Perspectives on systems modeling of human peripheral blood mononuclear cells

Frontiers in Molecular Biosciences

Volumn 4, Issue JAN, 2018, Pages

  Sen, Partho ^a   Kemppainen, Esko ^a   Orešič, Matej ^a,b  

^a UNIVERSITY OF TURKU   (Finland)

^b ÖREBRO UNIVERSITY   (Sweden)

Author keywords

Genome scale metabolic models; Immune system; Metabolomics; Multi omics; Pathways; PBMCs; Peripheral blood mononuclear cells; Systems biology

Indexed keywords

EID: 85041281300     PISSN: None     EISSN: 2296889X     Source Type: Journal    
DOI: 10.3389/fmolb.2017.00096     Document Type: Review

References (140)

1. Achiron, A., Feldman, A., Mandel, M., and Gurevich, M. (2007). Impaired expression of peripheral blood apoptotic-related gene transcripts in acute multiple sclerosis relapse. Ann. N. Y. Acad. Sci. 1107, 155-167. doi: 10.1196/annals.1381.017
2. Agren, R., Bordel, S., Mardinoglu, A., Pornputtapong, N., Nookaew, I., and Nielsen, J. (2012). Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput. Biol. 8:e1002518. doi: 10.1371/journal.pcbi.1002518
3. Akdis, M., Palomares, O., van de Veen, W., van Splunter, M., and Akdis, C. A. (2012). TH17 and TH22 cells: a confusion of antimicrobial response with tissue inflammation versus protection. J. Allergy Clin. Immunol. 129, 1438-1449. doi: 10.1016/j.jaci.2012.05.003
4. Almeida, L., Lochner, M., Berod, L., and Sparwasser, T. (2016). Metabolic pathways in T cell activation and lineage differentiation. Semin. Immunol. 28, 514-524. doi: 10.1016/j.smim.2016.10.009
5. Angelin, A., Gil-de-Gómez, L., Dahiya, S., Jiao, J., Guo, L., Levine, M. H., et al. (2017). Foxp3 reprograms T cell metabolism to function in low-glucose, high-lactate environments. Cell Metab. 25, 1282-1293. e1287. doi: 10.1016/j.cmet.2016.12.018
6. Aune, T. M., Collins, P. L., and Chang, S. (2009). Epigenetics and T helper 1 differentiation. Immunology 126, 299-305. doi: 10.1111/j.1365-2567.2008.03026.x
7. Autissier, P., Soulas, C., Burdo, T. H., and Williams, K. C. (2010). Evaluation of a 12-color flow cytometry panel to study lymphocyte, monocyte, and dendritic cell subsets in humans. Cytometry A. 77, 410-419. doi: 10.1002/cyto.a.20859
8. Bahr, T. M., Hughes, G. J., Armstrong, M., Reisdorph, R., Coldren, C. D., Edwards, M. G., et al. (2013). Peripheral blood mononuclear cell gene expression in chronic obstructive pulmonary disease. Am. J. Respir. Cell Mol. Biol. 49, 316-323. doi: 10.1165/rcmb.2012-0230OC
9. Baine, M. J., Chakraborty, S., Smith, L. M., Mallya, K., Sasson, A. R., Brand, R. E., et al. (2011). Transcriptional profiling of peripheral blood mononuclear cells in pancreatic cancer patients identifies novel genes with potential diagnostic utility. PLoS ONE 6:e17014. doi: 10.1371/journal.pone.0017014
10. Bauer, E., Laczny, C. C., Magnusdottir, S., Wilmes, P., and Thiele, I. (2015). Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires. Microbiome 3:55. doi: 10.1186/s40168-015-0121-6
11. Bauer, E., Zimmermann, J., Baldini, F., Thiele, I., and Kaleta, C. (2017). BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput. Biol. 13:e1005544. doi: 10.1371/journal.pcbi.1005544
12. Becker, S. A., and Palsson, B. O. (2008). Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 4:e1000082. doi: 10.1371/journal.pcbi.1000082
13. Becker, S. A., Feist, A. M., Mo, M. L., Hannum, G., Palsson, B. O., and Herrgard, M. J. (2007). Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat. Protoc. 2, 727-738. doi: 10.1038/nprot.2007.99
14. Belkaid, Y., and Hand, T. W. (2014). Role of the microbiota in immunity and inflammation. Cell 157, 121-141. doi: 10.1016/j.cell.2014.03.011
15. Bennett, L., Palucka, A. K., Arce, E., Cantrell, V., Borvak, J., Banchereau, J., et al. (2003). Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J. Exp. Med. 197, 711-723. doi: 10.1084/jem.20021553
