Changes in microbial and metabolic profiles of mice fed with long-term high salt diet

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However, less is known about long-term implications of HSD. Therefore, the aim of this study was to conduct a preliminary investigation into the effects of mice feeding with long-term HSD on gut microbial and metabolic profiles. Methods : In this study, C57BL/6J mice were fed with HSD for 22 weeks, after which fat and gut feces were collected. The composition of fecal microbiota was determined using 16S rRNA gene sequencing. Fecal metabolic profiling of mice was identified through untargeted ultrahigh-performance liquid chromatography-mass spectrometry. In addition, the serum levels of adipocytokines, including fibroblast growth factor 21 (FGF21) and adiponectin (APN), were measured. Results : Long-term HSD disrupted the growth performance of mice. Compared to those fed a normal salt diet, mice on a long-term HSD showed slower weight gain, as well as lower fat accumulation and serum levels of APN, while experiencing elevated blood pressure and levels of serum FGF21 and glucose. The 16S rRNA sequencing revealed changes in community richness and diversity, with long-term HSD affecting the abundance of certain gut microbiota, including Firmicutes , Christensenella , Barnesiella , and Lactococcus . Fecal metabolomic analysis also uncovered alterations in metabolites, such as myriocin, cerulenin, norcholic acid, 7-ketocholesterol, and prostaglandins B2. Further analysis indicated that these gut and microbiota and metabolites are predominantly involved in the lipid metabolism of the organism. Importantly, variations in these gut metabolites and microbiota were significantly correlated with body weight, fat accumulation, and the levels of FGF21 and APN. Conclusion : Long-term HSD affects physiological traits, alters gut metabolites profiles, and impacts the composition and function of gut microbiota, thus causes a certain impact on lipid metabolism. long-term high salt diet gut microbiota fecal metabolomics lipid metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Sodium is an essential nutrient that plays a vital role in maintaining normal cellular homeostasis and regulating fluid and electrolyte balance. It is equally crucial for maintaining extracellular fluid volume, muscle and nerve cell function, and the transmembrane transport of nutrients and substrates, attributed to its osmotic action[ 1 ]. While the daily minimum intake level required for physiological needs remains not well established, sodium deficiency is extremely unlikely in healthy individuals due to the presence of dietary salt in commonly consumed foods[ 1 ]. Conversely, an excess of salt intake, more than is physiologically necessary, is common in most populations worldwide. Salt intake is an important worldwide public health issue[ 2 ]. The World Health Organization (WHO) recommends that the daily maximum intake of salt should not exceed 5 g in adults and should be adjusted downward for children. Data from a global 2010 study showed that the average daily intake of salt per person in the surveyed regions was 10.06 g[ 3 ]. Salt intake in Asia is among the highest in the world (14.01 g/d per capita), more than twice the recommended limit. Excessive intake of salt can lead to acute toxicity, with the possibility of a fatal outcome[ 1 ]. Chronic excessive salt intake can also lead to pathological changes in the body, although they may not be evident in the early stages. Evidence suggests that salt consumption could predict the future development of metabolic syndrome[ 4 ], which, in turn, can serve as the foundation for various metabolism disorders including insulin resistance, abnormal lipid metabolism, and elevated blood pressure[ 5 , 6 ], contributing to the onset of diseases such as diabetes, non-alcoholic fatty liver disease, hypertension. Gut microbiota is considered a bridge between diet and host health[ 7 ]. The intestinal flora and their metabolites can directly impact physiological metabolism[ 8 ]. Although salt is primarily absorbed in the upper intestine, a part of it reaches the colon[ 1 ] and interacts with the gut microbiota. Previous studies have reported the relationships between high salt consumption, gut microbiota, and body metabolism[ 9 , 10 ]. However, a relatively short feeding duration may not fully reveal the long-term effects of a high salt diet (HSD). In the present study, we established a model fed with a long-term HSD and conducted exploratory investigations into the effects of this HSD on growth performance, serum adipocytokines, and fecal microbiota composition and metabolites. Materials and methods Animals and treatment All animal experiments were conducted in accordance with the Animal Research Ethics Committee of Wenzhou Medical University (Ethical approval number: SYXK 2020-0014). Seven-week-old male C57BL/6J wild-type mice were purchased from Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China) and housed in a room with free access to water and diet, at a controlled