Nutrigenomic influence of a curcumin-supplemented high glycemic diet on hippocampal microvasculature in male C57BL/6J mice

Abstract

Introduction

Curcumin, a dietary polyphenol primarily derived from turmeric, has potent antioxidant and anti-inflammatory capabilities against diet-related chronic diseases. A high glycemic diet (HGD) has been shown to contribute to cognitive decline and dysfunction of murine brain microvasculature. The goal of our study was to elucidate the multi-genomic effects of curcumin on hippocampal microvessels in mice during consumption of a high glycemic diet.

Methods

Male C57BL/6J mice were fed a low glycemic diet (LGD, 12% sucrose/weight), a high glycemic diet (HGD, 34% sucrose), or a HGD with 0.2% curcumin (HGD + Curc) for 12 weeks. Global transcriptomic profiles, including protein coding and non-coding genes, of laser-captured endothelial microvessels of the hippocampus were analyzed via microarrays. Bioinformatic tools were utilized to uncover networks and functional pathways of differentially expressed genes modulated by curcumin as well as interactivity between transcription factors and major curcumin metabolites via in silico docking analysis.

Results

The HGD + Curc treatment influenced the differential expression of 1887 genes compared to HGD alone, which included messenger RNAs, microRNAs, long noncoding RNAs, and small nucleolar RNAs. Of these modulated genes, 307 overlapped and were negatively correlated with the fold change expression of the HGD versus LGD comparison. These protein coding and non-coding gene targets regulated by HGD+Curc were involved in pathways related to neurodegeneration, oxidative phosphorylation, blood-brain barrier permeability, cell signaling, and cellular metabolism.

Discussion/conclusion

The results from this study show that curcumin induces complex nutrigenomic modifications that could elucidate its neuroprotective effect against hippocampal microvascular dysfunction induced by a high glycemic diet.

1. Introduction

Disruptions in the cerebral vascular system contribute to cognitive decline and the progression of dementia, which is the 5th leading cause of death in individuals aged 65 and older in the United States (1). The microvascular network, composed of capillary endothelial cells that maintain the blood brain barrier (BBB), is essential for the delivery of oxygen, nutrients, and hormones to the brain parenchyma (2) and thus dysfunction of this system via hypoperfusion (i.e., decreased cerebral blood flow) and atrophy is detrimental to neurological function (3, 4). Integrity of this BBB is maintained by tight junctions (TJ) composed of the transmembrane proteins such as occludin, claudins, zonula occuldens (ZOs), and junctional adhesion molecules (JAMs) (4, 5). As a key component of the limbic system, the hippocampus is responsible for memory formation, spatial organization, and behavioral regulation (6) though microvascular dysfunction, injury, and senescence (i.e., age-related deterioration) contribute to vascular dementia-related cognitive impairment (3). Nutrition and cognitive health have been strongly linked and consumption of diets that promote obesity development, like those high in sugar and saturated fats, can foster cognitive decline (7–9).

Metabolic syndrome (MetS) is a diet-induced cluster of conditions characterized by increased waist circumference, high triglycerides, reduced high-density lipoprotein cholesterol (HDL-C), raised blood pressure, and elevated fasting blood glucose that can operate in a concerted effort to increase the risk of type 2 diabetes, cardiovascular diseases, and cognitive dysfunction (10–13). Chronic hyperglycemia and insulin resistance, principal features of diabetes, brought on by a high glycemic diet (HGD) or a Western diet (high-fat, high-sugar) have reportedly contributed to cognitive deterioration and dementia characteristics like synaptic degeneration/plasticity impairment, neuroinflammation, memory loss/spatial learning issues, and dysfunction of glial cells (12, 14). Furthermore, hyperglycemia and other consequences of MetS establish a status of low-grade inflammation and mitochondrial oxidative stress that can contribute to endothelial dysfunction and BBB permeability, including within the hippocampus (15). Previous studies, including from members of our research group, have reported that HGD/Western diet is associated with deleterious effects on brain microvasculature due to neurovascular inflammation, apoptosis, and endothelial hyperpermeability (7, 16–19), even inducing a multi-omic effect on protein coding and non-coding genes (16, 20). A HGD and diabetes have also been linked to exacerbation of age-related detriments and major degenerative diseases like Alzheimer’s, Parkinson’s, Huntington’s, and amyotrophic lateral sclerosis (ALS) (10, 21–23) in addition to brain tumor progression (24, 25).

Dietary intervention of bioactives like polyphenols and carotenoids from fruits, vegetables, and other plant sources can act as nutraceutical solutions in alleviating metabolic syndrome-related cognitive impairment due to their antioxidant and anti-inflammatory capabilities (26–30). Curcumin is a dietary polyphenol found in the rhizome of turmeric (Curcuma longa), comprising ∼77% of its curcuminoid composition along with demethoxycurcumin (∼17%) and bisdemethoxycurcumin (∼3-6%), and has historically been utilized in South Asian cooking and herbal remedies (31, 32). An average daily consumption of turmeric within the Indian community is about 2–2.5 g per individual (60 kg), corresponding to 60–100 mg of curcumin (30). The three parent curcuminoids share their structure with two aromatic benzenemethoxy rings connected with a conjugated α,β-unsaturated β-diketo linker and curcumin specifically can tautomerize between keto-enol forms and exist as the keto form in neutral and acidic environments while the enol form is found in alkaline environments and the solid state (30, 33). These structural components of curcuminoids all contribute to a flexible yet hydrophobic nature that lead to poor bioavailability (34, 35), though nanoemulisons of curcumin have been developed to help facilitate absorption and bioactivity (36, 37). Once ingested, gut and liver enzymes are capable of degrading curcumin via phase I and II reactions (34). Phase I metabolism generates dihydrocurcumin, hexahydrocurcumin, tetrahydrocurcumin, and octahydrocurcumin via reduction of double bonds while phase II conjugates curcumin and metabolites with glucuronic acid and sulfate (34, 38). Phase II glucuronidation elevates solubility and thus is found predominantly in bodily fluids and organs (34). Interplay with colonic intestinal bacteria has been reported as certain species can modify curcuminoids by reduction, hydroxylation, demethylation, and demethoxylation (39) while consumption of curcumin can modulate the profile of the gut microbiota and alleviate dysbiosis associated with chronic diseases (40, 41), which may help drive the reported biological activities.

Nutraceutical bioactive effects of curcuminoids include antioxidant, anti-inflammatory, antiproliferative, and antimicrobial actions in addition to notable ones relevant to this study such as antidiabetic, cardioprotective and neuroprotective effects (30, 33, 42, 43). Furthermore, several studies have reported curcumin-mediated modulation of neurodegenerative disease severity in animal and human models (43–46). In high glycemic conditions, curcumin treatment has antidiabetic efficacy by improving insulin resistance, fasting blood sugar levels, dyslipidemia, neuropathy, and inflammatory/oxidative status (42, 47, 48). Mechanistic drivers of curcumin bioactivity relate to modulation of nuclear factor kappa B (NF-kB), nuclear factor E2-related factor (Nrf2), Janus kinase/signal transducers and activators of transcription (JAK-STAT), phosphoinositide 3-kinase (PI3K)/v-akt murine thymoma viral oncogene homolog 1 (AKT), and mammalian target of rapamycin (mTOR) pathways (30, 33, 43, 49).

Goal of study: The objective of this study was to assess the nutrigenomic effects of curcumin (0.2% in diet) in male wild-type mice on a high glycemic diet (HGD) and its potential impact in neurovascular function of microvessels within the hippocampus.

2. Materials and methods

2.1. Animals and diet treatments

The research in this study was performed in compliance with the Public Health Service Policy on Humane Care and Use of Laboratory Animals and all animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at the University of California, Davis (protocol code 20943 and date of approval: 04/18/2019). Male C57BL/6J mice (n = 21, 19 weeks of age) were purchased from The Jackson Laboratory (Bar Harbor, ME, United States) and housed (n = 1 per cage) in a 12-h light/dark cycle in a temperature- and humidity-controlled environment within the University of California, Davis Mouse Biology Program. Mice were fed a standard chow diet (Teklad Custom Diets #0915, Madison, WI, United States) during the acclimation period of 1 week and then were randomly assigned to three experimental dietary intervention groups (n = 7/group) for 12 weeks: low glycemic diet (LGD, Teklad Custom Diets #TD.08485; 67.9% kcal of carbohydrate, 19.1% protein, 13% fat, with 12% sucrose by weight), high glycemic diet (HGD, #TD.05230, 68.7% kcal of carbohydrate, 18.7% protein, 12.6% fat, with 34% sucrose/weight), and HGD+Curc (#TD.05230 + 0.2% curcumin in diet, equivalent to 1 g/day in humans, achieved by replacing 2.0 g/kg of corn starch content in the HGD formulation with isolated curcumin); the composition of the experimental diets is provided in Supplementary Table 13. The supplementation of 0.2% curcumin has been utilized in animal studies (50, 51) and the human equivalency of 1 g/day falls in the range for recommended supplementation (52, 53). Food and water were administered ad libitum and consumption of experimental diets were monitored by lab members.

2.2. Body weight and serum tests

Body weight was recorded at the conclusion of the 12-week dietary intervention period and terminal blood was collected via ventricular puncture following an 8-h fasting period and stored at −80°C. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), insulin, and glucose levels were measured from fasted serum samples, which were isolated by centrifuging the blood at 1,500 × g for 10 min at 4°C. Levels of TC, TG, HDL-C, and LDL-C were determined via enzymatic assays from Fisher Scientific (Hampton, NH, United States) and precipitation separation assays from Abcam (Waltham, MA, United States) that were adapted for a microplate format. Serum glucose and insulin levels were measured by an enzymatic assay from Fisher Scientific (Hampton, NH, United States) and electrochemiluminescence from Meso Scale Discovery (Rockville, MD, United States), respectively, in accordance with manufacturer’s instructions. All serum analyses were carried out by the UC Davis Mouse Metabolic Phenotyping Center (MMPC) Metabolic Core.