16. Berod, L., Friedrich, C., Nandan, A., Freitag, J., Hagemann, S., Harmrolfs, K., et al. (2014). De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells. Nat. Med. 20, 1327-1333. doi: 10.1038/nm.3704
17. Blazier, A. S., and Papin, J. A. (2012). Integration of expression data in genome-scale metabolic network reconstructions. Front. Physiol. 3:299. doi: 10.3389/fphys.2012.00299
18. Bordbar, A., Feist, A. M., Usaite-Black, R., Woodcock, J., Palsson, B. O., and Famili, I. (2011). A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst. Biol. 5:180. doi: 10.1186/1752-0509-5-180
19. Bordbar, A., Mo, M. L., Nakayasu, E. S., Schrimpe-Rutledge, A. C., Kim, Y. M., Metz, T. O., et al. (2012). Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol. Syst. Biol. 8:558. doi: 10.1038/msb.2012.21
20. Bordbar, A., Monk, J. M., King, Z. A., and Palsson, B. O. (2014). Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 15, 107-120. doi: 10.1038/nrg3643
21. Bowler, R. P., Bahr, T. M., Hughes, G., Lutz, S., Kim, Y.-I., Coldren, C. D., et al. (2013). Integrative omics approach identifies interleukin-16 as a biomarker of emphysema. Omics 17, 619-626. doi: 10.1089/omi.2013.0038
22. Broere, F., Apasov, S. G., Sitkovsky, M. V., and van Eden, W. (2011). 'A2 T cell subsets and T cell-mediated immunity'. in Principles of Immunopharmacology: 3rd Revised and Extended Edition, eds F. P. Nijkamp and M. J. Parnham (Basel: Birkhäuser Basel), 15-27
23. Brown, C. T., Davis-Richardson, A. G., Giongo, A., Gano, K. A., Crabb, D. B., Mukherjee, N., et al. (2011). Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes. PLoS ONE 6:e25792. doi: 10.1371/journal.pone.0025792
24. Brugman, S., Klatter, F., Visser, J., Wildeboer-Veloo, A., Harmsen, H., Rozing, J., et al. (2006). Antibiotic treatment partially protects against type 1 diabetes in the Bio-Breeding diabetes-prone rat. Is the gut flora involved in the development of type 1 diabetes? Diabetologia 49, 2105-2108. doi: 10.1007/s00125-006-0334-0
25. Buck, M. D., O'Sullivan, D., and Pearce, E. L. (2015). T cell metabolism drives immunity. J. Exp. Med. 212, 1345-1360. doi: 10.1084/jem.20151159
26. Buonaguro, L., Wang, E., Tornesello, M. L., Buonaguro, F. M., and Marincola, F. M. (2011). Systems biology applied to vaccine and immunotherapy development. BMC Syst. Biol. 5:146. doi: 10.1186/1752-0509-5-146
27. Burczynski, M. E., Twine, N. C., Dukart, G., Marshall, B., Hidalgo, M., Stadler, W. M., et al. (2005). Transcriptional profiles in peripheral blood mononuclear cells prognostic of clinical outcomes in patients with advanced renal cell carcinoma. Clin. Cancer Res. 11, 1181-1189
28. Cakir, T., Patil, K. R., Onsan, Z., Ulgen, K. O., Kirdar, B., and Nielsen, J. (2006). Integration of metabolome data with metabolic networks reveals reporter reactions. Mol. Syst. Biol. 2:50. doi: 10.1038/msb4100085
29. Chaussabel, D., Quinn, C., Shen, J., Patel, P., Glaser, C., Baldwin, N., et al. (2008). A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 29, 150-164. doi: 10.1016/j.immuni.2008.05.012
30. Chen, L., Ge, B., Casale, F. P., Vasquez, L., Kwan, T., Garrido-Martin, D., et al. (2016). Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398-1414 e1324. doi: 10.1016/j.cell.2016.10.026
31. Chindelevitch, L., Trigg, J., Regev, A., and Berger, B. (2014). An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models. Nat. Commun. 5:4893. doi: 10.1038/ncomms5893
32. Ciofani, M., Madar, A., Galan, C., Sellars, M., Mace, K., Pauli, F., et al. (2012). A validated regulatory network for Th17 cell specification. Cell 151, 289-303. doi: 10.1016/j.cell.2012.09.016
