Dataset Information

MNos2 deletion and human NOS2 replacement in Alzheimer disease models.

ABSTRACT: Understanding the pathophysiologic mechanisms underlying Alzheimer disease relies on knowledge of disease onset and the sequence of development of brain pathologies. We present a comprehensive analysis of early and progressive changes in a mouse model that demonstrates a full spectrum of characteristic Alzheimer disease-like pathologies. This model demonstrates an altered immune redox state reminiscent of the human disease and capitalizes on data indicating critical differences between human and mouse immune responses, particularly in nitric oxide levels produced by immune activation of the NOS2 gene. Using the APPSwDI(+)/(+)mNos2(-/-) (CVN-AD) mouse strain, we show a sequence of pathologic events leading to neurodegeneration,which include pathologically hyperphosphorylated tau in the perforant pathway at 6 weeks of age progressing to insoluble tau, early appearance of ?-amyloid peptides in perivascular deposits around blood vessels in brain regions known to be vulnerable to Alzheimer disease, and progression to damage and overt loss in select vulnerable neuronal populations in these regions. The role of species differences between hNOS2 and mNos2 was supported by generating mice in which the human NOS2 gene replaced mNos2. When crossed with CVN-AD mice, pathologic characteristics of this new strain (APPSwDI(+)/(-)/HuNOS2(tg+)/(+)/mNos2(-/-)) mimicked the pathologic phenotypes found in the CVN-AD strain.

SUBMITTER: Colton CA

PROVIDER: S-EPMC4131941 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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mNos2 deletion and human NOS2 replacement in Alzheimer disease models.

Colton Carol A CA Wilson Joan G JG Everhart Angela A Wilcock Donna M DM Puoliväli Jukka J Heikkinen Taneli T Oksman Juho J Jääskeläinen Olli O Lehtimäki Kimmo K Laitinen Teemu T Vartiainen Nina N Vitek Michael P MP

Journal of neuropathology and experimental neurology 20140801 8

Understanding the pathophysiologic mechanisms underlying Alzheimer disease relies on knowledge of disease onset and the sequence of development of brain pathologies. We present a comprehensive analysis of early and progressive changes in a mouse model that demonstrates a full spectrum of characteristic Alzheimer disease-like pathologies. This model demonstrates an altered immune redox state reminiscent of the human disease and capitalizes on data indicating critical differences between human and ...[more]

PMID: 25003233

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Project description:Animal models aim to replicate the symptoms, the lesions or the cause(s) of Alzheimer disease. Numerous mouse transgenic lines have now succeeded in partially reproducing its lesions: the extracellular deposits of Abeta peptide and the intracellular accumulation of tau protein. Mutated human APP transgenes result in the deposition of Abeta peptide, similar but not identical to the Abeta peptide of human senile plaque. Amyloid angiopathy is common. Besides the deposition of Abeta, axon dystrophy and alteration of dendrites have been observed. All of the mutations cause an increase in Abeta 42 levels, except for the Arctic mutation, which alters the Abeta sequence itself. Overexpressing wild-type APP alone (as in the murine models of human trisomy 21) causes no Abeta deposition in most mouse lines. Doubly (APP x mutated PS1) transgenic mice develop the lesions earlier. Transgenic mice in which BACE1 has been knocked out or overexpressed have been produced, as well as lines with altered expression of neprilysin, the main degrading enzyme of Abeta. The APP transgenic mice have raised new questions concerning the mechanisms of neuronal loss, the accumulation of Abeta in the cell body of the neurons, inflammation and gliosis, and the dendritic alterations. They have allowed some insight to be gained into the kinetics of the changes. The connection between the symptoms, the lesions and the increase in Abeta oligomers has been found to be difficult to unravel. Neurofibrillary tangles are only found in mouse lines that overexpress mutated tau or human tau on a murine tau -/- background. A triply transgenic model (mutated APP, PS1 and tau) recapitulates the alterations seen in AD but its physiological relevance may be discussed. A number of modulators of Abeta or of tau accumulation have been tested. A transgenic model may be analyzed at three levels at least (symptoms, lesions, cause of the disease), and a reading key is proposed to summarize this analysis.

Project description:ImportancePredictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.ObjectiveTo characterize the algorithmic fairness of longitudinal prediction models for AD progression.Design, setting, and participantsThis prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.ExposuresFairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.Main outcomes and measuresOutcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.ResultsA total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)-MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).Conclusions and relevanceIn this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.

Dataset Information

MNos2 deletion and human NOS2 replacement in Alzheimer disease models.

Publications

mNos2 deletion and human NOS2 replacement in Alzheimer disease models.

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