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Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome.

Higuera C, Gardiner KJ, Cios KJ - PLoS ONE (2015)

Bottom Line: We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine.The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine.Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense, Madrid, Spain; Departamento de Inteligencia Artificial e Ingeniería del Software, Facultad de Informática, Universidad Complutense, Madrid, Spain.

ABSTRACT
Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

No MeSH data available.


Related in: MedlinePlus

SOM clustering of trisomic mice data using 77 proteins.Light blue: t-SC-s; dark blue: t-SC-m; light pink: t-CS-s; dark pink: t-CS-m. Nodes forming each cluster are outlined: blue: t-SC-s; black: t-SC-m; yellow: t-CS-s; green: t-CS-m.
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pone.0129126.g006: SOM clustering of trisomic mice data using 77 proteins.Light blue: t-SC-s; dark blue: t-SC-m; light pink: t-CS-s; dark pink: t-CS-m. Nodes forming each cluster are outlined: blue: t-SC-s; black: t-SC-m; yellow: t-CS-s; green: t-CS-m.

Mentions: The optimal SOM for trisomic mice is shown in Fig 6 where the clusters of nodes have been outlined. Similar to Fig 2 with control mice, t-SC mice are well separated from t-CS (blue vs. pink). In addition, mice in t-SC-s and t-SC-m classes are also completely separated, with t-SC-s mice (light blue) in two clusters of 4 and 3 nodes, and t-SC-m (dark blue) in a large compact cluster of eight nodes and a single node. The organization of t-CS clusters, however, is more complicated and provides interesting differences from the SOM for control mice. In Fig 6, t-CS-s measurements are found in a cluster of 5 nodes (light pink) and one single node that together contain only 64 (of 105) measurements. Unlike control mice, the t-CS-s mice fail to learn and these nodes are largely located immediately adjacent to SC nodes, whereas in control mice, the c-CS-s nodes (Fig 2B) were distributed throughout the CS region. This difference suggests that protein levels in trisomy mice in failed learning are more similar to those in t-SC-s mice (i.e. in mice not asked to learn) than they are to those in control mice in successful learning. This is supported by the observation of mixed SC-CS nodes. Nodes containing the t-CS-m class (red background), the only class in this SOM that learns successfully, form a large 6-node cluster plus one singleton. Lastly, there are three CS-s/CS-m mixed nodes, containing 49 measurements, suggesting that not all responses in failed learning are incorrect and that these more closely resemble rescued learning.


Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome.

Higuera C, Gardiner KJ, Cios KJ - PLoS ONE (2015)

SOM clustering of trisomic mice data using 77 proteins.Light blue: t-SC-s; dark blue: t-SC-m; light pink: t-CS-s; dark pink: t-CS-m. Nodes forming each cluster are outlined: blue: t-SC-s; black: t-SC-m; yellow: t-CS-s; green: t-CS-m.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482027&req=5

pone.0129126.g006: SOM clustering of trisomic mice data using 77 proteins.Light blue: t-SC-s; dark blue: t-SC-m; light pink: t-CS-s; dark pink: t-CS-m. Nodes forming each cluster are outlined: blue: t-SC-s; black: t-SC-m; yellow: t-CS-s; green: t-CS-m.
Mentions: The optimal SOM for trisomic mice is shown in Fig 6 where the clusters of nodes have been outlined. Similar to Fig 2 with control mice, t-SC mice are well separated from t-CS (blue vs. pink). In addition, mice in t-SC-s and t-SC-m classes are also completely separated, with t-SC-s mice (light blue) in two clusters of 4 and 3 nodes, and t-SC-m (dark blue) in a large compact cluster of eight nodes and a single node. The organization of t-CS clusters, however, is more complicated and provides interesting differences from the SOM for control mice. In Fig 6, t-CS-s measurements are found in a cluster of 5 nodes (light pink) and one single node that together contain only 64 (of 105) measurements. Unlike control mice, the t-CS-s mice fail to learn and these nodes are largely located immediately adjacent to SC nodes, whereas in control mice, the c-CS-s nodes (Fig 2B) were distributed throughout the CS region. This difference suggests that protein levels in trisomy mice in failed learning are more similar to those in t-SC-s mice (i.e. in mice not asked to learn) than they are to those in control mice in successful learning. This is supported by the observation of mixed SC-CS nodes. Nodes containing the t-CS-m class (red background), the only class in this SOM that learns successfully, form a large 6-node cluster plus one singleton. Lastly, there are three CS-s/CS-m mixed nodes, containing 49 measurements, suggesting that not all responses in failed learning are incorrect and that these more closely resemble rescued learning.

Bottom Line: We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine.The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine.Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense, Madrid, Spain; Departamento de Inteligencia Artificial e Ingeniería del Software, Facultad de Informática, Universidad Complutense, Madrid, Spain.

ABSTRACT
Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

No MeSH data available.


Related in: MedlinePlus