2.3. Isolation, cryosection, and laser capture microdissection of murine hippocampal microvessels

At the time of sacrifice following the 12-week study period, mice were anesthetized by intraperitoneal injection of xylazine/ketamine with dosing based on the amount required to achieve the surgical plane of anesthesia (24–25.5 mg/kg xylazine and 216–229.5 mg/kg ketamine) and euthanized by exsanguination. Intact brains were collected rapidly and the region containing the temporal lobe and hippocampus was isolated and embedded with HistoPrep Frozen Tissue Embedding Media (Fisher Scientific, Pittsburgh, PA, United States) under RNase-free conditions. Hematoxylin staining and microscopy visualization of the medial temporal lobe allowed for the identification of the hippocampus and hippocampal neurons, as previously reported (54). The verified hippocampal region was then coronally cryosectioned (8 μm, Leica Frigocut 2800 n Cryostat, Leica Biosystems, Buffalo Grove, IL, United States), bound to charged RNA-free PEN Membrane Glass slides, treated with RNAlater^®-ICE (Life Technologies, Grand Island, NY, United States) for RNA preservation, and stored at −80°C for further analysis. A flowchart portraying the workflow of subsequent analyses of the hippocampus is provided in Figure 1.

FIGURE 1.

A flowchart depicting the workflow of the study, including microarray and bioinformatic analyses for the two key comparisons (A) HGD vs. LGD and (B) HGD+Curc vs. HGD. LGD, low glycemic diet; HGD, high glycemic diet; mRNA, messenger RNA; miRNA, microRNA; lncRNA, long non-coding RNA; snoRNA, small nucleolar RNA; GO, gene ontology; PPI, protein-protein interactions; DEGs, differentially expressed genes.

Hippocampal cryosections were immersed in nuclease-free water and dehydrated in desiccant in preparation for the laser capture microdissection (LCM) of endothelial microvessels (<20 μm), which were identified via alkaline phosphatase staining with 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium chloride (BCIP/NBT) substrate (55). Extraction of the microvascular endothelium of the cryosectioned hippocampus via LCM was performed with direct microscopic visualization of the entire vessel wall using a Leica LMD6000 Laser Microdissection Microscope (Leica Microsystems, Wetzlar, Germany). The isolated microvessels largely represented the cornu ammonis CA1 and CA3 regions in dorsal segments of the hippocampus, though further regional specification was not conducted.

2.4. RNA extraction and microarray transcriptomic analysis of lcm-isolated hippocampal microvessels

Laser-captured hippocampal microvessels (300 microvessels/mouse, n = 3 mice/group) were further utilized, starting with total RNA extraction via Arcturus PicoPure™ RNA Isolation Kit (Thermo Fisher Scientific, Santa Clara, CA, United States) following the manufacturer’s instructions. The Affymetix (Santa Clara, CA) protocol for RNA quantification with SYBR Green I and ROX™ Passive Reference Dye was conducted and the RNA quality of the LCM-derived microvessels was assessed with a Nanodrop spectrophotometer.

Once extracted, RNA from the hippocampal microvessels was further processed for transcriptomic analysis with Clariom D Mouse Arrays (one per mouse) that contained over 7 million probes for protein-coding and protein non-coding genes such as microRNAs, long non-coding RNAs, and small nucleolar RNAs (Thermo Fisher, Santa Clara, CA, United States). Extracted RNA (122.3 pg/mouse) was utilized for preparation of complimentary RNA (cRNA) and single-stranded cDNA (sscDNA) with the GeneChip^®WT Pico Kit (Thermo Fisher, Santa Clara, CA, United States). Subsequently, the sscDNA (5.5 μg) obtained from 20 μg cRNA was fragmented via uracil-DNA glycosylase (UDG) and apurinic/apyrimidinic endonuclease 1 (APE 1) and labeled by terminal deoxynucleotidyl transferase (TdT) with the biotin-linked DNA Labeling Reagent. The UC Davis Genome Center performed the hybridization, staining, and scanning of the arrays in accordance with the Thermo Fisher Scientific WT array hybridization protocol. The fragmented and labeled sscDNA samples were hybridized in the GeneChip™ Hybridization Oven 645 for 16 h at 45°C and then washed and stained with the GeneChip™ Fluidics Station 450. Finally, the microarrays were scanned with the GeneChip™ Scanner 3000 7G (Thermo Fisher Scientific, Santa Clara, CA, United States) and the Thermo Fisher Scientific Transcriptome Analysis Console software was utilized for quality control of the microarrays and data analysis. The gene expression data of the low glycemic diet (LGD) and high glycemic diet (HGD) can be found in Gene Expression Omnibus (GEO) dataset GSE185057 while the genomic data from the HGD+Curc microarrays is deposited under accession number GSE314833.

2.5. Bioinformatics methods

2.5.1. RNA classification

The ShinyGO version 0.82 online tool¹ (56) was utilized for the identification and classification of protein coding and non-coding RNA types (mRNA; miRNA, lncRNA, snoRNA, respectively) present in the microarray analysis with default settings applied (p-value cutoff: 0.05; species: mouse).

2.5.2. Pathway analysis

Pathway enrichment analysis of differentially expressed genes (DEGs) was conducted via the Enrichr² online bioinformatic tool (57–59) in conjunction with the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2021 (60) and Wiki Pathways 2024 (61) databases (p-value cutoff: 0.05). Pathway histograms and pie charts were created via the horizontal bar plot and 2D pie chart options on the SR Plot website³ (62).

2.5.3. Transcription factors and in silico docking

Transcription factors (TFs) potentially involved in the modulation of gene expression identified in our study were identified with the Enrichr tool through the Transcriptional Regulatory Relationships Unraveled by Sentence-based Text-mining (TRUUST) (63) and TRANScription FACtor (TRANSFAC) (64) databases (p-value cutoff: 0.05; species mouse). For in silico docking analysis, the online SwissDock⁴ tool (65, 66) was utilized to assess the molecular complementarity (i.e., binding energies) between these identified TFs and major dietary metabolites of curcumin via the Attracting Cavities approach (binding energy cutoff: < −7.0 kcal/mol) (67, 68). The SwissDock results (.doc4 files) were then imported into UCSF Chimera v1.19 (69) to visualize docking of the interaction with the lowest binding energy or other representative interactions. For curcumin and related dietary metabolites (ligands), the 2D and 3D structures were obtained from ChemSpider⁵ or PubChem⁶ (70), respectively. The 3D structures of TF proteins were acquired from the UniProt⁷ repository and electrostatic maps depicting charge distribution of the TFs were portrayed in the UCSF Chimera software when applicable. Docking analysis with SP1 could not be performed due to the 3D structure reported in UniProt at the time of this publication, which is not fully intact with high confidence in structure prediction.

2.5.4. Non-coding (miRNA and lncRNA)-target gene analysis

Functional strand identification (5p/3p) of differentially expressed miRNAs was achieved through the miRBase⁸ online resource (71). Subsequently, the MicroRNA ENrichment TURned NETwork (MIENTURNET)⁹ bioinformatic tool (72) was used to determine the gene targets regulated by the relevant miRNA strands using prediction from the TargetScan mode; the minimum number of gene-target interactions threshold was set to 1. Additionally, the gene targets of differentially expressed lncRNAs were obtained with the lncRRIsearch¹⁰ online tool using Ensembl identification numbers (73) (species: mouse; minimum of energy threshold: < −20 kcal/mol). Finally, gene targets of miRNA or lncRNAs were submitted to Enrichr for pathway ontology analysis with the KEGG 2021 and Wiki Pathways 2024 databases.

2.5.5. Comparative analysis and network mapping

Venn diagrams for comparisons between multi-level genomic regulation (i.e., different RNA types) by dietary intervention groups were generated via InteractiVenn¹¹ (74). STRING¹² (Protein-Protein Interaction Networks Functional Enrichment Analysis) software v12.0 (75) was utilized to construct a network of experimentally determined and predicted protein-protein interactions between coding DEGs (high confidence: 0.700). Furthermore, network maps between DEGs (mRNA, miRNA, lncRNAs) and their respective targets and/or functional pathways were assembled in the Cytoscape¹³ v3.10.3 software (76). The heatmap and correlation plot representing the fold change values between the HGD/LGD and HGD+Curc/HGD comparisons were generated through the heatmap with Ward clustering and Spearman scatter plot options, respectively, on the SR plot website.

2.5.6. Human disease association

Significant associations between identified differentially expressed genes and human diseases (nervous system and neurodegenerative) were assessed with the Comparative Toxicogenomics Database¹⁴ (CTD) (77) (corrected p-value cutoff: 0.05) and the Genome-Wide Association Studies (GWAS) Catalog¹⁵ (78); GWAS catalog numbers for neurodegenerative (EFO_0005772) and nervous system (EFO_0000618) diseases were utilized. The bubble plot for CTD results was generated with the enrichment bubble option in the SR plot website whereas the GWAS Venn diagram was created via InteractiVenn.

2.6. Statistical methods

For microarrays, statistical analysis of microvessel transcriptomes was conducted using ANOVA ebayes (Thermo Fisher Scientific Transcriptome Analysis Console software, Santa Clara, CA) with false discovery rate (FDR) correction. Differentially expressed genes (DEGs) from the microarray with significant p < 0.05 were considered as significantly differentially expressed. Diet intervention effects on body weight, lipid levels, glucose, and insulin were expressed as means ± standard error of the mean (SEM). Statistical significance (p ≤ 0.05) was assessed using unpaired t-tests (GraphPad software, La Jolla, CA, United States), or the Mann–Whitney test for non-normally distributed data.

