Brain Behavior and ImmunityNeurosciencePeer-reviewed journal article
Intermittent Cytomegalovirus Infection Alters Neurobiological Metabolism and Induces Cognitive Deficits in Mice
Mark A. A. HarrisoniD, Sara L. Morris, Grace A. Rudman, Daniel J. RittenhouseiD, Chandler H. MonkiD, Siva S. V. P. SakamuriiD, MaryJane Jones, Md Mehedi HasaniD, Mst Shamima KhatuniD, Hanyun WangiD, Lucas P. GarfinkeliD, Elizabeth B. NortoniD, Chad Steele, Sangku Kim, Jay K. KollsiD, S. Michal Jazwinski, Ricardo MostanyiD, Prasad V. G. KatakamiD, Elizabeth B. Engler-ChiurazziiD, Kevin J. ZwezdarykiD
Abstract
Risk factors contributing to dementia are multifactorial. Pathogens as risk factors for dementia is largely correlative with few causal relationships. Here, we demonstrate that intermittent cytomegalovirus (CMV) infection in mice, mimicking human chronic infection and reactivation/reinfection events, alters blood brain barrier (BBB) metabolic pathways. An increase in basal mitochondrial function is observed in brain microvasculature endothelial cells (BMEC) at 12 months post infection but not at earlier time points and is accompanied by elevated levels of superoxide, indicative of oxidative stress. Further, these mice score lower in cognitive assays as compared to age-matched controls. Our data show that repeated systemic infection with CMV, alters BBB metabolic function and impacts cognition. These observations provide mechanistic insights through which pathogens contribute to the progression of pathologies associated with dementia. In BriefMechanistic evidence supporting an infectious etiology of dementia (e.g. Alzheimers Disease) are poorly defined. Harrison et al., show that intermittent infection with cytomegalovirus metabolically rewires the blood brain barrier and neighboring glial cells altering their function, resulting in decreased cognitive function.
- organism: mouse
- assay: proteomics
- assay: metabolomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
- Citations
- 17
- FWCI
- 5.16
- Field percentile
- top 4%
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