RNAseq transcriptome profiling reveals activation of immune responses and disrupted circadian patterns in the cerebral white matter of aged mice
Hidehiro Ishikawa1, Gen Hamanaka1, Emiri Mandeville1, Shuzhen Guo1, Wenlu Li1, Akihiro Shindo2, Eng Lo1, Ken Arai1
1Neuroprotection Research Laboratory, Departments of Radiology and Neurology, Massachusetts General Hospital and Harvard Medical School, 2Department of Neurology, Mie University
Objective:
We aim to map transcriptome profiles of mouse corpus callosum using young (5-month-old) and aged (24-month-old) mice.
Background:
Aging is a risk factor for several neurological diseases. Although the approach of transcriptome profiling by RNAseq has been used to examine age-related changes in RNA expression levels in the brain, how aging affects the transcriptome of cerebral white matter is still mostly unknown.
Design/Methods:
All experimental procedures followed NIH guidelines and were approved by the MGH Institutional Animal Care and Use Committee. Male C57BL6J mice (N=12 for the young; N=12 for the aged) were used. Mice were sacrificed at either ZT1, ZT7, ZT13, or ZT19 to obtain corpus callosum samples (N=3/group). Then, RNA samples were prepared and used for bulk RNAseq.
Results:
Regardless of the time zones for sample preparation, inflammatory responses were activated in aged mice. Especially, genes that are related to complement classical pathways were significantly upregulated in the aged corpus callosum compared to the young group. On the other hand, there were several upregulated genes in the young group. The most upregulated gene in the young corpus callosum was tenascin C, which is a major extracellular matrix glycoprotein, and the pathway analysis showed that the pathways of glial cell development (GO:0021782) and regulation of nervous system development (GO:0051960) were highly enriched in the list of differentially expressed genes (DEGs). Interestingly, the circadian patterns of some genes were disrupted in the aged corpus callosum. In addition, the number of DEGs between young and aged mice was more significant in ZT1 (inactive phase for mice) compared to ZT13 (active phase), suggesting that age-related differences in gene expression patterns may depend on circadian time points.
Conclusions:
Our current study provides a novel dataset for white-matter-specific transcriptome profiles in mice. Our data would help us understand the mechanism of age-related transcriptome changes in cerebral white matter.