3. Results

3.1. Biochemical data: effect of diets on body weight and serum parameters

At the conclusion of the 12-week diet intervention period, overall body weight amongst the LGD and HGD controls did not differ and the HGD+Curc treatment did not have a significant effect (Figure 2A). Similarly, serum levels of total cholesterol (Figure 2B) and triglycerides (Figure 2C) were unchanged across the three study groups. Notably, HGD+Curc significantly increased high-density lipoprotein cholesterol (HDL-C) levels (91.39 ± 26.35 vs. 11.83 ± 14.43 mg/dL, p < 0.05) (Figure 2D) and decreased low-density lipoprotein cholesterol (LDL-C) (9.89 ± 5.86 vs. 1.98 ± 0.76 mg/dL, p < 0.05) compared to HGD alone (Figure 2E). In addition, HGD+Curc elevated circulatory insulin (108.84 ± 61.14 vs. 291.43 ± 112.59 mg/dL, p < 0.01) significantly (Figure 2F) but did not affect glucose levels (Figure 2G) in relation to LGD and HGD.

FIGURE 2.

Body weight and circulatory measurements following 12 weeks of diet intervention. (A) Mean total body weight (g); (B–G) Bar graphs denoting serum levels of (B) total cholesterol (TC, mg/dL), (C) triglycerides (TG, mg/dL), (D) high-density lipoprotein cholesterol (HDL-C, mg/dL), (E) low-density lipoprotein cholesterol [LDL-(C), mg/dL], (F) insulin (pg/mL), and (G) glucose (mg/dL). All endpoints were analyzed by one-way ANOVA with post-hoc Tukey HSD. Values are mean ± standard error of the mean (SEM); statistical significance *(p < 0.05) and **(p < 0.01).

3.2. HGD/LGD: effect of the high glycemic diet on male murine hippocampal microvasculature

Statistical analysis of the microarray data revealed that the HGD treatment compared to LGD modulated 786 differentially expressed genes (DEGs) in male murine hippocampal microvessels (Supplementary Table 1). More specifically, 201 protein-coding genes and 65 non-coding genes were regulated by the HGD treatment; the latter category included 19 microRNAs (miRNAs), 16 long non-coding RNAs (lncRNAs), and 30 small nucleolar RNAs (snoRNAs). Of these characterized genes, 190 were upregulated and 76 were down-regulated by HGD intervention compared to LGD (Figure 3A). Furthermore, the remaining 520 DEGs were categorized as pseudogenes, multi-complex genes, or unassigned genes with symbols (known) or without symbols (unidentified).

FIGURE 3.

HGD genomic regulation on murine brain hippocampal microvasculature. (A) Bar graph depicting the number of differentially expressed coding (mRNA, orange) and non-coding (miRNAs, green; lncRNAs, blue; snoRNAs, purple) genes that were up-/down-regulated by the HGD intervention compared to LGD. (B–D) Histograms of functional pathways involving (B) coding mRNA genes, (C) miRNA gene targets, and (D) lncRNA gene targets; statistically regulated pathways (p < 0.05) were identified using the Enrichr online database tool- KEGG (no asterisk), Wiki Pathways*, common to both databases**.

Amongst those classified, a total of 201 protein-coding genes were differentially expressed by HGD relative to LGD, which were mostly upregulated (n = 185; fold change range 1.5–11.4), while a few underwent downregulation (n = 16; fold change range of −4.38 to −1.5) (Supplementary Table 2). Bioinformatic analysis to uncover gene and pathway ontology indicated that the coding DEGs were involved in pathway regulation of major neurodegenerative diseases like Huntington’s, Parkinson’s, Alzheimer’s, and prion diseases and amyotrophic lateral sclerosis (ALS) as well as cellular metabolism (e.g., mitochondrial complex assembly, oxidative phosphorylation, and thermogenesis) (Figure 3B).

The hippocampal microarray analysis indicated that the HGD treatment also differentially expressed non-coding RNAs such as miRNAs, lncRNAs, and snoRNAs in comparison to LGD. Firstly, the 19 modulated miRNAs were primarily downregulated (n = 18; fold change (fc) range of −3.93 to −1.5) while only one (mmu-miR-692) was upregulated with a fold change of 4.97 (Supplementary Table 3). Targets of the modulated miRNAs totaled 527 genes that were involved in phosphoinositide 3-kinase (PI3K)/v-akt murine thymoma viral oncogene homolog 1 (AKT), mitogen-activated protein kinase (MAPK), and Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathways and extracellular matrix (ECM) maintenance such as ECM-receptor interactions and cytoskeletal focal adhesion (Figure 3C). Furthermore, all 16 differentially expressed lncRNAs were downregulated by HGD relative to LGD with a fold change range of −6.23 to −1.51 (Supplementary Table 4). Identification of 542 genes targeted by differentially expressed lncRNAs were involved in neuronal function pathways like neural crest differentiation, nitric oxide (NO)/cyclic guanosine monophosphate (cGMP)/protein kinase G (PKG) mediated neuroprotection, and phosphodiesterases in addition to synaptic signaling (Wingless-related integration site (Wnt) and glutamatergic synapse regulation) (Figure 3D). All pathways significantly regulated (p < 0.05) by HGD intervention relative to LGD, organized by identification with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Wiki Pathways databases, have been provided in Supplementary Figure 1 for coding genes (1A) as well as gene targets for miRNAs (1B) and lncRNAs (1C). Finally, relative expressions of the 30 snoRNAs were primarily downregulated (n = 26; fc −3.27 to −1.51) with some upregulation present (n = 4; fc 1.92–19.32) (Supplementary Table 5). Relevant target genes and pathways of these snoRNAs were not unveiled with literature searches and bioinformatic tools.

3.3. HGD + curcumin/HGD: nutraceutical effect of the high glycemic diet + curcumin on the hippocampal microvascular genome

Microarray analysis was also conducted with the curcumin supplemented group to uncover how this dietary polyphenol influenced the genome of the murine hippocampal microvessels that were exposed to a high glycemic load. Statistical analysis of the microarray data demonstrated that 1,887 DEGs were affected by HGD+Curc treatment relative to the HGD control group (Supplementary Table 6). The DEGs identified with gene symbols included 560 protein coding and 146 non-coding genes (42 miRNAs, 40 lncRNAs, and 64 snoRNAs). Within those classified into these categories, 168 genes were upregulated and 538 downregulated by HGD+Curc in comparison to HGD (Figure 4A). The remaining 1,181 DEGs were categorized as pseudogenes, multi-complex genes, immunoglobulin (Ig) variable chain genes, or unassigned genes with gene symbols (known) or without symbols (unidentified).

FIGURE 4.

HGD + Curcumin intervention on the murine hippocampal microvascular genome involving protein-coding genes. (A) Bar graph portraying the number of differentially expressed protein coding (mRNA, orange) and non-coding (miRNAs, green; lncRNAs, blue; snoRNAs, purple) genes up-/down-regulated by HGD+Curc versus HGD treatment. (B) Histogram of pathways for differentially expressed coding genes organized by gene count and pathway type accompanied by Cytoscape network maps of pathways (rectangles) and respective coding genes (circles); neuro-related (violet), physiological system (burgundy), cell signaling (teal), cell metabolism (blue), cell adhesion (peach), genetic regulation (mustard yellow), and other (light green); statistically regulated pathways (p < 0.05) were identified using the Enrichr online database tool- KEGG (no asterisk), Wiki Pathways*, common to both databases**. (C) Pie chart of pathway types and respective percentages for coding genes. (D) STRINGv12 interaction map of coding genes: experimentally determined (pink), curated databases (light blue), gene neighborhood (green), gene co-occurence (dark blue), gene fusions (red), co-expression (black), protein homology (purple), text mining (mentioned together in Pubmed abstracts; yellow).

3.3.1. Influence of HGD+Curc on coding mRNA

The protein coding genes differentially expressed by HGD+Curc were primarily downregulated (n = 509; fold change −32.64 to −1.5) with some upregulation (n = 51; fold change 1.51–9.68) compared to the HGD group (Supplementary Table 7). Pathway enrichment analysis demonstrated that coding DEGs influenced by HGD+Curc were involved in various neurological and physiological system associated functions as well as pathways of cellular signaling, metabolism, genetic regulation and adhesion (Figure 4B). Cognitive and neurological related functions, which took up 24.62% of overall significantly regulated pathways (Figure 4C), involving the coding DEGs were predominately associated with neurodegenerative diseases (e.g., Huntington’s, Parkinson’s, and Alzheimer’s diseases; prion disease; ALS) in addition to signaling of hormones (oxytocin, corticotropin) and/or growth factors like brain-derived neurotrophic factor (BDNF). Pathways concerning other physiological processes (23.08%) were identified such as diabetic cardiomyopathy, non-alcoholic fatty liver disease (NAFLD), and signaling of insulin and thyroid hormones. Cellular transduction pathways (16.92%) were involved in vascular endothelial growth factor and receptor VEGF/VEGFR2, cAMP/cGMP-PKG, and transforming growth factor-beta (TGF-β) signaling while functionality of coding DEGs in cellular metabolism (16.92%) were heavily implicated with the regulation of oxidative phosphorylation and thermogenesis. Though a small portion but important for endothelial maintenance, pathways related to cellular adhesion (3.85%) included the maintenance of the actin/microtubule cytoskeleton, focal adhesion, and tight/adherens junctions. Pathways of genetic regulation (8.46%) like mRNA processing, translation factors, and regulation of ribosomal proteins were affected, which is characteristic of this protein coding level of nutrigenomic modulation. Additionally, some identified genes related to chromosomal abnormalities such as copy number variations (CNV) and proximal deletions, influenced by dietary intervention. Finally, coding DEGs modulated by HGD+Curc were also involved in other regulatory pathways (6.15%) related to cellular processes like endocytosis, apoptosis, and senescence.

The STRING map highlights the potential interactions between differentially expressed protein coding genes (Figure 4D). Twenty eight of the 560 coding genes participated in 25 or more interactions (Table 1), the highest amongst them with 38 interactions being ATP synthase subunit alpha (Atp5a1). These highly interactive coding genes were involved in pathways of neurodegenerative diseases, diabetic complications (i.e., NAFLD and cardiomyopathy), and cellular metabolic processes of oxidative phosphorylation and thermogenesis.

