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Molecular systems evaluation of oligomerogenic APP(E693Q) and fibrillogenic APP(KM670/671NL)/PSEN1(Δexon9) mouse models identifies shared features with human Alzheimer's brain molecular pathology.

Readhead B, Haure-Mirande JV, Zhang B, Haroutunian V, Gandy S, Schadt EE, Dudley JT, Ehrlich ME - Mol. Psychiatry (2015)

Bottom Line: We also compared these results with datasets derived from human AD brain.Comparative molecular analysis converged on FMR1 (Fragile X Mental Retardation 1), an important negative regulator of APP translation and oligomerogenesis in the post-synaptic space.Integration of these transcriptomic results with human postmortem AD gene networks, differential expression and differential splicing signatures identified significant similarities in pathway dysregulation, including ECM regulation and neurogenesis, as well as strong overlap with AD-associated co-expression network structures.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

ABSTRACT
Identification and characterization of molecular mechanisms that connect genetic risk factors to initiation and evolution of disease pathophysiology represent major goals and opportunities for improving therapeutic and diagnostic outcomes in Alzheimer's disease (AD). Integrative genomic analysis of the human AD brain transcriptome holds potential for revealing novel mechanisms of dysfunction that underlie the onset and/or progression of the disease. We performed an integrative genomic analysis of brain tissue-derived transcriptomes measured from two lines of mice expressing distinct mutant AD-related proteins. The first line expresses oligomerogenic mutant APP(E693Q) inside neurons, leading to the accumulation of amyloid beta (Aβ) oligomers and behavioral impairment, but never develops parenchymal fibrillar amyloid deposits. The second line expresses APP(KM670/671NL)/PSEN1(Δexon9) in neurons and accumulates fibrillar Aβ amyloid and amyloid plaques accompanied by neuritic dystrophy and behavioral impairment. We performed RNA sequencing analyses of the dentate gyrus and entorhinal cortex from each line and from wild-type mice. We then performed an integrative genomic analysis to identify dysregulated molecules and pathways, comparing transgenic mice with wild-type controls as well as to each other. We also compared these results with datasets derived from human AD brain. Differential gene and exon expression analysis revealed pervasive alterations in APP/Aβ metabolism, epigenetic control of neurogenesis, cytoskeletal organization and extracellular matrix (ECM) regulation. Comparative molecular analysis converged on FMR1 (Fragile X Mental Retardation 1), an important negative regulator of APP translation and oligomerogenesis in the post-synaptic space. Integration of these transcriptomic results with human postmortem AD gene networks, differential expression and differential splicing signatures identified significant similarities in pathway dysregulation, including ECM regulation and neurogenesis, as well as strong overlap with AD-associated co-expression network structures. The strong overlap in molecular systems features supports the relevance of these findings from the AD mouse models to human AD.

No MeSH data available.


Related in: MedlinePlus

Enrichments for differentially expressed genes and differentially spliced genes with human LOAD signatures(a) Similarity of AD model transcriptional changes with postmortem LOAD gene expression collected from 6 brain regions (AD vs. non-demented controls). Spearman’s rho (shown in heatmap cells), reflect correlation between log2 fold change of orthologous mouse-human genes. All correlations were positive and significant. (b) Gene coexpression modules constructed from human postmortem brain samples (AD and non-demented controls) were intersected with DE and DEX gene sets, identifying multiple significant overlaps, including modules that are significantly differentially connected in LOAD. (c) Bayesian network built from human LOAD postmortem prefrontal cortex samples, subset by fibrillogenic APPKM670/671NL/PSEN1Δexon9 DE genes (FDR < 0.1), and their immediate neighbors. TYROBP, the key driver in the subnetwork most strongly associated with LOAD status, remained the most strongly connected gene in this induced subnetwork and is shown here with its local network neighborhood (first and second degree neighbors).(DE and DEX genes with FDR < 0.05 (unless otherwise stated), and gene coexpression module enrichments with FDR < 0.1 are shown)
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Figure 4: Enrichments for differentially expressed genes and differentially spliced genes with human LOAD signatures(a) Similarity of AD model transcriptional changes with postmortem LOAD gene expression collected from 6 brain regions (AD vs. non-demented controls). Spearman’s rho (shown in heatmap cells), reflect correlation between log2 fold change of orthologous mouse-human genes. All correlations were positive and significant. (b) Gene coexpression modules constructed from human postmortem brain samples (AD and non-demented controls) were intersected with DE and DEX gene sets, identifying multiple significant overlaps, including modules that are significantly differentially connected in LOAD. (c) Bayesian network built from human LOAD postmortem prefrontal cortex samples, subset by fibrillogenic APPKM670/671NL/PSEN1Δexon9 DE genes (FDR < 0.1), and their immediate neighbors. TYROBP, the key driver in the subnetwork most strongly associated with LOAD status, remained the most strongly connected gene in this induced subnetwork and is shown here with its local network neighborhood (first and second degree neighbors).(DE and DEX genes with FDR < 0.05 (unless otherwise stated), and gene coexpression module enrichments with FDR < 0.1 are shown)

