A Predictive Transcriptomic Approach to the Resveratrol-Mediated Reversal of Hypothalamic Alterations in a Mouse Model of Obesity

Abstract

Background: Obesity is associated with hypothalamic dysfunction characterized by neuroinflammation and altered transcriptional programs. While resveratrol (RSV) has shown beneficial metabolic effects in peripheral tissues, its central effects on hypothalamic gene expression in obesity remain poorly understood. This study provides the first predictive transcriptomic analysis of the hypothalamic response to RSV in a mouse model of diet-induced obesity. C57BL/6 male mice were fed a high-fat diet (HFD) to induce obesity and then subsequently treated with RSV. Methods: Hypothalamic RNA was extracted and analyzed using RNA sequencing. Differentially expressed genes (DEGs) were identified and functionally analyzed through KEGG pathway analysis. Results: Although RSV did not significantly alter body weight, it reversed the expression of several HFD-induced DEGs. Key genes modulated by RSV included Aqp7, Ccl27a, Lta, Rilp, M6pr-ps, C1ra, Snail1, Gbgt1, and Ppargc1b, which are involved in inflammation, lipid metabolism, mitochondrial function, and immune signaling. Pathway enrichment analysis revealed significant modulation of TNF and NF-κB signaling, cytokine–cytokine receptor interactions, glycosphingolipid biosynthesis, and phagosome-related activity. Remarkably, 45% of RSV-responsive transcripts were non-coding RNAs, suggesting epigenetic regulation. Conclusions: RSV reprograms the hypothalamic transcriptome in obesity, targeting both coding and non-coding RNAs associated with inflammation and metabolic regulation, independently of weight loss. These findings identify RSV as a potential central modulator of metabolic dysfunction and highlight the hypothalamus as a promising therapeutic target in obesity-related disease.

1. Introduction

Obesity represents a growing global pandemic and is currently acknowledged as one of the primary risk factors affecting human health. It is defined as an excessive accumulation of lipids due to an imbalance between energy intake and expenditure [1]. Obesity caused by the consumption of a high-fat diet (HFD) is characterized by increased circulating levels of lipopolysaccharides [2] and pro-inflammatory cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), which trigger low-grade chronic inflammation in peripheral organs [3] and in the central nervous system (CNS) [2,4].

In the CNS, an HFD disrupts neuronal communication, impairs the function of the blood–brain barrier (BBB) [5], and promotes the recruitment of peripheral immune cells, facilitating the entry of pro-inflammatory mediators into the brain and activating microglia [6]. These effects are particularly evident in the hypothalamus, which is located near the median eminence, a region where the BBB is more permeable and maintains close communication with peripheral signals [2].

Within the hypothalamus, diet-induced obesity alters nutrient-sensing mechanisms, feeding behavior regulation, and energy homeostasis [4]. These alterations include disrupted insulin and leptin signaling hormones secreted by the pancreas and adipose tissue, respectively, in response to glucose levels and increased adipocyte energy stores [7]. These hormones bind to their receptors (IR and LepR, respectively) on first-order neurons in the hypothalamus [8], activating the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) and the Janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3) pathways [8,9]. These signaling cascades regulate the expression of anorexigenic genes (e.g., Proopiomelanocortin (Pomc) and Cocaine- and amphetamine-regulated transcript (Cart)) [10], as well as orexigenic genes (e.g., Neuropeptide Y (Npy) and Agouti-related protein (Agrp)) [9,10,11].

Chronic HFD consumption in mice promotes systemic metabolic dysfunction and alters the hypothalamic expression of genes involved in food intake regulation, thereby stimulating appetite and increasing caloric intake [12,13,14], ultimately contributing to obesity.

In addition to protein-coding genes, growing evidence indicates that non-coding RNAs, particularly long non-coding RNAs (lncRNAs) and antisense transcripts, play a critical role in the regulation of hypothalamic inflammation and metabolic homeostasis. lncRNAs modulate gene expression through transcriptional, post-transcriptional, and epigenetic mechanisms, influencing metabolic and inflammatory signaling pathways and responding to nutritional and hormonal cues in key metabolic tissues [15,16]. Similarly, antisense RNAs fine-tune gene expression by regulating mRNA stability, transcriptional interference, and chromatin modifications, contributing to the control of metabolic and immune-related genes [17,18]. Dysregulation of these non-coding regulatory layers has been increasingly implicated in chronic inflammation and metabolic dysfunction associated with obesity, highlighting their potential relevance as mediators of central metabolic regulation [15,16,19].