TABLE 1.

Genes with 25 or more interactions in the STRING map of HGD+Curc DEGs in Figure 4.

Symbol	Gene name	Count
Atp5a1	ATP synthase subunit alpha, mitochondrial	38
Eef2	Elongation factor 2	34
Cox5a	Cytochrome c oxidase subunit 5A, mitochondrial	33
Rpl4	60S ribosomal protein L4	32
Uqcrc2	Cytochrome b-c1 complex subunit 2, mitochondrial	32
Actb	Actin, cytoplasmic 1, N-terminally processed	31
Cox4i1	Cytochrome c oxidase subunit 4 isoform 1, mitochondria	31
Gm11808	Ubiquitin-60S ribosomal protein L40	31
Uqcrc1	Cytochrome b-c1 complex subunit 1, mitochondrial	31
Cox5b	Cytochrome c oxidase subunit 5B	30
Ndufs3	NADH dehydrogenase [ubiquinone] iron-sulfur protein 3, mitochondrial	30
Rpl8	60S ribosomal protein L8	29
Rps15	40S ribosomal protein S15	29
Sdhb	Succinate dehydrogenase [ubiquinone] iron-sulfur subunit, mitochondrial	29
Hsp90ab1	Heat shock protein HSP 90-beta	28
Cox6a1	Cytochrome c oxidase subunit 6A1, mitochondrial	27
Ndufa4	Cytochrome c oxidase subunit NDUFA4	27
Ndufa9	NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 9, mitochondrial	27
Ndufv2	NADH dehydrogenase [ubiquinone] flavoprotein 2, mitochondrial	27
Uqcr11	Cytochrome b-c1 complex subunit 10	27
Ndufb7	NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 7	26
Ndufs4	NADH dehydrogenase [ubiquinone] iron-sulfur protein 4, mitochondrial	26
Rpl3	60S ribosomal protein L3	26
Rplp0	60S acidic ribosomal protein P0	26
Sdha	Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial	26
Arl6ip1	ADP-ribosylation factor-like protein 6-interacting protein 1	25
Eif5a	Eukaryotic translation initiation factor 5A-1	25
Ndufa13	NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 13	25

3.3.2. Transcription factors and in silico docking of curcumin metabolites

Another level of nutrigenomic modulation by curcumin consumption was observed in this study through the interactions between protein coding DEGs modulated by HGD+Curc and potential transcription factors (TFs) whose activity could be affected by curcumin and underlying observed genomic changes. Seven statistically significant TFs were identified (p < 0.05) included CREB1, SP1, FOXF1, NRF1, MAF, TCF12, HDAC3, and TFAP2A (Table 2). The largest number of coding DEGs (n = 33) was associated with CREB1, while the most significant relationship (p = 0.0011) was with MAF, which was likely due to Maf itself being a DEG. To further investigate how curcumin consumption potentially influenced TF activity, in silico docking analysis was performed to determine binding energies between the identified TFs and major dietary metabolites or derivatives of curcumin. The structures of curcumin and the related compounds of demethoxycurcumin, dihydrocurcumin, hexahydrocurcumin, tetrahydrocurcumin, curcumin glucuronide, and curcumin sulfate are provided in Figure 5A as well as their respective binding energies to the putative TFs in Table 3. Most metabolites had significant docking (<−7 kcal/mol) to one or more TF, with the lowest binding energy consistently being with TFAP2A, with the exception of tetrahydrocurcumin and NRF1. Among the metabolites, demethoxycurcumin and curcumin glucuronide showed potential interactions with the highest number of proteins. Representative depictions of significant docking amongst the dietary compounds and TFs are provided in Figures 5B–H.

TABLE 2.

Potential transcription factors (TFs) involved in gene regulation by HGD + Curc intervention.

TF (symbol)	Name	UniProt ID	P-value	Gene count
CREB1**	cAMP response element-binding protein 1	Q01147	0.036512	33
SP1*	Specificity Protein 1	O89090	0.004082	16
FOXF1**	Forkhead box protein F1	Q61080	0.030939	11
NRF1*	Nuclear respiratory factor 1	Q9WU00	0.002978	4
MAF*	MAF bZIP	P54843	0.001101	3
TCF12*	Transcription factor 12	Q61286	0.014969	2
HDAC3*	Histone deacetylase 3	O88895	0.014969	2
TFAP2A*	Transcription factor AP-2 alpha (Activating enhancer binding Protein 2 alpha)	P34056	0.030344	2

Table organized by number of protein coding genes influenced by TF activity identified using the Enrichr online database tool;

TRRUST*,

TRANSFAC**.

FIGURE 5.

In silico docking of curcumin-related metabolites to transcription factors (TFs). (A) Skeletal 2D structures of curcumin and major dietary curcumin-derived metabolites. (B–G) Representative docking interactions between metabolites and TF with significant binding energies, (B) curcumin and TFAP2A, (C) demethoxycurcumin and NRF1, (D) dihydrocurcumin and NRF1, (E) hexahydrocurcumin and TFAP2A, (F) tetrahydrocurcumin and HDAC3, (G) curcumin Glucuronide and FOXF1, and (H) curcumin Sulfate and MAF; 3D visualization via UCSF Chimera v1.19 (left) and SwissDock (right).

TABLE 3.

Binding energy of major dietary curcumin-related metabolites to transcription factors (TFs) according to the online SwissDock in silico docking tool.

Dietary metabolites	Binding energy to TFs (kcal/mol)
	CREB1	FOXF1	HDAC3	MAF	NRF1	TCF12	TFAP2A
Curcumin	–6.1655	–6.4461	–7.0956	–7.1561	–7.4551	–7.1376	–7.5784
Demethoxycurcumin	–6.1034	–6.3629	–6.4598	–6.9189	–7.3666	–6.6069	–7.6787
Dihydrocurcumin	–6.136	–6.6939	–6.9701	–6.9603	–7.5831	–6.9597	–7.8057
Hexahydrocurcumin	–5.9812	–6.8071	–6.9923	–6.9079	–7.2298	–6.8901	–7.8222
Tetrahydrocurcumin	–6.1399	–6.4305	–7.0385	–7.151	–7.5008	–6.8022	–7.3203
Curcumin glucuronide	–7.1127	–7.7135	–7.2736	–7.2426	–8.034	–7.1559	–8.8235
Curcumin sulfate	–6.966	–6.7967	–7.0248	–7.2852	–7.9576	–6.833	–8.9096

3.3.3. HGD+Curc non-coding miRNA

As seen with the HGD, several categories of non-coding RNAs were differentially expressed in the hippocampal microvessels by curcumin supplementation. Firstly, 42 miRNAs were modulated by HGD+Curc in comparison to HGD, of which 31 were upregulated (fold change range of 1.53–3.75) and 11 were downregulated (fold change range of −16.41 to −1.6) (Figure 6A and Supplementary Table 8A). Bioinformatic analysis identified 470 target genes for only 12 of the 42 DE miRNAs (p < 0.05), 10 of such miRNAs were upregulated (fold change 1.55–2.46) and two were downregulated (fold change −16.41 to −1.65) (Supplementary Figure 4A). A network map of the participating miRNAs and respective gene targets is provided in Figure 6B, demonstrating that genes are associated with up to eight miRNAs. In particular, three miRNAs were grouped together (mmu-let-7a-5p, -let-7k, and -miR-98-5p) in a network node as these are members of the let-7 family of miRNAs, which were upregulated (fold change 2.46, 1.65, 2.44, respectively) and associated with the largest subset (n = 179) of target genes (Supplementary Table 8B). Functions of overall genes targeted by the DE miRNAs were related to brain-derived neurotrophic factor (BDNF), forkhead box O (FoxO), and insulin signaling as well as the angiopoietin-like protein-8 regulatory pathway (Figure 6C), which were all pathways in common with the coding genes differentially expressed by HGD+Curc as mentioned previously. Additional pathways that were exclusive to miRNA targets were related to regulation of stem cell pluripotency, neuroinflammation, and glutamatergic signaling as well as biosynthesis of N-glycans and glycoaminoglycans (GAGs) and metabolism of galactose and amino/nucleotide sugars.

FIGURE 6.

Curcumin modulation of hippocampal non-coding miRNAs and targets. (A) Fold changes depicting upregulation or downregulation of differentially expressed miRNAs by HGD+Curc compared to HGD. (B) Network map of miRNAs (dark green rectangles) and their respective gene targets (light gene circles) generated via Cytoscape. (C) Histogram of functional pathways for miRNA gene targets categorized by gene count and pathway type; statistically regulated pathways (p < 0.05) were identified using the Enrichr online database tool KEGG (no asterisk), Wiki Pathways*, common to both**.

3.3.4. HGD+Curc non-coding lncRNA

Curcumin supplementation also modulated 40 lncRNAs in the hippocampal microvessels, the majority of which were upregulated (n = 36; fold change 1.5–3.62) while only four were downregulated (fold change: −8.53 to −1.93) (Figure 7A and Supplementary Table 9). The majority of DE lncRNAs (82.5%) were discovered to have a total of 656 gene targets and depicted in the network map provided in Figure 7B. Pathway enrichment analysis shows that these lncRNA targets were predominantly involved with neurological functionalities. However, these genes regulated by lncRNAs were associated with neurodevelopmental disorders, rather than neurodegenerative diseases, such as fragile X syndrome, autism, Rett syndrome, methylenetetrahydrofolate reductase (MTHFR) deficiency, the hypothetical craniofacial development pathway, postsynaptic signaling disruption by copy number variations (CNV), and T-box G-rich interacting factor (Tgif) disruption of Sonic Hedgehog (Shh) signaling (Figure 7C). Other significantly regulated pathways of lncRNA target genes included neuronal differentiation, glutamatergic synapse regulation, and NO/cGMP/PKG neuroprotection. Alternatively organized by database (i.e., KEGG and Wiki Pathways) and alphabetically, significantly regulated pathways (p < 0.05) by HGD+Curc compared to HGD alone are provided for coding DEGs in addition to gene targets for miRNAs and lncRNAs in Supplementary Figures 2–4, respectively.

