Functional enrichment analysis of differentially expressed genes leads to dysregulation in biological processes networks in Alzheimer’s disease continuum Genetics/omics and systems biology

• Published in: Alzheimer's & dementia Vol. 16; no. S2
• Main Authors: Ferreira, Pamela C.L., De Bastiani, Marco Antônio, Bellaver, Bruna, Povala, Guilherme, Brum, Wagner Scheeren, Ramos, Vanessa G., Bieger, Andrei, Froehlich, Pedro E, Zimmer, Eduardo R.
• Format: Journal Article
• Language: English
• Published: 01.12.2020
• ISSN: 1552-5260; 1552-5279
• DOI: 10.1002/alz.039506

Abstract Background Reliable blood‐based biomarkers are of urgent need in Alzheimer’s disease (AD). In this context, whether changes in blood cells are present in AD remains unresolved. An interesting strategy to identify dysregulated biological pathways is the use of omics technologies, bioinformatic tools and, biological networks. Applying these contemporary techniques to blood samples of individuals in the Alzheimer's continuum may aid in understanding the periphery‐brain interface, providing the field with new pathophysiological insights and novel biomarkers. Here, we aim to investigate biological processes altered in peripheral blood cells across the Alzheimer's continuum. We hypothesized that blood transcriptomic profiles of mild cognitive impairment (MCI) and AD individuals will reflect changes in the brain. Method Whole blood transcriptomic profiles from 99 cognitively unimpaired (CU), 169 MCI and 48 AD individuals were obtained from the ADNI database. Afterward, we computed differential expression analyses between CU‐MCI and CU‐AD. Finally, differentially expressed genes (DEGs) (p‐value < 0.05) from each comparison were submitted to functional enrichment analysis (FEA) of Gene Ontology (GO) biological processes and pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Result Peripheral blood cells transcriptome analysis identified a total of 1232 DEGs for MCI and 1985 DEGs for AD patients. For MCI, FEA showed GO terms associated with energetic metabolism, lipid metabolism, and purines pathway (Figure 1A). For AD, GO terms are related to immune and inflammatory response and amino acid metabolism (Figure 1B). Curiously, DEGs are mainly downregulated in MCI but upregulated in AD patients. Conclusion Here, we demonstrate a prominent difference between blood transcriptomic profiles of MCI and AD individuals using FEA. Interestingly, the transcriptomic profile of MCI individuals presented changes in energetic metabolism, which is in line with the well‐established early cerebral glucose metabolism changes observed in the MCI stage. In AD individuals, more pronounced changes in immune and inflammatory processes were found, which also fits with the known late neuroinflammation peak verified in the AD stage. In summary, our findings indicate that blood transcriptomics may reflect aspects of pathophysiological brain events, offering a great opportunity for identifying new peripheral biomarkers and disease stage‐specific treatments.