Vitamin D Deficiency-Associated Comorbidities: A Protein Network Dynamics Perspective

Main Article Content

Anton F. Fliri Shama Kajiji

Abstract

Vitamin D deficiency has been linked to numerous comorbid diseases. Despite this recognition, the effectiveness of vitamin D supplementation in improving clinical outcomes remains uncertain. To shed light on the underlying cause-effect relationships and molecular mechanisms involved, we analyzed effects of over 4000 diseases on information transfers between biological processes in human tissues. Spectral clustering of the data generated identified relationships between disease phenotypes and perturbations of protein network-network interactions mediating information transfers in tissues. Examination of these relationships discovered an integrated regulatory scheme involving interactions between 188 proteins responsive to vitamin D (vitamin D interactome). Functional analysis established a central role of fifteen proteins (core vitamin D interactome) in vitamin D pharmacology and their involvement in multiple reciprocal feedback loops. Identification of functions affected by this core regulatory framework provides new insights into relationships between vitamin D deficiency-associated comorbidities, epigenetic regulation of vitamin D pharmacology, and impact of mutations on disease. Recognition of these functional relationships suggests that vitamin D associated comorbidities may not be fully treatable by vitamin D supplementation alone. Further examination of consequences of perturbations of protein interactions within the core vitamin D interactome identified 590 morbidities exhibiting physical changes co-expressed in vitamin D deficiency. Further analysis revealed that these comorbidities can be differentiated based on cause-effect relationships defined by characteristic patterns of physical abnormalities and effects on protein network interactions. These findings demonstrate the utility of systems biology-based cause-effect analyses to unravel complex relationships involving multiple diseases and multiple biological processes; thereby providing a more comprehensive understanding of comorbidities and the impact of vitamin D deficiency on health.

Article Details

How to Cite
FLIRI, Anton F.; KAJIJI, Shama. Vitamin D Deficiency-Associated Comorbidities: A Protein Network Dynamics Perspective. Medical Research Archives, [S.l.], v. 11, n. 6, june 2023. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/3996>. Date accessed: 22 dec. 2024. doi: https://doi.org/10.18103/mra.v11i6.3996.
Section
Research Articles

References

1. Hurst JR, Dickhaus J, Maulik PK, Miranda JJ, Pastakia SD, Soriano JB, et al. Global Alliance for Chronic Disease researchers’ statement on multimorbidity. Lancet Glob Health. 2018;6:e1270–1.
2. Gupta P, Prabhakaran D, Mohan S. Multimorbidity or multiple long-term conditions: need for bridging the evidence & care gaps to address an emerging priority public health issue in India. Indian J Med Res. 2022 Sep;156(3):381-383. doi: 10.4103/ijmr.ijmr_1822_21. PMID: 36751739; PMCID: PMC10101353.
3. Amrein K, Scherkl M, Hoffmann M, Neuwersch-Sommeregger S, Köstenberger M, Tmava Berisha A, Martucci G, Pilz S, Malle O. vitamin D deficiency 2.0: an update on the current status worldwide. Eur J Clin Nutr. 2020 Nov;74(11):1498-1513. doi: 10.1038/s41430-020-0558-y. Epub 2020 Jan 20. PMID: 31959942; PMCID: PMC7091696.
4. Bilezikian JP, Formenti AM, Adler RA, Binkley N, Bouillon R, Lazaretti-Castro M, Marcocci C, Napoli N, Rizzoli R, Giustina A. Vitamin D: Dosing, levels, form, and route of administration: Does one approach fit all? Rev Endocr Metab Disord. 2021 Dec;22(4):1201-1218. doi: 10.1007/s11154-021-09693-7. Epub 2021 Dec 23. PMID: 34940947; PMCID: PMC8696970.
5. Bouillon R, Manousaki D, Rosen C, Trajanoska K, Rivadeneira F, Richards JB. The health effects of vitamin D supplementation: evidence from human studies. Nat Rev Endocrinol. 2022;18(2):96-110. doi:10.1038/s41574-021-00593-z
6. Autier P, Boniol M, Pizot C, Mullie P. vitamin D status and ill health: a systematic review. Lancet Diabetes Endocrinol. 2014;2(1):76-89. doi:10.1016/S2213-8587(13)70165-7
7. Ivanov PC. The New Field of Network Physiology: Building the Human Physiolome. Front Netw Physiol. 2021 Jun 30;1:711778. doi: 10.3389/fnetp.2021.711778. PMID: 36925582; PMCID: PMC10013018.
8. Wang K. et al. Classification of common human diseases derived from shared genetic and environmental determinants. Nat. Genet. 2017; 49, 1319–1325.
9. Clauset A, Moore C, Newman ME. Hierarchical structure and the prediction of missing links in networks. Nature. 2008 May 1;453(7191):98-101.
10. Liu CC, Tseng YT, Li W, et al. DiseaseConnect: a comprehensive web server for mechanism-based disease-disease connections. Nucleic Acids Res. 2014;42(Web Server issue):W137-W146. doi:10.1093/nar/gku412
11. Zhang XY, Birtwistle MR, Gallo JM. A General Network Pharmacodynamic Model-Based Design Pipeline for Customized Cancer Therapy Applied to the VEGFR Pathway. CPT Pharmacometrics Syst Pharmacol. 2014;3(1):e92. Published 2014 Jan 15. doi:10.1038/psp.2013.65
12. Guo P, Chen S, Wang H, Wang Y, Wang J. A Systematic Analysis on the Genes and Their Interaction Underlying the Comorbidity of Alzheimer's Disease and Major Depressive Disorder. Front Aging Neurosci. 2022;13:789698. Published 2022 Jan 20. doi:10.3389/fnagi.2021.789698
13. Fliri Anton Franz Joseph, Method and descriptors for comparing object-induced information flows in a plurality of interaction networks. US11120346B2·2021.