FIGURE 7.

Curcumin modulation of hippocampal non-coding lncRNAs and targets. (A) Upregulation or downregulation of differentially expressed lncRNAs by HGD+Curc compared to HGD. (B) Cytoscape network map of lncRNAs (dark blue hexagons) and their respective gene targets (light blue rectangles). (C) Categorized histogram of lncRNA gene targets organized via gene count and pathway type; statistically regulated pathways (p < 0.05) were identified using the Enrichr online database tool- KEGG (no asterisk), Wiki Pathways*.

3.3.5. HGD+Curc non-coding snoRNA

Finally, non-coding snoRNAs (n = 64) were also differentially regulated by HGD+Curc intervention, of which 50 were upregulated (fold change 1.5–4.72) and the other 14 were downregulated (fold change −16.58 to −1.54) (Supplementary Table 10). Target genes and pathways of these snoRNAs were not observed with literature searches and bioinformatic tools, though a few C/D box snoRNAs (Snords) were identified. Notably, Snord16a and Snord59a were downregulated (fc: −4.07) and upregulated (fc: 1.59), respectively, by HGD+Curc.

3.3.6. HGD+Curc: integrative analysis of coding DEGs and non-coding gene targets

In order to further look into the multigenomic influence of HGD+Curc dietary intervention on the hippocampal microvasculature, we generated an integrated network of differentially expressed protein coding mRNAs, potential regulatory TFs, and non-coding miRNAs and lncRNAs in addition to their respective gene targets (Figure 8A). Targets were clustered around TFs and non-coding RNAs with some overlap, indicating that some genes undergo multi-level regulation through more than one RNA type. For example, various genes were identified as protein coding DEGs in addition to targets of miRNAs (n = 13; Aplp2, Dram2, Epm2aip1, Erh, Ghitm, Gm14325, Gm14326, Gm14391, Nlk, Ppp2r2a, Rab6b, Slc25a18, Vamp1) or lncRNAs (n = 10; Agap2, Arf3, Atp1b1, Atp2b4, Camk2n1, Cask, Epb41l1, Lgi1, Srp54a, Tgfb3) (Supplementary Figure 5A). Likewise, a few genes were targets of both miRNAs and lncRNAs (n = 8; Baz2a, Brwd3, Caln1, Ccnt1, Dnal1, Slitrk2, Trim56, Yy1) though no genes identified through bioinformatic tools were common amongst coding DEGs and targets of non-coding multi-regulation (i.e., all three groups).

FIGURE 8.

Global interactions and pathways of differentially expressed coding genes and non-coding gene targets by HGD+Curc in the murine hippocampal microvasculature. (A) Cytoscape network map of coding mRNA (light brown circles), transcription factors (brown diamonds), miRNAs (dark green rectangles) and miRNA gene targets (light green circles), lncRNAs (dark blue hexagons) and lncRNA gene targets (light blue circles), genes that are coding mRNA + miRNA targets (teal circles), coding mRNA + lncRNA targets (red circles), miRNA targets + lncRNA gene targets (purple circles). (B) Histogram of pathways involving coding genes in addition to gene targets of non-coding miRNAs and lncRNA categorized by gene count and pathway type; statistically regulated pathways (p < 0.05) were identified using the Enrichr online database tool- KEGG (no asterisk), Wiki Pathways*, common to both databases**.

Integrated pathway enrichment of all differentially expressed coding genes as well as gene targets of miRNAs and lncRNAs was conducted to gain insight on the global, multigenomic regulation induced by HGD+Curc intervention on the hippocampal microvessels (Figure 8B). Consistently, a large portion of coding DEGs and non-coding gene targets were involved with pathways of neurodegenerative diseases (Alzheimer’s, Huntington’s, Parksinson’s, ALS, prion disease) as well as BDNF and retrograde endocannabinoid neuronal signaling. Furthermore, diabetic sequelae such as cardiomyopathy, NAFLD, and interactivity of insulin and aldosterone were influenced by HGD+Curc consumption. Several transduction pathways like cAMP/cGMP-PKG, FoxO, hypoxia-inducible factor-1 (HIF-1), and angiopoietin-like protein 8 signaling as well as cellular metabolic processes of thermogenesis and oxidative phosphorylation were significantly modulated. As for endothelial adhesion maintenance, genes involved in microtubule and adherens junction regulation were identified. Finally, some DEGs were involved in other pathways regulating copy number variations, circadian rhythm, and phototransduction. Overlaps between pathways as well as common coding DEGs and/or non-coding gene targets across both HGD and HGD+Curc are provided in Supplementary Figures 5B,C, respectively.

3.3.7. Disease associations with genes differentially expressed by HGD+Curc

Further investigation was conducted into genomic associations between the differentially expressed genes identified in murine hippocampal genome and genes known to be involved in the development of human neurological diseases due to the consistent involvement of identified DEGs with neurological dysfunction via pathway enrichment analysis. According to the Genome-Wide Association Study (GWAS) catalog, coding and non-coding DEGs regulated by HGD+Curc overlapped with modulated genes seen in human nervous system diseases (n = 115), neurodegenerative diseases (n = 2), or both (n = 60) (Figure 9A). Taking a closer look with the Comparative Toxicogenomics Database (CTD), HGD+Curc modulated DEGs were highly associated with general nervous system disease (corrected p = 6.28 ×10⁻²⁴) and several subcategories like congenital abnormalities, mental disorders, and diseases of genetic, metabolic, and musculoskeletal nature (Figure 9B and Supplementary Table 11). Notably, the HGD+Curc-related DEGs were further shown to be associated with neurodegenerative (corrected p = 2.48 ×10⁻⁹)and neurodevelopmental (corrected p = 5.02 ×10⁻⁹)diseases. Associations with signs and symptoms of nervous system diseases such as intellectual disability, neurologic/neurobehavioral manifestations, dyskinesias, and ataxia were also observed.

FIGURE 9.

Associations between the differentially expressed genes (DEGs) by HGD+Curc and known human neurological diseases. (A) Venn diagram depicting the overlap of identified DEGs (mRNAs, miRNAs, lncRNAs, snoRNAs; orange) with genes associated with neurodegenerative diseases (pink) and nervous system diseases (blue) according to the Genome-Wide Association Study (GWAS) catalog. (B) Bubbleplot of disease associations according to the Comparative Toxicogenomics Database; neurological disease (y-axis), disease category (x-axis), number of associated DEGs (node size), corrected p-value (node color).

3.3.8. Curcumin counteracts HGD-induced differential gene expression

The overall goal of this study was to determine the differential genomic regulation of the murine brain microvasculature across the dietary intervention groups, of which the two key comparisons were between HGD versus LGD and HGD+Curc versus HGD. The HGD+Curc supplemented group, relative to HGD as seen in Figure 10A, differentially expressed more than double the number of identified protein coding (560 vs. 201) and non-coding genes (146 vs. 65) in comparison to the DEGs identified between the two control groups of HGD and LGD. There were 376 common DEGs across the two key comparisons, of which were 139 coding mRNAs, 5 miRNAs, 4 lncRNAs, and 14 snoRNAs while the rest were miscellaneous or unidentified genes (Figure 10B and Supplementary Table 12). All of these common DEGs had an upregulated fold change (3.2–18.14) by HGD relative to LGD while 307 of them had reversed expression to downregulation (−30.91 to −1.5), leaving 69 upregulated (1.5 to 8.53) by HGD+Curc compared to HGD. Furthermore, correlation analysis calculated a significant negative correlation (p < 2.2 ×10⁻¹⁶; R = −0.55) between the fold changes of common DEGs across the two key comparisons of HGD/LGD and HGD+Curc/HGD (Figure 10C), suggesting a counteractive effect by curcumin supplementation. Pathway enrichment (Figure 10D) demonstrated that these common DEGs were involved with neurodegenerative diseases (e.g., Huntington’s, Parkinson’s, and Alzheimer’s diseases; prion disease; ALS), diabetic complications (NAFLD and cardiomyopathy) and cellular metabolism (e.g., mitochondrial complex assembly, oxidative phosphorylation, and thermogenesis).

FIGURE 10.

Comparison of the nutrigenomic regulation of HGD + Curc and HGD interventions on murine hippocampal microvasculature. (A) Bar graph representing the number of differentially expressed coding (mRNA, orange) and non-coding (miRNAs, green; lncRNAs, blue; snoRNAs, purple) genes that were up-/down-regulated by the HGD intervention compared to LGD (dashed bars) or HGD + Curc versus HGD (solid bars). (B) Heatmap of common genes that were differentially regulated in both HGD/LGD (left column) and HGD+Curc/HGD (right column) comparisons; unidentified genes are represented by their Affymetrix ID number. (C) Spearman’s correlation plot representing the relationship for the expression of common genes between HGD/LGD and HGD+Curc/HGD fold changes (fc). (D) Pathway histogram of common identified genes, organized by gene count and pathway type; statistically regulated pathways (p < 0.05) were identified using the Enrichr online database tool- KEGG (no asterisk), Wiki Pathways*, common to both databases**.

4. Discussion

The goal of this study was to assess the nutrigenomic regulation of curcumin on hippocampal microvessels in mice fed on a high glycemic diet. Genes with reversed fold change expression induced HGD+Curc relative to HGD were associated with multiple neurodegenerative diseases (e.g., Alzheimer’s, Huntington’s, Parkinson’s, and prion diseases; ALS), diabetic complications (NAFLD and cardiomyopathy) in addition to the cellular metabolic processes of oxidative phosphorylation and thermogenesis. Consistently shown in this study, these HGD+Curc-modulated pathways were connected via genes regulating the assembly of mitochondrial complexes I (Ndufa13, Ndufb6, Ndufb7), II (Sdhb), III (Cox7a2l, Uqcr11, Uqcrc1), and IV (Cox5a, Cox5b, Cox6a1) which indicated a switch, from promotion by HGD to reduction by HGD + Curc, in detrimental reactive oxygen species (ROS) production. Overall, this current study offers some of the first evidence that curcumin has potential multigenomic neuroprotective effects on hippocampal microvasculature under the exposure to a high glycemic diet in a wild-type murine model.

