Skip to main content
SpringerLink
Log in
Book cover

Pediatric Nephrology pp 425–445Cite as

Translational Research Methods: Basics of Renal Molecular Biology

  • Reference work entry
  • First Online:

  • 7068 Accesses

Abstract

Molecular biology has developed between years 1940s and 1960s as a branch of science that spans from genetics to cell biology and biochemistry with the goal of understanding the interactions by which DNA, RNA, and protein synthesis operate into cells [1]. Since then, numerous technological innovations pushed the field forward, allowing recombinant DNA technology to be applied to a wide variety of biologic problems and to enter the clinical practice. The discovery of restriction nucleases and DNA ligases, development of DNA libraries, DNA cloning procedures, nucleic acid hybridization techniques, and the polymerase chain reaction (PCR) together with transgenic animals have all represented successive steps for a successful delineation of the role of genes in cellular physiology and pathophysiology. This molecular revolution has affected all of the sciences, including medical and clinical research, and has culminated in the completion of the human genome project [2, 3]. In the past 15 years, we have witnessed tremendous technological advances for high-throughput analysis of DNA, RNA, and protein in a comprehensive and cost-effective manner, with great promises to translate the analysis of the central dogma of biology into precision diagnosis and treatment for both rare and common diseases (Fig. 1). The recent advances in RNA biology identified numerous noncoding functional regions of the genome, which “escape” the central dogma but that can be analyzed and studied using a similar framework.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   949.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   1,299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Astbury WT. Molecular biology or ultrastructural biology? Nature. 1961;190:1124.

    Article  CAS  PubMed  Google Scholar 

  2. Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921.

    Article  CAS  PubMed  Google Scholar 

  3. Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science. 2001;291:1304–51.

    Article  CAS  PubMed  Google Scholar 

  4. Lander ES, Botstein D. Mapping complex genetic traits in humans: new methods using a complete RFLP linkage map. Cold Spring Harb Symp Quant Biol. 1986;51:49–62.

    Article  PubMed  Google Scholar 

  5. Lander ES, Schork NJ. Genetic dissection of complex traits. Science. 1994;265:2037–48.

    Article  CAS  PubMed  Google Scholar 

  6. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. 1977;74:5463–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931–45.

    Article  CAS  Google Scholar 

  8. Ng SB, Turner EH, Robertson PD, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461:272–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Choi M, Scholl UI, Ji W, et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci U S A. 2009;106:19096–101.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Lemaire M, Fremeaux-Bacchi V, Schaefer F, et al. Recessive mutations in DGKE cause atypical hemolytic-uremic syndrome. Nat Genet. 2013;45:531–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Ng SB, Bigham AW, Buckingham KJ, et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat Genet. 2010;42:790–3.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Ng SB, Buckingham KJ, Lee C, et al. Exome sequencing identifies the cause of a Mendelian disorder. Nat Genet. 2010;42:30–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. O’Roak BJ, Vives L, Girirajan S, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature. 2012;485:246–50.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  14. Sanna-Cherchi S, Burgess KE, Nees SN, et al. Exome sequencing identified MYO1E and NEIL1 as candidate genes for human autosomal recessive steroid-resistant nephrotic syndrome. Kidney Int. 2011;80:389–96.

    Article  CAS  PubMed  Google Scholar 

  15. Sanna-Cherchi S, Sampogna RV, Papeta N, et al. Mutations in DSTYK and dominant urinary tract malformations. N Engl J Med. 2013;369:621.

    Article  CAS  PubMed  Google Scholar 

  16. Westland R, Bodria M, Carrea A, et al. Phenotypic expansion of DGKE-associated diseases. J Am Soc Nephrol. 2014;25:1408.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Boyden LM, Choi M, Choate KA, et al. Mutations in kelch-like 3 and cullin 3 cause hypertension and electrolyte abnormalities. Nature. 2012;482:98–102.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Romberg N, Al Moussawi K, Nelson-Williams C, et al. Mutation of NLRC4 causes a syndrome of enterocolitis and autoinflammation. Nat Genet. 2014;46:1135–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. TG, HDL Working Group of the Exome Sequencing Project NHL, Blood Institute, et al. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N Engl J Med. 2014;371:22–31.