14. Fliri AF, Loging WT, Thadeio PF, Volkmann RA. Analysis of drug-induced effect patterns to link structure and side effects of medicines. Nat Chem Biol. 2005;1(7):389-397. doi:10.1038/nchembio747
15. Fliri AF, Loging WT, Volkmann RA. Analysis of information flows in interaction networks: implication for drug discovery and pharmacological research. Discov Med. 2011;11(57):133-143.
16. Ying KC, Lin SW. Maximizing cohesion and separation for detecting protein functional modules in protein-protein interaction networks. PLoS One. 2020 Oct 13;15(10):e0240628. doi: 10.1371/journal.pone.0240628. PMID: 33048996; PMCID: PMC7553341.
17. L. Gao, p. sun, j. song., "Clustering algorithms for detecting functional modules in protein interaction networks.": Journal of Bioinformatics and Computational Biology, 2009, Volume: 7, Issue: 1, pp. 217-242
18. Freitas, C.G.S., Aquino, A.L.L., Ramos, H.S. et al. A detailed characterization of complex networks using Information Theory. Sci Rep.; 2019; 9, 16689. https://doi.org/10.1038/s41598-019-53167-5
19. Oerton E, Roberts I, Lewis PSH, Guilliams T, Bender A. Understanding and predicting disease relationships through similarity fusion. Bioinformatics. 2019;35(7):1213-1220. doi:10.1093/bioinformatics/bty754
20. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL. The human disease network. Proc Natl Acad Sci U S A. 2007;104(21):8685-8690. doi:10.1073/pnas.0701361104
21. Vasam Manjveekar Prabantu, Nagarajan Naveenkumar, Narayanaswamy Srinivasan. Influence of Disease-Causing Mutations on Protein Structural Networks. Front Mol Biosci. 2020; 7: 620554.
22. Sommariva, S., Caviglia, G., Ravera, S. et al. Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells. Sci Rep.2021; 11, 19602. https://doi.org/10.1038/s41598-021-99073-7
23. Takahashi, K., Abe, K., Kubota, S.I. et al. An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis. Nat Commun 2022;13: 5239.
24. Yadav KK, Singh AK. Topology-based protein–protein interaction analysis of oral cancer proteins. CURRENT SCIENCE. 2022 Nov 25;123(10):1216.
25. Panditrao, G., Bhowmick, R., Meena, C. et al. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci. 2022;47, 24. https://doi.org/10.1007/s12038-022-00253-y
26. D. Hitchcock, C.A. Glasbey, K. Ritz. Image analysis of space-filling by networks: application to a fungal mycelium Biotechnol. Tech. 1996; 10, pp. 205-210
27. de Lichtenberg U, Jensen LJ, Brunak S, Bork P. Dynamic complex formation during the yeast cell cycle. Science. 2005;307(5710):724-727. doi:10.1126/science.1105103
28. Taylor IW, Linding R, Warde-Farley D, et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009;27(2):199-204. doi:10.1038/nbt.1522
29. Langhauser F, et al. A diseasome cluster-based drug repurposing of soluble guanylate cyclase activators from smooth muscle relaxation to direct neuroprotection. NPJ Syst. Biol. Appl. 2018;4:1–13. doi: 10.1038/s41540-017-0039-7
30. Yue R, Dutta A. Computational systems biology in disease modeling and control, review and perspectives. NPJ Syst Biol Appl. 2022 Oct 3;8(1):37. doi: 10.1038/s41540-022-00247-4. PMID: 36192551; PMCID: PMC9528884.
31. Hidalgo CA, Blumm N, Barabási AL, Christakis NA. A dynamic network approach for the study of human phenotypes. PLoS Comput Biol. 2009;5(4):e1000353. doi:10.1371/journal.pcbi.1000353
32. Zhong Q, Simonis N, Li QR, et al. Edgetic perturbation models of human inherited disorders. Mol Syst Biol. 2009;5:321. doi:10.1038/msb.2009.80
33. Ma X, Gao L, Tan K. Modeling disease progression using dynamics of pathway connectivity. Bioinformatics. 2014;30(16):2343-2350. doi:10.1093/bioinformatics/btu298
34. Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways. Science. 1999;283(5400):381-387. doi:10.1126/science.283.5400.381
35. Bhalla, U. S., & Iyengar, R.. Emergent properties of networks of biological signaling pathways. Science, 1999; 283(5400), 381-387.
36. Fesce R. Subjectivity as an Emergent Property of Information Processing by Neuronal Networks. Front. Neurosci. 2020; 14:548071. doi: 10.3389/fnins.2020.548071
37. Bashan A, Bartsch RP, Kantelhardt JW, Havlin S, Ivanov PCh. Network physiology reveals relations between network topology and physiological function. Nat Commun. 2012;3:702. Published 2012 Feb 28. doi:10.1038/ncomms1705
38. Shah, N.A., Sarkar, C.A. Variable cellular decision-making behavior in a constant synthetic network topology. BMC Bioinformatics;2,019; 20, 237 https://doi.org/10.1186/s12859-019-2866-6
39. R. Schmidt et al.: Inferring Topology of Networks With Hidden Dynamic Variables. IEEE Access;2022;10;76682-76692
40. Dumont, Grégory, and Boris Gutkin. "Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits." PLoS computational biology; 2019; 15.5): e1007019.