Mentions: We accessed publicly available, postmortem gene expression profiles for 6 brain regions from a comparison across 34 individuals diagnosed with LOAD, along with 14 age-matched, non-demented controls37 to evaluate how the oligomerogenic APPE693Q and fibrillogenic APPKM670/671NL/PSEN1Δexon9 mouse transcriptomes approximate changes observed in human LOAD. We mapped human genes to mouse orthologs where available, and calculated the Spearman correlations between the gene expression log-fold-change (Figure 4a). We observed significant global similarity between all pairs of mouse DE, across all 6 LOAD brain regions. The oligomerogenic APPE693Q transgenic mouse EC was most similar to the LOAD EC signature, and the DG was most similar to the LOAD posterior cingulate cortex signature. For the fibrillogenic APPKM670/671NL/PSEN1Δexon9 transgenic mouse, the EC signature was also most similar to the LOAD posterior cingulate cortex signature, and the DG was most similar to the hippocampal signature.


Molecular systems evaluation of oligomerogenic APP(E693Q) and fibrillogenic APP(KM670/671NL)/PSEN1(Δexon9) mouse models identifies shared features with human Alzheimer's brain molecular pathology.

Readhead B, Haure-Mirande JV, Zhang B, Haroutunian V, Gandy S, Schadt EE, Dudley JT, Ehrlich ME - Mol. Psychiatry (2015)

Enrichments for differentially expressed genes and differentially spliced genes with human LOAD signatures(a) Similarity of AD model transcriptional changes with postmortem LOAD gene expression collected from 6 brain regions (AD vs. non-demented controls). Spearman’s rho (shown in heatmap cells), reflect correlation between log2 fold change of orthologous mouse-human genes. All correlations were positive and significant. (b) Gene coexpression modules constructed from human postmortem brain samples (AD and non-demented controls) were intersected with DE and DEX gene sets, identifying multiple significant overlaps, including modules that are significantly differentially connected in LOAD. (c) Bayesian network built from human LOAD postmortem prefrontal cortex samples, subset by fibrillogenic APPKM670/671NL/PSEN1Δexon9 DE genes (FDR < 0.1), and their immediate neighbors. TYROBP, the key driver in the subnetwork most strongly associated with LOAD status, remained the most strongly connected gene in this induced subnetwork and is shown here with its local network neighborhood (first and second degree neighbors).(DE and DEX genes with FDR < 0.05 (unless otherwise stated), and gene coexpression module enrichments with FDR < 0.1 are shown)
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Related In: Results  -  Collection