temperature of 23 ± 1°C, and subjected to a 12-hour/day light cycle. After 7 days of acclimatization, all the 8-week-old male mice were randomly divided into two groups: a high salt diet (HSD) group fed an 8% NaCl diet and a normal salt diet (NSD) group fed a 0.6% NaCl diet, for 22 weeks, with n = 4 in each group. The HSD containing 8% NaCl was purchased from Dytes Co., Ltd. (Suzhou, China) and the NSD containing 0.6% NaCl was purchased from Cyagen Co., Ltd. (Guangzhou, China). Sample collection and index determination During the feeding period, the body weight of the mice was recorded every two weeks, and blood pressure was measured using tail-cuff systems (BP 2000, Visitech Systems, USA) at the start and the end of the experiment. Glucose tolerance test (GTT) was performed at the end of the experiment and glucose levels were detected and recorded from blood sample collected at 10, 20, 30, 45, 60, 75 and 90 minutes after glucose injection (0.01 mL/g administration). The blood glucose (BG) meter and BG test strips were purchased from Braun Co., Ltd. (Melsungen, Germany). The area under the curve (AUC) of GTT was calculated according to the formula: AUC = 0.5 × (BG0min + BG30min)/2 + 0.5 × (BG30min + BG60min)/2 + 1 × (BG60min + BG90min)/2. On the day of sacrifice, a blood sample was collected from the eyes of the mice under anesthesia (with sodium pentobarbital at 0.1mL/10g body weight). The heart, liver, kidney, and various adipose tissues (white, peri-renal, and brown) of the mice were weighed. The intestinal feces were collected under sterile conditions, and all tissues were stored at -80 ℃ after cataloging. The ELISA kits for the determination of serum fibroblast growth factor 21 (FGF21) and adiponectin (APN) were purchased from Antibody and Immunoassay Service Co., Ltd. (Hong Kong, China). Fecal metabonomics The lyophilized feces samples were sent to Sangon Biotech (Shanghai) Co., Ltd (Shanghai, China) for metabonomics analysis. In brief, untargeted ultrahigh-performance liquid chromatography-mass spectrometry was performed using an AB Sciex Triple TOF 5600 mass spectrometer(Waters, UK) combined with ACQUITY UPLC H-Class system (Waters, UK). Extracted samples were injected into a chromatographic column (100mm × 2.1mm, 1.7um, ACQUITY UPLC BEH C-18, Waters, UK) at a flow rate of 0.4 mL/min, an injection volume of 5uL, and a column temperature of 45 ℃. The Mobile phase consisted of phase A (water with 0.1% formic acid) and phase B (methanol with 0.1% formic acid). The elution program was as follows: 0-2min, 5% mobile phase B; 2–4 min, 20% mobile phase B; 4–9 min, 25% mobile phase B; 9–17 min, 60% mobile phase B; 17–19 min,100% mobile phase B; 19-20.1min, 5%mobile phase B for equilibration. Mass spectrometry data were acquired by scanning in both positive and negative ion modes using an MS system equipped with an electrospray ionization source. The mass spectrometry raw data were acquired using the UNIFI Scientific Information System (Ver. 1.8.1.) and processed with Progenesis QI software(Ver.2.3, Nonlinear Dynamics, Newcastle, UK). The analytical parameters set were a precursor tolerance of 5 ppm, product tolerance of 10 ppm, and a product ion threshold of 5%. The raw abundance of metabolites was normalized as relative abundance for subsequent analysis. Metabolites annotation was performed using the Lipid metabolites and pathways strategy (Ver.2.3) ( http://www.lipidmaps.org ), Human Metabolome Database ( https://hmdb.ca/ ), and Metabolite Link database ( https://metlin.scripps.edu ). To identify potential differential metabolites, mutilvariable analysis was applied for metabolite profiling. The orthogonal partial least square discriminant analysis (OPLS-DA) was implemented using “ropls” R package[ 11 ] (Ver. 1.32.0). Variable importance in the projection (VIP) was used to evaluate the importance of each metabolite in OPLS-DA model. The p -values from the Student’s t-test and VIP values were used to screen differential metabolites. Metabolite with a p -value 1, and the value of VIP > 1 was considered a differential metabolite and was extracted for further analysis. KEGG pathway enrichment analysis of differential metabolites was conducted using MetaboAnalyst software (Ver.5.0) ( https://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml ). 16S rRNA gene sequencing and data analysis The lyophilized feces samples were sent to Sangon Biotech (Shanghai) Co., Ltd (Shanghai, China) for 16S rRNA gene sequencing. Briefly, total genomic DNA was extracted, and the V3 and V4 regions of bacterial 16S rRNA gene were amplified using dual-indexed primers: 341F: (5’-CCTACGGGNGGCWGCAG-3’) and 805R: (5’-GACTACHVGGGTATCTAATCC-3’). The quality-qualified library was used for high-throughput sequencing on the Illumina Hiseq 2500 system (Illumina, San Diego, USA). Raw data generated from the Illumina platform were merged into sequences using PEAR[ 12 ] software ( Ver.0.9.6) based on the overlapping relationship of paired-end reads. These sequences were then divided into individual samples based on their barcode