    Article  CAS  Google Scholar 

  20. Chaki M, Airik R, Ghosh AK, et al. Exome capture reveals ZNF423 and CEP164 mutations, linking renal ciliopathies to DNA damage response signaling. Cell. 2012;150:533–48.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Keramati AR, Fathzadeh M, Go GW, et al. A form of the metabolic syndrome associated with mutations in DYRK1B. N Engl J Med. 2014;370:1909–19.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  22. Metzker ML. Sequencing technologies – the next generation. Nat Rev Genet. 2010;11:31–46.

    Article  CAS  PubMed  Google Scholar 

  23. O’Roak BJ, Vives L, Fu W, et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science. 2012;338:1619–22.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  24. Halbritter J, Diaz K, Chaki M, et al. High-throughput mutation analysis in patients with a nephronophthisis-associated ciliopathy applying multiplexed barcoded array-based PCR amplification and next-generation sequencing. J Med Genet. 2012;49:756–67.

    Article  CAS  PubMed  Google Scholar 

  25. Halbritter J, Porath JD, Diaz KA, et al. Identification of 99 novel mutations in a worldwide cohort of 1,056 patients with a nephronophthisis-related ciliopathy. Hum Genet. 2013;132:865–84.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. International HapMap Consortium. The International HapMap Project. Nature. 2003;426:789–96.

    Article  CAS  Google Scholar 

  27. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273:1516–7.

    Article  CAS  PubMed  Google Scholar 

  28. Haines JL, Hauser MA, Schmidt S, et al. Complement factor H variant increases the risk of age-related macular degeneration. Science. 2005;308:419–21.

    Article  CAS  PubMed  Google Scholar 

  29. Edwards AO, Ritter 3rd R, Abel KJ, Manning A, Panhuysen C, Farrer LA. Complement factor H polymorphism and age-related macular degeneration. Science. 2005;308:421–4.

    Article  CAS  PubMed  Google Scholar 

  30. Klein RJ, Zeiss C, Chew EY, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308:385–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Palotie A, Widen E, Ripatti S. From genetic discovery to future personalized health research. N Biotechnol. 2013;30:291–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Weiss KM, Terwilliger JD. How many diseases does it take to map a gene with SNPs? Nat Genet. 2000;26:151–7.

    Article  CAS  PubMed  Google Scholar 

  33. Terwilliger JD, Hiekkalinna T. An utter refutation of the “fundamental theorem of the HapMap”. Eur J Hum Genet. 2006;14:426–37.

    Article  CAS  PubMed  Google Scholar 

  34. Gharavi AG, Kiryluk K, Choi M, et al. Genome-wide association study identifies susceptibility loci for IgA nephropathy. Nat Genet. 2011;43:321–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Kiryluk K, Li Y, Scolari F, et al. Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens. Nat Genet. 2014;46:1187–96.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Stanescu HC, Arcos-Burgos M, Medlar A, et al. Risk HLA-DQA1 and PLA(2)R1 alleles in idiopathic membranous nephropathy. N Engl J Med. 2011;364:616–26.

    Article  CAS  PubMed  Google Scholar 

  37. Lupski JR. Genomic rearrangements and sporadic disease. Nat Genet. 2007;39:S43–7.

    Article  CAS  PubMed  Google Scholar 

  38. Conrad DF, Pinto D, Redon R, et al. Origins and functional impact of copy number variation in the human genome. Nature. 2010;464:704–12.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Sebat J, Lakshmi B, Troge J, et al. Large-scale copy number polymorphism in the human genome. Science. 2004;305:525–8.

