Multidimensional analysis of behavior predicts genotype with high accuracy in a mouse model of Angelman syndrome.
Multidimensional analysis of behavior predicts genotype with high accuracy in a mouse model of Angelman syndrome.
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DOI:
10.1038/s41398-022-02206-3
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发表时间:
2022-10-03
影响因子:
6.8
通讯作者:
Sidorov, Michael S.
中科院分区:
文献类型:
--
作者:
Tanas, Joseph K.;Kerr, Devante D.;Wang, Li;Rai, Anika;Wallaard, Ilse;Elgersma, Ype;Sidorov, Michael S.
Angelman syndrome (AS) is a neurodevelopmental disorder caused by loss of expression of the maternal copy of the UBE3A gene. Individuals with AS have a multifaceted behavioral phenotype consisting of deficits in motor function, epilepsy, cognitive impairment, sleep abnormalities, as well as other comorbidities. Effectively modeling this behavioral profile and measuring behavioral improvement will be crucial for the success of ongoing and future clinical trials. Foundational studies have defined an array of behavioral phenotypes in the AS mouse model. However, no single behavioral test is able to fully capture the complex nature of AS—in mice, or in children. We performed multidimensional analysis (principal component analysis + k-means clustering) to quantify the performance of AS model mice (n = 148) and wild-type littermates (n = 138) across eight behavioral domains. This approach correctly predicted the genotype of mice based on their behavioral profile with ~95% accuracy, and remained effective with reasonable sample sizes (n = ~12–15). Multidimensional analysis was effective using different combinations of behavioral inputs and was able to detect behavioral improvement as a function of treatment in AS model mice. Overall, multidimensional behavioral analysis provides a tool for evaluating the effectiveness of preclinical treatments for AS. Multidimensional analysis of behavior may also be applied to rodent models of related neurodevelopmental disorders, and may be particularly valuable for disorders where individual behavioral tests are less reliable than in AS.
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影响因子:
8
作者:
Judson MC;Shyng C;Simon JM;Davis CR;Punt AM;Salmon MT;Miller NW;Ritola KD;Elgersma Y;Amaral DG;Gray SJ;Philpot BD
通讯作者:
Philpot BD
影响因子:
11
作者:
Keute M;Miller MT;Krishnan ML;Sadhwani A;Chamberlain S;Thibert RL;Tan WH;Bird LM;Hipp JF
通讯作者:
Hipp JF
影响因子:
4.9
作者:
Delling JP;Boeckers TM
通讯作者:
Boeckers TM
影响因子:
6.8
作者:
Heinz DE;Schöttle VA;Nemcova P;Binder FP;Ebert T;Domschke K;Wotjak CT
通讯作者:
Wotjak CT
影响因子:
64.8
作者:
Huang, Hsien-Sung;Allen, John A.;Mabb, Angela M.;King, Ian F.;Miriyala, Jayalakshmi;Taylor-Blake, Bonnie;Sciaky, Noah;Dutton, J. Walter, Jr.;Lee, Hyeong-Min;Chen, Xin;Jin, Jian;Bridges, Arlene S.;Zylka, Mark J.;Roth, Bryan L.;Philpot, Benjamin D.
通讯作者:
Philpot, Benjamin D.