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Strategy of probe selection for studying mRNAs that participate in receptor-mediated apoptosis signaling

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Abstract

Death receptors (DRs) and the participants of DR-mediated signaling are characterized by a large number of mRNA isoforms generated by alternative splicing. Due to their high labor intensity and high cost, conventional methods (RT-PCR and RT-PCR in real time) are ineffective when the simultaneous detection of a plurality of mRNA isoforms is needed. In this regard, the use of DNA biochips is has prospective applications in analyzing the expression of many genes simultaneously. In this paper, we suggest an optimal strategy of probes selection aimed at detecting the maximum number of mRNA splice variants generated by major participants of DR-signaling. The objects of the study were 185 genes that form 1134 mRNA isoforms. As a result, a biochip design was developed that enables the detection of 499 mRNA isoforms (44% of total mRNA splice variants). The proposed strategy combines a high degree of modularity, the use of modern high-performance computers, and broad opportunities for setting up the selection criteria in accordance with the objectives of the study.

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Abbreviations

DR:

death receptor

TNF:

tumor necrosis factor

DD:

death domain

References

  1. Sessler T., Healy S., Samali A., Szegezdi E. 2013. Structural determinants of DISC function: New insights into death receptor-mediated apoptosis signalling. Pharmacol. Ther. 140, 186–199.

    Article  CAS  PubMed  Google Scholar 

  2. Schneider-Brachert W., Heigl U., Ehrenschwender M. 2013. Membrane trafficking of death receptors: Implications on signalling. Int. J. Mol. Sci. 14, 14475–14503.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Utkin O.V., Novikov V.V. 2007. Regulation of apoptosis by alternative messenger RNA splicing. Ross. Biother. Zh. 6, 13–20.

    CAS  Google Scholar 

  4. Schwerk C., Schulze-Osthoff K. 2005. Regulation of apoptosis by alternative pre-mRNA splicing. Moll. Cell. 19, 1–13.

    Article  CAS  Google Scholar 

  5. Borysenko C.W., A-Palacios V.N.G., Griswold R.D., Li Y., Iyer A.K., Yaroslavskiy B.B., Sharrow A.C., Blair H.C. 2006. Death receptor-3 mediates apoptosis in human osteoblasts under narrowly regulated conditions. J. Cell. Physiol. 209, 1021–1028.

    Article  CAS  PubMed  Google Scholar 

  6. Cascino I., Papoff G., Eramo A., Ruberti G. 1996. Soluble Fas/Apo-1 splicing variants and apoptosis. Front. Biosci. 1, d12–d18.

    CAS  PubMed  Google Scholar 

  7. Utkin O.V., Novikov V.V. 2012. Death receptors in apoptosis modulation. Usp. Sovrem. Biol. 132, 288–297.

    Google Scholar 

  8. Koch Y., Wolf T., Sorger P.K., Eils R., Brors B. 2013. Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states. PLoS ONE. 8, e82593.

    Article  PubMed Central  PubMed  Google Scholar 

  9. Utkin O.V., Svintsova T.A., Kravchenko G.A., Shmeleva O.A., Novikov D.V., Babaev A.A., Sobchak D.M., Karaulov A.V., Novikov V.V. 2012. Expression of alternative forms of CD95/Fas gene in blood cells of patients with herpesvirus infection. Immunologiya. 33, 189–193.

    CAS  Google Scholar 

  10. Porquet N., Poirier A., Houle F., Pin A.L., Gout S., Tremblay P.L., Paquet E.R., Klinck R., Auger F.A., Huot J. 2011. Survival advantages conferred to colon cancer cells by E-selectin-induced activation of the PI3K-NFκB survival axis downstream of death receptor-3. BMC Cancer. 11, 285.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Lin S., Wang W., Palm C., Davis R.W., Juneau K. 2010. A molecular inversion probe assay for detecting alternative splicing. BMC Genomics. 11, 712.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Srinivasan K., Shiue L., Hayes J.D., Centers R., Fitzwater S., Loewen R., Edmondson L.R., Bryant J., Smith M., Rommelfanger C., Welch V., Clark T.A., Sugnet C.W., Howe K.J., Mandel-Gutfreund Y., Ares M., Jr. 2005. Detection and measurement of alternative splicing using splicing-sensitive microarrays. Methods. 37, 345–359.

    Article  CAS  PubMed  Google Scholar 

  13. Wang X., Seed B. 2003. Selection of oligonucleotide probes for protein coding sequences. Bioinformatics. 19, 796–802.

