Dokument: Modellierung des Spleißergebnisses durch Kombination von 5'ss Stärke und spleißregulierenden Elementen

Titel:

Modellierung des Spleißergebnisses durch Kombination von 5'ss Stärke und spleißregulierenden Elementen

Weiterer Titel:

Modelling splicing outcome by combining 5’ss strength and splicing regulatory elements

URL für Lesezeichen:

https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=66177

URN (NBN):

urn:nbn:de:hbz:061-20240722-101023-9

Kollektion:

Dissertationen

Sprache:

Deutsch

Dokumententyp:

Wissenschaftliche Abschlussarbeiten » Dissertation

Medientyp:

Text

Autor:

Ptok, Johannes [Autor]

Dateien:

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Dateien vom 17.06.2024 / geändert 17.06.2024

Beitragende:

Prof. Dr. Schaal, Heiner [Gutachter]
Prof. Dr. Feldbrügge, Michael [Gutachter]

Stichwörter:

Modeling Splicing

Dewey Dezimal-Klassifikation:

500 Naturwissenschaften und Mathematik » 570 Biowissenschaften; Biologie

Beschreibungen:

Prä-mRNA-Spleißen ist ein Schritt der mRNA Prozessierung, bei dem intronische Sequenzen entfernt und exonische Sequenzen ligiert werden. Variable Spleißstellen-Wahl, sogenanntes alternatives Spleißen, ermöglicht die Expression unterschiedlichster Proteine, ausgehen vom gleichen prä-mRNA-Transkript. Zunächst wird ein RNA-Duplex zwischen der 5'-Spleißstelle (5'ss) und dem freien Ende der U1 snRNA gebildet. Spleißregulatorische Elemente (SREs) rekrutieren spleißregulatorische Proteine (SRPs), wie hnRNP- oder SR-Proteine, die je nach ihrer Position die Erkennung von Spleißstellen stark beeinflussen können. Mutationen, die diese regulatorischen Elemente stören, können zu fehlerhaftem Spleißen führen, was verschiedensten Krankheiten auslösen kann. Algorithmen wie der HBond-Score (HBS), die die intrinsische Stärke von 5‘ss abschätzten, wie z.B. der HBond-Score (HBS), reichen daher alleine nicht immer aus, um die Auswirkungen von Mutationen auf die Nutzung von Spleißstellen korrekt zu erfassen und dadurch festzustellen, ob sie möglicherweise zu einer Verringerung an funktionalem Protein führen. In der Veröffentlichung I wurde der Einfluss von SREs auf die 5'ss-Nutzung und Algorithmen, die diese vorhersagen, wie z.B. der HEXplorer, untersucht. Das Splice Site HEXplorer Weight (SSHW) fasst die insgesamt fördernden oder hemmenden Eigenschaften des unmittelbaren Sequenzkontexts von Spleißstellen zusammen. Die Untersuchung von 5'ss Konkurrenz Situationen in einem großen RNA-seq-Datensatz von Fibroblasten in Publikation II zeigte, dass Unterschiede in HBS und SSHW zusammen berücksichtigt werden müssen, um die vorhergesagte 5'ss-Nutzung am besten zu modellieren. Die intrinsische Stärke hatte jedoch einen größeren Einfluss auf 5'ss-Erkennung als das SSHW, was auch für wahrscheinlich nicht-pathogene Mutationen gesunder Individuen des 1000-Genom-Projekts gezeigt werden konnte (unveröffentlichtes Manuskript I). Um schnell Sequenzvariationen auf ihren Einfluss auf Spleißstellen-Nutzung zu analysieren, wurde in Publikation III der VarCon-Algorithmus entwickelt, der mehrdeutige Positionsinformationen in genomische Positionen umwandelt. Um zu verstehen, warum kardiovaskuläre Endothelzellen, die mit hohen Konzentrationen von Lipoprotein niedriger Dichte (LDL) behandelt wurden, niedrige Konzentrationen von funktionellem NO-Synthase-3-Protein (NOS3) und hohen oxidativen Stress aufweisen, wurde in Publikation IV alternatives NOS3-Spleißen untersucht. Doch nicht Fehl-Spleißen, sondern Nutzung eines internen Promotors führte höchstwahrscheinlich zu einem verkürzten NOS3-Protein, das schließlich Apoptose auslöste. Die ersten 20 Aminosäuren der Apurin-/Apyrimidin-Endodeoxyribonuklease 1 (APEX1) waren in der Lage, die stressinduzierte Apoptose in Endothelzellen über eine Hochregulierung von SELENOT zu reduzieren, wie in Publikation V beschrieben. Um das SSHW von 5'ss in Reportersystemen oder Expressions-vektoren zu manipulieren, wurde in Publikation VI der ModCon-Algorithmus entwickelt, der einen genetischen Algorithmus anwendet, um SRP-Bindung über synonyme Substitutionen zu manipulieren. Veränderungen in der SRP-Besetzung von RNAs könnten sich auch auf Prozesse wie den RNA-Export auswirken, wie in Publikation VII beobachtet wurde, in der exportunterdrückende virale Sequenzelemente analysiert wurden, die vorzugsweise hnRNP-Proteine rekrutieren.