    Article  CAS  PubMed  Google Scholar 

  40. Redon R, Ishikawa S, Fitch KR, et al. Global variation in copy number in the human genome. Nature. 2006;444:444–54.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Iafrate AJ, Feuk L, Rivera MN, et al. Detection of large-scale variation in the human genome. Nat Genet. 2004;36:949–51.

    Article  CAS  PubMed  Google Scholar 

  42. Wellcome Trust Case Control Consortium, Craddock N, Hurles ME, et al. Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature. 2010;464:713–20.

    Article  CAS  Google Scholar 

  43. Cooper GM, Coe BP, Girirajan S, et al. A copy number variation morbidity map of developmental delay. Nat Genet. 2011;43:838–46.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Greenway SC, Pereira AC, Lin JC, et al. De novo copy number variants identify new genes and loci in isolated sporadic tetralogy of Fallot. Nat Genet. 2009;41:931–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Osoegawa K, Vessere GM, Utami KH, et al. Identification of novel candidate genes associated with cleft lip and palate using array comparative genomic hybridisation. J Med Genet. 2008;45:81–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Serra-Juhe C, Rodriguez-Santiago B, Cusco I, et al. Contribution of rare copy number variants to isolated human malformations. PLoS One. 2012;7:e45530.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  47. Brunetti-Pierri N, Berg JS, Scaglia F, et al. Recurrent reciprocal 1q21.1 deletions and duplications associated with microcephaly or macrocephaly and developmental and behavioral abnormalities. Nat Genet. 2008;40:1466–71.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Sanders SJ, Ercan-Sencicek AG, Hus V, et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron. 2011;70:863–85.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Sebat J, Lakshmi B, Malhotra D, et al. Strong association of de novo copy number mutations with autism. Science. 2007;316:445–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  50. Stefansson H, Rujescu D, Cichon S, et al. Large recurrent microdeletions associated with schizophrenia. Nature. 2008;455:232–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  51. Sanna-Cherchi S, Kiryluk K, Burgess KE, et al. Copy-number disorders are a common cause of congenital kidney malformations. Am J Hum Genet. 2012;91:987–97.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  52. Reddy UM, Page GP, Saade GR, et al. Karyotype versus microarray testing for genetic abnormalities after stillbirth. N Engl J Med. 2012;367:2185–93.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  53. Wapner RJ, Martin CL, Levy B, et al. Chromosomal microarray versus karyotyping for prenatal diagnosis. N Engl J Med. 2012;367:2175–84.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  54. Abel HJ, Duncavage EJ. Detection of structural DNA variation from next generation sequencing data: a review of informatic approaches. Cancer Genet. 2013;206:432–40.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  55. O’Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem. 1975;250:4007–21.

    PubMed Central  PubMed  Google Scholar 

  56. Anderson L, Anderson NG. High resolution two-dimensional electrophoresis of human plasma proteins. Proc Natl Acad Sci U S A. 1977;74:5421–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Bruschi M, Musante L, Candiano G, et al. Soft immobilized pH gradient gels in proteome analysis: a follow-up. Proteomics. 2003;3:821–5.

    Article  CAS  PubMed  Google Scholar 

  58. Manabe T, Jin Y. Analysis of protein/polypeptide interactions in human plasma using nondenaturing micro-2-DE followed by 3-D SDS-PAGE and MS. Electrophoresis. 2007;28:2065–79.

    Article  CAS  PubMed  Google Scholar 

  59. Manabe T, Jin Y, Tani O. Assignment of human plasma polypeptides on a nondenaturing 2-D gel using MALDI-MS and PMF and comparisons with the results of intact protein mapping. Electrophoresis. 2007;28:843–63.

    Article  CAS  PubMed  Google Scholar 

  60. Candiano G, Santucci L, Petretto A, et al. High-resolution 2-DE for resolving proteins, protein adducts and complexes in plasma. Electrophoresis. 2008;29:682–94.

