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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/32417785
Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data - PubMed Skip to main page content
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. 2020;75(4):1417-1435.
doi: 10.3233/JAD-200207.

Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data

Barbara Kramarz  1 , Rachael P Huntley  1 , Milagros Rodríguez-López  2 , Paola Roncaglia  2 , Shirin C C Saverimuttu  1 , Helen Parkinson  2 , Rina Bandopadhyay  3 , Maria-Jesus Martin  2 , Sandra Orchard  2 , Nigel M Hooper  4 , David Brough  4 , Ruth C Lovering  1
Affiliations

Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data

Barbara Kramarz et al. J Alzheimers Dis. 2020.

Abstract

Background: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer's disease (AD) research.

Objective: We aim to demonstrate the applicability of GO for interpretation of AD datasets to improve the understanding of the underlying molecular disease mechanisms, including the involvement of inflammatory pathways and dysregulated microRNAs (miRs).

Methods: We have undertaken a systematic full article GO annotation approach focused on microglial proteins implicated in AD and the miRs regulating their expression. PANTHER was used for enrichment analysis of previously published AD data. Cytoscape was used for visualizing and analyzing miR-target interactions captured from published experimental evidence.

Results: We contributed 3,084 new annotations for 494 entities, i.e., on average six new annotations per entity. This included a total of 1,352 annotations for 40 prioritized microglial proteins implicated in AD and 66 miRs regulating their expression, yielding an average of twelve annotations per prioritized entity. The updated GO resource was then used to re-analyze previously published data. The re-analysis showed novel processes associated with AD-related genes, not identified in the original study, such as 'gliogenesis', 'regulation of neuron projection development', or 'response to cytokine', demonstrating enhanced applicability of GO for neuroscience research.

Conclusions: This study highlights ongoing development of the neurobiological aspects of GO and demonstrates the value of biocuration activities in the area, thus helping to delineate the molecular bases of AD to aid the development of diagnostic tools and treatments.

Keywords: Alzheimer’s disease; Cytoscape network analysis; Gene Ontology; PANTHER; microglia; neuroinflammation.

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Conflict of interest statement

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/20-0207r1).

Figures

Fig. 1
Fig. 1
Association of prioritized entities with neuroinflammation-relevant GO terms. a) Number of prioritized entities associated with the listed GO terms and their descendants, bars indicate total number of entities (1st bars), number of entities annotated by ARUK-UCL (2nd bars) or other contributors (3rd bars). b) Number of annotations for the prioritized entities, contributed by ARUK-UCL or other groups, categorized by entity type. c) A fragment of GO, representing the relationships among some of the terms selected for analyses shown in (a) and (b). (Data from QuickGO: accessed 18 September 2019, filtered by prioritized entities, GO terms listed in Supplementary Table 3 and their descendants, evidence used in manual assertion and contributor).
Fig. 2
Fig. 2
Target-centered miR-target molecular interaction network. This network describes interactions between miRs and the mRNAs encoding AD-relevant microglial proteins. The network was constructed in Cytoscape [73] by seeding with 40 AD-relevant microglial gene symbols (Supplementary Table 1) and importing molecular interaction data from the EBI-GOA-miR file (accessed 1 July 2019). The protein-protein interactions (PPIs) edges were added to the network manually, based on data from another network seeded with the 17 AD-relevant microglial proteins shown in this Figure 2 (Supplementary Figure 1, Supplementary Table 7). The colors of node fragments correspond to GO terms (see key). Data associated with the enriched GO terms displayed in this figure is summarized in Table 4.
Fig. 3
Fig. 3
MiR-centered miR-target molecular interaction sub-network constructed by selecting four miRs enriched for ‘regulation of neuroinflammatory response’ and their direct targets. The sub-network was constructed in Cytoscape [73]. BiNGO [38] results (Supplementary Figure 2 and Supplementary Table 10) were used to identify relevant nodes and the first neighbors of the selected nodes. The four hub nodes represent miRs. The hub nodes are linked to the target nodes by the dashed edges, which represent experimentally demonstrated associations between miRs and their targets shown as nodes labelled with the HGNC-approved gene symbols. The purple cap at the end of each edge faces the target of miR silencing. The BiNGO enrichment analysis had been performed on the original large network of 415 nodes, shown in Supplementary Figure 2 and Supplementary Table 9. Key BiNGO analysis results are shown Table 5; all of the analysis results are provided in Supplementary Table 10. The background colors of the nodes’ fragments correspond to GO terms shown on the same background colors in the ‘Key to background colors’ text box.
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