33. Colijn, C., Brandes, A., Zucker, J., Lun, D. S., Weiner, B., Farhat, M. R., et al. (2009). Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 5:e1000489. doi: 10.1371/journal.pcbi.1000489
34. Collares, C., Evangelista, A., Xavier, D., Takahashi, P., Almeida, R., Macedo, C., et al. (2013). Transcriptome meta-analysis of peripheral lymphomononuclear cells indicates that gestational diabetes is closer to type 1 diabetes than to type 2 diabetes mellitus. Mol. Biol. Rep. 40, 5351-5358. doi: 10.1007/s11033-013-2635-y
35. Colyer, H. A., Armstrong, R. N., and Mills, K. I. (2012). Microarray for epigenetic changes: gene expression arrays. Methods Mol. Biol. 863, 319-328. doi: 10.1007/978-1-61779-612-8_20
36. Cooper, M. D. (2015). The early history of B cells. Nat. Rev. Immunol. 15, 191-197. doi: 10.1038/nri3801
37. Croft, M., Carter, L., Swain, S. L., and Dutton, R. W. (1994). Generation of polarized antigen-specific CD8 effector populations: reciprocal action of interleukin (IL)-4 and IL-12 in promoting type 2 versus type 1 cytokine profiles. J. Exp. Med. 180, 1715-1728. doi: 10.1084/jem.180.5.1715
38. Crotty, S. (2011). Follicular helper CD4 T cells (Tfh). Annu. Rev. Immunol. 29, 621-663. doi: 10.1146/annurev-immunol-031210-101400
39. Crow, M. K., Kirou, K. A., and Wohlgemuth, J. (2003). Microarray analysis of interferon-regulated genes in SLE. Autoimmunity 36, 481-490. doi: 10.1080/08916930310001625952
40. de Goffau, M. C., Luopajärvi, K., Knip, M., Ilonen, J., Ruohtula, T., Härkönen, T., et al. (2013). Fecal microbiota composition differs between children with β-cell autoimmunity and those without. Diabetes 62, 1238-1244. doi: 10.2337/db12-0526
41. Dimeloe, S., Burgener, A. V., Grahlert, J., and Hess, C. (2017). T-cell metabolism governing activation, proliferation and differentiation; a modular view. Immunology 150, 35-44. doi: 10.1111/imm.12655
42. Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., et al. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. U.S.A. 104, 1777-1782. doi: 10.1073/pnas.0610772104
43. Ebrahim, A., Almaas, E., Bauer, E., Bordbar, A., Burgard, A. P., Chang, R. L., et al. (2015). Do genome-scale models need exact solvers or clearer standards? Mol. Syst. Biol. 11:831. doi: 10.15252/msb.20156157
44. Edwards, C. J., Feldman, J. L., Beech, J., Shields, K. M., Stover, J. A., Trepicchio, W. L., et al. (2007). Molecular profile of peripheral blood mononuclear cells from patients with rheumatoid arthritis. Mol. Med. 13, 40-58. doi: 10.2119/2006-00056.Edwards
45. El-Semman, I. E., Karlsson, F. H., Shoaie, S., Nookaew, I., Soliman, T. H., and Nielsen, J. (2014). Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction. BMC Syst. Biol. 8:41. doi: 10.1186/1752-0509-8-41
46. Falcai, A., Soeiro-Pereira, P., Kubo, C., Aranda, C., Solé, D., and Condino-Neto, A. (2015). Peripheral blood mononuclear cells from severe asthmatic children release lower amounts of IL-12 and IL-4 after LPS stimulation. Allergol. Immunopathol. 43, 482-486. doi: 10.1016/j.aller.2014.10.005
47. Fernandez, D. R., Telarico, T., Bonilla, E., Li, Q., Banerjee, S., Middleton, F. A., et al. (2009). Activation of mammalian target of rapamycin controls the loss of TCRζ in lupus T cells through HRES-1/Rab4-regulated lysosomal degradation. J. Immunol. 182, 2063-2073. doi: 10.4049/jimmunol.0803600
48. Filén, J.-J., Filén, S., Moulder, R., Tuomela, S., Ahlfors, H., West, A., et al. (2009). Quantitative proteomics reveals GIMAP family proteins 1 and 4 to be differentially regulated during human T helper cell differentiation. Mol. Cell. Proteomics 8, 32-44. doi: 10.1074/mcp.M800139-MCP200
49. Förster, J., Famili, I., Fu, P., Palsson, B. Ø., and Nielsen, J. (2003). Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13, 244-253. doi: 10.1101/gr.234503