    Article  CAS  PubMed  Google Scholar 

  61. Panfoli I, Calzia D, Santucci L, Ravera S, Bruschi M, Candiano G. A blue dive: from ‘blue fingers’ to ‘blue silver’. A comparative overview of staining methods for in-gel proteomics. Expert Rev Proteomics. 2012;9:627–34.

    Article  CAS  PubMed  Google Scholar 

  62. Alban A, David SO, Bjorkesten L, et al. A novel experimental design for comparative two-dimensional gel analysis: two-dimensional difference gel electrophoresis incorporating a pooled internal standard. Proteomics. 2003;3:36–44.

    Article  CAS  PubMed  Google Scholar 

  63. Bruschi M, Grilli S, Candiano G, et al. New iodo-acetamido cyanines for labeling cysteine thiol residues. A strategy for evaluating plasma proteins and their oxido-redox status. Proteomics. 2009;9:460–9.

    Article  CAS  PubMed  Google Scholar 

  64. Knowles MR, Cervino S, Skynner HA, et al. Multiplex proteomic analysis by two-dimensional differential in-gel electrophoresis. Proteomics. 2003;3:1162–71.

    Article  CAS  PubMed  Google Scholar 

  65. Rosenthal N. Molecular medicine. Recognizing DNA. N Engl J Med. 1995;333:925–7.

    Article  CAS  PubMed  Google Scholar 

  66. Ott J. Estimation of the recombination fraction in human pedigrees: efficient computation of the likelihood for human linkage studies. Am J Hum Genet. 1974;26:588–97.

    PubMed Central  CAS  PubMed  Google Scholar 

  67. Lathrop GM, Lalouel JM, Julier C, Ott J. Strategies for multilocus linkage analysis in humans. Proc Natl Acad Sci U S A. 1984;81:3443–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. Cottingham Jr RW, Idury RM, Schaffer AA. Faster sequential genetic linkage computations. Am J Hum Genet. 1993;53:252–63.

    PubMed Central  PubMed  Google Scholar 

  69. Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES. Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet. 1996;58:1347–63.

    PubMed Central  CAS  PubMed  Google Scholar 

  70. Gudbjartsson DF, Jonasson K, Frigge ML, Kong A. Allegro, a new computer program for multipoint linkage analysis. Nat Genet. 2000;25:12–3.

    Article  CAS  PubMed  Google Scholar 

  71. Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin – rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002;30:97–101.

    Article  CAS  PubMed  Google Scholar 

  72. Sobel E, Lange K. Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics. Am J Hum Genet. 1996;58:1323–37.

    PubMed Central  CAS  PubMed  Google Scholar 

  73. Hinrichs A, Lin JH, Reich T, Bierut L, Suarez BK. Markov chain Monte Carlo linkage analysis of a complex qualitative phenotype. Genet Epidemiol. 1999;17 Suppl 1:S615–20.

    Article  PubMed  Google Scholar 

  74. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  75. Lee AB, Luca D, Klei L, Devlin B, Roeder K. Discovering genetic ancestry using spectral graph theory. Genet Epidemiol. 2010;34:51–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  76. Lee AB, Luca D, Roeder K. A spectral graph approach to discovering genetic ancestry. Ann Appl Stat. 2010;4:179–202.

    Article  PubMed Central  PubMed  Google Scholar 

  77. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34:816–34.

    Article  PubMed Central  PubMed  Google Scholar 

  78. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  79. Colella S, Yau C, Taylor JM, et al. QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Res. 2007;35:2013–25.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  80. Wang K, Li M, Hadley D, et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 2007;17:1665–74.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  81. McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  82. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  83. Cingolani P, Platts A, le Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6:80–92.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  84. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4:1073–81.

    Article  CAS  PubMed  Google Scholar 

  85. Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  86. Cooper GM, Stone EA, Asimenos G, et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005;15:901–13.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  87. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  88. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89:82–93.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  89. Koskinen VR, Emery PA, Creasy DM, Cottrell JS. Hierarchical clustering of shotgun proteomics data. Mol Cell Proteomics. 2011;10:M110 003822.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  90. Stessl M, Noe CR, Lachmann B. Influence of image-analysis software on quantitation of two-dimensional gel electrophoresis data. Electrophoresis. 2009;30:325–8.

