bioRxivNeurosciencePreprint
Lower brain pH as a shared endophenotype of psychotic disorders
Hideo HagiharaiD, Vibeke Sorensen CattsiD, Yuta Katayama, Tsuyoshi TakagiiD, Freesia L. Huang, Kuo‐Ping HuangiD, Shunsuke IshiiiD, Isabella A. Graef, Gerald R. CrabtreeiD, Keiichi I. NakayamaiD, Cynthia Shannon WeickertiD, Tsuyoshi MiyakawaiD
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Abstract
Lower pH is a well-replicated finding in the postmortem brains of patients with schizophrenia and bipolar disorder. Interpretation of the data, however, is controversial as to whether this finding reflects a primary feature of the diseases or is a result of confounding factors such as medication, postmortem interval, and agonal state. To date, systematic investigation of brain pH has not been undertaken using animal models, which can be studied without confounds inherent in human studies. In the present study, we first confirmed that the brains of patients with schizophrenia and bipolar disorder exhibit lower pH values by conducting a meta-analysis of existing datasets. We then utilized neurodevelopmental mouse models of psychiatric disorders in order to test the hypothesis that lower brain pH exists in these brains compared to controls due to the underlying pathophysiology of the disorders. We measured pH, lactate levels, and related metabolite levels in brain homogenates from three mouse models of schizophrenia (Schnurri-2 KO, forebrain-specific calcineurin KO, and neurogranin KO mice) and one of bipolar disorder (Camk2a HKO mice), and one of autism spectrum disorders (Chd8 HKO mice). All mice were drug-naive with the same postmortem interval and agonal state at death. Upon postmortem examination, we observed significantly lower pH and higher lactate levels in the brains of model mice relative to controls. There was a significant negative correlation between pH and lactate levels. These results suggest that lower pH associated with increased lactate levels is a pathophysiology of such diseases rather than mere artifacts.
- organism: mouse
- assay: metabolomics
- research focus: differential expression
- approach: statistical model
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