Transcriptome analysis of the hippocampus in environmental noise-exposed SAMP8 mice reveals regulatory pathways associated with Alzheimer’s disease neuropathology

Abstract

Background: Chronic noise exposure is one environmental hazard that is associated with genetic susceptibility factors that increase Alzheimer's disease (AD) pathogenesis. However, the comprehensive understanding of the link between chronic noise stress and AD is limited. Herein, we investigated the effects of chronic noise exposure on AD-like changes in senescence-accelerated mouse prone 8 (SAMP8). Methods: A total of 30 male SAMP8 mice were randomly divided into the noise-exposed group, the control group, and aging group (positive controls), and mice in the exposure group were exposed to 98 dB SPL white noise for 30 consecutive days. Transcriptome analysis and AD-like neuropathology of hippocampus were examined by RNA sequencing and immunoblotting. Enzyme-linked immunosorbent assay and real-time PCR were used to further determine the differential gene expression and explore the underlying mechanisms of chronic noise exposure in relation to AD at the genome level. Results: Chronic noise exposure led to amyloid beta accumulation and increased the hyperphosphorylation of tau at the Ser202 and Ser404 sites in young SAMP8 mice; similar observations were noted in aging SAMP8 mice. We identified 21 protein-coding transcripts that were differentially expressed: 6 were downregulated and 15 were upregulated after chronic noise exposure; 8 genes were related to AD. qPCR results indicated that the expression of Arc, Egr1, Egr2, Fos, Nauk1, and Per2 were significantly high in the noise exposure group. These outcomes mirrored the results of the RNA sequencing data. Conclusions: These findings further revealed that chronic noise exposure exacerbated aging-like impairment in the hippocampus of the SAMP8 mice and that the protein-coding transcripts discovered in the study may be key candidate regulators involved in environment-gene interactions.

Environmental noise stress disturbs commensal microbiota homeostasis and induces oxi-inflammmation and AD-like neuropathology through epithelial barrier disruption in the EOAD mouse model

“…Environmental noise is one of the most pervasive health hazards [ 3 – 5 ], which has also been associated with AD-like cognitive dysfunction as well as persistent tau and amyloid-β (Aβ) pathology in murine model [ 6 – 10 ]. Furthermore, environmental noise alone or in combination with ApoE4 or aging, important internal risk factors for sporadic late-onset AD, can aggravate the occurrence and development of AD [ 11 , 12 ]. Environmental stress may activate the APP/PS1 gene, which may further cause cognitive deficits and an impaired neuroinflammatory response to amyloid pathology [ 13 ].…”

New insights into the Manila clam and PAMPs interaction based on RNA-seq analysis of clam through in vitro challenges with LPS, PGN, and poly(I:C)

“…GO enrichment analysis of differentially expressed genes (DEGs) was implemented by the cluster Profiler R package, in which gene length bias was corrected [ 80 ]. GO terms with corrected P -value less than 0.05 were considered significantly enriched by differential expressed genes [ 74 ]. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular data sets generated by genome sequencing and other high-throughput experimental technologies ( http://www.genome.jp/kegg/ ) [ 74 ].…”

“…GO terms with corrected P -value less than 0.05 were considered significantly enriched by differential expressed genes [ 74 ]. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular data sets generated by genome sequencing and other high-throughput experimental technologies ( http://www.genome.jp/kegg/ ) [ 74 ]. We used the cluster Profiler R package to test the statistical enrichment of differential expression genes in KEGG pathways [ 70 ].…”

“…Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5 [73]. We selected Hisat2 as the mapping tool for that Hisat2 can generate a database of splice junctions based on the gene model annotation file and thus a better mapping result than other non-splice mapping tools [74,75]. Feature Counts v1.5.0-p3 was used to count the reads numbers mapped to each gene, and then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene [76].…”

“…Differential expression analysis of two groups was performed using the DESeq2 R package [74]. DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution [74]. The resulting P-values were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate [79].…”