In addition to HFD-induced hypothalamic neuroinflammation, obesity triggers increased production of reactive oxygen species and reduced antioxidant defenses, resulting in oxidative stress [4]. Oxidative stress contributes to insulin and leptin resistance and alters the function of effector molecules, including the aforementioned neuropeptides, as well as genes involved in inflammation [14]. Therefore, both neuroinflammation and oxidative stress play a critical role in gene expression changes that disrupt hypothalamic function and promote the persistence of obesity.

Natural compounds with antioxidant properties have shown promise in counteracting oxidative stress [20,21] and have demonstrated beneficial effects in obesity management [22]. Among these, resveratrol (RSV) [13,23,24,25], a polyphenolic compound abundant in grapes, berries, and red wine [26], has attracted considerable attention for its anti-inflammatory, antioxidant, neuroprotective, and anti-obesogenic properties [13,23,24,25].

RSV has been shown to confer neuroprotection through several mechanisms, many of which are associated with its antioxidant and anti-inflammatory actions [27,28,29]. In HFD-fed murine models with obesity, RSV supplementation protects against neuroinflammation, pyroptosis, and neuronal damage [28]; modulates proteolytic activity [27]; reduces neuronal apoptosis; and promotes hypothalamic neurogenesis toward anorexigenic POMC neurons [30].

In addition, RSV modulates the expression of various genes disrupted in obesity, including Sirtuin 1 (Sirt1) [28], Toll-like receptor 4 (Tlr4), Tumor necrosis factor (Tnf), Interleukin 1β (Il1b), Il6 [31,32,33], Il10 [26], Nuclear factor kappa-B (Nfkb), C-C motif chemokine ligand 2 (Ccl2) [33,34], and C-C chemokine receptor 2 (Ccr2) [34]. RSV also influences the expression of metabolic genes such as Peroxisome proliferator-activated receptor delta (Ppard), gamma (Pparg), and ATP citrate lyase (Acly) [33,34,35], as well as Leptin (Lep), Leptin receptor (Lepr), Suppressor of cytokine signaling 3 (Socs3) [36] and Insulin receptor substrate 1 (Irs1) [34].

Together, these findings support the hypothesis that antioxidant interventions may mitigate the transcriptomic alterations associated with diet-induced obesity.

The aim of this study was to characterize the transcriptomic alterations in the hypothalamus of HFD-induced mice with obesity, and to determine whether RSV supplementation modulates these changes in gene expression. To address this, we applied a predictive transcriptomic and bioinformatic approach to identify DEGs and the signaling pathways through which RSV may exert therapeutic effects in obesity. Unlike conventional descriptive RNA-seq studies, which primarily report differential gene expression profiles [37], this integrative strategy combines differential expression analysis with functional enrichment and pathway-based interpretation [38,39,40] to identify the coordinated gene networks and signaling pathways potentially targeted by resveratrol. This approach enabled the identification of candidate genes and signaling pathways with hypothalamic dysfunction, providing new insights into the central actions of RSV in obesity.

2. Materials and Methods

The feed for the standard diet (LabDiet 5001, St. Louis, MO, USA) and high-fat diet (HFD; Research Diets D12492, New Brunswick, NJ, USA) was obtained from commercial suppliers. Resveratrol (RSV; CAS No. 501-36-0) was purchased from Sigma-Aldrich (St. Louis, MO, USA), and TRIzol reagent (CAS No. 15596018) was obtained from Invitrogen (Carlsbad, CA, USA). All reagents used in this study were of analytical grade and confirmed to be of high purity.

2.1. Experimental Model of Obesity and RSV Treatment

Male C57BL/6 mice, 8 weeks old (n = 5 per group), were obtained from the Biotherium of the Faculty of Chemical-Biological Sciences, Autonomous University of Guerrero. All procedures involving animals were conducted in accordance with the Mexican guidelines (NOM-062-ZOO-1999) for the care and use of laboratory animals and approved by the Institutional Animal Care and Use Committee (CICUAL-01/2019) of the Autonomous University of Guerrero.

