NF-kB-p50-TFBS-complementary and c-myc-TFBS-complementary motifs in miRNAs involved in pathogenesis of different lympho- and myeloproliferative diseases
Summary. In our previous works, there were discovered different kinds of the c-myc-TFBS-complementary and NF-kB-p50-TFBS-complementary motifs in the stem-loop forms of the human, mouse and rat miRNAs. Also, there were described different types of the stem-loop miRNA informational redundancy as to these motifs. We have hypothesized these motifs, being the elements of the stem-loop miRNA degradome, can function as the alternative transcription factors. In the present paper, this methodology was applied to analysis of the characteristic miRNA expression profiles of different B-lymphoproliferative and myeloproliferative diseases. There were found the following main regularities: 1) significant part of the stem-loop miRNAs involved in pathogenesis of different hematologic diseases contain complementary motifs to the binding sites of c-myc and NF-kB-p50 transcription factors. Agreeably to this, the degradome elements of these stem-loop miRNAs are potentially able to function as alternative transcription factors; 2) each nosological unit has its own characteristic spectra of the TFBS-complementary motifs in its characteristic miRNA set, showing their real participation in the cell differentiation pathways; 3) different types of the stem-loop miRNA informational redundancy as to the TFBS-complementary motifs correlate with different directions of the cell differentiation. Also, our results permit to formulate the following recommendations for the differential diagnosis: 1) hyperexpression of miR-320a and miR-323b in the leukemia cells may be a weighty argument to diagnose chronic myelocytic leukemia (CML); 2) hyperexpression of the miRs 495 and 543 — alone and, moreover, in combination with hypoexpression of such miRs as 134, 542, 623, 671, 1182 — in the leukemia cells may be a weighty argument to diagnose CML; 3) hypoexpression of the miR-126 in the leukemia cells may be a weighty argument to diagnose acute myelocytic leukemia; 4) hyperexpression of the miR-185 and miR-324 in the lymphoma cells may be a weighty argument to diagnose Burkitt’s lymphoma; 5) hyperexpression of the miR-192a-2 in the lymphoma cells may be a weighty argument to diagnose mantle-cell lymphoma.
For today, an important role of miRNA up- and downregulation in pathogenesis and differential diagnosis of the malignancies, including different lympho- and myeloproliferative diseases, is completely proved and generally known. At the same time, it is known that certain miRNAs affect expression of certain transcription factors (TF), including, especially, ones being involved into transcription self-control of these miRNAs [1–3]. But we are still a long way from completeness of a list of such regulation. Thus, it would be logical to suppose these facts point to the imbalance of the corresponding miRNA-TF interrelations as one of the leading events in a malignancy pathogenesis. However, what is the mechanism of this imbalance appearance? Is the commonly known miRNA-caused disruption of translation enough for this? Most probably, not. Indeed, the crucial moment of any imbalance is differential disruption of the homeostasis supporting functions/agents .
Looking at the problem have been described above, it is rational to pay a special attention to such TFs as NF-kB (especially, its p50 protein subunit) and c-myc. Both these TFs are widely multifunctional and commonly known to be involved into regulation of all main biological functions, such as cell proliferation, inflammation, immune response, malignization, apoptosis, etc. c-myc expression is NF-kB-dependent  and, moreover, their expression may be cross-regulated . c-myc is well known as an oncoprotein, but modulations of its expression appear to be important not only for those malignancies in which it executes its direct oncoprotein’s function [7, 8]. NF-kB-p50 is not known as an oncoprotein, but its expression and activity play a distinguished role in practically all kinds and stages of oncogenesis [9, 10]. Especially, both these TFs play indirect but quite important role in development of different kinds of leukemia and lymphoma [11, 12]. At last, NF-kB TF was firstly discovered in proliferating lymphocytes . In this connection, we must point the followings.
In our previous papers [14, 15], we have described the NF-kB-p50-TFBS-complementary and c-myc-TFBS-complementary motifs in all Homo sapiens, Mus musculus and Rattus norvegicus stem-loop formed miRNAs, known up to October 2016. We have supposed these motifs enable the stem-loop miRNA degradome to function as a set of the alternative TF.
Note. The term «degradome» was not so long ago applied to the side products of protein processing, but now it is widely enlarged to the side products of different RNAs processing [16–18]. As we think, it is expedient to enlarge it up to the side products of any macromolecule processing.
