Transglutaminase 1-deficient recessive lamellar ichthyosis associated with a LINE-1 insertion in Jack Russell terrier dogs.

BACKGROUND: Congenital, nonepidermolytic cornification disorders phenotypically resembling human autosomal recessive ichthyosis have been described in purebred dog breeds, including Jack Russell terrier (JRT) dogs. One cause of gene mutation important to humans and dogs is transposon insertions. OBJECTIVES: To describe an autosomal recessive, severe nonepidermolytic ichthyosis resembling lamellar ichthyosis (LI) in JRT dogs due to insertion of a long interspersed nucleotide element (LINE-1) in the transglutaminase 1 (TGM1) gene. METHODS: Dogs were evaluated clinically, and skin samples were examined by light and electron microscopy. Phenotypic information and genotyping with a canine microsatellite marker suggested TGM1 to be a candidate gene. Genomic DNA samples and cDNA generated from epidermal RNA were examined. Consequences of the mutation were evaluated by Western blotting, quantitative reverse transcription-polymerase chain reaction (RT-PCR) and enzyme activity from cultured keratinocytes. RESULTS: Affected dogs had generalized severe hyperkeratosis. Histological examination defined laminated to compact hyperkeratosis without epidermolysis; ultrastructurally, cornified envelopes were thin. Affected dogs were homozygous for a 1980-bp insertion within intron 9 of TGM1. The sequence of the insertion was that of a canine LINE-1 element. Quantitative RT-PCR indicated a significant decrease in TGM1 mRNA in affected dogs compared with wild-type. TGM1 protein was markedly decreased on immunoblotting, and membrane-associated enzyme activity was diminished in affected dogs. CONCLUSIONS: Based on morphological and molecular features, this disease is homologous with TGM1-deficient LI in humans, clinically models LI better than the genetically modified mouse and represents its first spontaneous animal model. This is the first reported form of LI due to transposon insertion.

On-line event detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is an important issue in many chemical sensing applications. Leakage detection hy thresholds suffers from important drawbacks when sensors have serious drifts or they are affected by cross-sensitivities. Here we present an adaptive method based in a Dynamic Principal Component Analysis that models the relationships between the sensors in the may. In normal conditions a certain variance distribution characterizes sensor signals. However, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drifts the model is adaptive and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic signals and with real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method.