Mice were housed under controlled conditions (22 ± 2 °C; 50–60% humidity; 12 h light/12 h dark cycle) with ad libitum access to food and water. Animals were randomly assigned to three experimental groups:

• Control (C), fed a standard diet (LabDiet 5001, St. Louis, MO, USA).
• Obesity (Ob), fed a high-fat diet (HFD; D12492, Research Diets, New Brunswick, NJ, USA).
• Obesity treated with RSV (Ob + RSV), fed the same HFD and treated with 2.25 mg/kg body weight of resveratrol (RSV; Sigma-Aldrich, CAS No. 501-36-0, St. Louis, MO, USA).

Each group consisted of five animals for body weight measurements, and three samples of the animals were sent for RNA-seq analysis. The HFD regimen was initiated at week 8 in the Ob and Ob + RSV groups, while the C group remained on a standard diet. Beginning at week 16, RSV was administered daily for 4 weeks by oral gavage (diluted in saline solution) at a dose of 2.25 mg/kg/day using an esophageal cannula. The RSV dose was selected based on previously reported studies demonstrating the metabolic effects of this antioxidant in HFD-fed murine models [41].

The C and Ob groups received an equivalent volume of saline. Body weight was recorded weekly throughout the study.

2.2. Tissue Collection and Hypothalamus Dissection

At the end of the treatment period, animals were anesthetized with sodium pentobarbital (126 mg/kg; Cheminova, Mexico City, Mexico) and euthanized. The brain was rapidly removed, and the hypothalamus was dissected and washed with phosphate-buffered saline (pH 7.4). Tissue samples were immediately processed or stored at −80 °C until RNA extraction.

2.3. Total RNA Extraction and Quality Assessment

Total RNA was extracted from the hypothalamus using TRIzol reagent (Invitrogen, CAS No. 15596018; Carlsbad, CA, USA), following the manufacturer’s instructions. Tissue homogenization was performed using a PRO 200 homogenizer (PRO Scientific Inc., Oxford, CT, USA).

RNA purity and concentration were assessed using a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), considering samples with A260/A280 ratios ≥ 1.9 and concentrations ≥ 20 ng/μL as acceptable. All RNA samples were stored at −80 °C until sequencing.

2.4. RNA Sequencing and Bioinformatic Analysis

mRNA sequencing was performed by Novogene Co., Ltd. (Sacramento, CA, USA). Quality control metrics included Q20, Q30 scores, and GC content. Clean reads were aligned to the reference mouse genome using HISAT2 (v2.0.5). Gene-level quantification was performed using featureCounts (v1.5.0-p3). Transcript abundance was normalized as fragments per kilobase of transcript per million mapped reads (FPKM).

2.5. Differential Gene Expression Analysis

Differential expression analysis was conducted using the DESeq2 R package (v1.20.0) with three biological replicates per group. p-values were adjusted using the Benjamini–Hochberg procedure [42] to control the false discovery rate (FDR). Genes with adjusted p < 0.05 and log₂ FC ≥ 1 were considered DEGs.

2.6. Functional Enrichment Analysis

DEGs were annotated using the clusterProfiler R package (v3.8.1), which enabled the identification of gene families and their biological functions in the mouse genome based on public databases. Gene ontology (GO) enrichment analysis was conducted to determine the biological significance of each DEG in terms of biological process, molecular function, and cellular component. Additionally, statistical enrichment analysis of DEG was performed using the Kyoto encyclopedia of Genes and Genomes (KEGG) to identify associated signaling pathways. GO terms and KEGG pathways with an adjusted p < 0.05 and |log₂ fold change| ≥ 1. were considered significantly enriched.

2.7. Statistical Analysis

Body weight data were analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test. Results are presented as mean ± standard deviation (SD). Statistical analyses were performed using GraphPad Prism (v9.3.1), and p < 0.05 was considered statistically significant.