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Figure 4: Enrichments for differentially expressed genes and differentially spliced genes with human LOAD signatures(a) Similarity of AD model transcriptional changes with postmortem LOAD gene expression collected from 6 brain regions (AD vs. non-demented controls). Spearman’s rho (shown in heatmap cells), reflect correlation between log2 fold change of orthologous mouse-human genes. All correlations were positive and significant. (b) Gene coexpression modules constructed from human postmortem brain samples (AD and non-demented controls) were intersected with DE and DEX gene sets, identifying multiple significant overlaps, including modules that are significantly differentially connected in LOAD. (c) Bayesian network built from human LOAD postmortem prefrontal cortex samples, subset by fibrillogenic APPKM670/671NL/PSEN1Δexon9 DE genes (FDR < 0.1), and their immediate neighbors. TYROBP, the key driver in the subnetwork most strongly associated with LOAD status, remained the most strongly connected gene in this induced subnetwork and is shown here with its local network neighborhood (first and second degree neighbors).(DE and DEX genes with FDR < 0.05 (unless otherwise stated), and gene coexpression module enrichments with FDR < 0.1 are shown)
Mentions: We accessed publicly available, postmortem gene expression profiles for 6 brain regions from a comparison across 34 individuals diagnosed with LOAD, along with 14 age-matched, non-demented controls37 to evaluate how the oligomerogenic APPE693Q and fibrillogenic APPKM670/671NL/PSEN1Δexon9 mouse transcriptomes approximate changes observed in human LOAD. We mapped human genes to mouse orthologs where available, and calculated the Spearman correlations between the gene expression log-fold-change (Figure 4a). We observed significant global similarity between all pairs of mouse DE, across all 6 LOAD brain regions. The oligomerogenic APPE693Q transgenic mouse EC was most similar to the LOAD EC signature, and the DG was most similar to the LOAD posterior cingulate cortex signature. For the fibrillogenic APPKM670/671NL/PSEN1Δexon9 transgenic mouse, the EC signature was also most similar to the LOAD posterior cingulate cortex signature, and the DG was most similar to the hippocampal signature.

Bottom Line: We also compared these results with datasets derived from human AD brain.Comparative molecular analysis converged on FMR1 (Fragile X Mental Retardation 1), an important negative regulator of APP translation and oligomerogenesis in the post-synaptic space.Integration of these transcriptomic results with human postmortem AD gene networks, differential expression and differential splicing signatures identified significant similarities in pathway dysregulation, including ECM regulation and neurogenesis, as well as strong overlap with AD-associated co-expression network structures.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

ABSTRACT
Identification and characterization of molecular mechanisms that connect genetic risk factors to initiation and evolution of disease pathophysiology represent major goals and opportunities for improving therapeutic and diagnostic outcomes in Alzheimer's disease (AD). Integrative genomic analysis of the human AD brain transcriptome holds potential for revealing novel mechanisms of dysfunction that underlie the onset and/or progression of the disease. We performed an integrative genomic analysis of brain tissue-derived transcriptomes measured from two lines of mice expressing distinct mutant AD-related proteins. The first line expresses oligomerogenic mutant APP(E693Q) inside neurons, leading to the accumulation of amyloid beta (Aβ) oligomers and behavioral impairment, but never develops parenchymal fibrillar amyloid deposits. The second line expresses APP(KM670/671NL)/PSEN1(Δexon9) in neurons and accumulates fibrillar Aβ amyloid and amyloid plaques accompanied by neuritic dystrophy and behavioral impairment. We performed RNA sequencing analyses of the dentate gyrus and entorhinal cortex from each line and from wild-type mice. We then performed an integrative genomic analysis to identify dysregulated molecules and pathways, comparing transgenic mice with wild-type controls as well as to each other. We also compared these results with datasets derived from human AD brain. Differential gene and exon expression analysis revealed pervasive alterations in APP/Aβ metabolism, epigenetic control of neurogenesis, cytoskeletal organization and extracellular matrix (ECM) regulation. Comparative molecular analysis converged on FMR1 (Fragile X Mental Retardation 1), an important negative regulator of APP translation and oligomerogenesis in the post-synaptic space. Integration of these transcriptomic results with human postmortem AD gene networks, differential expression and differential splicing signatures identified significant similarities in pathway dysregulation, including ECM regulation and neurogenesis, as well as strong overlap with AD-associated co-expression network structures. The strong overlap in molecular systems features supports the relevance of these findings from the AD mouse models to human AD.

No MeSH data available.


Related in: MedlinePlus