Pre-mRNA splicing is an mRNA processing step in which intronic sequences are excised and exonic sequences are ligated. Variations in splice site selection, so-called alternative splicing, enables expression of different proteins, originating from the same pre-mRNA transcript. Initially, an RNA duplex is formed between the 5’ splice site (5’ss) and the free 5’ end of the U1 snRNA. Splicing regulatory elements (SREs) recruit splicing regulatory proteins (SRPs), like hnRNP or SR proteins, that can greatly influence 5’ss usage, depending on their position. Mutations affecting these regulatory elements can lead to aberrant splicing, which can induce several diseases. Thus, any algorithm that estimates only intrinsic splice site strength is insufficient to correctly capture the impact of mutations on splice site selection, and thus whether they potentially lead to a reduction in functional protein. Publication I reviewed the influence of SREs on 5’ss selection and predictive algorithms, like the HEXplorer. The Splice Site HEXplorer Weight summarizes the overall enhancing or repressing properties of the immediate sequence context of splice sites. Studying 5’ss usage competition between neighbouring 5’ss of a large RNA-seq data set of fibroblasts in Publication II showed, that the differences in HBS and SSHW had to be considered together, to best model predicted 5’ss usage. Intrinsic strength, however, had a greater impact on 5’ss recognition than the SSHW, which was also shown for most likely non-pathogenic mutations of healthy individuals of the 1000 Genome project (unpublished paper I). For fast analysis of sequence variations in context of splicing, the VarCon algorithm was developed in Publication III, which converts ambiguous positional information to genomic positions. To analyze why cardiovascular endothelial cells treated with high concentrations of low-density lipoprotein show low levels of functional NO-synthase 3 (NOS3) protein and high oxidative stress, alternative NOS3 splicing was studied in Publication IV. However, not miss-splicing, but an internal promotor most likely resulted in the truncated NOS3 protein, finally inducing apoptosis. The first 20 amino acids of the Apurinic/Apyrimidinic Endodeoxyribonuclease 1 (APEX1), were able to reduce stress-induced apoptosis in endothelial cells, via upregulation of SELENOT as described in Publication V. To manipulate the SSHW of 5’ss in reporter systems or expression vectors, the ModCon algorithm was developed in Publication VI, which applies a genetic algorithm to manipulate SRP binding via synonymous substitutions. Changes in SRP composition of RNAs, however, might also affect process like RNA export, as observed in Publication VII, analyzing export repressing viral sequence elements, that preferably recruited hnRNP proteins.

Quelle:

1. Geuens, T., D. Bouhy, and V. Timmerman, The hnRNP family: insights into their role in health and disease. Hum Genet, 2016. 135(8): p. 851-67.
2. Erkelenz, S., et al., Genomic HEXploring allows landscaping of novel potential splicing regulatory elements. Nucleic Acids Res, 2014. 42(16): p. 10681-97.
3. Will, C.L. and R. Luhrmann, Spliceosome structure and function. Cold Spring Harb Perspect Biol, 2011. 3(7).
4. Twyffels, L., C. Gueydan, and V. Kruys, Shuttling SR proteins: more than splicing factors. FEBS J, 2011. 278(18): p. 3246-55.
5. Berget, S.M., C. Moore, and P.A. Sharp, Spliced segments at the 5' terminus of adenovirus 2 late mRNA. Proc Natl Acad Sci U S A, 1977. 74(8): p. 3171-5.
6. Khodor, Y.L., et al., Nascent-seq indicates widespread cotranscriptional pre-mRNA splicing in Drosophila. Genes Dev, 2011. 25(23): p. 2502-12.
7. Nilsen, T.W. and B.R. Graveley, Expansion of the eukaryotic proteome by alternative splicing. Nature, 2010. 463(7280): p. 457-63.
8. Pan, Q., et al., Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet, 2008. 40(12): p. 1413-5.
9. Aebi, M., et al., Sequence requirements for splicing of higher eukaryotic nuclear pre-mRNA. Cell, 1986. 47(4): p. 555-65.
10. Moore, M.J., Intron recognition comes of AGe. Nat Struct Biol, 2000. 7(1): p. 14-6.
11. Matera, A.G. and Z. Wang, A day in the life of the spliceosome. Nat Rev Mol Cell Biol, 2014. 15(2): p. 108-21.
12. Michaud, S. and R. Reed, An ATP-independent complex commits pre-mRNA to the mammalian spliceosome assembly pathway. Genes Dev, 1991. 5(12B): p. 2534-46.
13. Raghunathan, P.L. and C. Guthrie, RNA unwinding in U4/U6 snRNPs requires ATP hydrolysis and the DEIH-box splicing factor Brr2. Curr Biol, 1998. 8(15): p. 847-55.
14. Wahl, M.C., C.L. Will, and R. Luhrmann, The spliceosome: design principles of a dynamic RNP machine. Cell, 2009. 136(4): p. 701-18.
15. Matlin, A.J., F. Clark, and C.W. Smith, Understanding alternative splicing: towards a cellular code. Nat Rev Mol Cell Biol, 2005. 6(5): p. 386-98.
16. Erkelenz, S., et al., Position-dependent splicing activation and repression by SR and hnRNP proteins rely on common mechanisms. RNA, 2013. 19(1): p. 96-102.
17. Ptok, J., et al., Context matters: Regulation of splice donor usage. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, 2019.
18. Freund, M., et al., A novel approach to describe a U1 snRNA binding site. Nucleic Acids Res, 2003. 31(23): p. 6963-75.
19. Zhuang, Y. and A.M. Weiner, A compensatory base change in U1 snRNA suppresses a 5' splice site mutation. Cell, 1986. 46(6): p. 827-35.
20. Yeo, G. and C.B. Burge, Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol, 2004. 11(2-3): p. 377-94.
21. Burset, M., I.A. Seledtsov, and V.V. Solovyev, Analysis of canonical and non-canonical splice sites in mammalian genomes. Nucleic Acids Res, 2000. 28(21): p. 4364-75.
22. Wang, Z. and C.B. Burge, Splicing regulation: from a parts list of regulatory elements to an integrated splicing code. RNA, 2008. 14(5): p. 802-13.
23. Muller, L., et al., Modeling splicing outcome by combining 5'ss strength and splicing regulatory elements. Nucleic Acids Res, 2022. 50(15): p. 8834-8851.
24. Baralle, F.E. and J. Giudice, Alternative splicing as a regulator of development and tissue identity. Nat Rev Mol Cell Biol, 2017. 18(7): p. 437-451.
25. Busch, A. and K.J. Hertel, Evolution of SR protein and hnRNP splicing regulatory factors. Wiley Interdiscip Rev RNA, 2012. 3(1): p. 1-12.
26. Wegener, M. and M. Muller-McNicoll, View from an mRNP: The Roles of SR Proteins in Assembly, Maturation and Turnover. Adv Exp Med Biol, 2019. 1203: p. 83-112.
27. Manley, J.L. and A.R. Krainer, A rational nomenclature for serine/arginine-rich protein splicing factors (SR proteins). Genes Dev, 2010. 24(11): p. 1073-4.
28. Anko, M.L., Regulation of gene expression programmes by serine-arginine rich splicing factors. Semin Cell Dev Biol, 2014. 32: p. 11-21.
29. Caceres, J.F., et al., Role of the modular domains of SR proteins in subnuclear localization and alternative splicing specificity. J Cell Biol, 1997. 138(2): p. 225-38.
30. Shepard, P.J. and K.J. Hertel, The SR protein family. Genome Biol, 2009. 10(10): p. 242.
31. Graveley, B.R., K.J. Hertel, and T. Maniatis, The role of U2AF35 and U2AF65 in enhancer-dependent splicing. RNA, 2001. 7(6): p. 806-18.
