Transcriptomic evidence of hypothalamic neuroinflammation in mice due to aspartame exposure through the TLR4/NF-κB/NLRP3 pathway

Abstract

The use of aspartame (ASP) instead of sugar is increasing. ASP exposure has been shown to affect the hypothalamus. However, whether it causes hypothalamic inflammation in mice and the specific molecular mechanisms are unknown. Here, we established an ASP exposure model with a body-weight-adjusted dose of 50 mg kg⁻¹ and employed Oxford Nanopore Technologies to investigate the differences in genes in the mice's hypothalamus. 491 differentially expressed genes were identified in the ASP group when compared with the control group. Functional enrichment analysis found that several terms and pathways related to immune function were altered in the hypothalamus. Furthermore, western blot outcomes showed that the TLR4/NF-κB/NLRP3 pathway was up-regulated. Immunofluorescence and enzyme-linked immunosorbent assay outcomes also suggested that the concentration of inflammatory factors increased. In conclusion, our results indicated that ASP exposure can induce inflammation in the hypothalamus tissue of mice and impair hypothalamic function in mice.

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1 Introduction

Aspartame (ASP) is a kind of artificial sweetener (AS), which can be found in many kinds of food products, and it received approval from the Food and Drug Administration (FDA) in 1981.¹ Its essence is methylated dipeptide,² and when consumed, ASP can be decomposed into phenylalanine, aspartic acid, and methanol by digestive enzymes, which are further broken down into formaldehyde and formic acid.³ As a non-nutritious food additive, ASP is favored by consumers because of its advantages of low calories and high sweetness, especially for people focused on weight loss or managing diabetes.⁴ But many researchers think that it has some negative effects, such as inflammation.⁵ Inflammation is the natural reaction to harmful stimuli and involves immune responses designed to help the body address threats and promote the repair of damaged tissues.⁶ It involves other substances such as cytokines, complement, and peptides.⁷ Neuroinflammation refers to inflammation that occurs in the central nervous system (CNS). In the CNS, the activation of astrocytes and microglia can intensify neuroinflammation.⁸ The activation of microglia can lead to an increase in pro-inflammatory cytokines, such as tumor necrosis factor (TNF)-α and interleukin (IL)-1β.⁹ As the name suggests, hypothalamic neuroinflammation is a neuroinflammatory response that occurs in the hypothalamus. The hypothalamus, as a special organ with both neural and endocrine functions, can integrate the stimulation signals from neurons and hormones, thereby precisely regulating endocrine altered transcription functions.¹⁰ More and more studies have proved that hypothalamic inflammation can induce other diseases, such as obesity, hypertension, and cognitive impairment.^11–13 Of course, it may also increase the risk of other diseases.¹⁴

ASP consumption has been found to cause neuronal morphological changes in the hypothalamus: increased vacuolation, decreased neuronal size and pyknotic nucleus, reduced hypothalamic neuronal viability, and significant astrocyte hyperplasia.¹⁵ Moreover, it can also induce endoplasmic reticulum stress in hypothalamic cells, disrupting axonal growth.¹⁶ However, whether ASP exposure causes hypothalamic inflammation is unclear, and the specific molecular mechanism has not also been elucidated.

Oxford Nanopore Technologies (ONT) is a third-generation sequencing technology that identifies base sequences based on electrical signals.¹⁷ Here, we hypothesized that ASP exposure would cause hypothalamic inflammation, and to elucidate the influence of ASP exposure on the transcriptional levels in the mouse hypothalamus, we recruited 4 week-old mice and provided them with a solution containing ASP for 12 weeks. Simultaneously, a research approach that integrated full-length (FL) sequencing and animal experiments was employed.

2 Materials and methods

2.1 Animal experiments

On the premise of fully complying with experimental norms and animal ethics requirements, we purchased 24 healthy C57BL/6 mice at the age of 3 weeks. After the purchase, the mice were first subjected to one-week adaptive feeding at the Laboratory Animal Center of China Medical University (CMU) (Shenyang, China). Subsequently, all the experimental protocols that had been approved by the Laboratory Animal Welfare and Ethics Committee of CMU (approval no.: CMU2023688). Throughout the entire experimental period, the mice were always housed at this center. The internal environmental temperature of this laboratory animal center is maintained at 24 ± 0.5 °C, the humidity is controlled within the range of 60 ± 20%, and the illumination duration is set from 6 am to 6 pm every day. At the age of 4 weeks, the mice were randomly assigned to the control (CON) group and the ASP group. During the period when the mice were 4 to 16 weeks old, both the CON group and the ASP group were fed with standard food. The difference was that the CON drank deionized water, while the ASP drank deionized water with ASP (Solarbio, Beijing, China) dissolved in it, and the intake was approximately 50 mg per kg body weight. When the mice reached 16 weeks of age, they were first anesthetized to an appropriate state. Subsequently, blood was rapidly collected by enucleation of the eyeballs. Concurrently, hypothalamic tissue samples were precisely harvested. Immediately after that, both the blood and tissue samples were properly stored to ensure their integrity and usability for subsequent experimental analyses. We made every possible effort to alleviate the pain and discomfort of the animals. In Fig. 1, the establishment process of the ASP diet model is presented.

