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Research Article
Dong Ni
Shenzhen University, China
Corresponding Author
Kun Huang
Indiana University, School of Medicine
Corresponding Author
Jie Zhang
Indiana University, School of Medicine
Corresponding Author
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Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies comparing AD versus control using bulk brain tissue samples have not considered cell composition changes in AD brains that can cause transcriptional changes not due to transcriptional regulation.
Using five large transcriptomic datasets, we mined conserved gene co-expression network modules, and applied differential expression and differential co-expression analysis on the modules in AD versus control brains. Combined with cell type deconvolution analysis, we addressed the question of whether the module expression changes are due to altered cellular composition or transcriptional regulation. Our findings were validated using four additional datasets.
We discovered that the increased expression of microglia modules can be explained by increased microglia population in AD brains rather than gene upregulation. In contrast, the decreased expression and perturbed co-expression in AD neuron modules are due to both neuron loss and regulation of neuronal pathways and several transcriptional factors are identified for such regulation. Similarly, the strong changes in expression and co-expression in astrocyte modules can also be attributed to a combinatory effect from astrogliosis and astrocyte gene activation in AD brains. The astrocyte modules expressions also strongly correlated with the clinicopathological biomarkers.
In summary, we demonstrated that combinatorial analysis is a powerful approach to delineate the origin of transcriptomic changes in bulk tissue data, which leads to a deeper understanding of key genes/pathways in AD.
Keywords
Alzheimer’s Disease, transcriptomic analysis, differential co-expression, gene co-expression network, cell type deconvolution, microglia, astrocyte
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Figure 5
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
- Supplementarymaterials.zip
Supplementary Figures and Tables. Table 1. All of the FGCN modules are classified into four groups based on the DC and DE scores and their characteristics. Table 2. FGCN module genes from AD/Normal brain samples and the overlap (Jaccard index) between each pair of AD and normal control FGCN modules. (sup-Table2.xlsx) Table 3. DE and DC score summary for all AD/Normal control FGCN modules. (sup-Table3.xlsx) Table 4. Top 10 enriched GO/pathway terms of each AD/normal FGCN module. (sup-Table4.xlsx) Table 5-6. Frequently down/up expressed genes (in at least two datasets) between AD and normal samples, merged for all five datasets. (sup-Table5-6.xlsx) Table 7. Enrichment of gene markers in five major cell types in all AD/normal control FGCN modules. (sup-Table7.xlsx) Table 8. Enriched pathway/GO terms for overlapping genes of AD1 and N1. (sup-Table8-10.xlsx) Table 9. Enriched pathway/GO terms in genes uniquely to AD1. (sup-Table8-10.xlsx) Table 10. Enriched pathway/GO terms in genes uniquely to N1. (sup-Table8-10.xlsx) Table 11. Enriched transcription factors and their targets for each FGCN modules. (sup-Table11.xlsx) Table 12. Transcription factor BCL6/STAT3 targeted genes in AD1 and N1(sup-Table12.xlsx). Table 13. The overlap of AD3/N6 microglia module with two previously identified disease associated microglia modules (sup-Table13.xlsx). Table 14. The full 547 hub gene lists for AD samples and normal control samples combined for all five datasets and the overlaps with microglia module genes. Table 15. The Correlation of FGCN module eigengene and cell types with clinicopathological measurements and associated p values in MSBB dataset. (sup-Table15.xlsx) Table 16. The gene ontology enrichment analysis on FGCN modules using DAVID and 10,931 genes are background.
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Dong Ni
Shenzhen University, China
Corresponding Author
Kun Huang
Indiana University, School of Medicine
Corresponding Author
Jie Zhang
Indiana University, School of Medicine
Corresponding Author
Badges
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This manuscript passed our Prescreen assessment
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History
CURRENT STATUS: Posted
Version 2
Posted 13 Oct, 2020
No community comments so far
No comments provided
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Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies comparing AD versus control using bulk brain tissue samples have not considered cell composition changes in AD brains that can cause transcriptional changes not due to transcriptional regulation.
Using five large transcriptomic datasets, we mined conserved gene co-expression network modules, and applied differential expression and differential co-expression analysis on the modules in AD versus control brains. Combined with cell type deconvolution analysis, we addressed the question of whether the module expression changes are due to altered cellular composition or transcriptional regulation. Our findings were validated using four additional datasets.
We discovered that the increased expression of microglia modules can be explained by increased microglia population in AD brains rather than gene upregulation. In contrast, the decreased expression and perturbed co-expression in AD neuron modules are due to both neuron loss and regulation of neuronal pathways and several transcriptional factors are identified for such regulation. Similarly, the strong changes in expression and co-expression in astrocyte modules can also be attributed to a combinatory effect from astrogliosis and astrocyte gene activation in AD brains. The astrocyte modules expressions also strongly correlated with the clinicopathological biomarkers.
In summary, we demonstrated that combinatorial analysis is a powerful approach to delineate the origin of transcriptomic changes in bulk tissue data, which leads to a deeper understanding of key genes/pathways in AD.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
- Supplementarymaterials.zip
Supplementary Figures and Tables. Table 1. All of the FGCN modules are classified into four groups based on the DC and DE scores and their characteristics. Table 2. FGCN module genes from AD/Normal brain samples and the overlap (Jaccard index) between each pair of AD and normal control FGCN modules. (sup-Table2.xlsx) Table 3. DE and DC score summary for all AD/Normal control FGCN modules. (sup-Table3.xlsx) Table 4. Top 10 enriched GO/pathway terms of each AD/normal FGCN module. (sup-Table4.xlsx) Table 5-6. Frequently down/up expressed genes (in at least two datasets) between AD and normal samples, merged for all five datasets. (sup-Table5-6.xlsx) Table 7. Enrichment of gene markers in five major cell types in all AD/normal control FGCN modules. (sup-Table7.xlsx) Table 8. Enriched pathway/GO terms for overlapping genes of AD1 and N1. (sup-Table8-10.xlsx) Table 9. Enriched pathway/GO terms in genes uniquely to AD1. (sup-Table8-10.xlsx) Table 10. Enriched pathway/GO terms in genes uniquely to N1. (sup-Table8-10.xlsx) Table 11. Enriched transcription factors and their targets for each FGCN modules. (sup-Table11.xlsx) Table 12. Transcription factor BCL6/STAT3 targeted genes in AD1 and N1(sup-Table12.xlsx). Table 13. The overlap of AD3/N6 microglia module with two previously identified disease associated microglia modules (sup-Table13.xlsx). Table 14. The full 547 hub gene lists for AD samples and normal control samples combined for all five datasets and the overlaps with microglia module genes. Table 15. The Correlation of FGCN module eigengene and cell types with clinicopathological measurements and associated p values in MSBB dataset. (sup-Table15.xlsx) Table 16. The gene ontology enrichment analysis on FGCN modules using DAVID and 10,931 genes are background.
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