41. Wodak SJ, Vlasblom J, Turinsky AL, Pu S. Protein-protein interaction networks: the puzzling riches. Curr Opin Struct Biol. 2013;23(6):941-953. doi:10.1016/j.sbi.2013.08.002
42. Patrick Finze. How we become ill. EMBO Reports (2017)18:515-518
43. Galas, D.. "systems biology." Encyclopedia Britannica, 2018; December 7. https://www.britannica.com/science/systems-biology.
44. Khan A, Uddin S, Srinivasan U. Comorbidity network for chronic disease: A novel approach to understand type 2 diabetes progression. Int J Med Inform. 2018;115:1-9. doi:10.1016/j.ijmedinf.2018.04.001
45. Pedro A. M. Mediano, Fernando E. Rosas et al. Greater than the parts: a review of the information decomposition approach to causal emergence. Philos Trans A Math Phys Eng Sci. July 11, 2022; 380(2227): 20210246.
46. Lage K, et al. Dissecting spatio-temporal protein networks driving human heart development and related disorders. Mol Syst Biol. 2010 Jun 22;6:381. doi: 10.1038/msb.2010.36.
47. Clauset, A., Moore, C., & Newman, M. E.. Hierarchical structure and the prediction of missing links in networks. Nature, 2008; 453(7191), 98-101.
48. McLennan-Smith TA, Roberts DO, Sidhu HS. Emergent behavior in an adversarial synchronization and swarming model. Phys Rev E. 2020;102(3-1):032607. doi:10.1103/PhysRevE.102.032607
49. Lei X, Yang X, Wu FX. Artificial Fish Swarm Optimization Based Method to Identify Essential Proteins. IEEE/ACM Trans Comput Biol Bioinform. 2020;17(2):495-505. doi:10.1109/TCBB.2018.2865567
50. Dresp-Langley, B. Seven properties of self-organization in the human brain. Big Data and Cognitive Computing, 2020; 4(2), 10.
51. Cubillos-Ruiz, A., Guo, T., Sokolovska, A. et al. Engineering living therapeutics with synthetic biology. Nat Rev Drug Discov .2021; 20, 941–960). https://doi.org/10.1038/s41573-021-00285-3
52. Olaf Witkowski and Takashi Ikegami. Emergence of Swarming Behavior: Foraging Agents Evolve Collective Motion Based on Signaling. PLoS One. 2016; 11(4): e0152756.
53. See Reference 10
54. Fliri AF, Kajiji S. Functional characterization of nutraceuticals using spectral clustering: Centrality of caveolae-mediated endocytosis for management of nitric oxide and vitamin D deficiencies and atherosclerosis. Front Nutr. 2022;9:885364. Published 2022 Aug 15. doi:10.3389/fnut.2022.885364
55. Berahmand, Kamal, Elahe Nasiri, and Yuefeng Li. "Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding." Computers in Biology and Medicine 138 (2021): 104933.
56. Manivasakam P, Ravi A, Ramesh J, et al. Autophagy: An Emerging Target for Developing Effective Analgesics. ACS Omega. 2023;8(10):9445-9453. Published 2023 Mar 3. doi:10.1021/acsomega.2c06949
57. Fliri Anton; Manivasakam Palaniyandi; Sutcliffe Joyce. Autophagy Activators and Inhibitors Of Ferroptosis For Preventing Acute Renal Failure And Neurotoxicity Induced By Certain Antibiotics. US2019056855W·2019-10-18
58. Fliri Anton Franz Joseph; Method For Identifying Treatment Of Infections Caused By Pathogens Of Diverse Origin.US2022249467A1·2022-08-11
59. Schaefer MH, Serrano L, Andrade-Navarro MA. Correcting for the study bias associated with protein-protein interaction measurements reveals differences between protein degree distributions from different cancer types. Front Genet. 2015 Aug 4;6:260. doi: 10.3389/fgene.2015.00260. PMID: 26300911; PMCID: PMC4523822.
60. Fierro-Monti I, Wright JC, Choudhary JS, Vizcaíno JA. Identifying individuals using proteomics: are we there yet? Front Mol Biosci. 2022 Nov 29;9:1062031. doi: 10.3389/fmolb.2022.1062031.
61. Zhu X, Shen X, Qu J, Straubinger RM, Jusko WJ. Multi-Scale Network Model Supported by Proteomics for Analysis of Combined Gemcitabine and Birinapant Effects in Pancreatic Cancer Cells. CPT Pharmacometrics Syst Pharmacol. 2018 Sep;7(9):549-561. doi: 10.1002/psp4.12320.
62. Kustatscher G, Collins T, Gingras AC, et al. Understudied proteins: opportunities and challenges for functional proteomics. Nat Methods. 2022;19(7):774-779.
63. Ivanov PC, Bartsch RP. Network physiology: mapping interactions between networks of physiologic networks. In: D’Angostino G, Scala A, editors. Networks of networks: the last frontier of complexity. Cham: Springer; 2014. p. 203–22.
64. Broido, A.D., Clauset, A. Scale-free networks are rare. Nat Commun.2019; 10, 1017. https://doi.org/10.1038/s41467-019-08746-5
65. Woessmann J, Kotol D, Hober A, Uhlén M, Edfors F. Addressing the Protease Bias in Quantitative Proteomics. J Proteome Res. 2022 Oct 7;21(10):2526-2534. doi: 10.1021/acs.jproteome.2c00491.
66. Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B. Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem. 2007;389(4):1017-1031. doi:10.1007/s00216-007-1486-6
67. US National Library of Medicine. MEDLINE: Overview 2021.
68. Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419. doi:10.1126/science.1260419
69. Damian Szklarczyk, Annika L Gable, Katerina C Nastou, David Lyon, Rebecca Kirsch, Sampo Pyysalo, Nadezhda T Doncheva, Marc Legeay, Tao Fang, Peer Bork, Lars J Jensen, Christian von Mering, The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets, Nucleic Acids Research,2021; Volume 49, Issue D1, Pages D605–D612, https://doi.org/10.1093/nar/gkaa1074
70. Fliri AF, Loging WT, Thadeio PF, Volkmann RA. Biospectra analysis: model proteome characterizations for linking molecular structure and biological response. J Med Chem. 2005;48(22):6918-6925. doi:10.1021/jm050494g
71. Dunn K, Marshall JG, Wells AL, Backus JEB. Examining the role of MEDLINE as a patient care information resource: an analysis of data from the Value of Libraries study. J Med Libr Assoc. 2017 Oct;105(4):336-346. doi: 10.5195/jmla.2017.87.
72. Finlayson SG, LePendu P, Shah NH. Building the graph of medicine from millions of clinical narratives. Sci Data. 2014 Sep 16;1:140032. doi: 10.1038/sdata.2014.32.
73. Szklarczyk D, Franceschini A, Kuhn M, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011;39(Database issue):D561-D568. doi:10.1093/nar/gkq973
74. Kaushal D, Naeve CW. An overview of Spotfire for gene-expression studies. Curr Protoc Hum Genet. 2005;Chapter 11:. doi:10.1002/0471142905.hg1109s45
75. Pike JW, Meyer MB. The vitamin D receptor: new paradigms for the regulation of gene expression by 1,25-dihydroxyvitamin D(3). Endocrinol Metab Clin North Am. 2010 Jun;39(2):255-69, table of contents. doi: 10.1016/j.ecl.2010.02.007. PMID: 20511050; PMCID: PMC2879406.
76. Hii CS, Ferrante A. The Non-Genomic Actions of vitamin D. Nutrients. 2016 Mar 2;8(3):135. doi: 10.3390/nu8030135. PMID: 26950144; PMCID: PMC4808864.
77. Missiuro PV, Liu K, Zou L, et al. Information flow analysis of interactome networks. PLoS Comput Biol. 2009;5(4):e1000350. doi:10.1371/journal.pcbi.1000350
78. Soong TT, Wrzeszczynski KO, Rost B. Physical protein-protein interactions predicted from microarrays. Bioinformatics. 2008;24(22):2608-2614. doi:10.1093/bioinformatics/btn498
79. Kotlyar M, Pastrello C, Malik Z, Jurisica I. IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species. Nucleic Acids Res. 2019;47(D1):D581-D589. doi:10.1093/nar/gky1037
80. Chen JC, Alvarez MJ, Talos F, Dhruv H, Rieckhof GE, Iyer A, Diefes KL, Aldape K, Berens M, Shen MM, Califano A. Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks. Cell. 2014 Oct 9;159(2):402-14.
81. Csermely P, Korcsmáros T, Kiss HJ, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther. 2013;138(3):333-408. doi:10.1016/j.pharmthera.2013.01.016
82. Valente TW, Coronges K, Lakon C, Costenbader E. How Correlated Are Network Centrality Measures? Connect (Tor). 2008 Jan 1;28(1):16-26. PMID: 20505784; PMCID: PMC2875682
83. Jeong H, Mason SP, Barabási AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411(6833):41-42. doi:10.1038/35075138
84. Malloy PJ, Feldman D. Genetic disorders and defects in vitamin D action. Endocrinol Metab Clin N Am 2010;39:333–346
85. Shrestha Bhattarai, T., Shamu, T., Gorelick, A.N. et al. AKT mutant allele-specific activation dictates pharmacologic sensitivities. Nat Commun. 2022; 13, 2111. https://doi.org/10.1038/s41467-022-29638-1.
86. Luke Humphreys, Margarita Espona-Fiedler, Daniel B. Longley. FLIP as a therapeutic target in cancer. The FEBS Journal. 2018; Volume 285, Issue 22 p. 4104-4123.https://doi.org/10.1111/febs.14523
87. Schlingmann KP, Kaufmann M, Weber S, et al. Mutations in CYP24A1 and idiopathic infantile hypercalcemia. N Engl J Med. 2011;365(5):410-421. doi:10.1056/NEJMoa1103864
88. Demir K, Kattan WE, Zou M, et al. Novel CYP27B1 Gene Mutations in Patients with vitamin D-Dependent Rickets Type 1A. PLoS One. 2015;10(7):e0131376. Published 2015 Jul 1. doi:10.1371/journal.pone.0131376
89. Ding H, Wu B, Wang H, Lu Z, Yan J, Wang X, Shaffer JR, Hui R, Wang DW. A novel loss-of-function DDAH1 promoter polymorphism is associated with increased susceptibility to thrombosis stroke and coronary heart disease. Circ Res.2010; 106:1145–1152
90. Hallmark B, Karafet TM, Hsieh P, Osipova LP, Watkins JC, Hammer MF. Genomic Evidence of Local Adaptation to Climate and Diet in Indigenous Siberians. Mol Biol Evol. 2019;36(2):315-327. doi:10.1093/molbev/msy211
91. Okano M, Bell DW, Haber DA, Li E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell. 1999;99(3):247-257. doi:10.1016/s0092-8674(00)81656-6
92. Zou Y, Deng W, Wang F, et al. A novel somatic MAPK1 mutation in primary ovarian mixed germ cell tumors. Oncol Rep. 2016;35(2):725-730. doi:10.3892/or.2015.4402
93. Petrackova A, Turcsanyi P, Papajik T, Kriegova E. Revisiting Richter transformation in the era of novel CLL agents. Blood Rev. 2021;49:100824. doi:10.1016/j.blre.2021.100824
94. Yue Y, Liu S, Han X, et al. Pathogenic variants of PROC gene caused type I activity deficiency in a familial Chinese venous thrombosis. J Cell Mol Med. 2019;23(10):7099-7104. doi:10.1111/jcmm.14563
95. Cleary JD, Pattamatta A, Ranum LPW. Repeat-associated non-ATG (RAN) translation. J Biol Chem. 2018;293(42):16127-16141. doi:10.1074/jbc.R118.003237
96. Mahamdallie SS, Hanks S, Karlin KL, et al. Corrigendum: Mutations in the transcriptional repressor REST predispose to Wilms tumor. Nat Genet. 2016;48(4):473. doi:10.1038/ng0329-473d
97. Blaschek A, V Kries R, Lohse P, et al. TNFRSF1A and MEFV mutations in childhood onset multiple sclerosis. Eur J Paediatr Neurol. 2018;22(1):72-81. doi:10.1016/j.ejpn.2017.08.00
98. Cordelli DM, Di Pisa V, Fetta A, et al. Neurological Phenotype of Mowat-Wilson Syndrome. Genes (Basel). 2021;12(7):982. Published 2021 Jun 27. doi:10.3390/genes12070982
99. Wang H, Chen W, Li D, Yin X, Zhang X, Olsen N, Zheng SG. vitamin D and Chronic Diseases. Aging Dis. 2017 May 2;8(3):346-353. doi: 10.14336/AD.2016.1021. PMID: 28580189; PMCID: PMC5440113.