Fig. 1 Diagram of the animal model. CON, control group; ASP, aspartame group.

2.2 Differentially expressed genes (DEGs) were screened between groups by ONT

The hypothalami of four mice were haphazardly chosen from each group for FL sequencing. According to the company's instructions, they employed Trizol reagent (Vazyme, Nanjing, China) to extract the RNA of these tissues, and 1 μg RNA was used to construct the cDNA libraries by using a cDNA-PCR Sequencing Kit (SQKPCS109). Subsequently, the repaired and purified DNA was sequenced. The sequences with a length of less than 200 bp and a Q score of less than 6 were filtered out.

FL transcriptomes quantify transcripts by aligning FL sequences to the reference transcriptome. The summed quantification of all transcripts of a gene was taken as the gene's quantification result, with counts per million (CPM) as the metric for gene expression level. The fold change (FC) indicated the expression ratio between two groups, and genes with FC ≥ 1.2 and P < 0.05 were defined as DEGs.

2.3 Gene function analysis

The Gene Ontology (GO) database can reveal the functions of genes and gene products in three aspects: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF).

Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, DEGs can be accurately annotated to corresponding metabolic pathways and biological function categories. In this study, we utilized the KOBAS software to match and enrich the screened DEGs with the pathways in the KEGG database, hoping to deeply explore the changes that occurred in the mouse hypothalamus tissue from the perspective of biological functions and further reveal its underlying molecular regulatory mechanisms.

2.4 Weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA)

WGCNA¹⁸ is based on the hypothesis of scale-free distribution of gene networks. In this way, co-expressed gene modules can be identified, and the relationship between genes and biological phenotypes can be explored.

Traditional KEGG and GO enrichment analyses only enrich significant DEGs, and some less significant but important genes will be ignored. However, GSEA can analyze all DEGs together, making the results more convincing.

2.5 Protein–protein interaction (PPI)

To explore the relationships among the proteins of these DEGs, we first used the blastx tool to align the sequences of DEGs with the genomic sequences in the STRING database. Subsequently, Cytoscape software was utilized to visualize the PPI.

2.6 Immunofluorescence (IF)

IF was utilized to survey the production of TNF-α in the hypothalamic paraventricular nucleus (PVN) and arcuate nucleus (ARC). First of all, the cerebral tissues were fixed and enclosed in paraffin blocks and then they were sliced into sections with a thickness of 6 μm. Secondly, the sections were heated at 60 °C for a duration of 2 hours and then soaked in xylene solution twice, with each soak lasting 30 minutes. Subsequently, rehydration was carried out using a gradient of alcohol. Then, the sections were heated with 0.1 mol L⁻¹ citrate repair solution (pH 6.0) for 5 minutes, blocked for one hour, and incubated with rabbit anti-TNF-α antibody (cat. no.: #11948; 1: 200, Cell Signaling Technology, Boston, USA) overnight at 4 °C. After washing with phosphate-buffered saline (PBS), sections were incubated with rhodamine marker goat anti-rabbit IgG (H + L) (cat. no.: ZF-0316; 1: 50, ZSGB-BIO, Beijing, China) in the dark for 2 hours. Finally, sections were observed using a fluorescence microscope (Nikon Corporation, Tokyo, Japan).

2.7 Western blot (WB)

Hypothalamic tissues were randomly selected from four mice in each group to detect the relative expression of toll-like receptor 4 (TLR4), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), NOD-like receptor pyrin domain-containing protein 3 (NLRP3), and Caspase-1 (CASP1). First, proteins were extracted using a radioimmunoprecipitation assay (RIPA) solution (RIPA : phosphatase inhibitor : protease inhibitor = 99 : 1 : 1). Subsequently, the extracted proteins were subjected to conventional electrophoresis. After electrophoresis, the separated proteins were transferred onto a polyvinylidene difluoride (PVDF) membrane. Following the transfer, the PVDF membrane was blocked and rinsed to remove excess blocking agent and impurities. Next, the treated membrane was immersed in the corresponding primary antibody solution [rabbit anti-NLRP3 (cat. no.: A24294), rabbit anti-CASP1 (cat. no.: A20470), rabbit anti-NF-κB p65 (cat. no.: A11202), rabbit anti-TLR4 (cat. no.: A11226; diluted 1: 1000; ABclonal, Wuhan, China) and rabbit anti-β-actin (cat. no.: #4970; diluted 1: 1000; Cell Signaling Technology, Boston, MA, USA)] and incubated overnight at 4 °C to allow the primary antibody to fully bind to the target proteins. The next day, the membrane was rinsed again with PBST buffer to remove unbound primary antibody. Then, the membrane was incubated with the goat anti-rabbit (cat. no.: AS014; diluted 1: 3000; Proteintech, Wuhan, China) antibody solution for 2 hours. After incubation, the membrane was detected using a Tanon-5200 Imager (Tanon, Shanghai, China).

