Clinical evaluation and COL2A1 gene analysis in 21 Brazilian families with Stickler syndrome: Identification of novel mutations, further genotype/phenotype correlation, and its implications for the diagnosis

We present clinical and molecular evaluation from a large cohort of patients with Stickler syndrome: 78 individuals from 21 unrelated Brazilian families. The patients were selected in a Hospital with a craniofacial dysmorphology assistance service and clinical diagnosis was based on the presence of cleft palate associated to facial and ocular anomalies of Stickler syndrome. Analysis of COL2A1 gene revealed 9 novel and 4 previously described pathogenic mutations. Except for the mutation c.556G>T (p.Gly186X), all the others were located in the triple helical domain. We did not find genotype/phenotype correlation in relation to type and position of the mutation in the triple helical domain. However, a significantly higher proportion of myopia in patients with mutations located in this domain was observed in relation to those with the mutation in the non-tripe helical domain (c.556G>T; P < 0.04). A trend towards a higher prevalence of glaucoma, although not statistically significant, was observed in the presence of the mutation c.556G>T. It is possible. that this mutation alters the splicing of the mRNA instead of only creating a premature stop codon and therefore it can lead to protein products of different ocular effects. One novel DNA variation (c.1266+7G>C) occurs near a splice site and it was observed to co-segregate with the phenotype in one of the two families with this DNA variation. As in silico analysis predicted that the c.1266+7G>C DNA variation can affect the efficiency of the splicing, we still cannot rule it out as non-pathogenic. Our study also showed that ascertainment through cleft palate associated to other craniofacial signs can be very efficient for identification of Stickler syndrome patients. Still, high frequency of familial cases and high frequency of underdevelopment of distal lateral tibial epiphyses observed in our patients suggested that the inclusion of this information can improve the clinical diagnosis of Stickler syndrome. (C) 2008 Elsevier Masson SAS. All rights reserved.

Prevalence and nonrandom distribution of exonic mutations in interferon regulatory factor 6 in 307 families with Van der Woude syndrome and 37 families with popliteal pterygium syndrome

Purpose: Interferon regulatory factor 6 encodes a member of the IRF family of transcription factors. Mutations in interferon regulatory factor 6 cause Van der Woude and popliteal pterygium syndrome, two related orofacial clefting disorders. Here, we compared and contrasted the frequency and distribution of exonic Mutations in interferon regulatory factor 6 between two large geographically distinct collections of families with Van der Woude and between one collection of families with popliteal pterygium syndrome. Methods: We performed direct sequence analysis of interferon regulatory factor 6 exons oil samples from three collections, two with Van der Woude and one with popliteal pterygium syndrome. Results: We identified mutations in interferon regulatory factor 6 exons in 68% of families in both Van der Woude collections and in 97% of families with popliteal pterygium syndrome. In sum, 106 novel disease-causing variants were found. The distribution of mutations in the interferon regulatory factor 6 exons in each collection was not random; exons 3, 4, 7, and 9 accounted for 80%. In the Van der Woude collections, the mutations were evenly divided between protein truncation and missense, whereas most mutations identified in the popliteal pterygium syndrome collection were missense. Further, the missense mutations associated with popliteal pterygium syndrome were localized significantly to exon 4, at residues that are predicted to bind directly to DNA. Conclusion: The nonrandom distribution of mutations in the interferon regulatory factor 6 exons suggests a two-tier approach for efficient mutation screens for interferon regulatory factor 6. The type and distribution of mutations are consistent with the hypothesis that Van der Woude is caused by haploinsufficiency of interferon regulatory factor 6. Oil the other hand, the distribution of popliteal pterygium syndrome-associated mutations suggests a different, though not mutually exclusive, effect oil interferon regulatory factor 6 function. Genet Med 2009:11(4):241-247.

The Immunomodulatory Action of Sialostatin L on Dendritic Cells Reveals Its Potential to Interfere with Autoimmunity

Sialostatin L (SialoL) is a secreted cysteine protease inhibitor identified in the salivary glands of the Lyme disease vector Ixodes scapularis. In this study, we reveal the mechanisms of SialoL immunomodulatory actions on the vertebrate host. LPS-induced maturation of dendritic cells from C57BL/6 mice was significantly reduced in the presence of SialoL. Although OVA degradation was not affected by the presence of SialoL in dendritic cell cultures, cathepsin S activity was partially inhibited, leading to an accumulation of a 10-kDa invariant chain intermediate in these cells. As a consequence, in vitro Ag-specific CD4(+) T cell proliferation was inhibited in a time-dependent manner by SialoL, and further studies engaging cathepsin S(-/-) or cathepsin L(-/-) dendritic cells confirmed that the immunomodulatory actions of SialoL are mediated by inhibition of cathepsin S. Moreover, mice treated with SialoL displayed decreased early T cell expansion and recall response upon antigenic stimulation. Finally, SialoL administration during the immunization phase of experimental autoimmune encephalomyelitis in mice significantly prevented disease symptoms, which was associated with impaired IFN-gamma and IL-17 production and specific T cell proliferation. These results illuminate the dual mechanism by which a human disease vector protein modulates vertebrate host immunity and reveals its potential in prevention of an autoimmune disease. The Journal of Immunology, 2009, 182: 7422-7429.

The importance of hormone receptor analysis in osteosarcoma cells growth submitted to treatment with estrogen in association with thyroid hormone

Bone tumor incidence in women peaks at age 50-60, coinciding with the menopause. That estrogen (E2) and triiodothyronine (T3) interact in bone metabolism has been well established. However, few data on the action of these hormones are available. Our purpose was to determine the role of E2 and T3 in the expression of bone activity markers, namely alkaline phosphatase (AP) and receptor activator of nuclear factor kappa B ligand (RANKL). Two osteosarcoma cell lines: MG-63 (which has both estrogen (ER) and thyroid hormone (TR) receptors) and SaOs-29 (ER receptors only) were treated with infraphysiological E2 associated with T3 at infraphysiological, physiological, and supraphysiological concentrations. Real-time RT-PCR was used for expression analysis. Our results show that, in MG-63 cells, infraphysiological E2 associated with supraphysiological T3 increases AP expression and decreases RANKL expression, while infraphysiological E2 associated with either physiological or supraphysiological T3 decreases both AP and RANKL expression. On the other hand, in SaOs-2 cells, the same hormone combinations had no significant effect on the markers` expression. Thus, the analysis of hormone receptors was shown to be crucial for the assessment of tumor potential growth in the face of hormonal changes. Special care should be provided to patients with T3 and E2 hormone receptors that may increase tumor growth. Copyright (C) 2007 John Wiley & Sons, Ltd.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.