32. Legartova, S., et al., The SC-35 Splicing Factor Interacts with RNA Pol II and A-Type Lamin Depletion Weakens This Interaction. Cells, 2021. 10(2).
33. Lemaire, R., et al., Stability of a PKCI-1-related mRNA is controlled by the splicing factor ASF/SF2: a novel function for SR proteins. Genes Dev, 2002. 16(5): p. 594-607.
34. Muller-McNicoll, M., et al., SR proteins are NXF1 adaptors that link alternative RNA processing to mRNA export. Genes Dev, 2016. 30(5): p. 553-66.
35. Sanford, J.R., et al., A novel role for shuttling SR proteins in mRNA translation. Genes Dev, 2004. 18(7): p. 755-68.
36. Pelisch, F., et al., The serine/arginine-rich protein SF2/ASF regulates protein sumoylation. Proc Natl Acad Sci U S A, 2010. 107(37): p. 16119-24.
37. Schwich, O.D., et al., SRSF3 and SRSF7 modulate 3'UTR length through suppression or activation of proximal polyadenylation sites and regulation of CFIm levels. Genome Biol, 2021. 22(1): p. 82.
38. Xiao, W., et al., Nuclear m(6)A Reader YTHDC1 Regulates mRNA Splicing. Mol Cell, 2016. 61(4): p. 507-519.
39. Zhu, Y., et al., Molecular Mechanisms for CFIm-Mediated Regulation of mRNA Alternative Polyadenylation. Mol Cell, 2018. 69(1): p. 62-74 e4.
40. Zhou, Z. and X.D. Fu, Regulation of splicing by SR proteins and SR protein-specific kinases. Chromosoma, 2013. 122(3): p. 191-207.
41. Blackie, A.C. and D.J. Foley, Exploring the roles of the Cdc2-like kinases in cancers. Bioorg Med Chem, 2022. 70: p. 116914.
42. Ninomiya, K., N. Kataoka, and M. Hagiwara, Stress-responsive maturation of Clk1/4 pre-mRNAs promotes phosphorylation of SR splicing factor. J Cell Biol, 2011. 195(1): p. 27-40.
43. Shi, Y. and J.L. Manley, A complex signaling pathway regulates SRp38 phosphorylation and pre-mRNA splicing in response to heat shock. Mol Cell, 2007. 28(1): p. 79-90.
44. Lai, M.C., R.I. Lin, and W.Y. Tarn, Transportin-SR2 mediates nuclear import of phosphorylated SR proteins. Proc Natl Acad Sci U S A, 2001. 98(18): p. 10154-9.
45. Misteli, T. and D.L. Spector, Serine/threonine phosphatase 1 modulates the subnuclear distribution of pre-mRNA splicing factors. Mol Biol Cell, 1996. 7(10): p. 1559-72.
46. Tacke, R., Y. Chen, and J.L. Manley, Sequence-specific RNA binding by an SR protein requires RS domain phosphorylation: creation of an SRp40-specific splicing enhancer. Proc Natl Acad Sci U S A, 1997. 94(4): p. 1148-53.
47. Botti, V., et al., Cellular differentiation state modulates the mRNA export activity of SR proteins. J Cell Biol, 2017. 216(7): p. 1993-2009.
48. Howard, J.M. and J.R. Sanford, The RNAissance family: SR proteins as multifaceted regulators of gene expression. Wiley Interdiscip Rev RNA, 2015. 6(1): p. 93-110.
49. Graveley, B.R., Sorting out the complexity of SR protein functions. RNA, 2000. 6(9): p. 1197-211.
50. Bradley, T., M.E. Cook, and M. Blanchette, SR proteins control a complex network of RNA-processing events. RNA, 2015. 21(1): p. 75-92.
51. Jamison, S.F., et al., U1 snRNP-ASF/SF2 interaction and 5' splice site recognition: characterization of required elements. Nucleic Acids Res, 1995. 23(16): p. 3260-7.
52. Shen, H. and M.R. Green, A pathway of sequential arginine-serine-rich domain-splicing signal interactions during mammalian spliceosome assembly. Mol Cell, 2004. 16(3): p. 363-73.
53. Shen, H. and M.R. Green, RS domains contact splicing signals and promote splicing by a common mechanism in yeast through humans. Genes Dev, 2006. 20(13): p. 1755-65.
54. Zuo, P. and T. Maniatis, The splicing factor U2AF35 mediates critical protein-protein interactions in constitutive and enhancer-dependent splicing. Genes Dev, 1996. 10(11): p. 1356-68.
55. Wang, Z., H.M. Hoffmann, and P.J. Grabowski, Intrinsic U2AF binding is modulated by exon enhancer signals in parallel with changes in splicing activity. RNA, 1995. 1(1): p. 21-35.
56. Kanopka, A., O. Muhlemann, and G. Akusjarvi, Inhibition by SR proteins of splicing of a regulated adenovirus pre-mRNA. Nature, 1996. 381(6582): p. 535-8.
57. Shen, M. and W. Mattox, Activation and repression functions of an SR splicing regulator depend on exonic versus intronic-binding position. Nucleic Acids Res, 2012. 40(1): p. 428-37.
