Deletion of complement factor H-related genes CFHR1 and CFHR3 is associated with atypical hemolytic uremic syndrome.

Atypical hemolytic uremic syndrome (aHUS) is associated with defective complement regulation. Disease-associated mutations have been described in the genes encoding the complement regulators complement factor H, membrane cofactor protein, factor B, and factor I. In this study, we show in two independent cohorts of aHUS patients that deletion of two closely related genes, complement factor H-related 1 (CFHR1) and complement factor H-related 3 (CFHR3), increases the risk of aHUS. Amplification analysis and sequencing of genomic DNA of three affected individuals revealed a chromosomal deletion of approximately 84 kb in the RCA gene cluster, resulting in loss of the genes coding for CFHR1 and CFHR3, but leaving the genomic structure of factor H intact. The CFHR1 and CFHR3 genes are flanked by long homologous repeats with long interspersed nuclear elements (retrotransposons) and we suggest that nonallelic homologous recombination between these repeats results in the loss of the two genes. Impaired protection of erythrocytes from complement activation is observed in the serum of aHUS patients deficient in CFHR1 and CFHR3, thus suggesting a regulatory role for CFHR1 and CFHR3 in complement activation. The identification of CFHR1/CFHR3 deficiency in aHUS patients may lead to the design of new diagnostic approaches, such as enhanced testing for these genes.

Down-regulation of beta1,4-galactosyltransferase V is a critical part of etoposide-induced apoptotic process and could be mediated by decreasing Sp1 levels in human glioma cells.

beta1,4-Galactosyltransferase V (beta1,4GalT V; EC 2.4.1.38) is considered to be very important in glioma for expressing transformation-related highly branched N-glycans. Recently, we have characterized beta1,4GalT V as a positive growth regulator in several glioma cell lines. However, the role of beta1,4GalT V in glioma therapy has not been clearly reported. In this study, interfering with the expression of beta1,4GalT V by its antisense cDNA in SHG44 human glioma cells markedly promoted apoptosis induced by etoposide and the activation of caspases as well as processing of Bid and expression of Bax and Bak. Conversely, the ectopic expression of beta1,4GalT V attenuated the apoptotic effect of etoposide on SHG44 cells. In addition, both the beta1,4GalT V transcription and the binding of total or membrane glycoprotein with Ricinus communis agglutinin-I (RCA-I) were partially reduced in etoposide-treated SHG44 cells, correlated well with a decreased level of Sp1 that has been identified as an activator of beta1,4GalT V transcription. Collectively, our results suggest that the down-regulation of beta1,4GalT V expression plays an important role in etoposide-induced apoptosis and could be mediated by a decreasing level of Sp1 in SHG44 cells, indicating that inhibitors of beta1,4GalT V may enhance the therapeutic efficiency of etoposide for malignant glioma.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.