100. J. Wesley Pike et al. The vitamin D receptor: contemporary genomic approaches reveal new basic and translational insights. J Clin Invest. 2017;127(4):1146-1154. https://doi.org/10.1172/JCI88887.
101. Nitulescu GM, Van De Venter M, Nitulescu G, Ungurianu A, Juzenas P, Peng Q, Olaru OT, Grădinaru D, Tsatsakis A, Tsoukalas D, Spandidos DA, Margina D. The Akt pathway in oncology therapy and beyond (Review). Int J Oncol. 2018 Dec;53(6):2319-2331. doi: 10.3892/ijo.2018.4597.
102. Kopan R. Notch signaling. Cold Spring Harb Perspect Biol. 2012 Oct 1;4(10):a011213. doi: 10.1101/cshperspect.a011213.
103. Braicu C, Buse M, Busuioc C, Drula R, Gulei D, Raduly L, Rusu A, Irimie A, Atanasov AG, Slaby O, Ionescu C, Berindan-Neagoe I. A Comprehensive Review on MAPK: A Promising Therapeutic Target in Cancer. Cancers (Basel). 2019 Oct 22;11(10):1618. doi: 10.3390/cancers11101618.
104. Tsuchiya Y, Nakabayashi O, Nakano H. FLIP the Switch: Regulation of Apoptosis and Necroptosis by cFLIP. Int J Mol Sci. 2015 Dec 18;16(12):30321-41. doi: 10.3390/ijms161226232.
105. Kümpfel, T., Hohlfeld, R. TNFRSF1A, TRAPS and multiple sclerosis. Nat Rev Neurol 5, 528–529 (2009). https://doi.org/10.1038/nrneurol.2009.154
106. Birkhoff JC, Huylebroeck D, Conidi A. ZEB2, the Mowat-Wilson Syndrome Transcription Factor: Confirmations, Novel Functions, and Continuing Surprises. Genes (Basel). 2021 Jul 3;12(7):1037. doi: 10.3390/genes12071037.
107. Oh D, Yu CH, Needleman DJ. Spatial organization of the Ran pathway by microtubules in mitosis. Proc Natl Acad Sci U S A. 2016;113(31):8729-8734. doi:10.1073/pnas.1607498113
108. Sedaghat Y, Bui HH, Mazur C, Monia BP. Identification of REST-regulated genes and pathways using a REST-targeted antisense approach. Nucleic Acid Ther. 2013;23(6):389-400. doi:10.1089/nat.2013.0445
109. Esmon CT. The protein C pathway. Chest. 2003;124(3 Suppl):26S-32S. doi:10.1378/chest.124.3_suppl.26s
110. Okano M. Bell D.W. Haber D.A. Li E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell. 1999; 99: 247-257
111. Liu X, Hou L, Xu D, et al. Effect of asymmetric dimethylarginine (ADMA) on heart failure development. Nitric Oxide. 2016;54:73-81. doi:10.1016/j.niox.2016.02.006
112. Alwhaibi A, Verma A, Adil MS, Somanath PR. The unconventional role of Akt1 in the advanced cancers and in diabetes-promoted carcinogenesis. Pharmacol Res. 2019;145:104270. doi:10.1016/j.phrs.2019.104270
113. Chen J, Somanath PR, Razorenova O, Chen WS, Hay N, Bornstein P, Byzova TV. Akt1 regulates pathological angiogenesis, vascular maturation and permeability in vivo. Nat Med. 2005;11:1188–96.
114. Takayanagi H. Osteoimmunology: shared mechanisms and crosstalk between the immune and bone systems. Nat Rev Immunol. 2007; 7(4): 292– 304
115. Tamayo M, Martin-Nunes L, Val-Blasco A, Piedras MJ, Larriba MJ, Gómez-Hurtado N, Fernández-Velasco M, Delgado C. Calcitriol, the Bioactive Metabolite of vitamin D, Increases Ventricular K+ Currents in Isolated Mouse Cardiomyocytes. Front Physiol. 2018 Aug 24;9:1186. doi: 10.3389/fphys.2018.01186.
116. Mahmoud AM, Szczurek M, Hassan C, Masrur M, Gangemi A, Phillips SA. vitamin D Improves Nitric Oxide-Dependent Vasodilation in Adipose Tissue Arterioles from Bariatric Surgery Patients. Nutrients. 2019 Oct 18;11(10):2521. doi: 10.3390/nu11102521. PMID: 31635396; PMCID: PMC6835261.
