Multi-omics analysis reveals neuroinflammation, activated glial signaling, and dysregulated synaptic signaling and metabolism in the hippocampus of aged mice

Abstract

Aging is an intricate biological event that occurs in both vertebrates and invertebrates. During the aging process, the brain, a vulnerable organ, undergoes structural and functional alterations, resulting in behavioral changes. The hippocampus has long been known to be critically associated with cognitive impairment, dementia, and Alzheimer's disease during aging; however, the underlying mechanisms remain largely unknown. In this study, we hypothesized that altered metabolic and gene expression profiles promote the aging process in the hippocampus. Behavioral tests showed that exploration, locomotion, learning, and memory activities were reduced in aged mice. Metabolomics analysis identified 69 differentially abundant metabolites and showed that the abundance of amino acids, lipids, and microbiota-derived metabolites (MDMs) was significantly altered in hippocampal tissue of aged animals. Furthermore, transcriptomic analysis identified 376 differentially expressed genes in the aged hippocampus. A total of 35 differentially abundant metabolites and 119 differentially expressed genes, constituting the top 200 correlations, were employed for the co-expression network. The multi-omics analysis showed that pathways related to inflammation, microglial activation, synapse, cell death, cellular/tissue homeostasis, and metabolism were dysregulated in the aging hippocampus. Our data revealed that metabolic perturbations and gene expression alterations in the aged hippocampus were possibly linked to their behavioral changes in aged mice; we also provide evidence that altered MDMs might mediate the interaction between gut and brain during the aging process.

Keywords: brain ageing; learning and memory; metabolomics; microbiota-derived metabolite; multi-omics analysis; neuroinflammation; synaptic plasticity; transcriptomics.

FIGURE 1

Open field test to study the behaviors of young and middle-aged mice. (A) Representative trajectory of young or middle-aged (Mid-aged) male mice in the open-field test. (B–F) Statistic data of the open field test parameters. Data shown are means with SEM. The distance travel (B), speed (C), freezing time (D), center entries (E), and center time (F) were quantified by the Open field test during 5-min monitoring. Student’s t-test and the p-value were shown in each bar chart, *p < 0.05, **p < 0.01; the hollow dot and the solid circle show the sampling dataset collected from the young group (n = 12) and the middle-aged group (n = 9, Mid-aged).

FIGURE 2

Aging impaired the active avoidance performance in male mice. Data shown are means with SEM. (A) Progression of active avoidance responses during the training phase. Aged mice (Old) did not increase the number of avoidance responses during the training phase (**p < 0.001). (B) Time course of crossing latencies for aged mice (Old) during the training phase. Old mice did not decrease escape latency during training (*p < 0.01, **p < 0.001). (C) Number of intertrial crosses. From day 1, there was no significant difference between young and aged mice (Old) in the number of intertrial crosses. Statistics were performed with repeated-measures two-way ANOVA, followed by post-Bonferroni’s test, n = 3–4 per group.

FIGURE 3

Metabolic profiling and altered metabolism-related pathways of hippocampus tissue in male aged mice. (A,B) Pie graph of the metabolites class composition of identified and significantly altered metabolites in the hippocampus of male aged mice. (C) Hierarchical heatmap analysis of the relative content of DEMs in hippocampus from male aged (O1–O3, n = 3) and young mice (Y1–Y3, n = 3). (D) Bubble illustration of top20 ranked enriched KEGG pathway terms. The diameter of the solid circle denotes the number of DEMs enriched and the color showing the p-value in the corresponding pathway.

FIGURE 4

The contents of bioactive lipids and microbiota-derived metabolites were changed in the aged hippocampus of male mice. The relative contents of bioactive lipids, OEA, and PEA (A); microbiota-derived metabolites, TMAO, spermidine, creatine, and hypoxanthine (B), allantoin, betaine, HPLA, taurine, thiamine, and Neu5Ac (C) were altered in the aged (O) and young (Y) hippocampus. Data are calculated as the relative contents of the young group and represent the means ± SEM. Student’s t-test, n = 3 per group, *p < 0.05, **p < 0.01). OEA, N-oleoylethanolamine; PEA, palmitoylethanolamide; TMAO, trimethylamine N-oxide; HPLA, hydroxyphenyl lactic acid; Neu5Ac, N-acetylneuraminic acid.

FIGURE 5

RNA-seq analysis of the differential expressional genes enriched in aging. (A,B) Hierarchical heatmap analysis [(A), n = 3 per group] and pie graph of the up-and down-regulated genes composition (B) of the differentially expressed genes (DEGs) as determined by the selection criteria: | FC| > 1.5 and p < 0.01. (C) Top 30 GO terms enriched by the MetaScape tool. The diameter of the solid circle denotes the number of DEGs enriched and the color showing the p-value in the corresponding pathway. (D–H) Gene cluster enriched in gliogenesis, glia cell proliferation, and microglia activation (D); positive regulation of cell death, hippocampus neuron death, and synapse pruning (E); phagocytosis (F); positive regulation of cytosolic calcium ion concentration (G); and regulation of endopeptidase activity and positive regulation of hydrolase activity (H). (I) qRT-PCR analysis of selected genes related to microglial activation and neuroinflammation. Data are calculated as the mRNA foldchange of the young group and represent the means ± SEM. Unpaired Student’s t-test, n = 3–6 per group *p < 0.05. (J) The Hub genes clustering by the protein-protein interaction network as determined by the MCODE tool of MetaScape.

FIGURE 6

Integrative analysis of transcriptomics and metabolomics identifies neuroinflammation, amino acids metabolism, sphingolipid signaling pathways, and neuroactive ligand and receptor interaction over presented in the hippocampus of aged mice. Pearson correlation between genes and metabolites was calculated, and the top-ranked correlations (| r| ≥ 0.9825 and p < 0.00046) were employed to construct the genes-metabolites co-expression network. The genes and metabolites were incorporated into their corresponding KEGG pathways.