PREreview del Insulin resistance alters cortical inhibitory neurons and microglia to exacerbate Alzheimer’s knock-in mouse phenotypes

Summary

            A growing body of work suggests that metabolic dysfunction, such as diabetes mellitus, can aggravate the cognitive decline of patients with Alzheimer’s disease (AD); however, the molecular basis for the overlap in metabolic dysfunction in both Alzheimer’s and diabetes mellitus is uncertain. Here, the authors investigate the contribution of metabolic dysfunction in a double knock-in (DKI) mouse model for Alzheimer's disease with mice expressing mutant human APP and wild-type tau protein.

            Using their DKI model, the authors compared two models of hyperglycemia, streptozotocin, which impairs insulin production, and the high-fat high-sugar (HFHS) diet, which induces insulin resistance. Metabolic and cognitive tests revealed that streptozotocin was insufficient to induce obesity or exacerbate AD-related phenotypes. The HFHS diet was sufficient to induce obesity, and it decreased cognitive performance in DKI mice. These data support the authors’ claim that metabolic stress driven by insulin resistance, rather than hyperglycemia, can exacerbate AD.

            Microglial morphology revealed greater infiltration in DKI mice, but there was no difference in DAM activation between diets. Instead, the authors identified AD-related DAM morphology remodeling in DKI mice, but this was mildly increased in DKI-HFHS mice. Trem2 was upregulated in both DKI mice, but it was concentrated in puncta only within DKI-HFHS mice. There was no difference between DKI-Lean and DKI-HFHS mice with respect to Aß deposition or tau phosphorylation. These data suggest that metabolic stress could alter microglial morphology and Trem2 expression, expanding our knowledge of the role Trem2 plays in neurodegeneration.

            The authors claim to identify a glial metabolic impairment state within DKI-HFHS mice that they term metabolic impairment in neurodegeneration (MinD). Single-nucleus RNA-seq identified a microglial sub-cluster that was predominantly enriched for DKI-HFHS microglia and 62 DEGs that were specific to the compound effects of diet and AD. A MinD-like state was observed in astrocytes and oligodendrocytes as well. Of the MinD state genes, the authors emphasized Nrg3 and claimed that its diet-and-disease-induced upregulation was responsible for decreased inhibitory synaptic density. These data provide strong support for the MinD state identified, but the evidence for Nrg3-mediated synaptic modulation is circumstantial.

            Most transcriptional changes found within neuronal populations were attributed to diet, because DEGs were commonly shared between WT-HFHS and DKI-HFHS mice. Using pathway analysis, the authors claimed that diet-induced stress alters inhibitory neuron excitability, but transcriptional data is insufficient to support this claim. These inhibitory neurons, later classified as L2/3 inhibitory neurons, showed dysregulated Meis2, a transcription factor important for pancreatic glucose dysregulation. Additionally, the authors focused on L2/3 excitatory neurons and found overlapping pathways suggestive of a diet-induced transcriptional program to alter synaptic function.

            In summary, the authors expand our understanding of the role of metabolic stress in AD pathology, but the current data is incomplete to support some of the authors’ conclusions, such as the effects of Nrg3 upregulation, altered neuronal excitability, and the role of Meis2 in the proposed MinD state. Provided below are recommendations for further experimentation and textual clarification.

Major Points

·         The title claims that insulin resistance exacerbates AD phenotypes through inhibitory neurons and microglia. The authors provide strong evidence of this diet-and-genotype interaction within microglia, but the majority of DEGs from inhibitory neurons are shared between WT- and DKI-HFHS nuclei. Please consider revising this title to more accurately represent the data in this report.

·         In Ext Figure 1, the authors claim that insulin deficiency is not sufficient to exacerbate AD-related phenotypes in this Alzheimer’s knock-in mouse model. The authors could strengthen this claim by citing literature showing whether there is no increase in AD risk within T1DM individuals.

·         In Figure 2 / Ext Figure 3, the authors claim that metabolic stress alters microglial morphology. The effect sizes in the Scholl analysis provided were small, and the differences between mice were subtle. Providing more information about this experimental design in the methods, including the blinding scheme, will improve the interpretability of these results.

·         In Figure 4, the authors link the decrease in synaptic density to the upregulation of Nrg3 in glial cells and ErbB4 in inhibitory neurons. While this is believable, the data presented are insufficient to define a causal relationship between Nrg3/ErbB4 upregulation and decreased synaptic density. The authors could strengthen this claim by determining whether independent overexpression of Nrg3 is sufficient to decrease inhibitory synaptic density.

·         In Figure 5, the authors identified a HFHS-induced altered transcriptional program within inhibitory neurons that suggests altered excitability. While this is an exciting observation, this claim can be further supported by providing neurophysiological data to confirm altered excitability within these neurons.

·         In Figure 6, the authors link Meis2 upregulation to both pancreatic and brain metabolic impairment. While this could be supportive of the overall claim of the paper, the data provided is insufficient without the context-dependent impact of Meis2 dysregulation. Because Meis2 is a transcription factor, the authors could strengthen their claim by verifying whether the canonical targets of Mes2 are also dysregulated within the brain

Minor Points

·         The authors make no reference to Figure 1 panels I and J within the text of the Results section. It appears that these data are important for displaying the combined effects of diet and genotype on AD phenotypes. Please provide an explanation for the data presented in these panels.

·         In Figure 2 panel D, the four separate lines on the graph are too thin and too faint to easily visualize the data. Please consider revising the formatting of this graph to improve readability.

In Ext Figure 1, the authors explained that they tested the effects of streptozotocin on WT and DKI mice, but the figure legends are incorrectly labeled. For example, panels A, B, and C refer to HFHS diet conditions, and panel D references App^NLF/hTau-WT mice (presumably App^NLF/hTau-STZ). Please revise this figure to improve clarity.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.