METHODS: One hundred two study participants were identified through a network of collaborating clinicians and the Amy and Friends CS support groups. Families with a diagnosis of CS could also self-recruit. Comprehensive clinical information for analysis was obtained directly from families and their clinicians.
RESULTS AND CONCLUSION: We present the most complete evaluation of Cockayne syndrome to date, including detailed information on the prevalence and onset of clinical features, achievement of neurodevelopmental milestones, and patient management. We confirm that the most valuable prognostic factor in CS is the presence of early cataracts. Using this evidence, we have created simple guidelines for the care of individuals with CS. We aim to assist clinicians in the recognition, diagnosis, and management of this condition and to enable families to understand what problems they may encounter as CS progresses.Genet Med 18 5, 483-493.
METHODS: We utilized a combination of chromosome microarray, exome sequencing, and genome sequencing with structural variant and trio analysis to investigate a cohort of 41 predominantly sporadic cases.
RESULTS: We identified likely causative variants in 54% (22/41) of cases, including 51% (19/37) of sporadic cases and 75% (3/4) of cases initially referred as familial ASD. Two-thirds of sporadic cases were found to have heterozygous variants, which in most cases were de novo. Approximately one-third (7/22) of genetic diagnoses were found in rarely reported or recently identified ASD genes including PXDN, GJA8, COL4A1, ITPR1, CPAMD8, as well as the new phenotypic association of Axenfeld-Rieger anomaly with a homozygous ADAMTS17 variant. The remainder of the variants were in key ASD genes including FOXC1, PITX2, CYP1B1, FOXE3, and PAX6.
CONCLUSIONS: We demonstrate the benefit of detailed phenotypic, genomic, variant, and segregation analysis to uncover some of the previously "hidden" heritable answers in several rarely reported and newly identified ocular ASD-related disease genes.
AREAS COVERED: Despite numerous methods employed in generating induced pluripotent stem cells (iPSCs) from cancer cells only a few studies have successfully reprogrammed malignant human cells. In this review we will provide an overview on i) methods to reprogram cancer cells, ii) characterization of the reprogrammed cancer cells, and iii) the differential effects of reprogramming on malignancy, epigenetics and response of the cancer cells to chemotherapeutic agents.
EXPERT OPINION: Continued technical progress in cancer cell reprogramming technology will be instrumental for more refined in vitro disease models and ultimately for the development of directed and personalized therapy for cancer patients in the future.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.