In the last few years there has been an increasing interest in exploring the proteome of human urine. A catalog of these proteins cannot only improve our knowledge of kidney physiology [ 1 x 1. Thongboonkerd, V., Klein, J.B., Pierce, W.M. et al. Sodium loading changes urinary protein excretion: A proteomic analysis. Am J Physiol Renal Physiol. 2003; 284: F1155–F1163 Crossref | PubMed | Google Scholar See all References ] [1] , but can also allow the identification of novel proteins associated with pathologic states. Indeed, it may be possible to identify potential biomarkers to diagnose and/or monitor renal disease [ 2. x 2. Knepper, M.A. Proteomics and the kidney. J Am Soc Nephrol. 2002; 13: 1398–1408 Crossref | PubMed | Scopus (98) | Google Scholar See all References , 3. x 3. Hampel, D.J., Sansome, C., Sha, M. et al. Toward proteomics in uroscopy: Urinary protein profiles after radiocontrast medium administration. J Am Soc Nephrol. 2001; 12: 1026–1035 PubMed | Google Scholar See all References , 4. x 4. Cutillas, P.R., Norden, A.G., Cramer, R. et al. Detection and analysis of urinary peptides by on-line liquid chromatography and mass spectrometry: Application to patients with renal Fanconi syndrome. Clin Sci (Lond). 2003; 104: 483–490 Crossref | PubMed | Scopus (81) | Google Scholar See all References ].

At present, the diagnosis of renal allograft rejection requires a renal biopsy. Clinical management of renal transplant patients would be improved by the development of non-invasive markers of rejection that can be measured frequently. This study sought to determine whether such candidate proteins can be detected in urine using mass spectrometry. Four patient groups were rigidly defined on the basis of allograft function, clinical course, and allograft biopsy result: acute clinical rejection group (n = 18), stable transplant group (n = 22), acute tubular necrosis group (n = 5), and recurrent (or de novo) glomerulopathy group (n = 5). Urines collected the day of the allograft biopsy were analyzed by mass spectrometry. As a normal control group, 28 urines from healthy individuals were analyzed the identical manner, as well as 5 urines from non-transplanted patients with lower urinary tract infection. Furthermore, sequential urine analysis was performed in patients in the acute clinical rejection and the stable transplant group. Three prominent peak clusters were found in 17 of 18 patients (94%) with acute rejection episodes, but only in 4 of 22 patients (18%) without clinical and histologic evidence for rejection and in 0 of 28 normal controls (P < 0.001). In addition, the presence or absence of these peak clusters correlated with the clinicopathologic course in most patients. Acute tubular necrosis, glomerulopathies, lower urinary tract infection, and cytomegalovirus viremia were not confounding variables. In conclusion, proteomic technology together with stringent definition of patient groups can detect urine proteins associated with acute renal allograft rejection. Identification of these proteins may prove useful as non-invasive diagnostic markers for rejection and the development of novel therapeutic agents. E-mail: peter.nickerson@bloodservices.ca

Our aim is to develop noninvasive tests to monitor the renal allograft posttransplant. Previously, we have reported that an unbiased proteomic-based approach can detect urine protein peaks associated with acute tubulointerstitial renal allograft rejection. Identification of these proteins peaks by mass spectrometry demonstrated that they all derive from nontryptic cleaved forms of β2-microglobulin. In vitro experiments showed that cleavage of intact β2-microglobulin requires a urine pH < 6 and the presence of aspartic proteases. Patients with acute tubulointerstitial rejection had lower urine pH than stable transplants and healthy individuals. In addition, they had higher amounts of aspartic proteases and intact β2-microglobulin in urine. These factors ultimately lead to increased amounts of cleaved urinary β2-microglobulin. Cleaved β2-microglobulin as an indicator of acute tubular injury may become a useful tool for noninvasive monitoring of renal allografts.

Limb girdle muscular dystrophy type 2B (LGMD2B) and Miyoshi myopathy (MM), a distal muscular dystrophy, are both caused by mutations in the recently cloned gene dysferlin, gene symbol DYSF. Two large pedigrees have been described which have both types of patient in the same families. Moreover, in both pedigrees LGMD2B and MM patients are homozygous for haplotypes of the critical region. This suggested that the same mutation in the same gene would lead to both LGMD2B or MM in these families and that additional factors were needed to explain the development of the different clinical phenotypes. In the present paper we show that in one of these families Pro791 of dysferlin is changed to an Arg residue. Both the LGMD2B and MM patients in this kindred are homozygous for this mutation, as are four additional patients from two previously unpublished families. Haplotype analyses suggest a common origin of the mutation in all the patients. On western blots of muscle, LGMD2B and MM patients show a similar abundance in dysferlin staining of 15 and 11%, respectively. Normal tissue sections show that dysferlin localizes to the sarcolemma while tissue sections from MM and LGMD patients show minimal staining which is indistinguishable between the two types. These findings emphasize the role for the dysferlin gene as being responsible for both LGMD2B and MM, but that the distinction between these two clinical phenotypes requires the identification of additional factor(s), such as modifier gene(s).

Objective: To test for associations between non–major histocompatibility complex susceptibility loci previously reported in autoimmune diseases and juvenile idiopathic arthritis (JIA).Methods: Published autoimmune disease genome‐wide association studies were reviewed, and 519 single‐nucleotide polymorphisms (SNPs) were selected for association testing. The initial cohort included 809 JIA cases and 3,535 controls of non‐Hispanic, European ancestry. Of the SNPs, 257 were successfully genotyped, while 168 were imputed with quality. Based on findings in the initial cohort, replication was sought for 21 SNPs in a second cohort of 1,015 JIA cases and 1,569 controls collected in the US and Germany. For the initial cohort, tests for association were adjusted for potential confounding effects of population structure by including principal components derived from a genome‐wide association study as covariates in logistic regression models. Odds ratios (ORs) and 95% confidence intervals were calculated.Results: Testing for association of previously reported autoimmune disease genetic associations in the initial cohort suggested associations with JIA in 13 distinct loci. Of these, 7 were validated in the replication cohort. Meta‐analysis results for the replicating loci included PTPN22 (rs6679677 [OR 1.58, P = 1.98 × 10⁻¹²], rs2476601 [OR 1.64, P = 1.90 × 10⁻¹³], and rs2488457 [OR 1.32, P = 6.74 × 10⁻⁸]), PTPN2 (rs1893217 [OR = 1.33, P = 1.60 × 10⁻⁹] and rs7234029 [OR 1.35, P = 1.86 × 10⁻¹⁰]), ADAD1‐IL2‐IL21 (rs17388568 [OR 1.24, P = 1.13 × 10⁻⁶] and rs13143866 [OR 0.83, P = 1.95 × 10⁻⁴]), STAT4 (rs3821236 [OR = 1.27, P = 2.36 × 10⁻⁶] and rs7574865 [OR = 1.31, P = 2.21 × 10⁻⁶]), C12orf30 (rs17696736 [OR = 1.19, P = 2.59 × 10⁻⁵]), COG6 (rs7993214 [OR = 0.76, P = 1.10 × 10⁻⁵]), and ANGPT1 (rs1010824 [OR = 0.79, P = 2.91 × 10⁻⁴]). These polymorphisms have been reported in diseases such as rheumatoid arthritis, type 1 diabetes mellitus, Crohn's disease, and multiple sclerosis.Conclusion: General susceptibility loci for autoimmunity are shared across diseases, including JIA, suggesting the potential for common therapeutic targets and mechanisms.