58. Sharma, S., et al., Polypyrimidine tract binding protein controls the transition from exon definition to an intron defined spliceosome. Nat Struct Mol Biol, 2008. 15(2): p. 183-91.
59. Sharma, S., et al., U1 snRNA directly interacts with polypyrimidine tract-binding protein during splicing repression. Mol Cell, 2011. 41(5): p. 579-88.
60. More, D.A. and A. Kumar, SRSF3: Newly discovered functions and roles in human health and diseases. Eur J Cell Biol, 2020. 99(6): p. 151099.
61. Ortiz-Sanchez, P., et al., Loss of SRSF3 in Cardiomyocytes Leads to Decapping of Contraction-Related mRNAs and Severe Systolic Dysfunction. Circ Res, 2019. 125(2): p. 170-183.
62. Van Nostrand, E.L., et al., A large-scale binding and functional map of human RNA-binding proteins. Nature, 2020. 583(7818): p. 711-719.
63. Tacke, R. and J.L. Manley, Determinants of SR protein specificity. Curr Opin Cell Biol, 1999. 11(3): p. 358-62.
64. Liu, H.X., M. Zhang, and A.R. Krainer, Identification of functional exonic splicing enhancer motifs recognized by individual SR proteins. Genes Dev, 1998. 12(13): p. 1998-2012.
65. Sliskovic, I., H. Eich, and M. Muller-McNicoll, Exploring the multifunctionality of SR proteins. Biochem Soc Trans, 2022. 50(1): p. 187-198.
66. Dreyfuss, G., et al., hnRNP proteins and the biogenesis of mRNA. Annu Rev Biochem, 1993. 62: p. 289-321.
67. Hoffman, D.W., et al., RNA-binding domain of the A protein component of the U1 small nuclear ribonucleoprotein analyzed by NMR spectroscopy is structurally similar to ribosomal proteins. Proc Natl Acad Sci U S A, 1991. 88(6): p. 2495-9.
68. Han, S.P., Y.H. Tang, and R. Smith, Functional diversity of the hnRNPs: past, present and perspectives. Biochem J, 2010. 430(3): p. 379-92.
69. Pinol-Roma, S., HnRNP proteins and the nuclear export of mRNA. Semin Cell Dev Biol, 1997. 8(1): p. 57-63.
70. Shen, E.C., et al., Arginine methylation facilitates the nuclear export of hnRNP proteins. Genes Dev, 1998. 12(5): p. 679-91.
71. Khan, M.I., J. Zhang, and Q. Liu, HnRNP F and hnRNP H1 regulate mRNA stability of amyloid precursor protein. Neuroreport, 2021. 32(9): p. 824-832.
72. Kajitani, N., et al., hnRNP L controls HPV16 RNA polyadenylation and splicing in an Akt kinase-dependent manner. Nucleic Acids Res, 2017. 45(16): p. 9654-9678.
73. Martinez-Contreras, R., et al., hnRNP proteins and splicing control. Adv Exp Med Biol, 2007. 623: p. 123-47.
74. Mayeda, A. and A.R. Krainer, Regulation of alternative pre-mRNA splicing by hnRNP A1 and splicing factor SF2. Cell, 1992. 68(2): p. 365-75.
75. Zhang, Q.S., et al., hnRNP A1 associates with telomere ends and stimulates telomerase activity. RNA, 2006. 12(6): p. 1116-28.
76. Lee, E.K., et al., hnRNP C promotes APP translation by competing with FMRP for APP mRNA recruitment to P bodies. Nat Struct Mol Biol, 2010. 17(6): p. 732-9.
77. Quinones-Valdez, G., et al., Regulation of RNA editing by RNA-binding proteins in human cells. Commun Biol, 2019. 2: p. 19.
78. Fung, P.A., R. Labrecque, and T. Pederson, RNA-dependent phosphorylation of a nuclear RNA binding protein. Proc Natl Acad Sci U S A, 1997. 94(4): p. 1064-8.
79. Jean-Philippe, J., S. Paz, and M. Caputi, hnRNP A1: the Swiss army knife of gene expression. Int J Mol Sci, 2013. 14(9): p. 18999-9024.
80. Wang, Y., et al., A complex network of factors with overlapping affinities represses splicing through intronic elements. Nat Struct Mol Biol, 2013. 20(1): p. 36-45.
81. Okunola, H.L. and A.R. Krainer, Cooperative-binding and splicing-repressive properties of hnRNP A1. Mol Cell Biol, 2009. 29(20): p. 5620-31.
82. Zhu, J., A. Mayeda, and A.R. Krainer, Exon identity established through differential antagonism between exonic splicing silencer-bound hnRNP A1 and enhancer-bound SR proteins. Mol Cell, 2001. 8(6): p. 1351-61.
83. Zarnack, K., et al., Direct competition between hnRNP C and U2AF65 protects the transcriptome from the exonization of Alu elements. Cell, 2013. 152(3): p. 453-66.
84. Cao, W., et al., Control of alternative splicing by forskolin through hnRNP K during neuronal differentiation. Nucleic Acids Res, 2012. 40(16): p. 8059-71.
85. Patton, J.G., et al., Characterization and molecular cloning of polypyrimidine tract-binding protein: a component of a complex necessary for pre-mRNA splicing. Genes Dev, 1991. 5(7): p. 1237-51.