3. Results

3.1. Effect of RSV on Body Weight in an Obesity Model

To induce obesity, mice were fed an HFD for eight weeks. One group remained with obesity (Ob), while the other received RSV treatment (Ob + RSV) during the final four weeks of the HFD regimen. A control group (C) was maintained on a standard diet. A significant increase in body weight was observed in both groups with obesity (Ob and Ob + RSV) compared with the control group, beginning at week 4 and persisting through week 12 (p < 0.05; Figure 1). However, no significant difference in body weight gain was detected between the Ob and Ob + RSV groups, indicating that RSV did not significantly affect weight gain under the experimental conditions.

3.2. Global Transcriptomic Analysis of Differentially Expressed Genes in the Hypothalamus

To investigate the global gene expression landscape, RNA-seq analysis was performed. Prior to differential gene expression analysis, the constructed libraries were subjected to quality control, ensuring their suitability (Supplementary Materials).

To identify hypothalamic DEGs associated with obesity and those modulated by RSV, gene-level counts were estimated for the three study groups based on gene length and the number of reads assigned to each gene. Differential expression analysis was performed using DESeq2, with DEGs defined by an adjusted p < 0.05 and |log₂ fold change| ≥ 1.

RNA-seq analysis of hypothalamic tissue revealed 393 DEGs in Ob vs. C and 483 DEGs in Ob + RSV vs. C (adjusted p < 0.05 and log₂ fold change (FC) ≥ 1) (Figure 2a). Of these, 54 genes were shared between the Ob vs. C and Ob + RSV vs. Ob comparisons (Figure 2b), indicating that RSV modulates a subset of obesity-induced gene expression changes.

Hierarchical clustering showed distinct transcriptomic profiles across the three groups. Heatmaps revealed obesity-associated gene alterations (Figure 2c) and their partial normalization in response to RSV (Figure 2d).

3.3. Classification of Resveratrol-Regulated Genes

Among the 54 DEGs modulated by RSV in mice with obesity, 55% were protein-coding genes (53.3% downregulated and 46.7% upregulated), whereas a substantial proportion (45%) corresponded to non-coding RNAs. This highlights non-coding transcriptional regulation as a prominent feature of the RSV-responsive hypothalamic transcriptome. The non-coding fraction included antisense RNAs (20.8%, 80% downregulated), long intergenic non-coding RNAs (37.5%, 66.7% downregulated), pseudogenes (20.8%, all downregulated), unprocessed pseudogenes (4.2%, all upregulated), processed transcripts (4.2%, all downregulated), and transcripts to be experimentally confirmed (TEC; 12.5%, all upregulated). Gene classifications and expression data are detailed in

Table 1. Differentially expressed genes (DEGs) by biotype in the hypothalamus of obese mice and RSV-treated obese mice.