Thus, the aim of our investigation was a search for any characteristic differences in distribution of the TFBS-complementary motifs named above between the stem-loop forms of the miRNAs involved in pathogenesis of the lymphoproliferative and myeloproliferative diseases.
We are fully aware of the incompleteness of modern information on these issues and, accordingly, the imperfection of our attempt to systematize this information. Therefore, we consider this article mainly as a draft algorithm for analyzing available and newly received information.
Materials and methods
This search was performed using the literature data and miRBase. We have used only such papers in which the authors have compared miRNA spectra in the leukemia/lymphoma cells obtained immediately from the patients versus their normal homologues obtained from the healthy donors [19–26]. The papers describing the in vitro cultured leukemia/lymphoma cells were not used because of significant biochemical disturbances appearing in all kinds of cells under in vitro conditions.
Distribution of the corresponding TFBS-complementary motifs in the whole set of the human stem-loop miRNAs have been discovered up to the end of October 2016 is shown in our previous papers [14, 15] and the supplementary materials to them. So, our task in the present work was to compile a list of miRNAs involved in the pathogenesis of the corresponding diseases, compare it with these supplementary tables and analyze the results using the criteria described in [14, 15].
Statistical analysis. Statistical significance of the final results was estimated using the Fisher’s exact method. In these calculations, we used the total number of all — both upregulated and downregulated — miRNAs involved in the pathogenesis of B-lymphoproliferative diseases have been studied here and the analogous number for myeloproliferative diseases. The approximate values of high number factorials were taken from the  tables.
Results and discussion
It is very important to note that we have initiated the present research without any preliminary hypothesis. Thus, all results described below were obtained only owing to the search algorithm have been elaborated in our previous papers [14, 15].
The primary results of our search are presented in the Tables 1 and 2.
Table 1. List of miRNAs involved in pathogenesis of some hematological diseases and containing c-myc-TFBS-complementary motifs
Notes. In this and next tables:
miR-xxx — stem-loop miRNA containing conventional motif(s);
miR-xxx — stem-loop miRNA containing motif(s) with an exchange;
miR-xxx — stem-loop miRNA containing motif(s) with a deletion;
miR-xxx — stem-loop miRNA containing motife(s) with an insert;
miR-xxx — stem-loop miRNA containing inner inversion;
miR-xxx — miRNAs not known in human (most probably, the contaminants); pseudo-miRNAs (indeed tRFs or other short RNAs);
miR-xxx* — mature (not stem-loop) miRNA, especially -5p; but its hyper- or hypoexpression indirectly testifies the corresponding stem-loop form expression modulations;
miR-xxx, miR–xxx, etc — stem-loop miRNA containing all kinds of motifs signed with the corresponding colors;
miR-xxxoverlap, etc. — stem-loop miRNA containing the motifs of the different kinds, overlapping one another;
miR–xxx, etc. — stem-loop miRNA containing 2 motifs of the same kind.
Table 2. List of miRNAs involved in pathogenesis of some hematological diseases and containing NF-κB-p50-TFBS-complementary motifs
As one can see, the main regularities of the TFBS-complementary motifs distribution in the set of pathogenesis-involved miRNAs are the same as ones in the total set of human, mouse and rat miRNAs have been described previously [14, 15]. Namely:
It is of a special interest that NF-kB-p50-TFBS-complementary motifs in B-lymphoproliferative diseases occur only in 2 of 7 listed nosological units (in BL and MCL) and in both cases — in upregulated miRNAs. In controversial, in CML all NF-kB-p50-TFBS-complementary motifs occur only in downregulated miRNAs.
Adding the Tables 3 and 4 to the analysis, one can see that the miRNA sets involved in pathogenesis of different nosological units are quite not equal in composition of their TFBS-complementary motifs.
Table 3. Number of c-myc-TFBS-complementary motifs in the stem-loop human miRNAs involved in pathogenesis of the hematological diseases listed above
Table 4. Total number of NF-κB-p50-TFBS-complementary motifs in the stem-loop human miRNAs involved in pathogenesis of the hematological diseases listed above
Cross-links between the characteristic miRNA sets of listed nosological units are shown in the Tables 5 and 6.
Table 5. Cross-links between the hematological diseases listed above in c-myc-TFBS-complementary motif containing miRNAs involved in their pathogenesis
Table 6. Cross-links between the hematological diseases listed above in NF-κB-p50-TFBS-complementary motif containing miRNAs involved in their pathogenesis
As to the c-myc-TFBS-complementary motifs (see Table 5), the characteristic miRNA sets of B-CLL and AML have not any cross-links with any of other listed nosological forms.
