Systems biology as an emerging paradigm in transfusion medicine

BMC Systems Biology

Volumn 12, Issue 1, 2018, Pages

  Yurkovich, James T ^a   Bordbar, Aarash ^b   Sigurjónsson, Ólafur E ^c,d   Palsson, Bernhard O ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Sinopia Biosciences   (United States)

^c REYKJAVIK UNIVERSITY   (Iceland)

^d LANDSPITALI UNIVERSITY HOSPITAL   (Iceland)

Author keywords

Metabolism; Red blood cell; Storage lesion; Systems biology; Transfusion medicine

Indexed keywords

ERYTHROCYTE; HUMAN; METABOLISM; MULTIVARIATE ANALYSIS; PROCEDURES; SYSTEMS BIOLOGY; TRANSFUSION MEDICINE;

ERYTHROCYTES; HUMANS; MULTIVARIATE ANALYSIS; SYSTEMS BIOLOGY; TRANSFUSION MEDICINE;

EID: 85043483498     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/s12918-018-0558-x     Document Type: Review

References (77)

1. Sender R, Fuchs S, Milo R. Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 2016; 14(8):1002533.
2. Joshi A, Palsson BO. Metabolic dynamics in the human red cell: Part I-A comprehensive kinetic model. J Theor Biol. 1989; 141(4):515-28.
3. Joshi A, Palsson BO. Metabolic dynamics in the human red cell. part II-Interactions with the environment. J Theor Biol. 1989; 141(4):529-45.
4. Joshi A, Palsson BO. Metabolic dynamics in the human red cell. part III-Metabolic reaction rates. J Theor Biol. 1990; 142(1):41-68.
5. Joshi A, Palsson BO. Metabolic dynamics in the human red cell. part IV-Data prediction and some model computations. J Theor Biol. 1990; 142(1):69-85.
6. Bordbar A, Jamshidi N, Palsson BO. iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states. BMC Syst Biol. 2011; 5:110.
7. Bazanovas AN, Evstifeev AI, Khaiboullina SF, Sadreev II, Skorinkin AI, Kotov NV. Erythrocyte: A systems model of the control of aggregation and deformability. Biosystems. 2015; 131:1-8.
8. D'Alessandro A, Kriebardis AG, Rinalducci S, Antonelou MH, Hansen KC, Papassideri IS, Zolla L. An update on red blood cell storage lesions, as gleaned through biochemistry and omics technologies. Transfusion. 2015; 55(1):205-19.
9. Takei T, Amin NA, Schmid G, Dhingra-Kumar N, Rugg D. Progress in global blood safety for HIV. J Acquir Immune Defic Syndr. 2009; 52(Suppl 2):127-31.
10. Simon TL, McCullough J, Snyder EL, Solheim BG, Strauss RG. Rossi's Principles of Transfusion Medicine. Hoboken: Wiley-Blackwell, 2016.
11. Frank SM, Johnson DJ, Resar LMS. Ultramassive transfusion: give blood, save a life. Transfusion. 2016; 56(3):546-8.
12. García-Roa M, del Carmen Vicente-Ayuso M, Bobes A. M, Pedraza A. C, González-Fernández A, Martín M. P, Sáez I, Seghatchian J, Gutiérrez L. Red blood cell storage time and transfusion: current practice, concerns and future perspectives. Blood Transfusion. 2017. https://doi.org/10.2450/2017.0345-16.
13. Glynn SA, Klein HG, Ness PM. The red blood cell storage lesion: the end of the beginning. Transfusion. 2016; 56(6):1462-8.
14. D'Alessandro A, Seghatchian J. Hitchhiker's guide to the red cell storage galaxy: Omics technologies and the quality issue. Transfus Apher Sci. 2017; 56(2):248-53.
15. Chen D, Serrano K, Devine DV. Introducing the red cell storage lesion. ISBT Sci Ser. 2016; 11(S1):26-33.
16. D'Alessandro A. Red blood cell storage lesion. VOXS. 2017; 12(1):207-13.
17. Paglia G, Palsson BØ, Sigurjonsson OE. Systems biology of stored blood cells: can it help to extend the expiration date?J. Proteomics. 2012; 76 Spec No:163-7.
18. Nemkov T, Hansen KC, Dumont LJ, D'Alessandro A. Metabolomics in transfusion medicine. Transfusion. 2016; 56(4):980-93.
19. Patti GJ, Yanes O, Siuzdak G. Innovation: Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012; 13(4):263-9.
20. Bordbar A. Interpreting the deluge of omics data: new approaches offer new possibilities. Blood Transfus. 2017; 15(2):189-90.
21. Hod EA, Francis RO, Spitalnik SL. Red blood cell storage Lesion-Induced adverse effects: More smoke; is there fire?. Anesth Analg. 2017; 124(6):1752-4.