2.8 Enzyme-linked immunosorbent assay (ELISA)

Serum was extracted from the blood samples obtained. Then, serum samples from six mice randomly selected from each group were used for ELISA. We measured the concentrations of IL-1β (cat. no.: JM-02323M1) and IL-18 (cat. no.: JM-02452M1) in the serum according to the kit (JINMGMEI, Jiangsu, China).

2.9 Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

RNA stock solutions were randomly selected from the hypothalamus of four mice from each group. The purity of the RNA was ensured to be between 1.9 and 2.2, and the concentration was maintained in the range of 500–1000 ng μL⁻¹. Then, the RNA was reverse transcribed into cDNA using a kit (Vazyme, Nanjing, China). The primers were provided by Sangon Biotech (Shanghai, China) (Table 1). PCR was performed on a QuantStudio 5 Flex Real-Time System (Thermo Fisher Scientific, Waltham, MA, USA) to quantify the expression of each target gene. β-Actin (Sangon Biotech, Shanghai, China) was used as an internal reference gene. The relative expression of each target gene was calculated using the 2^−ΔΔCT method.

Table 1 Gene primer sequence

Gene	Primers		Length
Ripk2	F	TCGTGTTCCTTGGCTGTAATAAGTC	117
	R	CATCTGGCTCACAATGGCTTCC
Gbp3	F	GCTGCATCTGAGGGAGCATT	148
	R	CAGCGTCAGGGAACACTACT
Gvin1	F	CTACCCTACTGAATGCCCTGTTTG	117
	R	AGCCAAGTTCTTCTGTGAATGTCTC
Irf7	F	TGAGCGAAGAGAGCGAAGAGG	119
	R	CGTACACCTTATGCGGATCAACTG
Gm4070	F	CCAGTTGCCTCTTTGTCCATCAG	80
	R	CTCCAGTCTCTTCCGTCCTTCC
Ifitm3	F	AGCCTATGCCTACTCCGTGA	214
	R	TTCAGGACCGGAAGTCGGAA
Rsad2	F	GATGTTTCTGAAGCGTGGCG	119
	R	ATAGCTGGGCGTGAATGTCC
Ifit3	F	ACTCTTTGGTCATGTGCCGTTAC	104
	R	TCGTCTCAGTTCTGCCATCCTC

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2.10 Statistical analysis

SPSS 21.0 data analysis software (IBM SPSS, Inc., Chicago, IL, USA) was used with Mean ± SEM for data between groups. The t-test of two independent samples was used for experimental data analysis with correction for multiple comparisons, and a corrected P < 0.05 is considered to indicate statistical significance. The statistical charts were plotted using GraphPad Prism 8.0 software (GraphPad Software, San Diego, CA, USA).

3 Results

3.1 Description of DEG results

We used FL RNA sequencing to explore the changes in the hypothalamus in mice between the two groups. The FLNC is shown in Table 2. All samples exhibited high levels of gene expression (Fig. 2A) and the sample correlation heat map showed that there was a strong correlation among the samples within the group (Fig. 2B). The analysis outcomes of DEGs in the CON group and ASP group showed that there were 491 DEGs, including 211 up-regulated DEGs and 280 down-regulated DEGs (Fig. 2C). Fig. 2D shows the clustering results of these DEG expression patterns.

Fig. 2 Statistics of transcriptomics. (A) CPM box line diagram; X-axis: different samples; Y-axis: logarithm of the sample expression amount CPM. (B) Sample correlation heat map; the colors ranging from blue to red represent Pearson correlation coefficients ranging from 0 to 1, indicating low to high correlations. (C) The volcano of DEGs in the CON/ASP comparisons; X-axis: the logarithm of the difference between the two samples’ expression levels of a certain transcript; Y-axis: P-value; blue dots: down-regulated DEGs; red dots: up-regulated DEGs; black dots: non-DEGs. (D) Cluster diagram of DEGs expression patterns in grouped samples; X-axis: table sample names and clustering results of samples; Y-axis: differential genes and gene clustering results; columns: different samples; rows: different genes; color: the level of gene expression in the sample log 2 (CPM + 1e-6).