117. Doronzo G, Viretto M, Russo I, et al. Nitric oxide activates PI3-K and MAPK signalling pathways in human and rat vascular smooth muscle cells: influence of insulin resistance and oxidative stress. Atherosclerosis. 2011;216(1):44-53. doi:10.1016/j.atherosclerosis.2011.01.01
118. Pike JW, Meyer MB. The unsettled science of nonrenal calcitriol production and its clinical relevance. J Clin Invest. 2020 Sep 1;130(9):4519-4521. doi: 10.1172/JCI141334. PMID: 32716362; PMCID: PMC7456237.
119. Miloudi K, Oubaha M, Ménard C, et al. NOTCH1 signaling induces pathological vascular permeability in diabetic retinopathy. Proc Natl Acad Sci U S A. 2019;116(10):4538-4547. doi:10.1073/pnas.1814711116
120. Pei, H., Yu, Q., Xue, Q. et al. Notch1 cardioprotection in myocardial ischemia/reperfusion involves reduction of oxidative/nitrative stress. Basic Res Cardiol.2013; 108, 373. https://doi.org/10.1007/s00395-013-0373
121. Boopathy AV, Pendergrass KD, Che PL, Yoon YS, Davis ME. Oxidative stress-induced Notch1 signaling promotes cardiogenic gene expression in mesenchymal stem cells. Stem Cell Res Ther. 2013 Apr 18;4(2):43. doi: 10.1186/scrt190. PMID: 23597145; PMCID: PMC3706823.
122. Kannan K, Jain SK. Oxidative stress and apoptosis. Pathophysiology. 2000;7:153–163. doi: 10.1016/S0928-4680(00)00053-5.
123. Dash PR, McCormick J, Thomson MJ, Johnstone AP, Cartwright JE, Whitley GS. Fas ligand-induced apoptosis is regulated by nitric oxide through the inhibition of fas receptor clustering and the nitrosylation of protein kinase Cepsilon. Exp Cell Res. 2007;313(16):3421-3431. doi:10.1016/j.yexcr.2007.05.030
124. Uriarte SM, Joshi-Barve S, Song Z, et al. Akt inhibition upregulates FasL, downregulates c-FLIPs and induces caspase-8-dependent cell death in Jurkat T lymphocytes. Cell Death Differ. 2005;12(3):233-242. doi:10.1038/sj.cdd.4401549
125. Tabasi N, Rastin M, Mahmoudi M, Ghoryani M, Mirfeizi Z, Rabe SZ, Reihani H. Influence of vitamin D on cell cycle, apoptosis, and some apoptosis related molecules in systemic lupus erythematosus. Iran J Basic Med Sci. 2015 Nov;18(11):1107-11. PMID: 26949498; PMCID: PMC4764112.
126. Ho SY, Ling TY, Lin HY, et al. SDF-1/CXCR4 Signaling Maintains Stemness Signature in Mouse Neural Stem/Progenitor Cells. Stem Cells Int. 2017;2017:2493752. doi:10.1155/2017/2493752
127. Datta S, Snow CJ, Paschal BM. A pathway linking oxidative stress and the Ran GTPase system in progeria. Mol Biol Cell. 2014 Apr;25(8):1202-15. doi: 10.1091/mbc.E13-07-0430. Epub 2014 Feb 12. PMID: 24523287; PMCID: PMC3982987.
128. Lai GR, Lee YF, Yan SJ, Ting HJ. Active vitamin D induces gene-specific hypomethylation in prostate cancer cells developing vitamin D resistance. Am J Physiol Cell Physiol. 2020;318(5):C836-C847. doi:10.1152/ajpcell.00522.2019
129. Mirza I, Mohamed A, Deen H, et al. Obesity-Associated vitamin D Deficiency Correlates with Adipose Tissue DNA Hypomethylation, Inflammation, and Vascular Dysfunction. Int J Mol Sci. 2022;23(22):14377. Published 2022 Nov 19. doi:10.3390/ijms232214377
130. Zimnicka AM, Husain YS, Shajahan AN, Sverdlov M, Chaga O, Chen Z, Toth PT, Klomp J, Karginov AV, Tiruppathi C, Malik AB, Minshall RD. Src-dependent phosphorylation of caveolin-1 Tyr-14 promotes swelling and release of caveolae. Mol Biol Cell. 2016 Jul 1;27(13):2090-106. doi: 10.1091/mbc.E15-11-0756. Epub 2016 May 11. PMID: 27170175; PMCID: PMC4927282.
131. Fliri AF, Kajiji S. Functional characterization of nutraceuticals using spectral clustering: Centrality of caveolae-mediated endocytosis for management of nitric oxide and vitamin D deficiencies and atherosclerosis. Front Nutr. 2022;9:885364. Published 2022 Aug 15. doi:10.3389/fnut.2022.885364
132. Schaefer RJ, Bonor JC, Joglekar MS, van Golen KL, Nohe AG. 1,25 Dihydroxyvitamin D3 uptake is localized at caveolae and requires caveolar function. J Biomed Nanotechnol. 2013;9(10):1707-1715. doi:10.1166/jbn.2013.1662
133. Xie Z, Hou L, Shen S, Wu Y, Wang J, Jie Z, Zhao X, Li X, Zhang X, Chen J, Xu W, Ning L, Ma Q, Wang S, Wang H, Yuan P, Fang X, Qin A, Fan S. Mechanical force promotes dimethylarginine dimethylaminohydrolase 1-mediated hydrolysis of the metabolite asymmetric dimethylarginine to enhance bone formation. Nat Commun. 2022 Jan 10;13(1):50. doi: 10.1038/s41467-021-27629-2. PMID: 35013196; PMCID: PMC8748781.
