Intragraft transcriptional profiling of renal transplant patients with tubular dysfunction reveals mechanisms underlying graft injury and recovery

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art_AZEVEDO_Intragraft_transcriptional_profiling_of_renal_transplant_patients_with_2016.PDF (2.67 MB)
Citações na Scopus
4
Tipo de produção
article
Data de publicação
2016
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
BIOMED CENTRAL LTD
Autores
RENESTO, Paulo Guilherme
CHINEN, Rogerio
NAKA, Erika
MATOS, Ana Cristina Carvalho de
CENEDEZE, Marcos Antonio
CAMARA, Niels Olsen Saraiva
PACHECO-SILVA, Alvaro
Citação
HUMAN GENOMICS, v.10, article ID 2, 12p, 2016
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Background: Proximal tubular dysfunction (PTD) is associated with a decreased long-term graft survival in renal transplant patients and can be detected by the elevation of urinary tubular proteins. This study investigated transcriptional changes in biopsies from renal transplant patients with PTD to disclose molecular mechanisms underlying graft injury and functional recovery. Methods: Thirty-three renal transplant patients with high urinary levels of retinol-binding protein, a biomarker of PTD, were enrolled in the study. The initial immunosuppressive scheme included azathioprine, cyclosporine, and steroids. After randomization, 18 patients (group 2) had their treatment modified by reducing cyclosporine dosage and substituting azathioprine for mycophenolate mofetil, while the other 15 patients (group 1) remained under the initial scheme. Patients were biopsied at enrollment and after 12 months of follow-up, and paired comparisons were performed between their intragraft gene expression profiles. The differential transcriptome profiles were analyzed by constructing gene co-expression networks and identifying enriched functions and central nodes in each network. Results: Only the alternative immunosuppressive scheme used in group 2 ameliorated renal function and tubular proteinuria after 12 months of follow-up. Intragraft molecular changes observed in group 2 were linked to autophagy, extracellular matrix, and adaptive immunity. Conversely, gene expression changes in group 1 were related to fibrosis, endocytosis, ubiquitination, and endoplasmic reticulum stress. Conclusion: These results suggest that molecular networks associated with the control of endocytosis, autophagy, protein overload, fibrosis, and adaptive immunity may be involved in improvement of graft function.
Palavras-chave
Network analysis, Kidney transplantation, Genomics, Proximal tubular dysfunction, Transcriptional profiling
URI
https://observatorio.fm.usp.br/handle/OPI/13483
Referências
  1. Abbate M, 1999, AM J PHYSIOL-RENAL, V277, pF454
  2. Akoh Jacob A, 2011, Saudi J Kidney Dis Transpl, V22, P637
  3. Wuthrich RP, 1998, TRANSPLANT P, V30, P4096, DOI 10.1016/S0041-1345(98)01355-4
  4. van den Brand JAJG, 2011, CLIN J AM SOC NEPHRO, V6, P2846, DOI 10.2215/CJN.04020411
  5. Scardoni G, 2009, BIOINFORMATICS, V25, P2857, DOI 10.1093/bioinformatics/btp517
  6. Lebeau C, 2005, NEPHROL DIAL TRANSPL, V20, P2321, DOI 10.1093/ndt/gfi042
  7. Chinen R, 2006, BRAZ J MED BIOL RES, V39, P1305, DOI 10.1590/S0100-879X2006001000006
  8. Camara NOS, 2004, TRANSPLANTATION, V78, P269, DOI 10.1097/01.TP.0000128333.46949.A4
  9. Bozic M, 2011, J AM SOC NEPHROL, V22, P1099, DOI 10.1681/ASN.2010070701
  10. Jaconi S, 1996, EUR J ENDOCRINOL, V134, P576
  11. Ling XB, 2010, J AM SOC NEPHROL, V21, P646, DOI 10.1681/ASN.2009080876