50. Foss-Freitas, M. C., Foss, N. T., Rassi, D. M., Donadi, E. A., and Foss, M. C. (2008). Evaluation of cytokine production from peripheral blood mononuclear cells of type 1 diabetic patients. Ann. N.Y. Acad. Sci. 1150, 290-296. doi: 10.1196/annals.1447.053
51. Geiger, R., Rieckmann, J. C., Wolf, T., Basso, C., Feng, Y., Fuhrer, T., et al. (2016). L-Arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell 167, 829-842 e813. doi: 10.1016/j.cell.2016.09.031
52. Gerriets, V. A., and Rathmell, J. C. (2012). Metabolic pathways in T cell fate and function. Trends Immunol. 33, 168-173. doi: 10.1016/j.it.2012.01.010
53. Giongo, A., Gano, K. A., Crabb, D. B., Mukherjee, N., Novelo, L. L., Casella, G., et al. (2011). Toward defining the autoimmune microbiome for type 1 diabetes. ISME J. 5, 82-91. doi: 10.1038/ismej.2010.92
54. Golubovskaya, V., and Wu, L. (2016). Different subsets of T cells, memory, effector functions, and CAR-T immunotherapy. Cancers 8:36. doi: 10.3390/cancers8030036
55. Greenberg, S. A., Pinkus, J. L., Pinkus, G. S., Burleson, T., Sanoudou, D., Tawil, R., et al. (2005). Interferon-alpha/beta-mediated innate immune mechanisms in dermatomyositis. Ann. Neurol. 57, 664-678. doi: 10.1002/ana.20464
56. Han, F., Li, G., Dai, S., and Huang, J. (2016). Genome-wide metabolic model to improve understanding of CD4+ T cell metabolism, immunometabolism and application in drug design. Mol. Biosyst. 12, 431-443. doi: 10.1039/C5MB00480B
57. Haudek-Prinz, V. J., Klepeisz, P., Slany, A., Griss, J., Meshcheryakova, A., Paulitschke, V., et al. (2012). Proteome signatures of inflammatory activated primary human peripheral blood mononuclear cells. J. Proteomics 76 Spec No., 150-162. doi: 10.1016/j.jprot.2012.07.012
58. Hirahara, K., Vahedi, G., Ghoreschi, K., Yang, X. P., Nakayamada, S., Kanno, Y., et al. (2011). Helper T-cell differentiation and plasticity: insights from epigenetics. Immunology 134, 235-245. doi: 10.1111/j.1365-2567.2011.03483.x
59. Hu, G., Tang, Q., Sharma, S., Yu, F., Escobar, T. M., Muljo, S. A., et al. (2013). Expression and regulation of intergenic long noncoding RNAs during T cell development and differentiation. Nat. Immunol. 14, 1190-1198. doi: 10.1038/ni.2712
60. Hyötyläinen, T., Jerby, L., Petäjä, E. M., Mattila, I., Jäntti, S., Auvinen, P., et al. (2016). Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease. Nat. Commun. 7:8994. doi: 10.1038/ncomms9994
61. Iikura, K., Katsunuma, T., Saika, S., Saito, S., Ichinohe, S., Ida, H., et al. (2011). Peripheral blood mononuclear cells from patients with bronchial asthma show impaired innate immune responses to rhinovirus in vitro. Int. Arch. Allergy Immunol. 155(Suppl. 1), 27-33. doi: 10.1159/000327262
62. Jensen, P. A., and Papin, J. A. (2011). Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27, 541-547. doi: 10.1093/bioinformatics/btq702
63. Kanduri, K., Tripathi, S., Larjo, A., Mannerstrom, H., Ullah, U., Lund, R., et al. (2015). Identification of global regulators of T-helper cell lineage specification. Genome Med. 7:122. doi: 10.1186/s13073-015-0237-0
64. Kew, S., Banerjee, T., Minihane, A. M., Finnegan, Y. E., Williams, C. M., and Calder, P. C. (2003). Relation between the fatty acid composition of peripheral blood mononuclear cells and measures of immune cell function in healthy, free-living subjects aged 25-72 y. Am. J. Clin. Nutr. 77, 1278-1286
65. Kleiveland, C. R. (2015). 'Peripheral blood mononuclear cells'. in The Impact of Food Bioactives on Health, eds K. Verhoeckx, P. Cotter, I. López-Expósito, C. Kleiveland, T. Lea, A. Mackie, T. Requena, D. Swiatecka, H. Wichers (Springer), 161-167
66. Knip, M., and Siljander, H. (2016). The role of the intestinal microbiota in type 1 diabetes mellitus. Nat. Rev. Endocrinol. 12, 154-167. doi: 10.1038/nrendo.2015.218