86. Robinson, R., Looping out introns to help splicing. PLoS Biol, 2006. 4(2): p. e41.
87. Martinez-Contreras, R., et al., Intronic binding sites for hnRNP A/B and hnRNP F/H proteins stimulate pre-mRNA splicing. PLoS Biol, 2006. 4(2): p. e21.
88. Fisette, J.F., et al., hnRNP A1 and hnRNP H can collaborate to modulate 5' splice site selection. RNA, 2010. 16(1): p. 228-38.
89. Nasim, F.U., et al., High-affinity hnRNP A1 binding sites and duplex-forming inverted repeats have similar effects on 5' splice site selection in support of a common looping out and repression mechanism. RNA, 2002. 8(8): p. 1078-89.
90. Li, S., et al., Identification of an aptamer targeting hnRNP A1 by tissue slide-based SELEX. J Pathol, 2009. 218(3): p. 327-36.
91. Han, K., et al., A combinatorial code for splicing silencing: UAGG and GGGG motifs. PLoS Biol, 2005. 3(5): p. e158.
92. Caputi, M. and A.M. Zahler, Determination of the RNA binding specificity of the heterogeneous nuclear ribonucleoprotein (hnRNP) H/H'/F/2H9 family. J Biol Chem, 2001. 276(47): p. 43850-9.
93. Freedman, M.L., et al., Principles for the post-GWAS functional characterization of cancer risk loci. Nat Genet, 2011. 43(6): p. 513-8.
94. Hindorff, L.A., et al., Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A, 2009. 106(23): p. 9362-7.
95. He, B., et al., Diverse noncoding mutations contribute to deregulation of cis-regulatory landscape in pediatric cancers. Sci Adv, 2020. 6(30): p. eaba3064.
96. Fox-Walsh, K.L., et al., The architecture of pre-mRNAs affects mechanisms of splice-site pairing. Proc Natl Acad Sci U S A, 2005. 102(45): p. 16176-81.
97. Pai, A.A., et al., The kinetics of pre-mRNA splicing in the Drosophila genome and the influence of gene architecture. Elife, 2017. 6.
98. Lim, K.H., et al., Using positional distribution to identify splicing elements and predict pre-mRNA processing defects in human genes. Proc Natl Acad Sci U S A, 2011. 108(27): p. 11093-8.
99. Fairbrother, W.G., et al., Predictive identification of exonic splicing enhancers in human genes. Science, 2002. 297(5583): p. 1007-13.
100. Brillen, A.L., et al., Succession of splicing regulatory elements determines cryptic 5ss functionality. Nucleic Acids Res, 2017. 45(7): p. 4202-4216.
101. Paz, I., et al., RBPmap: a web server for mapping binding sites of RNA-binding proteins. Nucleic Acids Res, 2014. 42(Web Server issue): p. W361-7.
102. Akerman, M., et al., A computational approach for genome-wide mapping of splicing factor binding sites. Genome Biol, 2009. 10(3): p. R30.
103. Cartegni, L., et al., ESEfinder: A web resource to identify exonic splicing enhancers. Nucleic Acids Res, 2003. 31(13): p. 3568-71.
104. Ke, S., et al., Quantitative evaluation of all hexamers as exonic splicing elements. Genome Res, 2011. 21(8): p. 1360-74.
105. Wang, Z., et al., Systematic identification and analysis of exonic splicing silencers. Cell, 2004. 119(6): p. 831-45.
106. Giudice, G., et al., ATtRACT-a database of RNA-binding proteins and associated motifs. Database (Oxford), 2016. 2016.
107. Piva, F., et al., SpliceAid: a database of experimental RNA target motifs bound by splicing proteins in humans. Bioinformatics, 2009. 25(9): p. 1211-3.
108. Jaganathan, K., et al., Predicting Splicing from Primary Sequence with Deep Learning. Cell, 2019. 176(3): p. 535-548 e24.
109. Pertea, M., X. Lin, and S.L. Salzberg, GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Res, 2001. 29(5): p. 1185-90.
110. Reese, M.G., et al., Improved splice site detection in Genie. J Comput Biol, 1997. 4(3): p. 311-23.
111. Scotti, M.M. and M.S. Swanson, RNA mis-splicing in disease. Nat Rev Genet, 2016. 17(1): p. 19-32.
112. Singh, R.K. and T.A. Cooper, Pre-mRNA splicing in disease and therapeutics. Trends Mol Med, 2012. 18(8): p. 472-82.
113. Cao, A. and R. Galanello, Beta-thalassemia. Genet Med, 2010. 12(2): p. 61-76.
114. Treisman, R., et al., A single-base change at a splice site in a beta 0-thalassemic gene causes abnormal RNA splicing. Cell, 1982. 29(3): p. 903-11.
115. Fletcher, S., et al., Antisense suppression of donor splice site mutations in the dystrophin gene transcript. Mol Genet Genomic Med, 2013. 1(3): p. 162-73.