4.1. HGD/LGD: implications of high glycemic diet consumption and neurodegeneration

In this study following 12 weeks of intervention, HGD induced multi-level genomic changes in the hippocampal microvasculature independently from significant differences in body weight and levels of total cholesterol, HDL-C, LDL-C, triglycerides, insulin, and glucose compared to LGD, which is largely consistent with our previously published study in female mice (20). Under these circumstances, this high glycemic load likely triggered early, tissue-specific cerebrovascular changes that either preceded or occurred independently of systemic alterations in insulin, glucose, and other biochemical markers. Coding genes were primarily upregulated by HGD, mostly for neurodegenerative diseases such as Alzheimer’s, Parkinson’s, Huntington’s, and ALS in addition to cellular metabolic processes of oxidative phosphorylation and thermogenesis and other diabetic issues like cardiomyopathy and NAFLD. The onset of hyperglycemic complications, such as NAFLD, have been closely linked to cognitive impairment, mainly due to the chronic-low grade inflammatory environment as neuroinflammation can be induced via chemokine infiltration across the BBB and activation of microglia (79). Furthermore, development of atherosclerosis and cardiomyopathy can lead to microvascular dysfunction and hippocampal impairment due to decreased cerebral blood flow (80, 81). In line with the reported literature regarding neurological complications brought on by a high-glycemic dietary challenge (10), this study showed that these pathways were linked through genes involved in the assembly of mitochondrial complexes I (Ndufa13, Ndufb6, Ndufb7, Ndufc1), II (Sdhb), III (Cox7a2l, Uqcr11, Uqcrc1), and IV (Cox5a, Cox5b, Cox6a1). The upregulation of these mitochondrial complex genes induced by HGD suggests possible accelerated production of ROS via oxidative phosphorylation, though not directly studied here, that has been linked to neurodegeneration such as aberrant oxidative damage, protein aggregation (i.e., protein folding), and promotion of neuroinflammatory and apoptotic pathways (82).

Meanwhile non-coding (miRNAs, lncRNAs, and snoRNAs) were primarily downregulated by HGD compared to LGD. Targets genes of HGD-regulated miRNAs were involved in the interplay between kinase phosphorylation (JAK/STAT, MAPK, PI3K-Akt) pathways responsible for influencing the neuroinflammatory and oxidative stress environment of neurological diseases (83). More specifically, JAK/STAT and MAPK signaling has been linked to abnormal accumulation of tau-protein and amyloid-beta (Aβ) characteristic of Alzheimer’s disease (83, 84) while disrupted PI3K/Akt activity impairs neuroplasticity (83, 85). Targets of DE miRNAs were also associated with extracellular matrix (ECM) maintenance like ECM-receptor interactions and cytoskeletal focal adhesion, mainly linked through targeting of collagen subunits. These ECM-interactions, including the perineuronal nets (PNNs) of the central nervous system, are disrupted by increased degradation activity via matrix metalloproteinases (MMPs) (86, 87) and binding of the cell-to-ECM connective integrins to Aβ (86, 87) in contribution to neurodegenerative conditions as these interactions are crucial for long-term potentiation (LTP) in hippocampal neurons (86, 88). Furthermore, all DE lncRNA were downregulated whose gene targets were associated with neuroprotection and neural crest differentiation mediated through NO-cGMP-PKG and Wnt signaling pathways as well as glutamatergic synapse regulation. NO-cGMP-PKG signaling is essential for LTP as NO aids in regulation of neuroplasticity, memory, and hypothalamic responsibilities, but Aβ accumulation impedes NO production and cGMP/PKG downstream activity (89) while Wnt signaling can protect against mitochondrial dysfunction (90). Finally, the structurally modifying ribosomal 2’-O-methylation activity of C/D box snoRNAs has been linked to CNS disorders and neurodegeneration (91), though the Snord82 downregulated by HGD in this study has not yet been directly implicated. Altogether, the neurodegenerative effect in the hippocampal microvasculature by HGD was once again seen in this study, irrespective of whole-body diabetic markers.

4.2. HGD + Curc/HGD: mechanisms of potential nutraceutical protection of hippocampal endothelium

Curcumin has gained traction in studies of its dietary bioactivity for neuroprotection in the central nervous system due to increasing evidence that it can act as a genomic and epigenetic modulator in multiple diseases like cancer, diabetes, and even neurodegenerative diseases (48, 92–96). Several studies have revealed that curcumin can impact large number of genes simultaneously, such as endothelial cells in vitro (97) or within the aorta of ApoE-/- mice (98), however, the multigenomic impact remains largely unknown. These epigenetic mechanisms by curcumin have included regulation of DNA methylation, histone modifications, and expression of non-coding RNAs like microRNAs, lncRNAs, and circular RNAs (92–96, 99–101). While the influence of curcumin has been documented in the brain within models of neurodegeneration (92, 100, 101) and diabetes (42, 92, 102, 103), even specifically in the hippocampus (104–106), they have focused on the temporal and/or hippocampal region as a whole and overall cognitive function. Therefore, these studies have not addressed the effect of curcumin on hippocampal microvessels, especially regarding epigenomic regulation. Notably, the differential expression of snoRNAs by curcumin in the cerebral endothelium has not been previously documented, thus this study highlights a potential new non-coding RNA-related level of epigenomic neuroprotection. Altogether, this current study emphasized the nutrigenomic influence of curcumin on hippocampal endothelial microvasculature subjected to a high glycemic dietary load within a wild-type model through multi-level modulation of coding mRNAs, putative transcription factors, and non-coding RNAs as summarized in Figure 11.

FIGURE 11.

Mechanisms of dietary curcumin regulation in hippocampal microvessels exposed to a high glycemic load.

The first indication of the curcumin’s bioactivity in this study was depicted in the circulation as HGD+Curc raised HDL-C and lowered LDL-C level compared to HGD, suggesting a mild corrective effect on dyslipidemia, though there was no overall effect on body weight, TC, and TG. Also, HGD+Curc significantly elevated circulatory insulin compared to HGD, which indicated an insulinotropic (i.e., promotion of insulin secretion) response as curcumin has been reportedly capable of protecting pancreatic β–cell damage under hyperglycemic conditions and diabetic patients (107–110). Thus, the curcumin-induced elevation in serum insulin was likely due to preservation of pancreatic β-cell production/secretory capacity of insulin and reduction of lipotoxic stress on β-cells via improved lipid homeostasis. However, more direct measurements of pancreatic β-cell health, such as the homeostatic model assessment for β-cell function (HOMA-β), need to be performed to further investigate the impact of curcumin consumption on circulatory insulin under the conditions of this study.

The HGD+Curc-induced regulatory pathways of coding genes were predominately linked through the downregulation of oxidative phosphorylation with implications for curcumin-mediated inhibition excessive mitochondrial ROS formation. Structural characteristics of curcumin can directly quench reactive free radicals as the phenolic hydroxyl groups can act as hydrogen or electron donors to form phenoxyl radicals and the keto-enol moiety can promote antioxidant activity via chelation with redox-active metal ions (i.e., Cu^{2 +}, Zn^{2 +}, and Fe^{3 +}) (111, 112). Furthermore, curcumin can indirectly affect ROS levels by promoting the activity of endogenous antioxidant enzymes like superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) via Nrf2 (111); these targets were not assessed in this study but would be important work for future investigations.

Differentially expressed genes related to distinct neurodegenerative diseases in this study leaned toward Alzheimer’s related neurofibril aggregation as HGD+Curc downregulated Aβ precursor protein (App), Aβ precursor-like proteins (Aplp1 and Aplp2) and Aβ precursor protein-binding, family B, member 1 (Apbb1). Interruption of Aβ aggregation was potentially facilitated by the decreased expression of Rho-associated coiled-coil containing protein kinase 1 (Rock1) by HGD+Curc and subsequent phosphorylation of App. A more direct inhibition of amyloidogenesis by curcumin was also possibly achieved here as the keto–enol ring and aromatic hydroxyl groups of curcumin reportedly can react covalently with the aromatic rings or form hydrogen bonds with amino acid residues with polar pockets of Aβ, respectively (113). Furthermore, tau-phosphorylation was potentially inhibited due to the decreased expression of calcium/calmodulin-dependent protein kinase IV (Camk4), glycogen synthase kinase 3 alpha (Gsk3a) as well as the increased expression of miR-142a-5p that targeted cyclin-dependent kinase 5 (Cdk5); while the CNS-associated Gsk3β isoform (114) was not identified by as a coding DEG, it was targeted by Gm20675. Gsk3 activity is also tied to insulin signaling in a negative-feedback loop as Gsk3-overexpression diminishes insulin-mediated glycogen synthesis and glucose homeostasis via pancreatic β-cell dysfunction, leading to peripheral insulin resistance, while insulin-receptor binding inhibits Gsk3 activation (115). Thus, this downregulation of Gsk3a and indirect action on Gsk3β may be tied to the potential insulinotropic capability of curcumin in this study. Targeting of caspase signaling, which has been linked to the neuronal apoptosis aspect of dementia (116, 117), was also observed in this study as Casp3 and Casp9 were targeted by lncRNA Gm20675 and miR-142a-5p/-199a-3p, respectively. Along with mitochondrial dysfunction, ALS pathology has been mainly linked to aggregation of misfolded SOD1 and transactive response DNA binding protein-43 (TDP-43, encoded by Tardbp) (118, 119), which were both downregulated by HGD+Curc.

