Somatic Mosaic Activating Mutations in PIK3CA Cause CLOVES Syndrome

Congenital lipomatous overgrowth with vascular, epidermal, and skeletal anomalies (CLOVES) is a sporadically occurring, nonhereditary disorder characterized by asymmetric somatic hypertrophy and anomalies in multiple organs. We hypothesized that CLOVES syndrome would be caused by a somatic mutation arising during early embryonic development. Therefore, we employed massively parallel sequencing to search for somatic mosaic mutations in fresh, frozen, or fixed archival tissue from six affected individuals. We identified mutations in PIK3CA in all six individuals, and mutant allele frequencies ranged from 3% to 30% in affected tissue from multiple embryonic lineages. Interestingly, these same mutations have been identified in cancer cells, in which they increase phosphoinositide-3-kinase activity. We conclude that CLOVES is caused by postzygotic activating mutations in PIK3CA. The application of similar sequencing strategies will probably identify additional genetic causes for sporadically occurring, nonheritable malformations.

Cellular interference in craniofrontonasal syndrome: males mosaic for mutations in the X-linked EFNB1 gene are more severely affected than true hemizygotes

Craniofrontonasal syndrome (CFNS), an X-linked disorder caused by loss-of-function mutations of EFNB1, exhibits a paradoxical sex reversal in phenotypic severity: females characteristically have frontonasal dysplasia, craniosynostosis and additional minor malformations, but males are usually more mildly affected with hypertelorism as the only feature. X-inactivation is proposed to explain the more severe outcome in heterozygous females, as this leads to functional mosaicism for cells with differing expression of EPHRIN-B1, generating abnormal tissue boundaries-a process that cannot occur in hemizygous males. Apparently challenging this model, males occasionally present with a more severe female-like CFNS phenotype. We hypothesized that such individuals might be mosaic for EFNB1 mutations and investigated this possibility in multiple tissue samples from six sporadically presenting males. Using denaturing high performance liquid chromatography, massively parallel sequencing and multiplex-ligation-dependent probe amplification (MLPA) to increase sensitivity above standard dideoxy sequencing, we identified mosaic mutations of EFNB1 in all cases, comprising three missense changes, two gene deletions and a novel point mutation within the 5' untranslated region (UTR). Quantification by Pyrosequencing and MLPA demonstrated levels of mutant cells between 15 and 69%. The 5' UTR variant mutates the stop codon of a small upstream open reading frame that, using a dual-luciferase reporter construct, was demonstrated to exacerbate interference with translation of the wild-type protein. These results demonstrate a more severe outcome in mosaic than in constitutionally deficient males in an X-linked dominant disorder and provide further support for the cellular interference mechanism, normally related to X-inactivation in females.

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.