116. Takeshima, Y., et al., Mutation spectrum of the dystrophin gene in 442 Duchenne/Becker muscular dystrophy cases from one Japanese referral center. J Hum Genet, 2010. 55(6): p. 379-88.
117. Yeo, G., et al., Variation in alternative splicing across human tissues. Genome Biol, 2004. 5(10): p. R74.
118. Su, C.H., D. D, and W.Y. Tarn, Alternative Splicing in Neurogenesis and Brain Development. Front Mol Biosci, 2018. 5: p. 12.
119. Braggin, J.E., et al., Alternative splicing in a presenilin 2 variant associated with Alzheimer disease. Ann Clin Transl Neurol, 2019. 6(4): p. 762-777.
120. Kar, A., et al., Tau alternative splicing and frontotemporal dementia. Alzheimer Dis Assoc Disord, 2005. 19 Suppl 1(Suppl 1): p. S29-36.
121. Tseng, E., et al., The Landscape of SNCA Transcripts Across Synucleinopathies: New Insights From Long Reads Sequencing Analysis. Front Genet, 2019. 10: p. 584.
122. Perrone, B., et al., Alternative Splicing of ALS Genes: Misregulation and Potential Therapies. Cell Mol Neurobiol, 2020. 40(1): p. 1-14.
123. Nikom, D. and S. Zheng, Alternative splicing in neurodegenerative disease and the promise of RNA therapies. Nat Rev Neurosci, 2023. 24(8): p. 457-473.
124. Oueslati, A., Implication of Alpha-Synuclein Phosphorylation at S129 in Synucleinopathies: What Have We Learned in the Last Decade? J Parkinsons Dis, 2016. 6(1): p. 39-51.
125. Ouyang, J., et al., The role of alternative splicing in human cancer progression. Am J Cancer Res, 2021. 11(10): p. 4642-4667.
126. Babic, I., et al., EGFR mutation-induced alternative splicing of Max contributes to growth of glycolytic tumors in brain cancer. Cell Metab, 2013. 17(6): p. 1000-1008.
127. Anczukow, O., et al., The splicing factor SRSF1 regulates apoptosis and proliferation to promote mammary epithelial cell transformation. Nat Struct Mol Biol, 2012. 19(2): p. 220-8.
128. Zhou, X., et al., BCLAF1 and its splicing regulator SRSF10 regulate the tumorigenic potential of colon cancer cells. Nat Commun, 2014. 5: p. 4581.
129. Jensen, M.A., J.E. Wilkinson, and A.R. Krainer, Splicing factor SRSF6 promotes hyperplasia of sensitized skin. Nat Struct Mol Biol, 2014. 21(2): p. 189-97.
130. Kim, H.R., et al., SRSF5: a novel marker for small-cell lung cancer and pleural metastatic cancer. Lung Cancer, 2016. 99: p. 57-65.
131. Zhou, X., et al., The RNA-binding protein SRSF1 is a key cell cycle regulator via stabilizing NEAT1 in glioma. Int J Biochem Cell Biol, 2019. 113: p. 75-86.
132. Gallardo, M., et al., Aberrant hnRNP K expression: All roads lead to cancer. Cell Cycle, 2016. 15(12): p. 1552-7.
133. Sandberg, R., et al., Proliferating cells express mRNAs with shortened 3' untranslated regions and fewer microRNA target sites. Science, 2008. 320(5883): p. 1643-7.
134. Mayr, C. and D.P. Bartel, Widespread shortening of 3'UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell, 2009. 138(4): p. 673-84.
135. Robberson, B.L., G.J. Cote, and S.M. Berget, Exon definition may facilitate splice site selection in RNAs with multiple exons. Mol Cell Biol, 1990. 10(1): p. 84-94.
136. Porubsky, D., et al., Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads. Nat Biotechnol, 2021. 39(3): p. 302-308.
137. Chan, J.J., et al., Pan-cancer pervasive upregulation of 3' UTR splicing drives tumourigenesis. Nat Cell Biol, 2022. 24(6): p. 928-939.
138. Ciolli Mattioli, C., et al., Alternative 3' UTRs direct localization of functionally diverse protein isoforms in neuronal compartments. Nucleic Acids Res, 2019. 47(5): p. 2560-2573.
139. Graveley, B.R., Alternative splicing: increasing diversity in the proteomic world. Trends Genet, 2001. 17(2): p. 100-7.
140. Dutertre, M., et al., The emerging role of pre-messenger RNA splicing in stress responses: sending alternative messages and silent messengers. RNA Biol, 2011. 8(5): p. 740-7.
141. Shattuck, J.E., et al., Sky1: at the intersection of prion-like proteins and stress granule regulation. Curr Genet, 2020. 66(3): p. 463-468.
142. Allemand, E., et al., Regulation of heterogenous nuclear ribonucleoprotein A1 transport by phosphorylation in cells stressed by osmotic shock. Proc Natl Acad Sci U S A, 2005. 102(10): p. 3605-10.
143. Buchan, J.R. and R. Parker, Eukaryotic stress granules: the ins and outs of translation. Mol Cell, 2009. 36(6): p. 932-41.