    Article  CAS  PubMed  Google Scholar 

  91. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2012.

    Google Scholar 

  92. Ejigu BA, Valkenborg D, Baggerman G, et al. Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments. OMICS. 2013;17:473–85.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  93. Pedreschi R, Hertog ML, Carpentier SC, et al. Treatment of missing values for multivariate statistical analysis of gel-based proteomics data. Proteomics. 2008;8:1371–83.

    Article  CAS  PubMed  Google Scholar 

  94. Fay MP, Proschan MA. Wilcoxon-Mann–Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat Surv. 2010;4:1–39.

    Article  PubMed Central  PubMed  Google Scholar 

  95. Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67:850–7.

    Article  PubMed  Google Scholar 

  96. Jensen KN, Jessen F, Jorgensen BM. Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples. J Proteome Res. 2008;7:1288–96.

    Article  PubMed  CAS  Google Scholar 

  97. Beck Jr LH, Bonegio RG, Lambeau G, et al. M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy. N Engl J Med. 2009;361:11–21.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  98. Kiryluk K, Li Y, Sanna-Cherchi S, et al. Geographic differences in genetic susceptibility to IgA nephropathy: GWAS replication study and geospatial risk analysis. PLoS Genet. 2012;8:e1002765.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  99. Saisawat P, Kohl S, Hilger AC, et al. Whole-exome resequencing reveals recessive mutations in TRAP1 in individuals with CAKUT and VACTERL association. Kidney Int. 2014;85:1310–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  100. Gee HY, Ashraf S, Wan X, et al. Mutations in EMP2 cause childhood-onset nephrotic syndrome. Am J Hum Genet. 2014;94:884–90.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  101. Prunotto M, Farina A, Lane L, et al. Proteomic analysis of podocyte exosome-enriched fraction from normal human urine. J Proteomics. 2013;82:193–229.

    Article  CAS  PubMed  Google Scholar 

  102. Hays T, Ma’ayan A, Clark NR, et al. Proteomics analysis of the non-muscle myosin heavy chain IIa-enriched actin-myosin complex reveals multiple functions within the podocyte. PLoS One. 2014;9:e100660.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  103. Bruschi M, Carnevali ML, Murtas C, et al. Direct characterization of target podocyte antigens and auto-antibodies in human membranous glomerulonephritis: alfa-enolase and borderline antigens. J Proteomics. 2011;74:2008–17.

    Article  CAS  PubMed  Google Scholar 

  104. Bruschi M, Sinico RA, Moroni G, et al. Glomerular autoimmune multicomponents of human lupus nephritis in vivo: alpha-enolase and annexin AI. J Am Soc Nephrol. 2014;25:2483.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  105. Candiano G, Musante L, Bruschi M, et al. Repetitive fragmentation products of albumin and alpha1-antitrypsin in glomerular diseases associated with nephrotic syndrome. J Am Soc Nephrol. 2006;17:3139–48.

    Article  CAS  PubMed  Google Scholar 

  106. Musante L, Bruschi M, Candiano G, et al. Characterization of oxidation end product of plasma albumin ‘in vivo’. Biochem Biophys Res Commun. 2006;349:668–73.

    Article  CAS  PubMed  Google Scholar 

  107. Santucci L, Candiano G, Petretto A, et al. From hundreds to thousands: widening the normal human Urinome (1). J Proteomics. 2014;112C:53.

    Google Scholar 

  108. Ngai HH, Sit WH, Jiang PP, Thongboonkerd V, Wan JM. Markedly increased urinary preprohaptoglobin and haptoglobin in passive Heymann nephritis: a differential proteomics approach. J Proteome Res. 2007;6:3313–20.