Gene Biotype	Ensembl Gene ID	KEGG Symbol	Gene Name	Log2 FC Ob vs. C	Log2 FC Ob + RSV vs. Ob
Antisense	ENSMUSG00000085323	9130410C08Rik	RIKEN cDNA 9130410C08 gene	3.88	−6.26
	ENSMUSG00000098720	Gm27239	Predicted gene 27239	1.77	−2.29
	ENSMUSG00000087004	Gm14154	Predicted gene 14154	4.40	−4.46
	ENSMUSG00000092130	D030025P21Rik	RIKEN cDNA D030025P21 gene	5.61	−5.64
	ENSMUSG00000085134	Gm11659	Predicted gene 11659	−3.99	4.25
LincRNA	ENSMUSG00000116256	Gm32405	Predicted gene 32405	5.36	−5.39
	ENSMUSG00000109149	Gm19656	Predicted gene 19656	3.45	−3.58
	ENSMUSG00000086952	Gm12596	Predicted gene 12596	5.42	−5.45
	ENSMUSG00000111399	Gm47328	Predicted gene 47328	5.86	−5.88
	ENSMUSG00000097819	Gm26813	Predicted gene 26813	5.14	−5.17
	ENSMUSG00000111970	Gm47715	Predicted gene 47715	5.42	−5.45
	ENSMUSG00000086464	Gm15835	Predicted gene 15835	−5.20	4.57
	ENSMUSG00000114148	Gm47701	Predicted gene 47701	−1.99	2.04
	ENSMUSG00000115545	Gm48908	Predicted gene 48908	−5.75	6.26
Processed pseudogene	ENSMUSG00000112048	Gm48750	Predicted gene 48750	5.28	−5.31
	ENSMUSG00000080849	Gm15702	Predicted gene 15702	4.35	−5.12
	ENSMUSG00000115593	Gm4824	Predicted gene 4824	4.48	−3.62
	ENSMUSG00000083305	Gm13315	Predicted gene 13315	4.03	−6.36
	ENSMUSG00000080832	M6pr-ps	Mannose-6-phosphate receptor pseudogene	2.65	−3.48
UP	ENSMUSG00000080888	Gm14387	Predicted gene 14387	−4.53	5.30
PT	ENSMUSG00000053749	Gm9920	Predicted gene 9920	5.44	−5.47
TEC	ENSMUSG00000109679	Gm45342	Predicted gene 45342	−5.38	5.50
	ENSMUSG00000111107	Gm48737	Predicted gene 48737	−5.46	5.24
	ENSMUSG00000107647	Gm44445	Predicted gene 44445	−4.57	4.98
Protein coding	ENSMUSG00000060981	Hist1h4h	Histone cluster 1 H4h	1.42	−1.39
	ENSMUSG00000025401	Myo1a	Myosin IA	1.13	−1.23
	ENSMUSG00000021345	Prl8a6	Prolactin family 8, subfamily a, member 6	5.27	−5.29
	ENSMUSG00000024402	Lta	Lymphotoxin A	1.96	−2.64
	ENSMUSG00000054626	Xlr	X-linked lymphocyte-regulated	3.54	−3.63
	ENSMUSG00000095247	Ccl27a	Chemokine (C-C motif) ligand 27A	3.92	−4.90
	ENSMUSG00000024175	Tekt4	Tektin 4	1.21	−1.46
	ENSMUSG00000053877	Srcap	Snf2-related CREBBP activator protein	−1.19	1.50
	ENSMUSG00000027801	Tm4sf4	Transmembrane 4 superfamily member 4	3.25	−4.97
	ENSMUSG00000023153	Tmem52	Transmembrane protein 52	1.44	−1.50
	ENSMUSG00000021506	Pitx1	Paired-like homeodomain transcription factor 1	4.72	−3.36
	ENSMUSG00000042821	Snail1	Snail family zinc finger 1	2.66	−2.59
	ENSMUSG00000028427	Aqp7	Aquaporin 7	5.37	−5.40
	ENSMUSG00000055172	C1ra	Complement component 1, r subcomponent A	3.41	−2.81
	ENSMUSG00000044814	Olfr543	Olfactory receptor 543	−3.02	3.24
	ENSMUSG00000073514	Dok6	Docking protein 6	−1.35	1.53
	ENSMUSG00000090053	Palm2	Paralemmin 2	−1.48	1.73
	ENSMUSG00000026829	Gbgt1	Globoside alpha-1,3-N-acetylgalactosaminyltransferase 1	−2.70	2.97
	ENSMUSG00000033871	Ppargc1b	Peroxisome proliferative activated receptor, gamma, coactivator 1 beta	−1.93	1.35
	ENSMUSG00000025776	Crispld1	Cysteine-rich secretory protein LCCL domain containing 1	−1.23	2.17
	ENSMUSG00000075596	B130006D01Rik	RIKEN cDNA B130006D01 gene	−5.12	5.76
	ENSMUSG00000038195	Rilp	Rab interacting lysosomal protein	1.01	−2.13
	ENSMUSG00000043972	Opn5	Opsin 5	3.27	−5.98
	ENSMUSG00000000263	Glra1	Glycine receptor, alpha 1 subunit	−2.41	2.82
	ENSMUSG00000038048	Cntnap5c	Contactin associated protein-like 5C	−2.40	2.62
	ENSMUSG00000047773	Ankfn1	Ankyrin-repeat and fibronectin type III domain containing 1	−2.00	1.45
	ENSMUSG00000073016	Uprt	Uracil phosphoribosyltransferase	−2.81	2.31
	ENSMUSG00000068130	Zfp442	Zinc finger protein 442	−2.42	2.96
	ENSMUSG00000097271	Gm9903	Predicted gene 9903	−4.38	5.18
	ENSMUSG00000096768	Gm47283	Predicted gene, 47283	1.01	−1.05

Note: log₂FC: Log₂ fold change in gene expression. Only genes showing differential expression between groups with an adjusted p < 0.05 and log₂ fold change ≥ 1 are shown. Biotypes: UP, unprocessed pseudogene; PT, processed transcript; TEC, to be experimentally confirmed. Groups: Ob, obese; C, control; Ob + RSV, obese treated with resveratrol.