Table 2 Clean data statistical results

File name	Read num	Base num	N50	Mean length	Max length	Mean Q score
File name: sample name; read num: number of sequences; base num: total base number; N50: N50 length; mean length: average read length; max length: the maximum length of reads.
ASP1	3502733	3667840839	1203	1047	30 513	Q13
ASP2	4672328	4939904791	1222	1057	201 408	Q13
ASP3	3887394	4106126166	1238	1056	55 763	Q13
ASP4	3090576	3679651688	1457	1190	111 436	Q13
CON1	3685755	4039720082	1270	1096	146 514	Q13
CON2	3660521	4163305860	1338	1137	104 607	Q13
CON3	3418180	3985567688	1422	1165	104 825	Q13
CON4	3839165	4435177279	1366	1155	66 228	Q13

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3.2 Function enrichment analysis results

We selected the top 10 KEGG pathways with P < 0.05 and then generated the bubble map (Fig. 3A and B). Fig. 3A shows the 10 up-regulated pathways: “NOD-like receptor signaling pathway”, “MicroRNAs in cancer”, “Hepatitis C”, “Influenza A”, “RIG-I-like receptor signaling pathway”, “Epstein–Barr virus infection”, “Human papillomavirus infection”, “Ribosome”, “Other glycan degradation”, and “Maturity onset diabetes of the young”. Fig. 3B showed the 10 down-regulated pathways: “Spliceosome”, “Folate biosynthesis”, “Notch signaling pathway”, “Primary bile acid biosynthesis”, “Intestinal immune network for IgA production”, “Arginine and proline metabolism”, “Oxidative phosphorylation”, “Ribosome biogenesis in eukaryotes”, “Parkinson disease” and “Valine, leucine and isoleucine degradation”.

Fig. 3 The functional annotation of the DEGs. (A and B) KEGG pathway enrichment analysis for upregulated genes (left panel) and downregulated genes (right panel) in the CON/ASP comparisons; X-axis: rich factor; Y-axis: the name of KEGG terms. Color depth: p-value; dot size: gene number. Top 5 up-and down-regulated GO terms based on BP (C), CC (D), and MF (E) in CON/ASP; X-axis: p-value of GO terms; Y-axis: the name of GO terms; blue bars: up-regulated GO terms; red bars: down-regulated GO terms.

We separately selected five up-regulated and five down-regulated entries in BP, CC, and MF (P < 0.05). In the BP, the up-regulated entries were: “negative regulation of prostaglandin biosynthetic process”, “negative regulation of glucocorticoid mediated signaling pathway”, “negative regulation of natural killer cell activation”, “activation of Janus kinase activity” and “negative regulation of DNA repair”, and the down-regulated entries were: “regulation of proton-transporting ATPase activity, rotational mechanism”, “convergent extension involved in neural plate elongation”, “aminoglycoside antibiotic metabolic process”, “negative regulation of type 2 immune response” and “T cell differentiation involved in immune response”. In the CC, the up-regulated entries were: “host”, “host cell cytoplasm”, “intrinsic component of mitochondrial inner membrane”, “host cell part” and “host intracellular part”, and the down-regulated entries were: “U2-type precatalytic spliceosome”, “nucleoplasm”, “MICOS complex”, “intermediate filament cytoskeleton” and “mitochondrial intermembrane space”. And in the MF, the up-regulated entries were: “protein tyrosine phosphatase activity”, “helicase activity”, “interleukin-4 receptor binding”, “NEDD8-specific protease activity” and “GTP binding”, and the down-regulated entries were: “NADHX epimerase activity”, “NADPHX epimerase activity”, “Ral GTPase binding”, “insulin-like growth factor receptor binding” and “translation initiation factor activity” (Fig. 3C–E).

3.3 WGCNA and GSEA of all genes

We screened genes with fragments per kilobase million greater than or equal to 1 for WGCNA analysis. First, we computed the correlation coefficient between genes, and the heat map showed the co-expression network (Fig. 4A). Then, we constructed a hierarchical clustering tree based on the correlation coefficients between genes (Fig. 4B) using a power value of 7, a module similarity threshold of 0.5, and a number of genes in each module of 30. To study the correlation between these modules and the relationship between different gene expression modules and samples, we plotted a hub gene heatmap (Fig. 4C) and module-trait correlation heatmap (Fig. 4D). Fig. 4E shows the terms with the highest GO enrichment of each module, as well as their proportion size.

