Metabolic stress and age drive inflammation and cognitive decline in mice and humans

Abstract

Introduction: Metabolic stressors (obesity, metabolic syndrome, prediabetes, and type 2 diabetes [T2D]) increase the risk of cognitive impairment (CI), including Alzheimer's disease (AD). Immune system dysregulation and inflammation, particularly microglial mediated, may underlie this risk, but mechanisms remain unclear.

Methods: Using a high-fat diet-fed (HFD) model, we assessed longitudinal metabolism and cognition, and terminal inflammation and brain spatial transcriptomics. Additionally, we performed hippocampal spatial transcriptomics and single-cell RNA sequencing of post mortem tissue from AD and T2D human subjects versus controls.

Results: HFD induced progressive metabolic and CI with terminal inflammatory changes, and dysmetabolic, neurodegenerative, and inflammatory gene expression profiles, particularly in microglia. AD and T2D human subjects had similar gene expression changes, including in secreted phosphoprotein 1 (SPP1), a pro-inflammatory gene associated with AD.

Discussion: These data show that metabolic stressors cause early and progressive CI, with inflammatory changes that promote disease. They also indicate a role for microglia, particularly microglial SPP1, in CI.

Highlights: Metabolic stress causes persistent metabolic and cognitive impairments in mice. Murine and human brain spatial transcriptomics align and indicate a pro-inflammatory milieu. Transcriptomic data indicate a role for microglial-mediated inflammatory mechanisms. Secreted phosphoprotein 1 emerged as a potential target of interest in metabolically driven cognitive impairment.

Keywords: cognitive impairment; hippocampus; human; inflammation; microglia; mouse; obesity; prediabetes; type 2 diabetes.

FIGURE 1

Study design and longitudinal metabolic phenotyping. Data from n = 15 SD and n = 15 HFD mice over 1 year of feeding. A, Study design. B, Body weight AUC (see Figure S1 in supporting information for details on body weight AUC calculation). C, GTT AUC. D, Difference between groups in body weight AUC. E, Difference between groups in GTT AUC. A Cox proportional hazards model was used to compare differences in AUC between groups. AUC, area under the curve; BW, body weight; GTT, glucose tolerance test; HFD, high‐fat diet; LB, lower bound; LPS, lipopolysaccharide; MO, month; MWM, Morris water maze; Nov., November; PB, puzzle box; SD, standard diet; Sept., September; UB, upper bound; wk, weeks; YR, year.

FIGURE 2

Kaplan–Meier curves for longitudinal puzzle box data. Data from n = 15 standard diet (SD) and n = 15 high‐fat diet (HFD) mice after (A) 6 months (May); (B) 8 months (July); (C) 10 months (September); (D) 12 months on diet (November). Kaplan–Meier survival probability was calculated and represents the average probability of each group to escape the light area of the box to the dark area. A longer escape time is the adverse event associated with poorer task performance.

FIGURE 3

Terminal hippocampal microglial morphology. Data from n = 13 SD (n = 6 saline, n = 7 LPS) and n = 11 HFD (n = 4 saline, n = 7 given LPS) mice after 1 year of feeding. Terminal hippocampal microglial morphology with (A) representative images from all four groups; (B) complexity score for hilus (top) and molecular layer (bottom) microglia. Data are presented individual data points (cells) with means (black bars) and analyzed using mixed model. Differences are annotated as *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. There were no significant differences in complexity in the CA1 region (data not shown). HFD, high‐fat diet; LPS, lipopolysaccharide; SD, standard diet.

FIGURE 4

Mouse brain spatial transcriptomics enrichment analysis. Data from the clusters of interest (C0, C7, C11, C15) from standard diet (SD; n = 3 biological and n = 1 technical replicates) and high‐fat diet (HFD; n = 3 biological and n = 1 technical replicates) mice given saline after 1 year of feeding. A, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Rich factor represents the ratio of significant differentially expressed genes within a pathway to the total number of genes and is denoted by dot size. Dot color indicates significance from less (pink) to most (red) significant on a log10 adjusted P value scale. CellChat incoming (B) and outgoing (C) signaling pathways based on the CellChat database, in which signaling pathways are composed of ligand–receptor complexes. Asterisks represent significant (P < 0.05) differences in signaling pathways due to HFD. Heatmaps indicate the strength of the incoming or outgoing signal from one cluster to another with a deeper red representing a greater upregulation of signal activity and a deeper blue indicating a greater downregulation of signal activity.