.

Notably, the high proportion of non-coding RNAs among RSV-responsive genes represents a distinctive feature of this transcriptomic response, distinguishing it from profiles dominated by protein-coding genes and highlighting the prominence of non-coding transcripts in the hypothalamic response to RSV under obesogenic conditions.

3.4. GO and KEGG Pathway Enrichment Analyses

To further predict the biological effects of RSV in the hypothalamus of mice with obesity, GO and KEGG pathway enrichment analyses were performed using the 54 RSV-responsive DEGs.

GO enrichment analysis identified biological processes including globoside metabolism, UMP biosynthesis, regression of the hyaloid vascular plexus, regulation of acrosome reaction and regulation of vesicle size (Figure 3a). Although diverse, these processes converge on lipid handling and vesicular dynamics, both of which are relevant to hypothalamic metabolic regulation.

KEGG pathway analysis revealed enrichment in pathways related to lipid metabolism, inflammation, and insulin resistance, including regulation of lipolysis in adipocytes, PPAR signaling, TNF signaling, NF-κB signaling, and complement cascades (Figure 3b). These enriched pathways support the prediction of RSV-mediated modulation of inflammatory and metabolic signaling within the hypothalamus.

3.5. Key Obesity-Related Genes Modulated by Resveratrol

RSV significantly modulated the expression of nine genes that were dysregulated by HFD-induced obesity. Of these, seven genes were upregulated in obesity and downregulated following RSV treatment, including Aquaporin 7 (Aqp7), Chemokine (C-C motif) ligand 27A (Ccl27a), Lymphotoxin alpha (Lta), Rab interacting lysosomal protein (Rilp), Mannose-6-phosphate receptor pseudogene (M6pr-ps), Complement component 1, r subcomponent A (C1ra), and Snail family zinc finger 1 (Snail1), and two genes were downregulated in obesity and upregulated by RSV, including Globoside alpha-1,3-N-acetylgalactosaminyltransferase 1 (Gbgt1) and Peroxisome proliferator-activated receptor gamma coactivator 1 beta (Ppargc1b).

Table 2. Genes regulated by RSV in the hypothalamus of mice with obesity and their impact on signaling pathways.

KEGG Pathway	KEGG Symbol	Gene Name	Log2 FC Ob vs. C	Log2 FC Ob + RSV vs. Ob
Glycosphingolipid biosynthesis	Gbgt1	Globoside alpha-1,3-N-acetylgalactosaminy ltransferase 1	↓ −2.70	↑ 2.97
PPAR signaling pathway	Aqp7	Aquaporin 7	↑ 5.37	↓ −5.40
Regulation of lipolysis in adipocytes
Viral protein interaction with cytokine and cytokine receptor	Ccl27a	Chemokin e (C-C motif) ligand 27A	↑ 3.92	↓ −4.90
NF-κB	Lta	Lymphotoxin alpha (TNF superfamily, member 1)	↑ 1.96	↓ −2.64
TNF
Phagosome	Rilp	Rab interacting lysosomal protein	↑ 1.01	↓ −2.13
	M6pr-ps	Mannose-6-phosphate receptor, pseudogene	↑ 2.65	↓ −3.48
	C1ra	Complement component 1, r subcomponent A	↑ 3.41	↓ −2.81
Complement and coagulation cascades	C1ra	Complement component 1, r subcomponent A	↑ 3.41	↓ −2.81
Adherens junction	Snail1	Snail family zinc finger 1	↑ 2.66	↓ −2.59
Insulin resistance	Ppargc1b	Peroxisome proliferative activated receptor, gamma, coactivator 1 beta	↓ −1.46	↑ 1.28

Note: log₂FC: Log₂ fold change in gene expression. Only genes showing differential expression between groups with an adjusted p < 0.05 and |log₂ fold change| ≥ 1 are shown. Up: ↑, down: ↓. Groups: Ob, obesity; C, control; Ob + RSV, obesity treated with resveratrol.

presents the RSV-regulated DEGs in the context of obesity, along with the KEGG signaling pathways influenced by these genes.