67. Kosiewicz, M. M., Dryden, G. W., Chhabra, A., and Alard, P. (2014). Relationship between gut microbiota and development of T cell associated disease. FEBS Lett. 588, 4195-4206. doi: 10.1016/j.febslet.2014.03.019
68. Kostic, A. D., Gevers, D., Siljander, H., Vatanen, T., Hyötyläinen, T., Hämäläinen, A.-M., et al. (2015). The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260-273. doi: 10.1016/j.chom.2015.01.001
69. Kröger, W., Mapiye, D., Entfellner, J.-B. D., and Tiffin, N. (2016). A meta-analysis of public microarray data identifies gene regulatory pathways deregulated in peripheral blood mononuclear cells from individuals with systemic lupus erythematosus compared to those without. BMC Med. Genomics 9:66. doi: 10.1186/s12920-016-0227-0
70. Lee, G. R., Kim, S. T., Spilianakis, C. G., Fields, P. E., and Flavell, R. A. (2006). T helper cell differentiation: regulation by cis elements and epigenetics. Immunity 24, 369-379. doi: 10.1016/j.immuni.2006.03.007
71. Levy, H., Wang, X., Kaldunski, M., Jia, S., Kramer, J., Pavletich, S. J., et al. (2012). Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes. Genes Immun. 13, 593-604. doi: 10.1038/gene.2012.41
72. Li, J., Jia, H., Cai, X., Zhong, H., Feng, Q., Sunagawa, S., et al. (2014a). An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834-841. doi: 10.1038/nbt.2942
73. Li, J., Zhang, S. X., Wang, W., Cheng, K., Guo, H., Rao, C. L., et al. (2017a). Potential antidepressant and resilience mechanism revealed by metabolomic study on peripheral blood mononuclear cells of stress resilient rats. Behav. Brain Res. 320, 12-20. doi: 10.1016/j.bbr.2016.11.035
74. Li, S., Nakaya, H. I., Kazmin, D. A., Oh, J. Z., and Pulendran, B. (2013). 'Systems biological approaches to measure and understand vaccine immunity in humans'. in Seminars in Immunology (Elsevier), 209-218
75. Li, S., Rouphael, N., Duraisingham, S., Romero-Steiner, S., Presnell, S., Davis, C., et al. (2014b). Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat. Immunol. 15, 195-204. doi: 10.1038/ni.2789
76. Li, S., Sullivan, N. L., Rouphael, N., Yu, T., Banton, S., Maddur, M. S., et al. (2017b). Metabolic phenotypes of response to vaccination in humans. Cell 169, 862-877.e17. doi: 10.1016/j.cell.2017.04.026
77. Liu, M. L., Zhang, X. T., Du, X. Y., Fang, Z., Liu, Z., Xu, Y., et al. (2015). Severe disturbance of glucose metabolism in peripheral blood mononuclear cells of schizophrenia patients: a targeted metabolomic study. J. Transl. Med. 13, 226. doi: 10.1186/s12967-015-0540-y
78. Loyet, K. M., Ouyang, W., Eaton, D. L., and Stults, J. T. (2005). Proteomic profiling of surface proteins on Th1 and Th2 cells. J. Proteome Res. 4, 400-409. doi: 10.1021/pr049810q
79. Lu, C., and Thompson, C. B. (2012). Metabolic regulation of epigenetics. Cell Metab. 16, 9-17. doi: 10.1016/j.cmet.2012.06.001
80. Luckheeram, R. V., Zhou, R., Verma, A. D., and Xia, B. (2012). CD4+T cells: differentiation and functions. Clin. Dev. Immunol. 2012:12. doi: 10.1155/2012/925135
81. Ma, E. H., Bantug, G., Griss, T., Condotta, S., Johnson, R. M., Samborska, B., et al. (2017). Serine is an essential metabolite for effector T cell expansion. Cell Metab. 25, 345-357. doi: 10.1016/j.cmet.2016.12.011
82. Ma, H., Sorokin, A., Mazein, A., Selkov, A., Selkov, E., Demin, O., et al. (2007). The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 3:135. doi: 10.1038/msb4100177
83. MacIver, N. J., Michalek, R. D., and Rathmell, J. C. (2013). Metabolic regulation of T lymphocytes. Annu. Rev. Immunol. 31, 259-283. doi: 10.1146/annurev-immunol-032712-095956
84. Magnúsdóttir, S., Heinken, A., Kutt, L., Ravcheev, D. A., Bauer, E., Noronha, A., et al. (2016). Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81-89. doi: 10.1038/nbt.3703