Fig. 4 WGCNA of all genes in sequence data and GSEA. (A) Gene co-expression network heatmap. (B) Phylogenetic tree diagram of genes and trait correlation heat map. (C) Heatmap of correlations between modules. (D) Module and trait correlation heatmap. (E) The GO entry with the highest proportion of module genes in each module of all genes. (F and G) GSEA.

For functional enrichment of all expressed genes and combined with the research purpose of this experiment, we selected the “Th1 and Th2 cell differentiation” in KEGG and the “positive regulation of inflammatory response” in BP of GO (Fig. 4F–G). The outcomes of GSEA enrichment analysis showed that it was downregulated in the CON/ASP group (Th1 and Th2 cell differentiation: NES = −1.59462987563198 and P = 0.00709219858156028; positive regulation of inflammatory response: NES = −1.54892727501248 and P = 0.0186170212765957).

3.4 The networks of PPI

We mapped the network with all DEGs and visualized them in Cytoscape to visualize the interactions between genes with high connectivity among these genes. Fig. 5A shows the PPI network of concentric circles in descending order from the inside to the outside of the degree of each gene; the degree of the central circle was from 38 to 48, and the degrees were 27 to 37, 14 to 26, and 1 to 13 outwards in turn, and the color shade also represented the size of the degree. Fig. 5B shows the interactions between DEGs enriched in the NOD-like receptor signaling pathway.

Fig. 5 The PPI network of all DEGs and the NOD-like signaling pathway. (A) The PPI of all DEGs in the CON and ASP group comparison. (B) The NOD-like signaling pathway PPI of DEGs in the CON and ASP group comparison. Note: the nodes represent genes, and the edges represent the interaction between genes. The size and color of the node indicate the size of the gene degree; the larger the node, the darker the color, and the larger the degree of the gene.

3.5 ASP exposure increased the expression of TNF-α in the hypothalamus

TNF-α was identified as an important regulator of the inflammatory response.¹⁹ It is capable of causing necrosis and apoptosis.²⁰ The PVN and ARC are the two most studied nuclei in the hypothalamus. We found a difference in TNF-α production in the PVN and ARC regions between the CON and ASP groups. The expression of TNF-α in the ASP was higher than that in the CON. This indicated that ASP intake can increase TNF-α in the PVN (Fig. 6A) and ARC (Fig. 6B) regions, potentially impacting hypothalamic functions. A schematic diagram of the mouse brain was quoted from The Mouse Brain in Stereotaxic Coordinates, Third Edition.²¹

Fig. 6 The TNF-α expression in the PVN and ARC of the hypothalamus. (A) The TNF-α expression in the PVN. (B) The TNF-α expression in the ARC. The scale bar = 20 μm; magnification = ×400. Drawing of a control section of mouse brain at Bregma −0.94 mm and Bregma −1.46 mm. The arrows indicate positive cells.

3.6 ASP exposure can activate the TLR4/NF-κB/NLRP3 pathway

The TLR4/NF-κB/NLRP3 pathway was inflammation-related,²² and the “NOD-like receptor signaling pathway” was also enriched in the function analysis of DEGs (P < 0.05), so we detected the production of proteins related to the TLR4/NF-κB/NLRP3 signaling pathway in the hypothalamus of mice. The outcomes revealed that the production of TLR4, NF-κB p65, NLRP3, and CASP1 in ASP was notably higher than that in CON (P < 0.05) (Fig. 7A–D).

Fig. 7 Protein levels of the TLR4/NF-κB/NLRP3 signaling pathway in the hypothalamus. (A) The expression of the TLR4 protein. (B) The expression of NF-κB protein. (C) The expression of NLRP3 protein. (D) The expression of CASP1 protein. Levels of these proteins were statistically significantly increased (P < 0.05) in the hypothalamus of mice. Note: each bar represents the mean ± SEM; each dot represents the sample; *P < 0.05 vs. CON group.

3.7 ASP exposure increased inflammatory cytokine levels in the serum of mice

IL-1β and IL-18 are proinflammatory cytokines; their elevation may be associated with inflammation in the body.^23,24 In our study, the IL-1β and IL-18 of mice increased significantly when compared to the CON (Fig. 8). The high production of IL-1β and IL-18 may induce hypothalamic inflammation, and these findings were supported by the outcomes of the IF and WB.

Fig. 8 Serum cytokine levels. (A) The IL-1β level of mice (n = 6). (B) The IL-18 level of mice (n = 6). Note: each bar represents the mean ± SEM; each dot represents the sample; *P < 0.05 vs. control group.