Regulation of multiple pathways by HGD+Curc in this study indicated potential BBB preservation. Pathological angiogenesis (i.e., formation of new blood vessels), or neovascularization in the context of microvascular networks, has been implicated with the neurodegenerative disease progression and is heavily regulated by VEGF/VEGFR interactions and downstream signaling (120–122). Binding of VEGF to its receptor VEGFR can activate PI3K and Rho GTPases like Ras homolog gene family, member A (Rhoa) and cell division cycle 42 (Cdc42), which can stimulate actin cytoskeleton/ECM-remodeling in the endothelium by Rock1 and cofilin-1 (Cfl1) (123, 124). This angiogenic process appears to be partially inhibited as genes involved in PI3K signaling (Pik3ca, Pik3r3, Pik3c2a), Rho GTPases (Rhoa, Cdc42/Cdc42ep1), and cytoskeleton migration (Rock1, Cfl1) were downregulated by HGD+Curc. As mentioned previously, permeability of the vascular endothelium/BBB can also be affected by ECM-interactions such as focal adhesion degradation by MMPs and transendothelial migration of leukocytes facilitated by adhesion molecules (86, 125). The miRNA-mediated targeting of Mmp11 (let-7a-5p, let-7k, miR-98-5p, miR-6394) and Mmp24 (miR-142a-5p, -199a-3p, -665-3p) along with reduction of neural cell adhesion molecule 1 (Ncam1) and aforementioned Rhoa, Rock1, and PI3K players by HGD+Curc appear to moderately affect these interactions. Angiogenesis can also be influenced by hypoxia, seen in cases like ischemic stroke (126) and is facilitated by hypoxia-inducible factor-1 (HIF-1) and VEGF/PI3K signaling that all can contribute to a neuroinflammatory environment (125, 127). Hyperglycemia has been linked to upregulation of HIF-1 and loss of endothelial tight junctions in brain microvascular endothelium due to VEGF-overexpression (128). The HIF-1 complex (heterodimer composed of α and β subunits) can be stabilized by heat shock protein 90 (Hsp90ab1, Hsp90b) (129), which were reduced by HGD+Curc. Hypoxic signaling can also contribute to Aβ formation and tau hyperphosphorylation (130). Taken together, observed changes in the expression of genes are suggestive of a decrease in BBB permeability, a key factor in neurodegenerative disease development.

Downregulation of these VEGF-mediated angiogenic and BBB-remodeling pathways by HGD+Curc may also indicate a moderate anti-cancer functionality of curcumin by restricting blood flow to tumor cells and reducing cell migration via ECM-degradation related to metastasis (131, 132). PI3k signaling governs cell cycle progression/proliferation, adherence and migration (133, 134) while blunting the cell cycle arrest and apoptotic activities of forkhead box O (FoxO) signaling (134). Therefore, downregulation of players in PI3K pathway (Pik3ca, Pik3r3, Pik3c2a) and cell cycle mediators like cyclin D2 (Ccnd2, G1 to S), cyclin B1 (Ccnb1, G2 to M), and Ras homolog enriched in brain (Rheb) by HGD+Curc may indicate an inhibitory effect on the HGD-induced influence toward tumor cell proliferation. Reduction of genes related to glucose transporter type 1 (Glut1, encoded by Slc2a1), ATP synthase (Atp5a1, Atp5b, Atp5g2, Atp5g3), sodium/potassium (Na⁺/K⁺) ATPase (Atp1a1, Atp1a2, Atp1a3, Atp1b1, Atp1b2), and the vacuolar ATPase (Atp6v0a1, Atp6v0b, Atp6v0c, Atp6v0e2, Atp6v1c1, Atp6v1d) as well as non-coding targeting of ATP-binding cassette (ABC) subunits (Abcc3, Abcc5, Abcc10) by curcumin in this study may further indicate anti-cancer capabilities due to inhibition by limiting the energy needs of tumorous cells (135–138), though further investigation is warranted.

Curcumin and its derivative demethoxycurcumin as well as related phase I and II metabolites (dihydrocurcumin, hexahydrocurcumin, tetrahydrocurcumin, curcumin glucuronide, and curcumin sulfate) had significant binding interactions with one or more TFs potentially involved in regulation of coding DEGs identified in this study. The largest number of coding DEGs modulated by HGD+Curc interacted with the cAMP-responsive element binding protein (CREB) transcription factor, which has widely reported to be involved with neuroprotection and neuroplasticity (139). Inhibitory phosphorylation of CREB (serine-129) (140) by Gsk3β can lead to decreased hippocampal neurogenesis and activity of pro-survival genes like brain-derived neurotrophic factor (BDNF) (141, 142). On the other hand, CREB can be activated by PI3K/Akt at a different site (serine-133) (143, 144) and bind to the promoter region of VEGF (145) and thus is related to tumorigenic vascularization and proliferation. Notably, TFAP2A had the lowest, most significant binding energies with curcumin and related metabolites, except for tetrahydrocurcumin. TFAP2A has a dual role as it is involved in differentiation of cranial neural crest cells and inhibition of this TF can lead to the development of facial clefts, specifically branchio-oculo-facial syndrome (146–148), though it can also influence ECM remodeling via MMPs and promote angiogenesis via VEGF and HIF-1α in oncogenic conditions (149). In another study, curcumin inhibited the oncogenic TFAP2A-induced ECM remodeling in colorectal cancer via downregulation of genes in the ECM-receptor pathway (150). All curcumin-related compounds had significant binding efficacy with nuclear respiratory factor-1 (NRF1), not to be confused with nuclear factor erythroid 2-related factor 1 (Nrf1, encoded by Nfe2l1). NRF1 activity reportedly mediates mitochondrial biogenesis and alleviates Aβ-induced degenerative mitochondrial dysfunction (151), which reiterates the potential influence curcumin had on mitochondrial function in the hippocampal microvasculature under high glycemic exposure.

SP1 is another transcription factor that has been seen as a driver of angiogenesis in microvascular endothelial cells under hyperglycemic (152) or hypoxic conditions (153) while another SP family member (SP3) has been associated with regulation of BBB players like transferrin receptor and occludin (152). Though SP1 may be related to neuronal survival and synaptogenesis (154), it is also involved in Alzheimer’s disease through binding to promoter regions of App and its cleaving enzyme β-secretase (155). Histone deacetylases (HDACs) are responsible for chromatin compacting that represses gene transcription thus HDAC3, which is the predominantly expressed class I HDAC in the brain and highly expressed in the hippocampus, is involved in the silencing of genes essential for neuronal survival and plasticity (156, 157). Though HDAC3 can be involved in normal brain development by regulating neural progenitor cells, it has been implicated in the progression of neurodegeneration and neurotoxicity largely due to phosphorylation by Gsk3β and interactions with HDAC1 (156, 157). HDAC3 can be overexpressed within the hippocampus in diabetic conditions (158), which can promote BBB transendothelial permeability. As curcumin has been reportedly can inhibit activity of multiple classes of HDACs (159), HGD+Curc dietary intervention may have facilitated neuroprotective functionality through the inhibition of HDAC3. TCF12, is a member of the basic helix-loop-helix (bHLH) protein family that has been linked to the promotion of neurogenesis, primarily mesodiencephalic dopaminergic neurons, and heterodimerization with other bHLHs can promote neuronal differentiation during cortical development (160, 161). In an endogenous antioxidant mechanism that is commonly seen with curcumin bioactivity (162), small MAF proteins form heterodimers with nuclear factor erythroid 2-related factor 1 (Nrf2) in order to bind antioxidant response elements (AREs) in the promoters of target genes (163). Finally, FOXF1 is reportedly involved with embryonic development of gut-derived organs such as the intestine, stomach, liver, gallbladder, and lung (164) and has been linked to microvascular and endothelial health, but this was primarily in the context of lung development (165, 166). These coding DEGs regulated by this transcription factor may be involved in modulation of neurodevelopmental disorders like other members of the forkhead box family (167), but it has not yet been directly linked to neuronal function.

Multiple miRNAs differentially expressed by HGD+Curc compared to HGD have been reportedly involved in endothelial health in terms of BBB integrity, angiogenesis, and vascular inflammation. As stated previously, the let-7 network node of mmu-let-7a-5p, -let-7k, and -miR-98-5p connected to the largest subset of target genes which was notable as several members of the let-7 family have been linked to regulation of cerebrovascular inflammation and angiogenesis (168). Of these, miR-let-7a and miR-98-5p have been reported to help preserve BBB integrity via prevention of tight junction loss as well as inhibition of proinflammatory cytokine release and immune cell infiltration, even under the case of hyperglycemic stress with miR-let-7a (169). Another study found an anti-angiogenic influence of miR-let-7a by targeting the TGFβ pathway, particularly Tgfb3 (170) which was downregulated by HGD+Curc. An additional group of miRNAs involved in angiogenic regulation is the miR-181 family, of which miR-181d-5p was upregulated by HGD+Curc. Particularly, miR-181d-5p has been found to negatively regulate hyperglycemia-induced VEGF-mediated angiogenesis in human retinal microvascular endothelial cells (171) and promote blood-tumor barrier permeability in glioma endothelial cells, which may aid in delivery of chemotherapeutic drugs (172). Retinal neovasularization related to diabetic retinopathy was also targeted by miR-384-3p, which inhibited this angiogenic process by targeting hexokinase 2 (173). MiR-384-3p activity has also shown relevance in the context of Alzheimer’s disease as it has been reported to target App and its cleaving enzyme β-secretase (174). Regarding neuroinflammation, NF-κB signaling in the vascular endothelium has been targeted in other studies by miR-193b-3p in a direct manner through promoting NF-κB p65 acetylation and inhibition of HDAC3 (175), a TF identified in this study, in addition to an indirect manner through miR-199a-3p-mediated targeting of mTOR signaling that reduced NF-κB p65 phosphorylation and adhesion molecule expression related to leukocyte adherence (176). Several other miRNAs outside of this study have also been associated with neurodegeneration (177) as well as endothelial homeostasis and inflammation (178, 179).