    Article  CAS  PubMed  Google Scholar 

  14. Lockhart D.J., Dong H., Byrne M. 1996. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 14, 1675–1680.

    Article  CAS  PubMed  Google Scholar 

  15. Rouillard J., Zuker M., Gulari E. 2003. OligoArray 2.0: Design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res. 31, 3057–3062.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Gordon P., Sensen C. 2004. Osprey: A comprehensive tool employing novel methods for the design of oligonucleotides for DNA sequencing and microarrays. Nucleic Acids Res. 32, e133.

    Article  PubMed Central  PubMed  Google Scholar 

  17. Li F., Stormo G. 2001. Selection of optimal DNA oligos for gene expression arrays. Bioinformatics. 17, 1067–1076.

    Article  CAS  PubMed  Google Scholar 

  18. Wernersson R., Nielsen H. 2005. OligoWiz 2.0: Integrating sequence feature annotation into the design of microarray probes. Nucleic Acids Res. 33, W611–W615.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Herwig R., Schmitt A., Steinfath M., O’Brien J., Seidel H., Meier-Ewert S., Lehrach H., Radelof U. 2000. Information theoretical probe selection for hybridisation experiments. Bioinformatics. 16, 890–898.

    Article  CAS  PubMed  Google Scholar 

  20. Chang P., Peck K. 2003. Design and assessment of a fast algorithm for identifying specific probes for human and mouse genes. Bioinformatics. 19, 1311–1317.

    Article  CAS  PubMed  Google Scholar 

  21. Tolonen A., Albeanu D., Corbett J., Handley H., Henson C., Malik P. 2002. Optimized in situ construction of oligomers on an array surface. Nucleic Acids Res. 30, e107.

    Article  PubMed Central  PubMed  Google Scholar 

  22. Kaderali L., Schliep A. 2002. Selecting signature oligonucleotides to identify organisms using DNA arrays. Bioinformatics. 18, 1340–1349.

    Article  CAS  PubMed  Google Scholar 

  23. Hughes T., Mao M., Jones A., Burchard J., Marton M.J., Shannon K.W., Lefkowitz S.M., Ziman M., Schelter J.M., Meyer M.R., Kobayashi S., Davis C., Dai H., He Y.D., Stephaniants S.B., Cavet G., Walker W.L., West A., Coffey E., Shoemaker D.D., Stoughton R., Blanchard A.P., Friend S.H., Linsley P.S. 2001. Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat. Biotechnol. 19, 342–347.

    Article  CAS  PubMed  Google Scholar 

  24. SantaLucia J. Jr. 1998. A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proc. Natl. Acad. Sci. U. S. A. 95, 1460–1465.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Aho A.V., Corasick M.J. 1975. Efficient string matching: An aid to bibliographic search. Commun. ACM. 18, 333–340.

    Article  Google Scholar 

  26. Iseli C., Ambrosini G., Bucher P., Jongeneel C.V. 2007. Indexing strategies for rapid searches of short words in genome sequences. PLoS ONE. 2, e579.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Kane M.D., Jatkoe T.A., Stumpf C.R., Lu J., Thomas J.D., Madore S.J. 2000. Assessment of the sensitivity and specificity of oligonucleotide (50 mer) microarrays. Nucleic Acids Res. 28, 4552–4557.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Xu G., Shi Y. 2007. Apoptosis signaling pathways and lymphocyte homeostasis. Cell Res. 17, 759–771.

    Article  CAS  PubMed  Google Scholar 

  29. Kanehisa M., Goto S., Sato Y., Kawashima M., Furumichi M., Tanabe M. 2014. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Stamova B.S., Apperson M., Walker W.L., Tian Y., Xu H., Adamczy P., Zhan X., Liu D.Z., Ander B.P., Liao I.H., Gregg J.P., Turner R.J., Jickling G., Lit L., Sharp F.R. 2009. Identification and validation of suitable endogenous reference genes for gene expression studies in human peripheral blood. BMC Med. Genomics. 2, 49.

    Article  PubMed Central  PubMed  Google Scholar 

  31. Benson D.A., Cavanaugh M., Clark K., Karsch-Mizrachi I., Lipman D.J., Ostell J., Sayers E.W. 2013. GenBank. Nucleic Acids Res. 41, D36–D42.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Gardner S.N., Jaing C.J., McLoughlin K.S., Slezak T.R. 2000. A microbial detection array (MDA) for viral and bacterial detection. BMC Genomics. 11, 668.