134. Chen Y, Li Y, Zhang P, Traverse JH, Hou M, Xu X, Kimoto M, Bache RJ. Dimethylarginine dimethylaminohydrolase and endothelial dysfunction in failing hearts. Am J Physiol Heart Circ Physiol. 2005;289:H2212–9.
135. Zhang P, Hu X, Xu X, Chen Y, Bache RJ. Dimethylarginine dimethylaminohydrolase 1 modulates endothelial cell growth through nitric oxide and Akt. Arterioscler Thromb Vasc Biol. 2011 Apr;31(4):890-7. doi: 10.1161/ATVBAHA.110.215640.
136. Choi HR, Lee SW, Yeom H, Jeon DH, Kim HC, Youm Y. Association between vitamin D status and asymmetric dimethylarginine (ADMA) concentration in the Korean elderly population. Maturitas. 2017;102:13-17. doi:10.1016/j.maturitas.2017.05.002
137. Barsacchi R, Perrotta C, Bulotta S, Moncada S, Borgese N, Clementi E. Activation of endothelial nitric-oxide synthase by tumor necrosis factor-alpha: a novel pathway involving sequential activation of neutral sphingomyelinase, phosphatidylinositol-3' kinase, and Akt. Mol Pharmacol. 2003;63(4):886-895. doi:10.1124/mol.63.4.886
138. Dargelos E, Renaud V, Decossas M, Bure C, Lambert O, Poussard S. Caveolae-mediated effects of TNF-α on human skeletal muscle cells. Exp Cell Res. 2018;370(2):623-631. doi:10.1016/j.yexcr.2018.07.027
139. Magrinat G, Mason SN, Shami PJ, Weinberg JB. Nitric oxide modulation of human leukemia cell differentiation and gene expression. Blood. 1992;80(8):1880-1884.
140. Jeong MH, Kim HR, Park YJ, Chung KH. Akt and Notch pathways mediate polyhexamethylene guanidine phosphate-induced epithelial-mesenchymal transition via ZEB2. Toxicol Appl Pharmacol. 2019;380:114691. doi:10.1016/j.taap.2019.
141. Rosati J, Spallotta F, Nanni S, et al. Smad-interacting protein-1 and microRNA 200 family define a nitric oxide-dependent molecular circuitry involved in embryonic stem cell mesendoderm differentiation. Arterioscler Thromb Vasc Biol. 2011;31(4):898-907. doi:10.1161/ATVBAHA.110.214478
142. Bassez G, Camand OJ, Cacheux V, Kobetz A, Dastot-Le Moal F, Marchant D, Catala M, Abitbol M, Goossens M (March 2004). "Pleiotropic and diverse expression of ZFHX1B gene transcripts during mouse and human development supports the various clinical manifestations of the "Mowat-Wilson" syndrome". Neurobiology of Disease. 15 (2): 240–50. doi:10.1016/j.nbd.2003.10.004. PMID 15006694. S2CID 25770329.
143. Theodossiou SK, Murray JB, Hold LA, Courtright JM, Carper AM, Schiele NR. Akt signaling is activated by TGFβ2 and impacts tenogenic induction of mesenchymal stem cells. Stem Cell Res Ther. 2021;12(1):88. Published 2021 Jan 26. doi:10.1186/s13287-021-02167-2
144. Shi C, Sun B, Wu H, et al. Dysfunction of Caveolae-Mediated Endocytic TβRI Degradation Results in Hypersensitivity of TGF-β/Smad Signaling in Osteogenesis Imperfecta. J Bone Miner Res. 2023;38(1):103-118. doi:10.1002/jbmr.4734
145. Pallotta F, Rosati J, Straino S, et al. Nitric oxide determines mesodermic differentiation of mouse embryonic stem cells by activating class IIa histone deacetylases: potential therapeutic implications in a mouse model of hindlimb ischemia [published correction appears in Stem Cells. 2014 Jun;32(6):1688-9]. Stem Cells. 2010;28(3):431-442. doi:10.1002/stem.300
146. Mohammad S, Mishra A, Ashraf MZ. Emerging Role of vitamin D and its Associated Molecules in Pathways Related to Pathogenesis of Thrombosis. Biomolecules. 2019 Oct 24;9(11):649. doi: 10.3390/biom9110649. PMID: 31653092; PMCID: PMC6920963.
147. Maehata Y, Miyagawa S, Sawa Y. Activated protein C has a protective effect against myocardial I/R injury by improvement of endothelial function and activation of AKT1. PLoS One. 2012;7(8):e38738. doi: 10.1371/journal.pone.0038738. Epub 2012 Aug 20. PMID: 22916090; PMCID: PMC3423409.
148. Mohammad S, Mishra A, Ashraf MZ. Emerging Role of Vitamin D and its Associated Molecules in Pathways Related to Pathogenesis of Thrombosis. Biomolecules. 2019 Oct 24;9(11):649. doi: 10.3390/biom9110649. PMID: 31653092; PMCID: PMC6920963.
149. Bouillon R, Carmeliet G, Lieben L, Watanabe M, Perino A, Auwerx J, et al. Vitamin D and energy homeostasis: of mice and men. Nat Rev Endocrinol. 2014;10:79–87. doi: 10.1038/nrendo.2013.226.
150. Luo L, Yan S, Lai PT, et al. PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology [published online ahead of print, 2021 Jan 20]. Bioinformatics. 2021;btab019. doi:10.1093/bioinformatics/btab019
151. Köhler S, Gargano M, Matentzoglu N, et al. The Human Phenotype Ontology in 2021. Nucleic Acids Res. 2021;49(D1):D1207-D1217. doi:10.1093/nar/gkaa1043
152. Choi KW, Batchelder AW, Ehlinger PP, Safren SA, O'Cleirigh C. Applying network analysis to psychological comorbidity and health behavior: Depression, PTSD, and sexual risk in sexual minority men with trauma histories. J Consult Clin Psychol. 2017 Dec;85(12):1158-1170. doi: 10.1037/ccp0000241.