3.8 RT-qPCR results were in line with sequencing

To verify the accuracy of transcriptomics outcomes, we chose eight genes related to the NLRP3 pathway and inflammation from the DEGs for validation, and β-actin as the reference. The eight genes were: radical S-adenosyl methionine domain containing 2 (Rsad2) (P = 0.008598982; log 2FC = 1.660822434), receptor (TNFRSF)-interacting serine-threonine kinase 2 (Ripk2) (P = 0.048013873; log 2FC = 1.304271684), interferon-induced protein with tetratricopeptide repeats 3 (Ifit3) (P = 0.043376832; log 2FC = 0.639091596), interferon induced transmembrane protein 3 (Ifitm3) (P = 0.002278406; log 2FC = 0.819401763), guanylate binding protein 3 (Gbp3) (P = 0.031836044; log 2FC = 0.985751197), GTPase, very large interferon inducible, family member 2 (Gm4070) (P = 0.038589391; log 2FC = 1.162379982), GTPase, very large interferon inducible 1 (Gvin1) (P = 0.023127812; log 2FC = 1.207305019), and interferon regulatory factor 7 (Irf7) (P = 0.042399413; log 2FC = 0.914758991). The outcomes showed that these genes were notably increased (P < 0.05) (Fig. 9A–H). The outcomes were in line with RNA-sequencing.

Fig. 9 The verified differentially expressed genes of inflammation-related and NOD-like signaling pathways in the mouse hypothalamus. (A) The mRNA expression of Ifit3. (B) The mRNA expression of Rsad2. (C) The mRNA expression of Ifitm3. (D) The mRNA expression of Irf7. (E) The mRNA expression of Ripk2. (F) The mRNA expression of Gvin1. (G) The mRNA expression of Gbp3. (H) The mRNA expression of Gm4070. Note: each bar represents the mean ± SEM; each dot represents the sample; *P < 0.05 vs. CON group.

4 Discussion

In recent years, along with the enhancement of individuals’ health consciousness and the advancement of food-related technologies, the use of ASs instead of sugar has become more and more extensive.²⁵ ASP, as one of the artificial sweeteners, is widely used in various low-sugar or sugar-free foods and beverage products.²⁶ The FDA stipulates that the acceptable daily intake of ASP in Europe is 40 mg per kg body weight (bw) and 50 mg per (kg bw) in the United States.²⁷ Multiple studies have shown that when mice consume ASP at a daily dose of 40 mg per (kg bw) or higher, significant neurotoxic effects occur. For instance, after rats were administered 40 mg per (kg bw) of ASP daily for 90 days, they exhibited increased levels of oxidative stress, elevated nitric oxide free radicals, and reduced hippocampal acetylcholinesterase activity, along with neurobehavioral changes such as impaired learning and memory abilities.²⁸ Additionally, gradient concentration studies have revealed that animals treated with 40 mg kg⁻¹, 80 mg kg⁻¹, and 160 mg kg⁻¹ of ASP showed abnormal neurons in brain tissue section examinations.²⁹ Based on these findings, to explore the specific molecular mechanisms underlying the effects of ASP on the hypothalamus, we chose a dosage of 50 mg kg⁻¹ for exposure in this study. The hypothalamus is the link between the endocrine system and the nervous system.³⁰ However, there is not enough research on the effects of ASP exposure on the hypothalamus, so we administered mice with 50 mg kg⁻¹ ASP, and FL RNA sequencing of the hypothalamus in mice was performed using ONT to explore whether ASP exposure has an impact on the development and function of the hypothalamus.

Our transcriptomic analysis revealed that all the samples under test manifested high expression levels. Furthermore, upon comparing the genes expressed in the CON group, we identified 491 DEGs in the ASP group. In order to study the function of DEGs, we used KEGG and GO analyses. In KEGG analysis, we found that the “NOD-like receptor signaling pathway” and “RIG-I-like receptor signaling pathway” are upregulated, and “Intestinal immune network for IgA production” is downregulated; these are signaling pathways related to immune.^31–33 In GO analysis, some terms related to the development of the hypothalamus and immune system were also altered, such as “negative regulation of DNA repair”, “negative regulation of type 2 immune response”, and “interleukin-4 receptor binding”. All in all, these results implied that ASP exposure caused alterations in immune-related functions in the hypothalamus of mice. Subsequently, we used IF to investigate the inflammatory infiltration of the hypothalamus. Compared with the CON group, there was more TNF-α expression in the hypothalamic PVN and ARC of mice in the ASP group. PVN is an area of the hypothalamus that regulates cardiovascular function. It occurs with inflammation that may affect cardiovascular function, which can lead to the occurrence of high blood pressure.³⁴ ARC can regulate homeostatic feeding and plays a crucial role in sensing nutrients and metabolic hormones,³⁵ which may lead to the development of obesity.³⁶ So, we determined that ASP exposure may induce hypothalamic inflammation.

