Anti-SMC1A (aa 1183-1233) polyclonal antibody

Cornelia de Lange syndrome (CdLS), Rubinstein-Taybi syndrome (RSTS), and KBG syndrome are three distinct developmental human disorders. Variants in seven genes belonging to the cohesin pathway, NIPBL, SMC1A, SMC3, HDAC8, RAD21, ANKRD11, and BRD4, were identified in about 80% of patients with CdLS, suggesting that additional causative genes remain to be discovered. Two genes, CREBBP and EP300, have been associated with RSTS, whereas KBG results from variants in ANKRD11. By exome sequencing, a genetic cause was elucidated in two patients with clinical diagnosis of CdLS but without variants in known CdLS genes. In particular, genetic variants in EP300 and ANKRD11 were identified in the two patients with CdLS. EP300 and ANKRD11 pathogenic variants caused the reduction of the respective proteins suggesting that their low levels contribute to CdLS-like phenotype. These findings highlight the clinical overlap between CdLS, RSTS, and KBG and support the notion that these rare disorders are linked to abnormal chromatin remodeling, which in turn affects the transcriptional machinery.

Facial analysis systems are becoming available to healthcare providers to aid in the recognition of dysmorphic phenotypes associated with a multitude of genetic syndromes. These technologies automatically detect facial points and extract various measurements from images to recognize dysmorphic features and evaluate similarities to known facial patterns (gestalts). To evaluate such systems' usefulness for supporting the clinical practice of healthcare professionals, the recognition accuracy of the Cornelia de Lange syndrome (CdLS) phenotype was examined with FDNA's automated facial dysmorphology novel analysis (FDNA) technology. In the first experiment, 2D facial images of CdLS patients with either an NIPBL or SMC1A gene mutation as well as non-CdLS patients which were assessed by dysmorphologists in a previous study were evaluated by the FDNA technology; the average detection rate of experts was 77% while the system's detection rate was 87%. In the second study, when a new set of NIPBL, SMC1A and non-CdLS patient photos was evaluated, the detection rate increased to 94%. The results from both studies indicated that the system's detection rate was comparable to that of dysmorphology experts. Therefore, utilizing such technologies may be a useful tool in a clinical setting.