Cross‐species evidence of differential expression of S100A6 and SLC11A1 in the hippocampus of Alzheimer’s disease patients and mouse models

Abstract

Background: The evaluation of similarities and differences between mouse models and human Alzheimer’s disease (AD) provides invaluable insights on disease pathophysiology. We compared hippocampal transcriptomic profiles of human late‐onset AD (LOAD) and early‐onset AD (EOAD) individuals with three mouse models (hAβ‐KI, APP/PS1 and 5xFAD) in an exploratory analysis. Afterwards, we validated the intersection of genes consistently altered in all groups using hippocampal tissue from APP/PS1 mice, and EOAD and LOAD patients. Methods: Five publicly available human AD/cognitively unimpaired (CU) transcriptomic profiles were collected from GEO (https://www.ncbi.nlm.nih.gov/geo/), merged and submitted to differential expression analysis (DEA) using R. APP/PS1 mouse model RNAseq data from two datasets (GSE149661 and GSE145907) were also collected. Finally, RNAseq data of 5xFAD and hAβ‐KI mouse models were obtained from AMP‐AD Knowledge Portal (https://www.synapse.org/) using synapser and synapserutils packages. All animal models were also submitted to DEA. Validation of selected genes was performed by qRT‐PCR. Target mRNA levels were normalized using β‐actin as a housekeeper gene. The results were expressed relative to wild‐type (WT) animals or CU controls using the 2−ΔΔCt method. Results: DEA identified 1164, 3261 and 1782 differentially expressed genes (DEGs) for the comparisons between hAβ‐KI, 5xFAD and APP/PS1 mutants versus WT controls, respectively. hAβ‐KI, APP/PS1, and 5xFAD mice exhibited more DEGs in common with LOAD than with EOAD patients. Interestingly, APP/PS1 and 5xFAD mice also presented more DEGs in common with LOAD than with EOAD patients. However, 5xFAD showed significantly higher DEGs intersection with EOAD than with hAβ‐KI and APP/PS1. Additionally, we identified S100A6, C1QB, SST, CD14, CD33, SLC11A1 and KCNK1 genes altered in both EOAD and LOAD as well as the three mouse models evaluated. qRT‐PCR analysis of these genes in EOAD, LOAD and APP/PS1 model revealed S100A6 and SLC11A1 consistently altered in human pathology and mouse model. Conclusion: Animal models seek to better simulate AD pathology, making the evaluation of their molecular overlap with human disease greatly important. S100A6, a calcium‐binding protein modulating several biological activities, and SLC11A1, a metal transporter associated with inflammatory processes, were consistently altered in our transcriptomics exploratory analyses and experimental validation, making them promising targets for further investigation.