144. Jain, S., et al., ATPase-Modulated Stress Granules Contain a Diverse Proteome and Substructure. Cell, 2016. 164(3): p. 487-98.
145. Gonnissen, S., et al., High Concentration of Low-Density Lipoprotein Results in Disturbances in Mitochondrial Transcription and Functionality in Endothelial Cells. Oxid Med Cell Longev, 2019. 2019: p. 7976382.
146. Janssen, W.J., et al., Inflammation-Induced Alternative Pre-mRNA Splicing in Mouse Alveolar Macrophages. G3 (Bethesda), 2020. 10(2): p. 555-567.
147. Ramirez-Peinado, S., et al., TRAPPC13 modulates autophagy and the response to Golgi stress. J Cell Sci, 2017. 130(14): p. 2251-2265.
148. Merk, D., et al., Selenoprotein T Protects Endothelial Cells against Lipopolysaccharide-Induced Activation and Apoptosis. Antioxidants (Basel), 2021. 10(9).
149. Ptok, J., et al., Modifying splice site usage with ModCon: Maintaining the genetic code while changing the underlying mRNP code. Comput Struct Biotechnol J, 2021. 19: p. 3069-3076.
150. Ostermann, P.N., et al., Let It Go: HIV-1 cis-Acting Repressive Sequences. J Virol, 2021. 95(15): p. e0034221.
151. Grosso, A.R., et al., Tissue-specific splicing factor gene expression signatures. Nucleic Acids Res, 2008. 36(15): p. 4823-32.
152. Zhang, X.H. and L.A. Chasin, Computational definition of sequence motifs governing constitutive exon splicing. Genes Dev, 2004. 18(11): p. 1241-50.
153. Wong, M.S., J.B. Kinney, and A.R. Krainer, Quantitative Activity Profile and Context Dependence of All Human 5' Splice Sites. Mol Cell, 2018. 71(6): p. 1012-1026 e3.
154. Krchnakova, Z., et al., Splicing of long non-coding RNAs primarily depends on polypyrimidine tract and 5' splice-site sequences due to weak interactions with SR proteins. Nucleic Acids Res, 2019. 47(2): p. 911-928.
155. Zhang, Z., et al., Noisy splicing, more than expression regulation, explains why some exons are subject to nonsense-mediated mRNA decay. BMC Biol, 2009. 7: p. 23.
156. Hall, S.L. and R.A. Padgett, Conserved sequences in a class of rare eukaryotic nuclear introns with non-consensus splice sites. J Mol Biol, 1994. 239(3): p. 357-65.
157. Turunen, J.J., et al., The significant other: splicing by the minor spliceosome. Wiley Interdiscip Rev RNA, 2013. 4(1): p. 61-76.
158. Alioto, T.S., U12DB: a database of orthologous U12-type spliceosomal introns. Nucleic Acids Res, 2007. 35(Database issue): p. D110-5.
159. Sheth, N., et al., Comprehensive splice-site analysis using comparative genomics. Nucleic Acids Res, 2006. 34(14): p. 3955-67.
160. Abril, J.F., R. Castelo, and R. Guigo, Comparison of splice sites in mammals and chicken. Genome Res, 2005. 15(1): p. 111-9.
161. Nurk, S., et al., The complete sequence of a human genome. Science, 2022. 376(6588): p. 44-53.
162. Buchner, N., et al., Unhealthy diet and ultrafine carbon black particles induce senescence and disease associated phenotypic changes. Exp Gerontol, 2013. 48(1): p. 8-16.
163. Le Gouill, E., et al., Endothelial nitric oxide synthase (eNOS) knockout mice have defective mitochondrial beta-oxidation. Diabetes, 2007. 56(11): p. 2690-6.
164. Corona, J.C. and M.R. Duchen, PPARgamma as a therapeutic target to rescue mitochondrial function in neurological disease. Free Radic Biol Med, 2016. 100: p. 153-163.
165. Tanguy, Y., et al., The PACAP-regulated gene selenoprotein T is highly induced in nervous, endocrine, and metabolic tissues during ontogenetic and regenerative processes. Endocrinology, 2011. 152(11): p. 4322-35.
166. Gehring, N.H., E. Wahle, and U. Fischer, Deciphering the mRNP Code: RNA-Bound Determinants of Post-Transcriptional Gene Regulation. Trends Biochem Sci, 2017. 42(5): p. 369-382.
167. Schwartz, S., B.K. Felber, and G.N. Pavlakis, Distinct RNA sequences in the gag region of human immunodeficiency virus type 1 decrease RNA stability and inhibit expression in the absence of Rev protein. J Virol, 1992. 66(1): p. 150-9.
168. Schwartz, S., et al., Mutational inactivation of an inhibitory sequence in human immunodeficiency virus type 1 results in Rev-independent gag expression. J Virol, 1992. 66(12): p. 7176-82.

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Heinrich-Heine-Universität Düsseldorf

Dokument: Modellierung des Spleißergebnisses durch Kombination von 5'ss Stärke und spleißregulierenden Elementen