85. Mak, T. W., Grusdat, M., Duncan, G. S., Dostert, C., Nonnenmacher, Y., Cox, M., et al. (2017). Glutathione primes T cell metabolism for inflammation. Immunity 46, 1089-1090. doi: 10.1016/j.immuni.2017.06.009
86. Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., et al. (2013). Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol. Syst. Biol. 9:649. doi: 10.1038/msb.2013.5
87. Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Uhlen, M., and Nielsen, J. (2014). Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5:3083. doi: 10.1038/ncomms4083
88. Mejía-León, M. E., and Barca, A. M. (2015). Diet, microbiota and immune system in type 1 diabetes development and evolution. Nutrients 7, 9171-9184. doi: 10.3390/nu7115461
89. Mouritsen, O. G. (2011). Lipidology and lipidomics-quo vadis? A new era for the physical chemistry of lipids. Phys. Chem. Chem. Phys. 13, 19195-19205. doi: 10.1039/c1cp22484k
90. Murri, M., Leiva, I., Gomez-Zumaquero, J. M., Tinahones, F. J., Cardona, F., Soriguer, F., et al. (2013). Gut microbiota in children with type 1 diabetes differs from that in healthy children: a case-control study. BMC Med. 11:46. doi: 10.1186/1741-7015-11-46
91. O'Brien, E. J., Monk, J. M., and Palsson, B. O. (2015). Using genome-scale models to predict biological capabilities. Cell 161, 971-987. doi: 10.1016/j.cell.2015.05.019
92. O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., and Palsson, B. O. (2013). Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol. Syst. Biol. 9:693. doi: 10.1038/msb.2013.52
93. Oestreich, K. J., and Weinmann, A. S. (2012). Encoding stability versus flexibility: lessons learned from examining epigenetics in T helper cell differentiation. Curr. Top. Microbiol. Immunol. 356, 145-164. doi: 10.1007/82_2011_141
94. Olafsdottir, T. A., Lindqvist, M., Nookaew, I., Andersen, P., Maertzdorf, J., Persson, J., et al. (2016). Comparative systems analyses reveal molecular signatures of clinically tested vaccine adjuvants. Sci. Rep. 6:39097. doi: 10.1038/srep39097
95. Orth, J. D., Thiele, I., and Palsson, B. Ø. (2010). What is flux balance analysis? Nat. Biotechnol. 28, 245-248. doi: 10.1038/nbt.1614
96. Pagani, M., Rockstroh, M., Schuster, M., Rossetti, G., Moro, M., Crosti, M., et al. (2015). Reference proteome of highly purified human Th1 cells reveals strong effects on metabolism and protein ubiquitination upon differentiation. Proteomics 15, 3644-3647. doi: 10.1002/pmic.201400139
97. Patil, K. R., and Nielsen, J. (2005). Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc. Natl. Acad. Sci. U.S.A. 102, 2685-2689. doi: 10.1073/pnas.0406811102
98. Patil, K. R., Rocha, I., Förster, J., and Nielsen, J. (2005). Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6:308. doi: 10.1186/1471-2105-6-308
99. Payne, K. K., Zoon, C. K., Wan, W., Marlar, K., Keim, R. C., Kenari, M. N., et al. (2013). Peripheral blood mononuclear cells of patients with breast cancer can be reprogrammed to enhance anti-HER-2/neu reactivity and overcome myeloid-derived suppressor cells. Breast Cancer Res. Treat. 142, 45-57. doi: 10.1007/s10549-013-2733-5
100. Pearce, E. L., and Pearce, E. J. (2013). Metabolic pathways in immune cell activation and quiescence. Immunity 38, 633-643. doi: 10.1016/j.immuni.2013.04.005
101. Pearce, E. L., Poffenberger, M. C., Chang, C.-H., and Jones, R. G. (2013). Fueling immunity: insights into metabolism and lymphocyte function. Science 342:1242454. doi: 10.1126/science.1242454
102. Pharkya, P., and Maranas, C. D. (2006). An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab. Eng. 8, 1-13. doi: 10.1016/j.ymben.2005.08.003
103. Pharkya, P., Burgard, A. P., and Maranas, C. D. (2004). OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 14, 2367-2376. doi: 10.1101/gr.2872004