Only BL, MCL and CML characteristic miRNA sets have cross-links with more than one other nosological forms. It is notable that the most frequent of these cross-links is miR-142.
As to the NF-kB-p50-TFBS-complementary motifs (see Table 6), only MCL and ABC+GCB have a single cross-link between one-another, and it is miR-92-a.
At last, Table 7 demonstrate distribution of the miRNAs, stem-loop forms of which have the signs of the informational redundancy as to c-myc-TFBS-complementary and NF-kB-p50-TFBS-complementary motifs.
Table 7. Different kinds of informational redundancy in the TFBS-complementary-motif-containing stem-loop miRNAs involved in pathogenesis of the hemathological diseases listed above
As one can see in the Table 7, such miRNAs were found to be involved only in myeloproliferative diseases (CML and AML) and in BL. Accordingly to the Fisher’s exact method (using approximate factorial values), this difference between B-lymphoproliferative (without BL) and myeloproliferative diseases is statistically significant under P≈0.003. It is notable that in most cases these miRNAs are downregulated. Upregulated from them are only miR-192 in BL; miR-495 and miR-543 in CML.
Thus, analysis of the TFBS-complementary motifs in the characteristic miRNA sets of different lympho- and myeloproliferative diseases demonstrate a series of signs which may be useful for both fundamental understanding of the leukemia genesis and differential diagnosis.
The conclusions of this work naturally divide in two groups — fundamentally biological (1–3) and diagnostic (4–8) ones.
1. Significant part of the stem-loop miRNAs involved in pathogenesis of different hematologic diseases contain complementary motifs to the binding sites of such TF as c-myc and NF-kB-p50. Agreeably to this, the degradome elements of these stem-loop miRNAs are potentially able to function as alternative TF.
2. Each nosological unit has its own characteristic spectra of the TFBS-complementary motifs in its characteristic miRNA set, showing their real participation in the cell differentiation pathways.
3. Different kinds of the stem-loop miRNA informational redundancy as to the TFBS-complementary motifs correlate with different directions of the cell differentiation.
4. Hyperexpression of miR-320a and miR-323b in the leukemia cells may be a weighty argument to diagnose CML.
5. Hyperexpression of the miRs 495 and 543 — alone and, moreover, in combination with hypoexpression of such miRs as 134, 542, 623, 671, 1182 — in the leukemia cells may be a weighty argument to diagnose CML.
6. Hypoexpression of the miR-126 in the leukemia cells may be a weighty argument to diagnose AML.
7. Hyperexpression of the miR-185 and miR-324 in the lymphoma cells may be a weighty argument to diagnose BL.
8. Hyperexpression of the miR-192a-2 in the lymphoma cells may be a weighty argument to diagnose MCL.
1. Tsang J., Zhu J., van Oudenaarden A. (2007) MicroRNA-mediated Feedback and Feedforward Loops are Recurrent Network Motifs in Mammals. Mol. Cell., 26(5): 753-767. doi:10.1016/j.molcel.2007.05.018.
2. Huang X.A., Lin H. (2012) The miRNA Regulation of Stem Cells. Wiley Interdiscip. Rev. Membr. Transp. Signal., 1(1): 83–95. doi:10.1002/wdev.5.
3. Lai X., Wolkenhauer O., Vera J. (2016) Understanding microRNA-mediated gene regulatory networks through mathematical modelling. Nucleic Acids Res., 44(13): 6019–6035. doi:10.1093/nar/gkw550.
4. Kernbach S. (2008) Structural Self-Organization in Multi-Agents and Multi-Robotic Systems. Logos Verlag Berlin GmbH, 250 p.
5. Pathway maps. Development PDGF signaling via STATS and NF-kB (http://lsresearch.thomsonreuters.com/maps/635).
6. Orlovsky O.A., Samoylenko O.A., Shlyakhovenko V.O. (2016) Transcription factor binding sites in a structural gene: what may be this? (by the example of the genes encoding the main enzymes of the polyamines metabolism). East Eur. Sci. J., № 6(10), Part 3: 62–68.
7. Dang C.V. (2012) MYC on the Path to Cancer. Cell, 149(1): 22–35. doi:10.1016/j.cell.2012.03.003.
8. Miller D.M., Thomas S.D., Islam A., Muench D., Sedoris K. (2012) c-Myc and Cancer Metabolism. Clin. Cancer Res.: an official journal of the American Association for Cancer Research, 18(20): 5546–5553. doi:10.1158/1078-0432.CCR-12-0977.
