Background Prenatal alcohol exposure (PAE) can result in an array of morphological, behavioural and neurobiological deficits that can range in their severity. Despite extensive research in the field and a significant progress made, especially in understanding the range of possible malformations and neurobehavioral abnormalities, the molecular mechanisms of alcohol responses in development are still not well understood. There have been multiple transcriptomic studies looking at the changes in gene expression after PAE in animal models, however there is a limited apparent consensus among the reported findings. In an effort to address this issue, we performed a comprehensive re-analysis and meta-analysis of all suitable, publically available expression data sets. Methods We assembled ten microarray data sets of gene expression after PAE in mouse and rat models consisting of samples from a total of 63 ethanol-exposed and 80 control animals. We re-analyzed each data set for differential expression and then used the results to perform meta-analyses considering all data sets together or grouping them by time or duration of exposure (pre- and post-natal, acute and chronic, respectively). We performed network and Gene Ontology enrichment analysis to further characterize the identified signatures. Results For each sub-analysis we identified signatures of differential expressed genes that show support from multiple studies. Overall, the changes in gene expression were more extensive after acute ethanol treatment during prenatal development than in other models. Considering the analysis of all the data together, we identified a robust core signature of 104 genes down-regulated after PAE, with no up-regulated genes. Functional analysis reveals over-representation of genes involved in protein synthesis, mRNA splicing and chromatin organization. Conclusions Our meta-analysis shows that existing studies, despite superficial dissimilarity in findings, share features that allow us to identify a common core signature set of transcriptome changes in PAE. This is an important step to identifying the biological processes that underlie the etiology of FASD.
Introduction: FISH is a powerful technique to detect specific genomic sequences within an individual cell. Differences in the genetic makeup of AC and SqCC of the lung have been documented. A robust genetic based test applicable to sections from FFPE for the classification of AC vs SqCC tissue could have clinical utility as this classification can determine treatment selection. The optimal translation of array based genetic alterations data (in the form of copy number alterations) into a select set of FISH probes is nontrivial. One must consider the type (gain or loss), the extent and fidelity of the alteration (length in base pairs of the alteration common across the data set being considered which will affect FISH probe signal strength). Also one must consider effects the sectioning process will have on the actual FISH measurements; thick sections – more complete nuclei (better genetic loci fidelity) but more nuclear overlap (more difficult recognition of individual nuclei), thin sections – less nuclei overlap between cells but less complete nuclei. We use an in silico simulation to model the effect sectioning will have on the robustness and efficacy of a FISH based classifier for the genetic based differentiation of AC from SqCC tissue. Methods: High resolution array comparative genomic hybridization (aCGH) tiling array data was used to identify recurrent copy number alterations that could be used to differentiate between 169 AC and 92 SqCC cases. An in silico simulation of the sectioning process on the detection of these alterations was programmed in MATLAB. This simulation modeled the physical distribution of the DNA sequence, chromosome by chromosome within individual modeled nuclei. For each of the 261 cases chromosomal loci gains were randomly inserted into the cell's DNA irrespective of chromosome and chromosomal loci deletions were assumed to occur in only one of that specific chromosome type. Gains and losses were assumed to linearly affect the size of chromosomes and cell nuclei. Cell nuclei sectioning was simulated 300 times for each case (10 different nuclear rotations for each of ten different chromosomal placement simulations within the nucleus for three different section thicknesses of 4um, 7um and 10 um). The number of spots that would be detected by FISH for each of these 300 simulations for each case for all 24,817 loci measured by the aCGH was saved for later classification accuracy analysis. Results: The accuracy of the in silico model was checked against expected out comes; 1) such as the average loss of FISH spots as a function of the loss of nuclear volume to sectioning for loci with 1, 2 or 3 expected copies and 2) the concurrent loss of adjacent loci, or the loss of all loci within a chromosome with the loss of that chromosome. These measures were validated across at least 10K simulations. From the simulations we computed graphs of the number of loci retained as a function of the maximum radius of the nuclear material left within the section for each cell (an observable feature under microscopy). Further we calculated what percentage of nuclei within the section would have a maximum radius larger than 50, 60, 70, 80 or 90% of the pre-section nuclear radius for each case. This was done the 3 section thickness. Finally we used the simulation data to see the effect of sectioning on case classification, the number of cells which must be measured per case for optimal classification and the selection of the optimal genomic loci to differentiate the two types of tissue.
The Communication Switch is designed for speech pathologists to monitor the progress of physically and mentally disabled individuals who have impaired verbal communication abilities. This communication switch consists of two modules -Simple Communication Switch (SCS) and Communication Switch Downloader (CSD).The SCS emits a light and tone to signal staff, counts the number of communications emitted and stores these data along with a device ID. The CSD connects to one or more SCSs to provide data collection, simple menu-driven LCD displq, SCS initialization and data transfer to computer.
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