To better clarify the function of these expressed genes and the link between them, we performed WGCNA on these expressed genes. Finally, we got nine modules, and to understand the association between these modules and samples, we conducted further analysis and found that ASP exposure had a positive effect on the four modules of greenyellow, darkolivegreen, firebrick4, and bisque4. The highest GO-enriched term in the green-yellow module was “synapse”, which can mediate fast point-to-point communication between neurons, which in turn connects neurons into circuits.³⁷ Its formation affects the connections between neurons and thus the normal functioning of the nervous system.³⁸ We also found that the highest GO term of the firebrick4 module was “nucleoid”. The nucleoid refers to the area within a virus, bacterial cell, mitochondrion, or chloroplast where the nucleic acid is restricted, and nucleoid occlusion drives polar aggregation of cytosolic proteins.³⁹ The ASP molecule undergoes a spontaneous self-assembly process under simulated physiological conditions to produce cytotoxic nanofibrils of regular β-sheet amyloid.⁴⁰ However, β-fibril formation and accumulation are closely related to inflammation.⁴¹ The highest GO-enriched term in the bisque4 module is “cell aggregation”. Neutrophil over-recruitment leads to inflammatory diseases, tissue destruction, and loss of organ function, and platelets were one of the essential factors at the sites of inflammation and infection.⁴² This is consistent with the previous research, such as that of Professor Dina Mostafa Mohammed, who found that ASP exposure at 40 mg kg⁻¹ caused an increase in red blood cell and platelet counts in rats.⁴⁰ Our GSEA results also suggested that ASP exposure could cause abnormalities in the immune system and inflammatory response in adolescent mice. “Th1 and Th2 cell differentiation” was down-regulated in the ASP group compared with the control. However, Th1 and Th2 cell differentiation was associated with the inflammatory response.⁴³ Positive regulation of the inflammatory response is also down-regulated. The positive regulation of inflammatory response-a series of protective reactions initiated by the body in response to pathogen invasion, tissue damage, or abnormal stimuli-saves four aspects: first, efficiently eliminating pathogens; second, initiating the process of tissue repair; third, participating in immune surveillance and the clearance of abnormal cells; fourth, maintaining immune balance and providing early warning.⁴⁴ In this study, it was found that ASP exposure could lead to the downregulation of the positive inflammatory response function in the hypothalamic tissue of mice, but the WB and IF results of this study showed an upward trend in inflammatory markers at the protein level. This seemingly contradictory phenomenon may reflect the multi-level regulatory characteristics of the inflammatory response: the downregulation at the transcriptional level may be a compensatory feedback mechanism triggered by the enhanced inflammation at the protein level, or it may stem from post-translational modifications that lead to “uncoupling” between gene expression and protein abundance.

Given that our KEGG analysis was enriched in the “NOD-like receptor signaling pathway” and in line with our research objectives, we opted to test the main proteins of the TLR4/NF-κB/NLRP3 pathway. This pathway has been proven to be important in inflammation.^45–47 TLR4 is a pattern recognition receptor;⁴⁸ it can recognize danger signals associated with pathogens and damage-associated molecular patterns.⁴⁹ The increase of TLR4 activity can increase the production of NF-κB and pro-inflammatory cytokines such as TNF-α and IL-1β.⁵⁰ NF-κB is a primitive protein transcription factor, which is a downstream factor of the TLR4 receptor, composed of p50 and p65 dimers, and plays a crucial role in coordinating innate immunity and inflammation,⁵¹ and it is a key mediator of priming signaling required for NLRP3 inflammasome activation.⁵² As a core component of the inflammasome, NLRP3 can bind to the adapter protein ASC and recruit pro-CASP1 to form an active complex, promoting the activation of pro-CASP1 into mature CASP1. On the one hand, activated CASP1 cleaves and produces active pro-inflammatory cytokines (such as IL-1β and IL-18) to trigger inflammation; on the other hand, it can also induce pyroptosis, which can also trigger a strong inflammatory response.⁵³ It is important to the immune system and the initiation of the inflammatory response.⁵⁴ Thus, we detected TLR4, NF-κB, NLRP3, and CASP1 in the hypothalamus and the levels of IL-1β and IL-18 in the serum. The results suggested that ASP exposure may induce hypothalamic inflammation by activating the TLR4/NF-κB/NLRP3 signaling pathway and increasing the level of pro-inflammatory factors in serum.