    Article  CAS  PubMed  Google Scholar 

  109. Shui HA, Huang TH, Ka SM, Chen PH, Lin YF, Chen A. Urinary proteome and potential biomarkers associated with serial pathogenesis steps of focal segmental glomerulosclerosis. Nephrol Dial Transplant. 2008;23:176–85.

    Article  CAS  PubMed  Google Scholar 

  110. Varghese SA, Powell TB, Budisavljevic MN, et al. Urine biomarkers predict the cause of glomerular disease. J Am Soc Nephrol. 2007;18:913–22.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  111. Decramer S, Wittke S, Mischak H, et al. Predicting the clinical outcome of congenital unilateral ureteropelvic junction obstruction in newborn by urinary proteome analysis. Nat Med. 2006;12:398–400.

    Article  CAS  PubMed  Google Scholar 

  112. O’Riordan E, Orlova TN, Podust VN, et al. Characterization of urinary peptide biomarkers of acute rejection in renal allografts. Am J Transplant. 2007;7:930–40.

    Article  PubMed  CAS  Google Scholar 

  113. Vilasi A, Cutillas PR, Maher AD, et al. Combined proteomic and metabonomic studies in three genetic forms of the renal Fanconi syndrome. Am J Physiol Renal Physiol. 2007;293:F456–67.

    Article  CAS  PubMed  Google Scholar 

  114. Psihogios NG, Kalaitzidis RG, Dimou S, Seferiadis KI, Siamopoulos KC, Bairaktari ET. Evaluation of tubulointerstitial lesions’ severity in patients with glomerulonephritides: an NMR-based metabonomic study. J Proteome Res. 2007;6:3760–70.

    Article  CAS  PubMed  Google Scholar 

  115. Murtas C, Bruschi M, Carnevali ML, et al. In vivo characterization of renal auto-antigens involved in human auto-immune diseases: the case of membranous glomerulonephritis. Proteomics Clin Appl. 2011;5:90–7.

    Article  CAS  PubMed  Google Scholar 

  116. Murtas C, Allegri L, Ghiggeri GM. Circulating antipodocyte antibodies in membranous nephropathy: new findings. Am J Kidney Dis. 2013;62:12–5.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Physiopathology of Uremia, Division of Nephrology, Dialysis and Transplantation, Istituto Giannina Gaslini, Genoa, Italy

    Gian Marco Ghiggeri & Maurizio Bruschi

  2. Division of Nephrology, Columbia University, College of Physicians and Surgeons, New York, NY, USA

    Simone Sanna-Cherchi

Authors
  1. Gian Marco Ghiggeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maurizio Bruschi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simone Sanna-Cherchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gian Marco Ghiggeri .

Editor information

Editors and Affiliations

  1. Department of Pediatrics, Medical College of Wisconsin, Children’s Research Institute, Children’s Hospital, Health System of Wisconsin, Milwaukee, Wisconsin, USA

    Ellis D. Avner

  2. Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

    William E. Harmon

  3. Service de Néphrologie Pédiatrique, Hôpital Necker-Enfants Malades, Université Paris-Descartes, Paris, France

    Patrick Niaudet

  4. Department of Pediatrics, Wakayama Medical University, Wakayama City, Japan

    Norishige Yoshikawa

  5. Division of Nephrology, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy

    Francesco Emma

  6. Division of Nephrology and Hypertension, The Heart Institute, Cincinnati Children’s Hospital Medical Center, College of Medicine, Cincinnati, Ohio, USA

    Stuart L. Goldstein

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this entry

Cite this entry

Ghiggeri, G.M., Bruschi, M., Sanna-Cherchi, S. (2016). Translational Research Methods: Basics of Renal Molecular Biology. In: Avner, E., Harmon, W., Niaudet, P., Yoshikawa, N., Emma, F., Goldstein, S. (eds) Pediatric Nephrology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43596-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-43596-0_13

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43595-3

  • Online ISBN: 978-3-662-43596-0

  • eBook Packages: MedicineReference Module Medicine

Publish with us

Policies and ethics

Search

Navigation