In the scope of this study, pathways for targets of DE miRNAs by HGD+Curc participated in conjunction with coding genes for synaptic signaling while exclusive pathways were involved in endothelial ECM maintenance in terms of biosynthesis of N-glycans and glycoaminoglycans (GAGs). BDNF, a type of neurotrophin synthesized in high concentrations within neuronal cell bodies and glia of the hippocampus, is important for synaptogenesis, synaptic plasticity/LTP, and neurotransmitter release (180). Multiple integrative downstream pathways are modulated by BDNF following cleavage and bondage to tropomyosin receptor kinase B (TrkB, Ntrk2) receptors, which can recruit SHC-transforming protein 3 (Shc3), that are all involved in neurotransmitter release such as PI3K-Akt/mTOR (neuronal survival) and MAPK/ERK (phosphorylation of synaptic vesicles) (180). Additionally, neuroprotective BDNF activation can inhibit Gsk3β through phosphorylation by dedicator of cytokinesis 3 (Dock3) (181). These BDNF-related processes were influenced by HGD+Curc through non-coding targeting of Ntrk2/TrkB by Gm16121, Shc3 by miR-142a-5p, -384-3p, -665-3p, and Dock3 by let-7a-5p/let-7k/miR-98-5p, miR-6394, miR-665-3p. However, BDNF is upregulated in a tumor environment due to its influence of pro-survivability cascades like PI3K/Akt/mTOR and MAPK (182).

GAGs and N-glycans are both essential components of the neurovascular ECM that regulate vascular homeostasis and cellular communication as GAGs primarily form the protective endothelial glycocalyx layer (EGL) (183), while N-glycans are attached to transmembrane proteins (i.e., N-glycosylation) to modulate protein folding, cell signaling, and adhesion (184). GAGs involved in the regulation of the PNNs of central nervous system ECM influenced by HGD+Curc were heparan sulfate and chondroitin sulfate as miRNAs (let-7a-5p, let-7k, miR-98-5p, -142a-5p, -199a-3p, -6394) targeted genes related to synthesis like chondroitin sulfate synthase 3 (Chsy3) and sulfation enzymes such as N-deacetylase/N-sulfotransferase 2 (Ndst2), heparan sulfate-glucosamine 3-O-sulfotransferase 3A1 (Hs3st3a1) and carbohydrate sulfotransferase 3 (Chst3) which may indicate an influence of curcumin on the glycocalyx aspect of the BBB. Sulfation of GAGs to generate heparan sulfate and chondroitin sulfate contribute to the protective negative charge of the EGL that helps regulate permeability of charged molecules (185), but dysregulation of sulfation patterns are linked to tumor migration and amyloid aggregation (186, 187). Furthermore, miRNAs (let-7a-5p, let-7k, miR-98-5p, -384-3p, -142a-5p) targeted N-glycosylation facilitators like dolichyl-diphosphooligosaccharide protein glycosyltransferase (Ddost), phosphomannomutase 2 (Pmm2), and dolichyl-phosphate beta-glucosyltransferase (Alg5). More to the point of regulating cell signaling of glycoproteins, DE miRNAs (let-7a-5p/let-7k/miR-98-5p, miR-6394) targeted β-galactoside alpha-2,6-sialyltransferase 1 (ST6Gal1) that influences tumorigenic cell adherence by adding sialic acid to glycoproteins (188). Altogether, targets of miRNA differentially expressed by HGD+Curc portrayed a potential versatile regulation of curcumin on aspects like BBB permeability, ECM-regulation and synaptic signaling.

Pathways unique to the gene targets of lncRNAs differentially expressed by HGD+Curc were predominantly involved in pathways regarding neurodevelopmental disorders, rather than neurodegenerative. Such disorders like autism as well as fragile X and Rett syndromes can be associated with mitochondrial dysfunction (189), but findings from this study highlight a link through glutamatergic synaptic signaling as subunits of N-methyl-D-aspartate (NMDA) receptors (Grin2A, Grin2B, Grin2D) were targeted by 6–14 lncRNAs differentially expressed by HGD+Curc. Important for synaptic plasticity and LTP for memory, glutamatergic NMDA receptor interactions can be regulated through the NO/cGMP pathway (190) but can be impaired by MTHFR deficiency, which is linked to increased phosphorylation of hippocampal Aβ precursor protein due to dysfunctional folate metabolism (191). Craniofacial development was also associated with DE lncRNAs through TFAP2A, the TF that significantly interacted with all curcumin-related metabolites, which was targeted by Gm16084. Again, lncRNA targets complementarity participated in pathways of neurodegeneration through the inclusion of these NMDA receptor subunits and the aforementioned targeting of Gsk3β and Casp3 by Gm20675. Regarding the C/D box snoRNAs, Snord82 has been reported to be a potential tumor suppressor of prostate cancer (192) was upregulated by our HGD+Curc group, which was a reversal of HGD regulation. Additionally, Snord16a is a potential biomarker for colon cancer (193) and Snord59a is reported to be a tumor immune infiltration-associated snoRNA (194), which were downregulated and upregulated, respectively, by HGD+Curc. However, further studies for relevancy of these Snords in the context of brain cancer would be necessary.

This study showed that curcumin supplementation within a high glycemic diet (HGD+Curc) may have a dual role of moderate neuroprotection and potential anti-tumorigenicity in our model of hippocampal microvasculature, though these often involve regulation of the same pathways in opposing directions. Since consequences of high glycemic diet consumption are complex, regulation of curcumin may have acted in a multi-faceted manner and could be elucidated in direct models of neurodegeneration and brain cancer, which has been reviewed previously (38, 43–45, 195–200). Studies investigating curcumin have included multiple models of aging, Alzheimer’s, Huntington’s Parkinson’s, multiple sclerosis, and ischemic/hemorrhagic stroke (43–45, 195–197). Additionally, these studies have included direct measurements of Aβ aggregation, mitochondrial function, and antioxidant/anti-inflammatory markers as well as cognitive tests (43–45, 195–197). Furthermore, some clinical studies have been conducted to assess the effect curcumin on cognitive function in healthy/non-demented older adults (201) as well as Alzheimer’s and Parkinson’s patients (43, 201). Studies of neuroprotection have also been conducted and included in these reviews that involved curcumin nanoparticles (45) and other curcumin related metabolites mentioned in this study (202) with an emphasis on tetrahydrocurcumin (203), demethoxycurcumin (202, 204), and hexahydrocurcumin (205). Finally, the anti-diabetic effect of turmeric and curcumin have been widely reviewed in multiple models (38, 47, 107).

Some limitations should be addressed as the results displayed are representative of the bioinformatics resources utilized and available at the time of the study and subsequent identification of target genes and pathways may vary with the use of other databases and gene ontology analysis tools. Large amounts of DEGs found in the hippocampal microarrays were either miscellaneous (i.e., pseudogenes, multi-complex, immunoglobulin (Ig) variable chain genes) or unidentified. Some of the DEGs common to both HGD/LGD and HGD+Curc comparisons represented in Figure 10 were unidentified, as indicated by only their Affymetrix IDs, leaving pathway enrichment analysis to be done with the remaining identified genes. It is important to note that conclusions about full regulation of the identified pathways cannot be made definitively as the DEGs and non-coding targets characterized within this study via microarray analysis and bioinformatic tools were not totally comprehensive in their respective pathways. Regarding the overall model, the HGD in this study did not appear to induce a hyperglycemic status systemically as changes in body weight and serum TG, TC/HDL-C/LDL-C, TG, insulin and glucose levels compared to LGD were not observed. Studies with endpoints of hyperglycemia and insulin resistance in mice have utilized a high fat diet alone or combined high-fat, high-sucrose diets for short-term studies (e.g., < 16 weeks) (206–209) or high-sucrose diets alone for extended study periods (e.g., 55 weeks) (210). As the diets in this study were isocaloric (∼3.6–3.7 kcal/g) with similar fat content (12.6–13.0 % kcals), the lack of changes in serum metabolic markers may indicate that the consumption of a high-sucrose diet alone at the starting age of 20 weeks and a duration of 12 weeks without the additional stressor of excess calories/fat were not sufficient to induce systemic hyperglycemia and insulin resistance. Additionally, the study was conducted with only male mice so further experimentation to account for sex differences in the analysis for bioactivity of curcumin alongside a high glycemic diet would be needed. Lastly, no direct measurements of abnormal protein aggregation associated with neurological diseases like Aβ, SOD1, α-synuclein were measured.

5. Conclusion

Overall, this study showed that dietary intervention of curcumin induced a multi-level, nutrigenomic effect on the hippocampal microvasculature as differentially expressed coding genes and targets of non-coding miRNAs and lncRNAs were involved in numerous pathways, mainly involving key signaling pathways regulating the interplay between neurodegeneration, mitochondrial dysfunction and thermogenesis, in addition to endothelial remodeling induced by the high glycemic diet challenge.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by an award from the Richard A. and Nora Eccles Foundation (A20-0111), the Richard A. and Nora Eccles Harrison Endowed Chair in Diabetes Research, and the Frances Lazda Endowed Chair in Women’s Cardiovascular Medicine for AV. DM was supported by the United States Department of Agriculture, National Institute of Food and Agriculture (USDA-NIFA), Hatch project 7010153.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.

Ethics statement

The animal study was approved by Public Health Service on the Humane Use and Care of Laboratory Animals and the University of California, Davis, Institutional Animal Care and Use Committee (IACUC) approved protocol number 20943 on 18 April 2019. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

EB: Data curation, Visualization, Formal analysis, Software, Writing – original draft, Writing – review & editing, Validation. JR: Writing – review & editing, Supervision, Methodology, Validation. SN: Writing – review & editing, Validation, Software, Methodology, Project administration. AV: Methodology, Data curation, Supervision, Conceptualization, Validation, Funding acquisition, Writing – review & editing. DM: Funding acquisition, Writing – review & editing, Writing – original draft, Software, Formal analysis, Visualization, Validation, Conceptualization, Methodology, Data curation, Supervision, Investigation.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2025.1736964/full#supplementary-material