These genes are implicated in processes such as inflammation, immune response, lipid metabolism, and insulin resistance, which are mechanisms predicted to be reprogrammed by RSV. Figure 4 illustrates the genes and biological functions regulated by RSV.

4. Discussion

In this study, we examined the impact of RSV treatment on the hypothalamic transcriptome of mice with HFD-induced obesity. Although several studies have reported that higher doses of RSV reduce body weight in animals fed an HFD [43,44], at the dose used in the present study, RSV administration did not significantly reduce body weight (Figure 1). Nevertheless, RSV exerted a marked effect on transcriptomic modulation. Our transcriptomic analyses revealed that RSV modulates a specific subset of genes involved in inflammation, immune response, lipid metabolism, and energy homeostasis in the hypothalamus. These findings support the hypothesis that RSV can exert neuroprotective and metabolic effects independent of weight loss, consistent with previous reports in rodent models of obesity [27].

Interestingly, a significant proportion (45%) of RSV-modulated transcripts were non-coding RNAs, including lncRNAs, antisense RNAs, and pseudogenes. Emerging evidence indicates that lncRNAs are key regulators of inflammatory signaling pathways and metabolic control in the context of obesity and insulin resistance [45]. Moreover, RSV has been shown to modulate the expression of hundreds of lncRNAs, reversing dysregulated transcriptomes in insulin-resistant skeletal muscle; for instance, the lncRNA NONMMUT044897.2 was downregulated upon RSV treatment, leading to improved insulin sensitivity via the SOCS1 pathway [46]. Although the functions of most RSV-modulated non-coding RNAs in the hypothalamus remain to be explored, their regulation suggests a potential epigenetic mechanism through which RSV exerts central metabolic and anti-inflammatory effects.

Consistent with the literature, our results suggest that an HFD promotes lipid accumulation in visceral adipose tissue, which in turn triggers systemic low-grade inflammation characterized by the release of pro-inflammatory cytokines, free fatty acids, and adipokines [6,12,13,47]. These factors impair hypothalamic function and disrupt circuits regulating energy balance and appetite, leading to a pro-inflammatory transcriptional profile in the hypothalamus [12,13]. In our model, an HFD altered hypothalamic gene expression, while RSV treatment counteracted these changes (Figure 2). KEGG analysis (Figure 3b) revealed that RSV-enriched pathways included NF-κB and TNF signaling, driven by modulation of genes such as Lta and Ccl27a, both of which are implicated in neuroinflammation and metabolic dysfunction [48,49,50].

Lta, encoding lymphotoxin-α, is upregulated in obesity and contributes to inflammation, insulin resistance, and weight gain, while its inhibition confers metabolic protection [48,49]. Similarly, Ccl27a, a chemokine involved in T-cell recruitment and neuroinflammatory signaling [50], was downregulated by RSV in our study. Elevated expression of chemokines, including Ccl2, Ccl4, and Ccl27a, has been reported in humans and murine specimens with obesity, correlating with inflammation and insulin resistance, and these expression levels are reduced following interventions such as bariatric surgery [50,51]. In the CNS, Ccl27 promotes T-cell chemotaxis and plays a role in hypothalamic inflammation [47]. Furthermore, HFD-induced microglial activation and chemokine overexpression have been associated with reduced energy expenditure and obesity progression [14].

The RSV-mediated downregulation of Lta and Ccl27a observed here may thus reflect a central anti-inflammatory mechanism with potential metabolic benefits. These findings support the proposed therapeutic value of targeting the chemokine-receptor signaling axis to mitigate neuroinflammation and metabolic dysfunction in obesity and related diseases [52,53,54].