    Article  Google Scholar 

  33. Jaing C., Gardner S., McLoughlin K., Mulakken N., Alegria-Hartman M., Banda P., Williams P., Gu P., Wagner M., Manohar C., Slezak T. 2008. A functional gene array for detection of bacterial virulence elements. PLoS ONE. 3, e2163.

    Article  PubMed Central  PubMed  Google Scholar 

  34. Lodes M.J., Suciu D., Wilmoth J.L., Ross M., Munro S., Dix K., Bernards K., Stöver A.G., Quintana M., Iihoshi N., Lyon W.J., Danley D.L., McShea A. 2007. Identification of upper respiratory tract pathogens using electrochemical detection on an oligonucleotide microarray. PLoS ONE. 2, e924.

    Article  PubMed Central  PubMed  Google Scholar 

  35. McLoughlin K.S. 2011. Microarrays for pathogen detection and analysis. Brief Funct. Genomics. 10, 342–353.

    Article  PubMed Central  PubMed  Google Scholar 

  36. Erlandsson L., Rosenstierne M.W., McLoughlin K., Jaing C., Fomsgaard A. 2011. The microbial detection array combined with random Phi29-amplification used as a diagnostic tool for virus detection in clinical samples. PLoS ONE. 6, e22631.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Li H., Zhao C., Chen H., Zhang F., He W., Wang X., Wang Q., Yang R., Zhou D., Wang H. 2013. Identification of gene clusters associated with host adaptation and antibiotic resistance in Chinese Staphylococcus aureus isolates by microarray-based comparative genomics. PLoS ONE. 8, e53341.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. You Y., He L., Zhang M., Fu J., Gu Y., Zhang B., Tao X., Zhang J. 2012. Comparative genomics of Helicobacter pylori strains of China associated with different clinical outcome. PLoS ONE. 7, e38528.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Gill S.R., McIntyre L.M., Nelson C.L., Remortel B., Rude T., Reller L.B., Fowler V.G., Jr. 2011. Potential associations between severity of infection and the presence of virulence-associated genes in clinical strains of Staphylococcus aureus. PLoS ONE. 6, e18673.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Emy Dorfman L., Leite J.C., Giugliani R., Riegel M. 2015. Microarray-based comparative genomic hybridization analysis in neonates with congenital anomalies: Detection of chromosomal imbalances. J. Pediatr. (Rio de Janeiro). 91, 59–67. doi 10.1016/j.jped.2014.05.007

    Article  Google Scholar 

  41. Miller D.T., Adam M.P., Aradhya S., Biesecker L.G., Brothman A.R., Carter N.P., Church D.M., Crolla J.A., Eichler E.E., Epstein C.J., Faucett W.A., Feuk L., Friedman J.M., Hamosh A., Jackson L., Kaminsky E.B., Kok K., Krantz I.D., Kuhn R.M., Lee C., Ostell J.M., Rosenberg C., Scherer S.W., Spinner N.B., Stavropoulos D.J., Tepperberg J.H., Thorland E.C., Vermeesch J.R., Waggoner D.J., Watson M.S., Martin C.L., Ledbetter D.H. 2010. Consensus statement: Chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am. J. Hum. Genet. 86, 749–764.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Yuen R.K., Merkoulovitch A., MacDonald J.R., Vlasschaert M., Lo K., Grober E., Marshall C.R., Jarvi K.A., Kolomietz E., Scherer S.W. 2014. Development of a high-resolution Y-chromosome microarray for improved male infertility diagnosis. Fertil. Steril. 101, 1079–1085.

    Article  PubMed  Google Scholar 

  43. Li X., Chen J., Lu B.J., Peng S., Desper R., Lai M. 2011. −8p12–23 and +20q are predictors of subtypes and metastatic pathways in colorectal cancer: Construction of tree models using comparative genomic hybridization data. OMICS. 15, 37–47.

    Article  CAS  PubMed  Google Scholar 

  44. Kang J.U. 2014. Chromosome 8q as the most frequent target for amplification in early gastric carcinoma. Oncol. Lett. 7, 1139–1143.

    CAS  PubMed Central  PubMed  Google Scholar 

  45. Li X., Peng S. 2013. Identification of metastasis-associated genes in colorectal cancer through an integrated genomic and transcriptomic analysis. Chin. J. Cancer Res. 25, 623–636.