22. Logan JA, Kelly ME, Ayers D, Shipillis N, Baier G, Day PJR. Systems biology and modeling in neuroblastoma: practicalities and perspectives. Expert Rev Mol Diagn. 2010; 10(2):131-45.
23. Verma M, Karimiani EG, Byers RJ, Rehman S, Westerhoff HV, Day PJR. Mathematical modelling of miRNA mediated BCR.ABL protein regulation in chronic myeloid leukaemia vis-a-vis therapeutic strategies. Integr Biol. 2013; 5(3):543-54.
24. Zhang W, Edwards A, Fan W, Flemington EK, Zhang K. miRNA-mRNA correlation-network modules in human prostate cancer and the differences between primary and metastatic tumor subtypes. PLoS ONE. 2012; 7(6):40130.
25. Mani KM, Lefebvre C, Wang K, Lim WK, Basso K, Dalla-Favera R, Califano A. A systems biology approach to prediction of oncogenes and molecular perturbation targets in b-cell lymphomas. Mol Syst Biol. 2008; 4:169.
26. Kitano H. Computational systems biology. Nature. 2002; 420(6912):206-10. https://doi.org/10.1038/nature01254.
27. Li S, Rouphael N, Duraisingham S, Romero-Steiner S, Presnell S, Davis C, Schmidt DS, Johnson SE, Milton A, Rajam G, Kasturi S, Carlone GM, Quinn C, Chaussabel D, Palucka AK, Mulligan MJ, Ahmed R, Stephens DS, Nakaya HI, Pulendran B. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nature Immunology. 2013; 15(2):195-204. https://doi.org/10.1038/ni.2789.
28. Church GM, Elowitz MB, Smolke CD, Voigt CA, Weiss R. Realizing the potential of synthetic biology. Nature Reviews Molecular Cell Biology. 2014; 15(4):289-94. https://doi.org/10.1038/nrm3767.
29. Thakur GS, Jr BJD, Dean KR, Zhang Y, Rodriguez-Fernandez M, Hammamieh R, Yang R, Jett M, Palma J, Petzold LR, III FJD. Systems biology approach to understanding post-traumatic stress disorder. Mol BioSyst. 2015; 11(4):980-93. https://doi.org/10.1039/c4mb00404c.
30. Bartel J, Krumsiek J, Theis FJ. Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J. 2013; 4:201301009.
31. Brereton RG, Lloyd GR. Partial least squares discriminant analysis: taking the magic away: PLS-DA: taking the magic away. J Chemom. 2014; 28(4):213-25.
32. Krumsiek J, Suhre K, Illig T, Adamski J, Theis FJ. Bayesian independent component analysis recovers pathway signatures from blood metabolomics data. J Proteome Res. 2012; 11(8):4120-31.
33. Worley B, Powers R. Multivariate analysis in metabolomics. Curr Metabolomics. 2013; 1(1):92-107.
34. Bordbar A, McCloskey D, Zielinski DC, Sonnenschein N, Jamshidi N, Palsson BO. Personalized Whole-Cell kinetic models of metabolism for discovery in genomics and pharmacodynamics. Cell Syst. 2015; 1(4):283-92.
35. Kleinman S, Busch MP, Murphy EL, Shan H, Ness P, Glynn SA. The national heart, lung, and blood institute recipient epidemiology and donor evaluation study (REDS-III): a research program striving to improve blood donor and transfusion recipient outcomes. Transfusion. 2013; 54(3pt2):942-55. https://doi.org/10.1111/trf.12468.
36. Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, Thorleifsson SG, Agren R, Bölling C, Bordel S, Chavali AK, Dobson P, Dunn WB, Endler L, Hala D, Hucka M, Hull D, Jameson D, Jamshidi N, Jonsson JJ, Juty N, Keating S, Nookaew I, Le Novère N, Malys N, Mazein A, Papin JA, Price ND, Sr Selkov E, Sigurdsson MI, Simeonidis E, Sonnenschein N, Smallbone K, Sorokin A, van Beek JHGM, Weichart D, Goryanin I, Nielsen J, Westerhoff HV, Kell DB, Mendes P, Palsson BØ. A community-driven global reconstruction of human metabolism. Nat Biotechnol. 2013; 31(5):419-25.
37. Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BØ. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA. 2007; 104(6):1777-82.
38. Thiele I, Palsson BØ. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010; 5(1):93-121.
39. Bordbar A, Johansson PI, Paglia G, Harrison SJ, Wichuk K, Magnusdottir M, Valgeirsdottir S, Gybel-Brask M, Ostrowski SR, Palsson S, Rolfsson O, Sigurjónsson OE, Hansen MB, Gudmundsson S, Palsson BO. Identified metabolic signature for assessing red blood cell unit quality is associated with endothelial damage markers and clinical outcomes. Transfusion. 2016; 56(4):852-62.
