bioRxivSystems BiologyPreprint
Functional dissection of Alzheimer’s disease brain gene expression signatures in humans and mouse models
Ying‐Wooi WaniD, Rami Al-Ouran, Carl Grant MangleburgiD, Tom V. Lee, Katherine Allison, Sarah Neuner, Catherine Kaczorowski, Vivek Phillip, Gareth R. HowelliD, Heidi Martini-Stoica, Hui ZhengiD, Jungwoo Wren Kim, Valina Dawson, Ted Dawson, Ping‐Chieh PaoiD, Li‐Huei TsaiiD, Jean‐Vianney Haure‐MirandeiD, Minghui Wang, Michelle E. EhrlichiD, Hongkang Mei, Xiaoyan Zhong, Paramita ChakrabartyiD, Yona LevitesiD, Todd E. Golde, Allan I. LeveyiD, , Joshua M ShulmaniD
Abstract
Human brain transcriptomes can highlight biological pathways associated with Alzheimers disease (AD); however, challenges remain to link expression changes with causal triggers. We have examined 30 AD-associated, gene coexpression modules from human brains for overlap with 251 differentially-expressed gene sets from mouse brain RNA-sequencing experiments, including from models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus neurofibrillary tangle pathology and further reveal age- and sex-dependent expression signatures for AD progression. Human coexpression modules enriched for neuronal and/or microglial genes overlap broadly with signatures from mouse models of AD, Huntingtons disease, Amyotrophic Lateral Sclerosis, and also aging. Several human AD coexpression modules, including those implicated in the unfolded protein response and oxidative phosphorylation, were not activated in AD models, but instead were detected following other, unexpected mouse genetic manipulations. Our results comprise a powerful, cross-species resource and pinpoint experimental models for diverse features of AD pathophysiology from human brain transcriptomes.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- approach: benchmark study
- Citations
- 6
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