9. Hoesel B., Schmid J.A. (2013) The complexity of NF-κB signaling in inflammation and cancer. Mol. Cancer, 12: 86–92. doi:10.1186/1476-4598-12-86.
10. Xia Y., Shen S., Verma I.M. (2014) NF-κB, an active player in human cancers. Cancer Immunol. Res., 2(9): 823–830. doi:10.1158/2326-6066.CIR-14-0112.
11. Delgado M.D., León J. (2010) Myc Roles in Hematopoiesis and Leukemia. Genes & Cancer, 1(6): 605–616. doi:10.1177/1947601910377495.
12. Yamamoto Y., Gaynor R.B. (2001) Role of the NF-kB Pathway in the Pathogenesis of Human Disease States. Curr. Mol. Med., 1(3): 287–296. doi: https://doi.org/10.2174/1566524013363816.
13. Baltimore D. (2009) Discovering NF-κB. Cold Spring Harbor Perspectives in Biology, 1(1): 26–32. doi:10.1101/cshperspect.a000026.
14. Orlovsky O.A., Samoylenko O.A., Shlyakhovenko V.O. (2017) C-myc-TFBS-complementary and NF-κB-p50-TFBS-complementary motifs in miRNA of human and laboratory rodents. East Eur. Sci. J., 1 (1): 9–12.
15. Orlovsky O.A., Shlyakhovenko V.O., Samoylenko O.A. (2017) Towards the informational redundancy of c-myc- and NF-κB-p50-TFBS-complementary motifs in miRNA. East Eur. Sci. J., 2 (2): 14–18.
16. Bracken C.P., Szubert J.M., Mercer T.R. et al. (2011) Global analysis of the mammalian RNA degradome reveals widespread miRNA-dependent and miRNA-independent endonucleolytic cleavage. Nucleic Acids Res., 39(13): 5658–5668. doi:10.1093/nar/gkr110.
17. Wang Y., Li L., Tang S. et al. (2016) Combined small RNA and degradome sequencing to identify miRNAs and their targets in response to drought in foxtail millet. DVC Genetics, 17: 57–65. DOI: 10.1186/s12863-016-0364-7.
18. Jackowiak P., Nowacka M., Strozycki P.M., Figlerowicz M. (2011) RNA degradome — its biogenesis and functions. Nucleic Acids Res., 39(17): 7361–7370. doi:10.1093/nar/gkr450.
19. Lim E.L., Marra M.A. (2013) MicroRNA dysregulation in B-cell non-Hodgkin lymphoma. Blood and Lymphatic Cancer: Targets and Therapy, 3: 25–40.
20. Yuan Y., Kasar S., Underbayev C., Prakash S., Raveche E. (2012) MicroRNAs in Acute Myeloid Leukemia and Other Blood Disorders. Leuk. Res. Treat., 1912: 1–11. doi:10.1155/2012/603830.
21. Zanette D.L., Rivadavia F., Molfetta G.A. et al. (2007) miRNA expression profiles in chronic lymphocytic and acute lymphocytic leukemia. Brazilian J. Med. Biol. Res., 40: 1435–1440.
22. Luan C., Yang Z., Chen B. (2015) The functional role of microRNA in acute lymphoblastic leukemia: relevance for diagnosis, differential diagnosis, prognosis and therapy. Onco Targets Ther., 8: 2903–2914.
23. Luna-Aguirre C. M., de la Luz Martinez-Fierro M., Mar-Aguilar F. et al. (2015) Circulating microRNA expression profile in B-cell acute lymphoblastic leukemia. Cancer Biomark., 15(3): 299–310. DOI: 10.3233/CBM-15046.
24. Mi S., Lu J., Sun M. et al. (2007) MicroRNA expression signatures accurately discriminate acute lymphoblastic leukemia from acute myeloid leukemia. PNAS, 104(50): 19971–19976.
25. Dixon-McIver A., East P., Mein C.A. et al. (2008) Distinctive Patterns of MicroRNA Expression Associated with Karyotype in Acute Myeloid Leukaemia. PloS ONE, 3(5): 2141–2146.
26. Таблицы факториалов (http://chursinvb.ucoz.ru/load/tablica_faktorialov_ot_1_ do_2000/7-1-0-113).
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