40. Sparrow RL. Time to revisit red blood cell additive solutions and storage conditions: a role for "omics" analyses. Blood Transfus. 2012; 10 Suppl 2:7-11.
41. Yurkovich JT, Palsson BO. Quantitative -omic data empowers bottom-up systems biology. Curr Opin Biotechnol. 2018; 51:130-6.
42. Strauss D, de Verdier CD. Preservation of red blood cells with purines and nucleosides. III, synthesis of adenine, guanine, and hypoxanthine nucleotides. Folia Haematol Int Mag Klin Morphol Blutforsch. 1980; 107(3):434-53.
43. Lockwood WB, Hudgens RW, Szymanski IO, Teno RA, Gray AD. Effects of rejuvenation and frozen storage on 42-day-old AS-3 RBCs. Transfusion. 2003; 43(11):1527-32.
44. D'Alessandro A, Nemkov T, Kelher M, Bernadette West F, Schwindt RK, Banerjee A, Moore EE, Silliman CC, Hansen KC. Routine storage of red blood cell (RBC) units in additive solution-3: a comprehensive investigation of the RBC metabolome. Transfusion. 2014; 55(6):1155-68.
45. D'Alessandro A, Nemkov T, Hansen KC, Szczepiorkowski ZM, Dumont LJ. Red blood cell storage in additive solution-7 preserves energy and redox metabolism: a metabolomics approach. Transfusion. 2015; 55(12):2955-66.
46. Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012; 10(4):291-305.
47. Bordbar A, Yurkovich JT, Paglia G, Rolfsson O, Sigurjónsson ÓE, Palsson BO. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci Rep. 2017; 7:46249.
48. Reisz JA, Wither MJ, Dzieciatkowska M, Nemkov T, Issaian A, Yoshida T, Dunham AJ, Hill RC, Hansen KC, DAlessandro A. Oxidative modifications of glyceraldehyde 3-phosphate dehydrogenase regulate metabolic reprogramming of stored red blood cells. Blood. 2016; 128(12):32-42.
49. D'Alessandro A, Nemkov T, Yoshida T, Bordbar A, Palsson BO, Hansen KC. Citrate metabolism in red blood cells stored in additive solution-3. Transfusion. 2016; 57(2):325-36.
50. Nemkov T, Sun K, Reisz JA, Yoshida T, Dunham A, Wen EY, Wen AQ, Roach RC, Hansen KC, Xia Y, D'Alessandro A. Metabolism of citrate and other carboxylic acids in erythrocytes as a function of oxygen saturation and refrigerated storage. Front Med. 2017; 4:175.
51. Rolfsson Ó, Sigurjonsson ÓE, Magnusdottir M, Johannsson F, Paglia G, Gudmundsson S, Bordbar A, Palsson S, Brynjólfsson S, Gudmundsson S, Palsson B. Metabolomics comparison of red cells stored in four additive solutions reveals differences in citrate anticoagulant permeability and metabolism. Vox Sang. 2017; 112(4):326-35.
52. D'Alessandro A, D'Amici GM, Vaglio S, Zolla L. Time-course investigation of SAGM-stored leukocyte-filtered red bood cell concentrates: from metabolism to proteomics. Haematologica. 2011; 97(1):107-15.
53. Högman CF, Hedlund K, Sahleström Y. Red cell preservation in protein-poor media. III, protection against in vitro hemolysis. Vox Sang. 1981; 41(5-6):274-81.
54. Heaton A, Miripol J, Aster R, Hartman P, Dehart D, Rzad L, Grapka B, Davisson W, Buchholz DH. Use of adsol preservation solution for prolonged storage of low viscosity AS-1 red blood cells. Br J Haematol. 1984; 57(3):467-78.
55. Simon TL, Marcus CS, Myhre B, Nelson EJ. Effects of AS-3 nutrient-additive solution on 42 and 49 days of storage of red cells. Transfusion. 1987; 27(2):178-82.
56. Walker WH, Netz M, Gänshirt KH. 49 day storage of erythrocyte concentrates in blood bags with the PAGGS-mannitol solution. Beitr Infusionsther. 1990; 26:55-9.
57. Dawson RB, Levine Z, Zuck T, Hershey RT, Myers C. Blood preservation XXVII, fructose and mannose maintain ATP and 2,3-DPG. Transfusion. 1978; 18(3):347-52.
58. Sharma V, Ichikawa M, Freeze HH. Mannose metabolism: more than meets the eye. Biochem Biophys Res Commun. 2014; 453(2):220-8.
59. Ha V, Jayalath VH, Cozma AI, Mirrahimi A, de Souza RJ, Sievenpiper JL. Fructose-containing sugars, blood pressure, and cardiometabolic risk: a critical review. Curr Hypertens Rep. 2013; 15(4):281-97.