    PubMed Central  PubMed  Google Scholar 

  46. Carrigan P.E., Bingham J.L., Srinvasan S., Brentnall T.A., Miller L.J. 2011. Characterization of alternative spliceoforms and the RNA splicing machinery in pancreatic cancer. Pancreas. 40, 281–288.

    CAS  PubMed Central  PubMed  Google Scholar 

  47. Lee C., Roy M. 2004. Analysis of alternative splicing with microarrays: Successes and challenges. Genome Biol. 5, 231.

    Article  PubMed Central  PubMed  Google Scholar 

  48. Lapuk A., Marr H., Jakkula L., Pedro H., Bhattacharya S., Purdom E., Hu Z., Simpson K., Pachter L., Durinck S., Wang N., Parvin B., Fontenay G., Speed T., Garbe J., Stampfer M., Bayandorian H., Dorton S., Clark T.A., Schweitzer A., Wyrobek A., Feiler H., Spellman P., Conboy J., Gray J.W. 2010. Programs in breast cancer exon-level microarray analyses identify alternative splicing. Mol. Cancer Res. 8, 961–974.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  49. Hall J.S., Leong H.S., Armenoult L.S., Newton G.E., Valentine H.R., Irlam J.J., Möller-Levet C., Sikand K.A., Pepper S.D., Miller C.J., West C.M. 2011. Exon-array profiling unlocks clinically and biologically relevant gene signatures from formalin-fixed paraffin-embedded tumour samples. Br. J. Cancer. 104, 971–981.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Xi L., Feber A., Gupta V., Bergemann A.D., Landreneau R.J., Litle V.R., Pennathur A., Luketich J.D., Godfrey T.E. 2008. Whole genome exon arrays identify differential expression of alternatively spliced, cancerrelated genes in lung cancer. Nucleic Acids Res. 36, 6535–6547.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  51. Paulo P., Ribeiro F.R., Santos J., Mesquita D., Almeida M., Barros-Silva J.D., Itkonen H., Henrique R., Jerónimo C., Sveen A., Mills I.G., Skotheim R.I., Lothe R.A., Teixeira M.R. 2012. Molecular subtyping of primary prostate cancer reveals specific and shared target genes of different ETS rearrangements. Neoplasia. 14, 600–611.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Guo X., Chen Q.R., Song Y.K., Wei J.S., Khan J. 2011. Exon array analysis reveals neuroblastoma tumors have distinct alternative splicing patterns according to stage and MYCN amplification status. BMC Med. Genomics. 18, 35.

    Article  Google Scholar 

  53. Li R., Ochs M.F., Ahn S.M., Hennessey P., Tan M., Soudry E., Gaykalova D.A., Uemura M., Brait M., Shao C., Westra W., Bishop J., Fertig E.J., Califano J.A. 2014. Expression microarray analysis reveals alternative splicing of LAMA3 and DST genes in head and neck squamous cell carcinoma. PLoS ONE. 9, e91263.

    Article  PubMed Central  PubMed  Google Scholar 

  54. Sveen A., Johannessen B., Teixeira M.R., Lothe R.A., Skotheim R.I. 2014. Transcriptome instability as a molecular pan-cancer characteristic of carcinomas. BMC Genomics. 10, 672.

    Article  Google Scholar 

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Authors and Affiliations

  1. Blokhina Scientific Research Institute of Epidemiology and Microbiology of Nizhny Novgorod, Nizhny Novgorod, 603950, Russia

    L. A. Solntsev, V. D. Starikova, N. A. Sakharnov, D. I. Knyazev & O. V. Utkin

  2. Nizhny Novgorod State Medical Academy, Nizhny Novgorod, 603005, Russia

    O. V. Utkin

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  1. L. A. Solntsev
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  2. V. D. Starikova
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  3. N. A. Sakharnov
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  4. D. I. Knyazev
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Correspondence to L. A. Solntsev.

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Original Russian Text © L.A. Solntsev, V.D. Starikova, N.A. Sakharnov, D.I. Knyazev, O.V. Utkin, 2015, published in Molekulyarnaya Biologiya, 2015, Vol. 49, No. 3, pp. 515–524.

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Solntsev, L.A., Starikova, V.D., Sakharnov, N.A. et al. Strategy of probe selection for studying mRNAs that participate in receptor-mediated apoptosis signaling. Mol Biol 49, 457–465 (2015). https://doi.org/10.1134/S0026893315030164

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