153. Fliri AF, Loging WT, Volkmann RA. Cause-effect relationships in medicine: a protein network perspective. Trends Pharmacol Sci. 2010;31(11):547-555. doi:10.1016/j.tips.2010.07.005.
154. See reference 4
155. Zhang Y, Zhang J, Studzinski GP. AKT pathway is activated by 1, 25-dihydroxyvitamin D3 and participates in its anti-apoptotic effect and cell cycle control in differentiating HL60 cells. Cell Cycle. 2006;5(4):447-451. doi:10.4161/cc.5.4.2467
156. Domingues-Faria C, Chanet A, Salles J, Berry A, Giraudet C, Patrac V, Denis P, Bouton K, Goncalves-Mendes N, Vasson MP, Boirie Y, Walrand S. Vitamin D deficiency down-regulates Notch pathway contributing to skeletal muscle atrophy in old wistar rats. Nutr Metab (Lond). 2014 Sep 30;11(1):47. doi: 10.1186/1743-7075-11-47. PMID: 25317198; PMCID: PMC4195890.
157. Bhat IA, Mir IR, Malik GH, et al. Comparative study of TNF-α and vitamin D reveals a significant role of TNF-α in NSCLC in an ethnically conserved vitamin D deficient population. Cytokine. 2022;160:156039. doi:10.1016/j.cyto.2022.156039
158. Nguyen, TP., Scotti, M., Morine, M.J. et al. Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins. BMC Syst Biol 5, 195 (2011). https://doi.org/10.1186/1752-0509-5-195
159. Lai GR, Lee YF, Yan SJ, Ting HJ. Active vitamin D induces gene-specific hypomethylation in prostate cancer cells developing vitamin D resistance. Am J Physiol Cell Physiol. 2020;318(5):C836-C847. doi:10.1152/ajpcell.00522.2019
160. Larriba MJ, García de Herreros A, Muñoz A. Vitamin D and the Epithelial to Mesenchymal Transition. Stem Cells Int. 2016;2016:6213872. doi: 10.1155/2016/6213872. Epub 2016 Jan 6. PMID: 26880977; PMCID: PMC4736588.
161. Fetahu IS, Höbaus J, Kállay E. Vitamin D and the epigenome. Front Physiol. 2014 Apr 29;5:164. doi: 10.3389/fphys.2014.00164. PMID: 24808866; PMCID: PMC4010791.
162. Rajendran G, Shanmuganandam K, Bendre A, Muzumdar D, Goel A, Shiras A. Epigenetic regulation of DNA methyltransferases: DNMT1 and DNMT3B in gliomas [published correction appears in J Neurooncol. 2011 Sep;104(2):495. Mujumdar, Dattatreya [corrected to Muzumdar, Dattatraya]]. J Neurooncol. 2011;104(2):483-494. doi:10.1007/s11060-010-0520-2
163. Skrypek N, Bruneel K, Vandewalle C, et al. ZEB2 stably represses RAB25 expression through epigenetic regulation by SIRT1 and DNMTs during epithelial-to-mesenchymal transition. Epigenetics Chromatin. 2018;11(1):70. Published 2018 Nov 16. doi:10.1186/s13072-018-0239-4
164. Li J, Sun L, Li Y. Regulation of dimethylarginine dimethylaminohydrolase 2 expression by NF-κB acetylation. Exp Ther Med. 2021 Feb;21(2):114. doi: 10.3892/etm.2020.9546. Epub 2020 Dec 3. PMID: 33335577; PMCID: PMC7739820.
165. Bouillon R, Carmeliet G, Verlinden L, et al. Vitamin D and human health: lessons from vitamin D receptor null mice. Endocr Rev. 2008;29(6):726-776. doi:10.1210/er.2008-0004
166. Bikle DD. Vitamin D: an ancient hormone. Exp Dermatol. 2011;20(1):7-13. doi:10.1111/j.1600-0625.2010.01202.x
167. Nguyen, TP., Scotti, M., Morine, M.J. et al. Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins. BMC Syst Biol 5, 195 (2011). https://doi.org/10.1186/1752-0509-5-195
168. Wodak SJ, Vlasblom J, Turinsky AL, Pu S. Protein-protein interaction networks: the puzzling riches. Curr Opin Struct Biol. 2013;23(6):941-953. doi:10.1016/j.sbi.2013.08.002
169. Gillis J, Ballouz S, Pavlidis P. Bias tradeoffs in the creation and analysis of protein-protein interaction networks. J Proteomics. 2014 Apr 4;100:44-54. doi: 10.1016/j.jprot.2014.01.020. Epub 2014 Jan 27. PMID: 24480284; PMCID: PMC3972268.
170. Alberca GGF, Alberca RW. Role of vitamin D deficiency and comorbidities in COVID-19. World J Virol. 2022 Jan 25;11(1):85-89. doi: 10.5501/wjv.v11.i1.85. PMID: 35117974; PMCID: PMC8788214.
171. Khan A, Dawoud H, Malinski T. Nanomedical studies of the restoration of nitric oxide/peroxynitrite balance in dysfunctional endothelium by 1,25-dihydroxy vitamin D3 - clinical implications for cardiovascular diseases. Int J Nanomedicine. 2018 Jan 19;13:455-466. doi: 10.2147/IJN.S152822. PMID: 29416330; PMCID: PMC5788997.
172. See reference 49.