Biomedical subjects

Travis Unger

Publications and source records attributed to Travis Unger.

4 recordsLinked to original sources

PubMed2006

Correlation of transcriptome profile with electrical activity in temporal lobe epilepsy.

The biology underlying epileptic brain activity in humans is not well understood and likely depends on changes in gene expression. We performed a microarray transcriptome profiling of 12 anterolateral temporal cortical samples originating from five individuals who suffered with temporal lobe epilepsy for at least 10 years. Prior to partial lobectomy, intraoperative electrocorticography was performed on the cortical surface of each patient. These recordings showed characteristic differences in frequency and amplitude that were defined as "spiking" (abnormal) or "non-spiking" (normal). Between the transcriptome of the two sample groups, transferrin (TF) was the most differentially expressed gene. Furthermore, gene expression profiling also revealed a downregulation of multiple GABA system-related genes (GABRA5, GABRB3, ABAT) in the spiking samples and an upregulation of oligodendrocyte and lipid metabolism transcripts (MOG, CA2, CNP, SCD, PLP1, FA2H, ABCA2). In addition, several transcripts related to the classical MAPK cascade showed expression level alterations between the spiking and non-spiking samples (G3BP2, MAPK1, PRKAR1A, and MAP4K4). Out of 12 genes chosen for verification by RT qPCR, 9 showed significant expression changes in the microarray-predicted direction. Furthermore, the microarray and qPCR data were highly correlated (r = 0.98; P < 0.001). We conclude that abnormal electrical brain activity in the spiking samples is strongly correlated with gene expression changes and we speculate that some of the observed transcriptome changes may be directly involved in the induction or prevention of the ictal events seen in epilepsy.

Action Potentials↗

PubMed2005

Presenilin-1-dependent transcriptome changes.

Familial forms of Alzheimer's disease (FADs) are caused by the expression of mutant presenilin 1 (PS1) or presenilin 2. Using DNA microarrays, we explored the brain transcription profiles of mice with conditional knock-out of PS1 (cKO PS1) in the forebrain. In parallel, we performed a transcription profiling of the hippocampus and frontal cortex of the FAD-linked DeltaE9 mutant transgenic (TG) mice and matched controls [TG mice expressing wild-type human PS1 (hPS1)]. When the TG and cKO datasets were cross-compared, the majority of the 30 common expression alterations were in opposite direction, suggesting that the FAD-linked PS1 variant produces transcriptome changes primarily by gain of aberrant function. Our microarray studies also revealed an unanticipated inverse correlation of transcript levels between the brains of mice that coexpress DeltaE9 hPS1+ amyloid precursor protein (APP)695 Swe and DeltaE9 hPS1 single transgenic mice. The opposite directionality of these changes in transcript levels must be a function of APP and/or APP derivatives.

Alzheimer Disease↗

PubMed2005

True and false discovery in DNA microarray experiments: transcriptome changes in the hippocampus of presenilin 1 mutant mice.

In transcriptome profiling experiments using DNA microarrays, it is critical to maximize putatively true data discovery while keeping the false discovery rate at acceptable levels. Using previously published and verified transcriptome datasets of mice with genetically altered PS1 physiology, we present a simple, robust, and system-specific assessment of type I and type II errors in two independent microarray experimental series. We provide evidence to suggest that for maximizing true discovery and minimizing false discovery, statistical criteria alone are inferior to statistical significance plus magnitude of change criteria. Furthermore, we found that, regardless of the exact criteria used for determining differential expression, different data extraction protocols give rise to different discovery and false discovery rates. In addition, a large proportion of expression differences were both dataset and analytical approach dependent. The data assessment methods presented and discussed in this manuscript can be easily carried out on any microarray dataset using basic spreadsheet functions as the only tool needed. Finally, we provide an in-depth analysis of the hippocampal transcriptome of DeltaE9 hPS1 transgenic mice and mice with a conditional ablation of the PS1 gene.

Animals↗

PubMed2005

Transcriptome differences between the frontal cortex and hippocampus of wild-type and humanized presenilin-1 transgenic mice.

OBJECTIVE: The authors investigated the differences between the frontal cortical (Fc) and hippocampal (Hc) transcriptomes of wild type (wt mPS1), humanized presenilin-1 (PS1 [wt hPS1]) and Alzheimer-disease (AD)-linked DeltaE9 hPS1 mutant mice. METHODS: Using high-density oligonucleotide arrays, they recently performed transcriptome profiling of wt mPS1, wt hPS1, and DeltaE9 hPS1 mutant mice. Whereas these studies analyzed the commonalities of gene expression patterns and commonly-regulated genes across the two brain areas and across the animal models, the current study focused on the gene-expression differences across Fc and Hc, two critical AD-affected brain regions. RESULTS: The data revealed that in the wild-type mice, there are significant transcriptome differences between the Fc and the Hc tissue, and these expression differences are maintained in humanized transgenic mice carrying the wt hPS1 gene or DeltaE9 hPS1 mutation. Also, they provide evidence that a subset of genes show disturbed regional Fc-Hc gene-expression ratios in the transgenic mice carrying the DeltaE9 hPS1 mutation. Some of these genes, including stearoyl-Coenzyme A desaturase-2 (Scd2) and Prostaglandin D2 synthase (Ptgds), have been previously implicated in the pathology of AD. CONCLUSIONS: Data suggest that disturbed gene-expression ratios between cortical regions may be an important event in altered brain physiology.

Alzheimer Disease↗