104. Price, N. D., Reed, J. L., and Palsson, B. Ø. (2004). Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol. 2, 886-897. doi: 10.1038/nrmicro1023
105. Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., et al. (2010). A human gut microbial gene catalog established by metagenomic sequencing. Nature 464:59. doi: 10.1038/nature08821
106. Ravikrishnan, A., and Raman, K. (2015). Critical assessment of genome-scale metabolic networks: the need for a unified standard. Brief. Bioinformatics 16, 1057-1068. doi: 10.1093/bib/bbv003
107. Roesch, L. F., Lorca, G. L., Casella, G., Giongo, A., Naranjo, A., Pionzio, A. M., et al. (2009). Culture-independent identification of gut bacteria correlated with the onset of diabetes in a rat model. ISME J. 3, 536-548. doi: 10.1038/ismej.2009.5
108. Rosengren, A. T., Nyman, T. A., and Lahesmaa, R. (2005). Proteome profiling of interleukin-12 treated human T helper cells. Proteomics 5, 3137-3141. doi: 10.1002/pmic.200401151
109. Round, J. L., and Mazmanian, S. K. (2009). The gut microbiota shapes intestinal immune responses during health and disease. Nat. Rev. Immunol. 9, 313-323. doi: 10.1038/nri2515
110. Sakaguchi, S., Yamaguchi, T., Nomura, T., and Ono, M. (2008). Regulatory T cells and immune tolerance. Cell 133, 775-787. doi: 10.1016/j.cell.2008.05.009
111. Salehzadeh-Yazdi, A., Asgari, Y., Saboury, A. A., and Masoudi-Nejad, A. (2014). Computational analysis of reciprocal association of metabolism and epigenetics in the budding yeast: a genome-scale metabolic model (GSMM) approach. PLoS ONE 9:e111686. doi: 10.1371/journal.pone.0111686
112. Sanders, V. M. (2006). Epigenetic regulation of Th1 and Th2 cell development. Brain Behav. Immun. 20, 317-324. doi: 10.1016/j.bbi.2005.08.005
113. Savaryn, J. P., Toby, T. K., Catherman, A. D., Fellers, R. T., LeDuc, R. D., Thomas, P. M., et al. (2016). Comparative top down proteomics of peripheral blood mononuclear cells from kidney transplant recipients with normal kidney biopsies or acute rejection. Proteomics 16, 2048-2058. doi: 10.1002/pmic.201600008
114. Schellenberger, J., Que, R., Fleming, R. M., Thiele, I., Orth, J. D., Feist, A. M., et al. (2011). Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6, 1290-1307. doi: 10.1038/nprot.2011.308
115. Sen, P., Mardinogulu, A., and Nielsen, J. (2017). Selection of complementary foods based on optimal nutritional values. Sci. Rep. 7:5413. doi: 10.1038/s41598-017-05650-0
116. Sen, P., Vial, H. J., and Radulescu, O. (2016). Mathematical modeling and omic data integration to understand dynamic adaptation of Apicomplexan parasites and identify pharmaceutical targets. Compr. Anal. Parasite Biol. 7:457. doi: 10.1002/9783527694082.ch20
117. Shlomi, T., Cabili, M. N., Herrgard, M. J., Palsson, B. O., and Ruppin, E. (2008). Network-based prediction of human tissue-specific metabolism. Nat. Biotechnol. 26, 1003-1010. doi: 10.1038/nbt.1487
118. Shoaie, S., and Nielsen, J. (2014). Elucidating the interactions between the human gut microbiota and its host through metabolic modeling. Front. Genet. 5:86. doi: 10.3389/fgene.2014.00086
119. Shoaie, S., Ghaffari, P., Kovatcheva-Datchary, P., Mardinoglu, A., Sen, P., Pujos-Guillot, E., et al. (2015). Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320-331. doi: 10.1016/j.cmet.2015.07.001
120. Simeoni, O., Piras, V., Tomita, M., and Selvarajoo, K. (2015). Tracking global gene expression responses in T cell differentiation. Gene 569, 259-266. doi: 10.1016/j.gene.2015.05.061
121. Smiljanovic, B., Grün, J. R., Biesen, R., Schulte-Wrede, U., Baumgrass, R., Stuhlmüller, B., et al. (2012). The multifaceted balance of TNF-α and type I/II interferon responses in SLE and RA: how monocytes manage the impact of cytokines. J. Mol. Med. 90, 1295-1309. doi: 10.1007/s00109-012-0907-y
