Microarray Analysis of Hippocampal CA1 Neurons Implicates Early Endosomal Dysfunction During Alzheimer's Disease Progression

Abstract

Background Endocytic dysfunction and neurotrophin signaling deficits may underlie the selective vulnerability of hippocampal neurons during the progression of Alzheimer’s disease (AD), although there is little direct in vivo and biochemical evidence to support this hypothesis. Methods Microarray analysis of hippocampal CA1 pyramidal neurons acquired via laser capture microdissection (LCM) was performed using postmortem brain tissue. Validation was achieved using real-time quantitative PCR (qPCR) and immunoblot analysis. Mechanistic studies were performed using human fibroblasts subjected to overexpression with viral vectors or knockdown via siRNA. Results Expression levels of genes regulating early endosomes (rab5) and late endosomes (rab7) are selectively up regulated in homogeneous populations of CA1 neurons from individuals with mild cognitive impairment (MCI) and AD. The levels of these genes are selectively increased as antemortem measures of cognition decline during AD progression. Hippocampal qPCR and immunoblot analyses confirmed increased levels of these transcripts and their respective protein products. Elevation of select rab GTPases regulating endocytosis paralleled the down regulation of genes encoding the neurotrophin receptors TrkB and TrkC. Overexpression of rab5 in cells suppressed TrkB expression, whereas knockdown of TrkB expression did not alter rab5 levels, suggesting that TrkB down regulation is a consequence of endosomal dysfunction associated with elevated rab5 levels in early AD. Conclusions These data support the hypothesis that neuronal endosomal dysfunction is associated with preclinical AD. Increased endocytic pathway activity, driven by elevated rab GTPase expression, may result in long term deficits in hippocampal neurotrophic signaling and represent a key pathogenic mechanism underlying AD progression.

7,8-Dihydroxyflavone, a Small-Molecule TrkB Agonist, Reverses Memory Deficits and BACE1 Elevation in a Mouse Model of Alzheimer's Disease

“…Levels of brain-derived neurotrophic factor (BDNF) and its receptor tyrosine receptor kinase B (TrkB) are decreased in the hippocampus and cerebral cortex early in the progression of Alzheimer's disease (AD) (Hu and Russek, 2008;Schindowski et al, 2008;Tapia-Arancibia et al, 2008;Zuccato and Cattaneo, 2009;Ginsberg et al, 2010). As BDNF-TrkB signaling is crucial for neuronal plasticity (Minichiello, 2009;Pang and Lu, 2004), it is hypothesized that deficient BDNF-TrkB pathways may underlie amyloidb (Ab)-induced neurotoxicity, synaptic dysfunction, and memory deficits in this devastating neurodegenerative disorder.…”

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

“…Therefore, robust methodological strategy will accelerate transcriptome studies by inferring more accurate spliced transcript variability and tissue specific (or condition specific) isoform diversity as well as allelic genetic differences in tissues (Chan et al, 2002;Li and Dickson, 1997;Robakis and Georgakopoulos, 2014). Thus, we here have a greatly important take-home message to be addressed in the upcoming years as the future direction, the focus on gene level analysis might be a partial analytical technique in transcriptional regulatory mechanisms, especially, when both is related with disease progression and therapeutic effects as mentioned in earlier studies (Gerns Storey et al, 2014;Ginsberg et al, 2010;Han and Jiang, 2014;Kim et al, 2012;Kumar et al, 2012;Li et al, 2014;Mills et al, 2013;Nishiu et al, 2002;Satoh et al, 2014;Wang et al, 2013;Wang et al, 2014;Yalamanchili et al, 2014). Accordingly, followed by differential gene expression analysis, differential expression at transcripts, exons, and isoforms, and allelic specific expression under the particular tissues (conditions) will be a next stage in the ultra-high-throughput community.…”

How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review

“…In conventional microarray platform, many Bayesian and non-parametric methods have been also popularly introduced. They has been performed analogously with better performance in the aspects of power of detection and false discovery rates in the situation of small samples, large number of probes and genes with noisy sets (Aryee et al, 2009;Gao and Song, 2005;Gerns Storey et al, 2014;Ginsberg et al, 2010;Lin et al, 2003;Nishiu et al, 2002;Stegle et al, 2010;Zhao et al, 2008). More recently, sequencing based platforms have revolutionized transcriptome studies by replacing arrays with RNA-seq.…”

“…With the advent of new elegant count based technology referred to as RNA-seq, newly developed RNA-seq specific methods and adaptively implemented ones from microarrays have been proposed and successfully contributed to transcriptome studies (Gerns Storey et al, 2014;Ginsberg et al, 2010;Han and Jiang, 2014;Kim et al, 2012;Kumar et al, 2012;Li et al, 2014;Mills et al, 2013;Nishiu et al, 2002;Satoh et al, 2014;Wang et al, 2013;Warren et al, 2015;Zhang et al, 2014). Representative statistical methods have been popularly utilized to quantify expression levels of mRNA abundance and identify differentially expressed genes between multiple conditions (Anders and Huber, 2010;Anders et al, 2013;Anders et al, 2012;Bar-Joseph et al, 2012;Bi and Davuluri, 2013;Bullard et al, 2010;Glaus et al, 2012;Hardcastle and Kelly, 2010;Lee et al, 2011;Marioni et al, 2008;Niu et al, 2014;Oh et al, 2013;Oshlack et al, 2010;Pollier et al, 2013;Roberts et al, 2011;Robinson and Oshlack, 2010;Tarazona et al, 2011;Trapnell et al, 2009;Trapnell et al, 2012;Young et al, 2010).…”