  12. Scherer A, 2009, NEPHROL DIAL TRANSPL, V24, P2567, DOI 10.1093/ndt/gfp183
  13. Cybulsky AV, 2013, KIDNEY INT, V84, P25, DOI 10.1038/ki.2012.390
  14. Ball ST, 2003, QJM-INT J MED, V96, P363, DOI 10.1093/qjmed/hcg052
  15. Smoot ME, 2011, BIOINFORMATICS, V27, P431, DOI 10.1093/bioinformatics/btq675
  16. Schaub S, 2007, TRANSPLANTATION, V84, P104, DOI 10.1097/01.tp.0000268808.39401.e8
  17. Seto S, 2012, CELL MICROBIOL, V14, P710, DOI 10.1111/j.1462-5822.2012.01754.x
  18. Badid C, 2000, KIDNEY INT, V58, P51, DOI 10.1046/j.1523-1755.2000.00140.x
  19. Prifti E, 2008, BIOINFORMATICS, V24, P2636, DOI 10.1093/bioinformatics/btn492
  20. McDermott JE, 2009, J COMPUT BIOL, V16, P169, DOI 10.1089/cmb.2008.04TT
  21. Maluf DG, 2008, MOL MED, V14, P276, DOI 10.2119/2007-00111.Maluf
  22. Rouas-Freiss N, 2003, AM J TRANSPLANT, V3, P11, DOI 10.1034/j.1600-6143.2003.30103.x
  23. Famulski KS, 2010, AM J TRANSPLANT, V10, P490, DOI 10.1111/j.1600-6143.2009.02983.x
  24. Nauta FL, 2011, AM J KIDNEY DIS, V57, P733, DOI 10.1053/j.ajkd.2010.12.022
  25. Tanjore H, 2013, BBA-MOL BASIS DIS, V1832, P940, DOI 10.1016/j.bbadis.2012.11.011
  26. Montojo J, 2010, BIOINFORMATICS, V26, P2927, DOI 10.1093/bioinformatics/btq562
  27. Benson M, 2006, CURR MOL MED, V6, P695, DOI 10.2174/156652406778195044
  28. Bradley SP, 2006, TRANSPLANT P, V38, P1740, DOI 10.1016/j.transproceed.2006.05.054
  29. Heymann D, 2006, CURR OPIN INVESTIG D, V7, P443
  30. Tsushima K, 2011, FASEB J, V25, P1531, DOI 10.1096/fj.10-174615
  31. Woitas RP, 2001, CLIN CHEM, V47, P2179
  32. Behrends C, 2010, NATURE, V466, P68, DOI 10.1038/nature09204
  33. Amer H, 2013, AM J TRANSPLANT, V13, P676, DOI 10.1111/ajt.12044
  34. Eisen MB, 1998, P NATL ACAD SCI USA, V95, P14863, DOI 10.1073/pnas.95.25.14863
  35. Dohi E, 2012, NEUROCHEM INT, V60, P431, DOI 10.1016/j.neuint.2012.01.020
  36. Ntemka A, 2011, HIPPOKRATIA, V15, P232
  37. Ding WX, 2007, AM J PATHOL, V171, P513, DOI 10.2353/ajpath.2007.070188
  38. Deretic Vojo, 2008, V445, P1, DOI 10.1007/978-1-59745-157-4_1
  39. Grzanka J, 2013, INT IMMUNOPHARMACOL, V16, P343, DOI 10.1016/j.intimp.2013.02.004
  40. Jolly EC, 2012, AM J TRANSPLANT, V12, P2845, DOI 10.1111/j.1600-6143.2012.04172.x
  41. Walker JL, 2010, P NATL ACAD SCI USA, V107, P13730, DOI 10.1073/pnas.0910382107
  42. Le Roux S, 2010, TRANSPL P, V42, P3475, DOI 10.1016/j.transproceed.2010.09.006
  43. Saeed AI, 2003, BIOTECHNIQUES, V34, P374
  44. Stacchiotti A, 2002, HISTOCHEM J, V34, P305, DOI 10.1023/A:1023378525471
  45. Pesenacker AM, 2013, BLOOD, V121, P2647, DOI 10.1182/blood-2012-08-443473
  46. Hsu CW, 2008, PROTEOMICS, V8, P1975, DOI 10.1002/pmic.200701004
  47. de Matos ACC, 2010, TRANSPL INT, V23, P493, DOI 10.1111/j.1432-2277.2009.01005.x
  48. Solez K, 2007, AM J TRANSPLANT, V7, P518, DOI 10.1111/j.1600-6143.2006.01688.x
  49. Bando SY, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0079913
  50. Correa-Costa M, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0049569
  51. Huang ZX, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-308
  52. Pallet N, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0084708
  53. Pinsky Brett W, 2012, Saudi J Kidney Dis Transpl, V23, P693, DOI 10.4103/1319-2442.98112
  54. Reddy Anupama, 2010, Genome Inform, V24, P139
  55. Rudra D, 2012, NAT IMMUNOL, V13, P1009
  56. Yu HY, 2007, PLOS COMPUT BIOL, V3, P713, DOI 10.1371/journal.pcbi.0030059

Coleções
Artigos e Materiais de Revistas Científicas - FM/MPE
Artigos e Materiais de Revistas Científicas - LIM/36