FIGURE 5

Human spatial transcriptomics enrichment results. Data from the hippocampal clusters of interest (C3, C4, C5, C7, C13) from control (n = 1) and Alzheimer's disease and type 2 diabetes (n = 1) patients. A, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Rich factor represents the ratio of significant differentially expressed genes within a pathway to the total number of genes and is denoted by dot size. Dot color indicates significance from less (pink) to most (red) significant on a log10 adjusted P value scale. CellChat incoming (B) and outgoing (C) signaling pathways based on the CellChat database, in which signaling pathways are composed of ligand–receptor complexes. Heatmaps indicate the strength of the incoming or outgoing signal from one cluster to another with a deeper red representing a greater upregulation of signal activity and a deeper blue indicating a greater downregulation of signal activity.

FIGURE 6

Overlapping cell–cell signaling pathways between murine and human spatial transcriptomics data. Murine data from standard diet (SD; n = 3 biological and n = 1 technical replicates) and high‐fat diet (HFD; n = 3 biological and n = 1 technical replicates) mice given saline and human data from a control (Ctrl) patient (n = 1) and a patient with Alzheimer's disease and type 2 diabetes (AD+T2D; n = 1). Amyloid precursor protein (APP) circle plots in humans (A) and mice (B). Autocrine and paracrine signaling interactions between clusters are represented with color indicating upregulated (red) or downregulated (blue) interactions in AD+T2D or with HFD. Bolded clusters indicate clusters of interest. Mapped APP in humans (C) and mice (D). Signaling interactions between clusters are represented with colors indicating upregulated (red) or downregulated (blue) interactions in AD+T2D or with HFD. Secreted phosphoprotein 1 (SPP1) circle plots in humans (E) and mice (F). Autocrine and paracrine signaling interactions between clusters are represented with colors indicating upregulated (red) or downregulated (blue) interactions in AD+T2D or with HFD. Mapped SPP1 in humans (G), and mice (H). Signaling interactions between clusters are represented with colors indicating upregulated (red) or downregulated (blue) interactions in AD+T2D or with HFD. Wilcoxon rank testing was performed to compare HFD and SD in mice, and only the significant interactions are displayed in the figures.

FIGURE 7

AMP‐AD participant demographics, post mortem brain single‐nucleus RNAseq CellChat analysis (based on ligand–receptor pairs), and overlapping signaling pathways between datasets. Data are presented as (A) demographics, (B) significantly altered cell–cell signaling pathways, (C) SPP1 signaling circle plot, (D) contributing ligand–receptor pairs to SPP1 signaling, and (E) overlapping significant CellChat signaling pathways in spatial transcriptomics and AMP‐AD data. If a single participant had data from multiple brain regions (caudate nucleus, superior temporal gyrus, and dorsolateral prefrontal cortex), data were pooled. In the circle plot, autocrine and paracrine signaling interactions between cell types are represented with color indicating upregulated (red) or downregulated (blue) interactions in AD. Overlapping pathways (red shaded cells) indicate signaling pathways shared between AMP‐AD data and human spatial transcriptomics data and/or the human/mouse spatial transcriptomic crossover analysis. AD, Alzheimer's disease; AMP‐AD, Accelerating Medicines Partnership–Alzheimer's Disease; Ctrl, control; PMI, post mortem interval; SD, standard deviation; SPP1, secreted phosphoprotein 1.

FIGURE 8

Heat map representing the correlative analysis among murine behavior, gene expression, and microglial morphology. Analysis measured the correlations between behavior (as average rank), gene expression data for APP (APP, SORL1, TYROBP, and CD74) and SPP1 (SPP1, ITGAV, ITGB1, ITGB5, ITGA8, and ITGA5) related signaling, and microglial morphology. Color represents the correlation strength (darker red = stronger positive correlation, darker blue = stronger negative correlation), with strong correlations (Pearson correlation coefficient; PCC > 0.5) included on the heat map. Data analyzed Pearson correlation analysis and significant PCC (P < 0.05) are indicated in bold and trending PCC in italics (P < 0.1). For quartiles, cellular complexity data were divided into four quartiles and the percentage of cells that fell within the two outermost quartiles of complexity (Q1 and Q4) were included in the analysis. A greater percentage of cells within Q1 is indicative of a high degree of activation, whereas a greater percentage of cells within Q4 is indicative of a low degree of activation (quartile 4). Avg, average; Cplx, complexity; Mol, molecular; Q, quartile.