RSV also modulated genes involved in lipid and glycerol metabolism. Gbgt1, which participates in glycosphingolipid biosynthesis [55], was upregulated by RSV. Altered glycosphingolipid metabolism has been linked to disrupted lipid handling in metabolic tissues [56], suggesting a potential protective role for RSV through modulation of membrane lipid composition.

Aqp7, an aquaglyceroporin involved in glycerol transport, was upregulated in obesity, likely to reflect increased glycerol availability due to HFD-induced lipolysis [57]. RSV treatment reduced Aqp7 expression, which may be metabolically beneficial, as excessive glycerol influx can contribute to lipotoxicity, oxidative stress, and impaired insulin signaling in the hypothalamus [58]. These findings suggest that RSV-mediated downregulation of Aqp7 may help protect hypothalamic function under obesogenic conditions.

RSV also restored the expression of Ppargc1b, a key regulator of mitochondrial function and energy metabolism that was reduced in obesity. The upregulation of Ppargc1b is consistent with studies demonstrating its protective effects against obesity-induced inflammation, insulin resistance, and mitochondrial dysfunction [3,59]. Knockout studies have linked Ppargc1b deficiency to increased fat accumulation, systemic inflammation, and impaired glucose metabolism, whereas its overexpression enhances fatty acid oxidation and energy expenditure, thereby conferring resistance to diet-induced obesity [60,61,62]. These findings suggest that RSV may exert protective metabolic effects, at least in part through Ppargc1b-mediated pathways.

In addition, RSV normalized the expression of Snail1, a gene associated with adiposity and metabolic dysfunction [63]. Given that Snail1 deletion leads to reduced adipocyte size and fat mass, its downregulation by RSV suggests potential anti-obesogenic effects. Furthermore, RSV suppressed obesity-induced overexpression of Rilp, M6pr-ps, and C1ra, genes involved in phagosome activation, complement signaling, and coagulation. These pathways are linked to microglial overactivation and excessive phagocytosis, processes associated with neurodegeneration [64,65,66,67]. By dampening these signals, RSV may confer neuroprotective effects by limiting complement-mediated synaptic loss, a phenomenon implicated in Alzheimer’s disease [66,67].

To our knowledge, this is the first study to investigate the impact of RSV on the hypothalamic transcriptome in an HFD-induced obesity model. The integration of RNA-seq and pathway analyses provides novel insight into predictive molecular targets and regulatory networks through which RSV may exert central effects. These findings highlight the potential of RSV as a modulator of hypothalamic inflammation and metabolic dysfunction, although functional validation is still required. The selected dose has previously been shown to exert metabolic effects in HFD-fed mice, supporting its suitability for exploratory transcriptomic analysis [41]. A limitation of this study is the use of a single RSV dose; therefore, dose-dependent transcriptomic effects were not assessed. Notably, despite using a relatively low dose (2.25 mg/kg), we detected significant modulation of hypothalamic gene expression. In contrast, other studies have required higher RSV doses to demonstrate overt neuroprotective and anti-inflammatory effects [68,69]. These findings suggest that transcriptomic changes may be detectable at lower doses, even in the absence of pronounced systemic or weight-related effects.

Future studies should evaluate how these transcriptomic signatures translate into changes in hypothalamic neuronal activity, systemic metabolism, and behavior. The pronounced modulation of non-coding RNAs further emphasizes the need for mechanistic studies assessing their roles in central metabolic regulation. Moreover, given the absence of significant weight loss in our model, it will be essential to determine the dose dependency, long-term efficacy, and translational feasibility of RSV, considering its limited bioavailability and complex pharmacokinetics.

In summary, RSV modulated the expression of hypothalamic genes dysregulated by obesity, including Aqp7, Ccl27a, Lta, Rilp, M6pr-ps, C1ra, Snail1, Gbgt1, and Ppargc1b. These transcriptional changes involve pathways associated with inflammation, lipid metabolism, immune activation, and mitochondrial function. Notably, RSV’s central effects appear to occur at least in part independently of body weight changes and involve coordinated regulation of both coding and non-coding RNAs. Together, these findings provide predictive insights into the molecular mechanisms through which RSV may ameliorate hypothalamic dysfunction in obesity.