60. Semchyshyn HM. FructationIn vivo: Detrimental and protective effects of fructose. BioMed Research International. 2013; 2013:1-9. https://doi.org/10.1155/2013/343914.
61. Beutler E, Duron O. Studies on blood preservation. the relative capacities of hexoses, hexitols, and ethanol to maintain red cell ATP levels during storage. Transfusion. 1966; 6(6):537-42.
62. Dawson RB, Hershey RT, Myers CS, Zuck TF. Blood preservation. XXVIII, galactose and maltose maintain red blood cell 2,3-DPG and ATP. Transfusion. 1980; 20(1):110-3.
63. Rolfsson Ó, Johannsson F, Magnusdottir M, Paglia G, Sigurjonsson ÓE, Bordbar A, Palsson S, Brynjólfsson S, Gudmundsson S, Palsson B. Mannose and fructose metabolism in red blood cells during cold storage in SAGM. Transfusion. 2017; 57(11):2665-76.
64. Concha II, Velásquez FV, Martínez JM, Angulo C, Droppelmann A, Reyes AM, Slebe JC, Vera JC, Golde DW. Human erythrocytes express GLUT5 and transport fructose. Blood. 1997; 89(11):4190-5.
65. Paglia G, Sigurjónsson ÓE, Bordbar A, Rolfsson Ó, Magnusdottir M, Palsson S, Wichuk K, Gudmundsson S, Palsson BO. Metabolic fate of adenine in red blood cells during storage in SAGM solution. Transfusion. 2016; 56(10):2538-47.
66. Yurkovich JT, Zielinski DC, Yang L, Paglia G, Rolfsson O, Sigurjonsson OE, Broddrick JT, Bordbar A, Wichuk K, Brynjolfsson S, Palsson S, Gudmundsson S, Palsson BO. Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks. J Biol Chem. 2017. https://doi.org/10.1074/jbc.M117.804914.
67. Wallas CH. Sodium and potassium changes in blood bank stored human erythrocytes. Transfusion. 1979; 19(2):210-5.
68. Högman CF, Meryman HT. Storage parameters affecting red blood cell survival and function after transfusion. Transfus Med Rev. 1999; 13(4):275-96.
69. Antonelou MH, Kriebardis AG, Papassideri IS. Aging and death signalling in mature red cells: from basic science to transfusion practice. Blood Transfus. 2010; 8(Suppl 3):39-47.
70. Flatt JF, Bawazir WM, Bruce LJ. The involvement of cation leaks in the storage lesion of red blood cells. Front Physiol. 2014; 5:214.
71. Paglia G, D'Alessandro A, Rolfsson Ó, Sigurjónsson ÓE, Bordbar A, Palsson S, Nemkov T, Hansen K. C, Gudmundsson S, Palsson BO. Biomarkers defining the metabolic age of red blood cells during cold storage. Blood. 2016; 128(13):e43-50.
72. Casali E, Berni P, Spisni A, Baricchi R, Pertinhez TA. Hypoxanthine: a new paradigm to interpret the origin of transfusion toxicity. Blood Transfus. 2015; 14(6):555-6.
73. Nemkov T, Sun K, Yoshida T, Reisz JA, Hansen KC, Francis R, Roach RC, Xia Y, de Wolski K, Spitalnik S, Hod EA, D'Alessandro A. Hypoxia modulates the purine salvage pathway and decreases cell and supernatant levels of hypoxanthine, a predictor of 24h in vivo survival of stored mouse and human red blood cells. In: AABB Annual Meeting. San Diego: 2017. p. 14-03.
74. Nemkov T, Sun K, Reisz JA, Song A, Yoshida T, Dunham A, Wither MJ, Francis RO, Roach RC, Dzieciatkowska M, Rogers SC, Doctor A, Kriebardis A, Antonelou M, Papassideri I, Young C, Thomas T, Hansen KC, Spitalnik SL, Xia Y, Zimring JC, Hod EA, D'Alessandro A. Hypoxia modulates the purine salvage pathway and decreases red blood cell and supernatant levels of hypoxanthine during refrigerated storage. Haematologica. 2017:2017-178608. https://doi.org/10.3324/haematol.2017.178608.
75. Yurkovich J. T, Yang L, Palsson BO. Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells. PLoS Comput Biol. 2017; 13(3):1005424.
76. Yurkovich JT, Yang L, Palsson BO. Utilizing biomarkers to forecast quantitative metabolite concentration profiles in human red blood cells. In: 1st IEEE Conference on Control Technology and Applications. Kohala Coast: 2017.
77. Bayer SB, Hampton MB, Winterbourn CC. Accumulation of oxidized peroxiredoxin 2 in red blood cells and its prevention. Transfusion. 2015; 55(8):1909-18. https://doi.org/10.1111/trf.13039.