122. Stockinger, B., and Veldhoen, M. (2007). Differentiation and function of Th17 T cells. Curr. Opin. Immunol. 19, 281-286. doi: 10.1016/j.coi.2007.04.005
123. Suthers, P. F., Zomorrodi, A., and Maranas, C. D. (2009). Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol. Syst. Biol. 5:301. doi: 10.1038/msb.2009.56
124. Swainston, N., Smallbone, K., Hefzi, H., Dobson, P. D., Brewer, J., Hanscho, M., et al. (2016). Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12:109. doi: 10.1007/s11306-016-1051-4
125. Tan, C., and Gery, I. (2012). The unique features of Th9 cells and their products. Crit. Rev. Immunol. 32, 1-10. doi: 10.1615/CritRevImmunol.v32.i1.10
126. Teixeira, V. H., Olaso, R., Martin-Magniette, M. L., Lasbleiz, S., Jacq, L., Oliveira, C. R., et al. (2009). Transcriptome analysis describing new immunity and defense genes in peripheral blood mononuclear cells of rheumatoid arthritis patients. PLoS ONE 4:e6803. doi: 10.1371/journal.pone.0006803
127. Thiele, I., Swainston, N., Fleming, R. M., Hoppe, A., Sahoo, S., Aurich, M. K., et al. (2013). A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419-425. doi: 10.1038/nbt.2488
128. Tuomela, S., and Lahesmaa, R. (2013). Early T helper cell programming of gene expression in human. Semin. Immunol. 25, 282-290. doi: 10.1016/j.smim.2013.10.013
129. Tuomela, S., Rautio, S., Ahlfors, H., Oling, V., Salo, V., Ullah, U., et al. (2016). Comparative analysis of human and mouse transcriptomes of Th17 cell priming. Oncotarget 7, 13416-13428. doi: 10.18632/oncotarget.7963
130. Twine, N. C., Stover, J. A., Marshall, B., Dukart, G., Hidalgo, M., Stadler, W., et al. (2003). Disease-associated expression profiles in peripheral blood mononuclear cells from patients with advanced renal cell carcinoma. Cancer Res. 63, 6069-6075
131. Väremo, L., Scheele, C., Broholm, C., Mardinoglu, A., Kampf, C., Asplund, A., et al. (2016). Proteome-and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes. Cell Rep. 14:1567. doi: 10.1016/j.celrep.2015.04.010
132. Wang, X., Jia, S., Geoffrey, R., Alemzadeh, R., Ghosh, S., and Hessner, M. J. (2008). Identification of a molecular signature in human type 1 diabetes mellitus using serum and functional genomics. J. Immunol. 180, 1929-1937. doi: 10.4049/jimmunol.180.3.1929
133. Wang, Y., Eddy, J. A., and Price, N. D. (2012). Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst. Biol. 6:153. doi: 10.1186/1752-0509-6-153
134. Wen, L., Ley, R. E., Volchkov, P. Y., Stranges, P. B., Avanesyan, L., Stonebraker, A. C., et al. (2008). Innate immunity and intestinal microbiota in the development of type 1 diabetes. Nature 455, 1109-1113. doi: 10.1038/nature07336
135. Wing, K., and Sakaguchi, S. (2010). Regulatory T cells exert checks and balances on self tolerance and autoimmunity. Nat. Immunol. 11, 7-13. doi: 10.1038/ni.1818
136. Yizhak, K., Chaneton, B., Gottlieb, E., and Ruppin, E. (2015). Modeling cancer metabolism on a genome scale. Mol. Syst. Biol. 11:817. doi: 10.15252/msb.20145307
137. Yuan, D., Koh, C., and Wilder, J. (1994). Interactions between B lymphocytes and NK cells. FASEB J. 8, 1012-1018
138. Yun, J., Johnson, J. L., Hanigan, C. L., and Locasale, J. W. (2012). Interactions between epigenetics and metabolism in cancers. Front. Oncol. 2:163. doi: 10.3389/fonc.2012.00163
139. Zhang, Z.-N., Xu, J.-J., Fu, Y.-J., Liu, J., Jiang, Y.-J., Cui, H.-L., et al. (2013). Transcriptomic analysis of peripheral blood mononuclear cells in rapid progressors in early HIV infection identifies a signature closely correlated with disease progression. Clin. Chem. 59, 1175-1186. doi: 10.1373/clinchem.2012.197335
140. Zur, H., Ruppin, E., and Shlomi, T. (2010). iMAT: an integrative metabolic analysis tool. Bioinformatics 26, 3140-3142. doi: 10.1093/bioinformatics/btq602