To investigate the interactions between DEGs in the ASP and CON groups, we performed PPI analysis. It was able to demonstrate the interaction between genes. PPI is represented as networks or graphs where proteins are nodes and the interactions between them are edges, and nodes with high centrality tend to be in the center of the network.⁵⁵ Our results suggested that the Isg15, Oasl2, Ddx49, Ppp5c, Mysm1, Dhx9, Ripk2, and Mast1 genes may be critical in all the DEGs. ISG15 is a type I interferon (IFN)-inducible gene encoding a protein with pleiotropic functions, acting both as a soluble molecule and as a protein modifier.⁵⁶ Studies have shown that increased production of ISG15 in monocytes represents a state of high immune stress and increases the secretion of IFN-γ to further enhance the immune response.⁵⁷ The allergic inflammation-related gene Oasl2 was up-regulated in skin inflammation by the sensitizers.⁵⁸ Up-regulation of the Ddx49 gene inhibits apoptosis.⁵⁹ However, in our study, the gene decreased, which also corroborates our experimental results. The Ripk2 is the inflammation-related gene,⁶⁰ mice with Ripk2 gene knockout had lower neuroinflammatory features at the acute stage of stroke-induced injury compared to mice that did not knock out the gene.⁶¹ Our RT-qPCR results further validated that the genes Ifit3, Rsad2, Ifitm3, and Irf7 were also up-regulated, which was in line with previous research findings.^62–64

This study has certain limitations because of the small sample size and the lack of an a priori power calculation. First, the design characteristics of the RNA sequencing and WB experiments may reduce the sensitivity of statistical tests and, at the same time, limit the generalizability of the study results. Second, no sex-stratified analysis was performed on the data, making it difficult to rule out the potential impact of sex factors on the results. Third, microglial/astrocytic markers (e.g., ionized calcium-binding adapter molecule 1 and glial fibrillary acidic protein) were not detected, which exerts a substantial influence on neuroinflammation. Future studies should expand the sample size to address these issues. To investigate the core mechanisms, the mice were subjected to a standardized, 12 week and 24 hour continuous exposure. This experimental protocol deviates from the human daily dietary pattern, which is characterized by non-24 hour continuous intake and the coexistence of a diverse array of dietary components. Consequently, when extrapolating the conclusions derived from the mouse experiment to human application contexts, it is imperative to conduct an objective assessment of the potential biases inherent in this translational process.

5 Conclusions

The RNA-sequencing results indicated that ASP exposure altered the transcription and translation of genes in the hypothalamus of mice. Our study has also suggested that ASP exposure may affect the function of the hypothalamus and lead to the occurrence of hypothalamic inflammation by activating TLR4/NF-κB/NLRP3 signaling pathways in mice.

Author contributions

Wenyu Yang: writing – original draft, project administration, data curation, and formal analysis. Tong Tong: data curation and project administration. Qiqi Li: data curation and project administration. Wenhui Ye: data curation. Wei Wei: writing – review & editing, methodology, guide, conceptualization, supervision, and funding acquisition.

Conflicts of interest

The authors declare no conflict of interest.

Abbreviations

ARC	Arcuate nucleus
AS	Artificial sweetener
ASP	Aspartame
BP	Biological process
CASP1	Caspase-1
CC	Cellular component
CMU	China Medical University
CNS	Central nervous system
CON	Control
CPM	Counts per million
DEGs	Differentially expressed genes
ELISA	Enzyme-linked immunosorbent assay
FC	Fold change
FDA	Food and Drug Administration
FL	Full-length
Gbp3	Guanylate binding protein 3
GO	Gene ontology
GSEA	Gene set enrichment analysis
Gm4070	GTPase, very large interferon inducible, family member 2
Gvin1	GTPase, very large interferon inducible 1
IF	Immunofluorescence
Ifit3	Interferon-induced protein with tetratricopeptide repeats 3
Ifitm3	Interferon-induced transmembrane protein 3
IL	Interleukin
Irf7	Interferon regulatory factor 7
KEGG	Kyoto encyclopedia of genes and genomes
MF	Molecular function
NF-κB	Nuclear factor kappa-light-chain-enhancer of activated B cells
NLRP3	NOD-like receptor pyrin domain-containing protein 3
ONT	Oxford nanopore technologies
PBS	Phosphate-buffered saline
PPI	Protein–protein interaction
PVDF	Polyvinylidene difluoride
PVN	Paraventricular nucleus
RIPA	Radioimmunoprecipitation assay
Ripk2	Receptor (TNFRSF)-interacting serine-threonine kinase 2
Rsad2	Radical S-adenosyl methionine domain containing 2
RT-qPCR	Reverse transcription-quantitative polymerase chain reaction
TLR4	Toll-like receptor 4
TNF	Tumor necrosis factor
WB	Western blot
WGCNA	Weighted gene co-expression network analysis

Data availability

Data can be made available upon request.

Supplementary information (SI) is available. See DOI: https://doi.org/10.1039/d5fo00831j.

Acknowledgements

The authors thank Biomarker Technology Company for technical support.

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