sequences. Subsequently, the data from each sample were processed to remove low-quality data and retain high-quality data using QIIME[ 13 ] software (Ver.1.8.0). Non-specific amplification and Chimeras were also eliminated using Usearch[ 14 ] software (Ver.5.2.236), and sequences with 97% similarity were clustered into one operational taxonomic unit (OTU). The representative sequences of each OTU were classified into different taxonomic levels (phylum, class, order, family, and genus) using the Ribosomal Database Project (RDP) classifier[ 15 ] (Ver.2.12) with a threshold of 0.8. Additionally, the relative abundance of species was quantified at each taxonomic level. Alpha diversity was evaluated by calculating Shannon, Simpson, Ace, and Chao1 indices with “vegan” R package ( https://github.com/vegandevs/vegan ) (Ver.2.6-4). Using Bray-Curtis distance, principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) analysis were performed to assess beta diversity. Species differences between groups were identified using Linear discriminate analysis effect size (LEfSe) based on “microeco” R package[ 16 ] (Ver.1.1.0). The KEGG functional profiles of the microbial community were predicted by using PICRUSt2[ 17 ] tool. Statistical analysis A double-blind experimental analysis mode was used, and all statistical analyses were performed using R software (Ver.4.3.1). The normality of data distribution was assessed with the Shapiro-Wilk test. Groups comparisons were performed using either Student’s t-test or the Wilcoxon test. The results of the statistical analysis were presented as the mean ± standard error of the mean (SEM). A level of p -value < 0.05 (two-sided) was considered statistically significant. Results Long-term HSD altered fat accumulation, glucose homeostasis, and levels of FGF21 and APN During the feeding period, the body weight of mice fed with NSD increased gradually, while mice fed with HSD experienced a lower weight gain that did not differ significantly from their initial weight until the end of the treatment (Fig. 1 A). Moreover, the body weight of HSD-fed mice was lower than that of NSD-fed mice at each stage. Organ and tissue weights also changed after long-term HSD treatment. The heart and kidney weight-to-body weight ratio was elevated in long-term HSD group, while the weights of white, peri-renal, and brown adipose tissue relative to body weight decreased (Fig. 1 B). In the terms of blood pressure, both systolic and diastolic blood pressure underwent a significant elevation after long-term HSD treatment compared with the initial blood pressure (Fig. 1 C, D). At the end of the treatment, systolic blood pressure was higher in long-term HSD group than that in NSD group, but diastolic blood pressure did not significantly differ between the groups. Additionally, several metabolic indicators underwent significantly changes. The serum level of adipocytokines FGF21 increased in response to long-term HSD treatment, while the level of APN decreased dramatically (Fig. 1 E, F). The glucose level and AUC value of GTT were higher in long-term HSD mice than in NSD group (Fig. 1 G, H and I ). To sum up, long-term HSD alters growth performance and metabolism homeostasis. Overview of gut microbial communities in response to long-term HSD The impact of intestinal microbiota on obesity and metabolic disease is widely doucumented[ 18 – 20 ]. Thus, we explored the changes in gut microbial communities in response to long-term HSD. In the present study, we acquired 749783 high-quality reads, from which 2357 representative OTUs were identified at a similarity level of 97%. The Shannon curve was plateaued with an increase in the sequences count, indicating adequate sequencing depth (Fig. 2 A). Alpha diversity was assessed using several indices. The Shannon and Simpson indices in HSD group were greater compared to those in NSD group, whereas the ACE and Chao1 indices showed the opposite trend (Fig. 2 B, C, D and E ), indicating that gut microbial community richness and diversity were altered in response to long-term HSD. Considering species composition and abundance, we performed PCoA and NMDS analyses to assess beta diversity at the OTU level. As shown in Fig. 2 F and G, long-term HSD and NSD groups were divided into two compartments in the 3D PCoA plot, and a similar pattern was observed in the NMDS score plot. Adonis (R 2 = 0.644, p = 0.029) and ANOSIM (R 2 = 0.865, p = 0.028) analyses further confirmed the significant differences between the groups. Species annotation information for representative OTUs was identified using the RDP classifier. At the phylum level, Firmicutes emerged as the most predominant phylum, constituting 65.99% and 94.10% of OTUs in long-term HSD and NSD groups, respectively (Fig. 3 A). The ratio of Firmicutes to Bacteroidetes was decreased in long-term HSD group (Fig. 3 B). Moreover, Bacteroidetes , Proteobacteria , Candidatus Saccharibacteria , Tenericutes , and Deferribacteres were more enriched in long-term HSD group compared to NSD group. At the genus level, among the total 257 annotated genera, Allobaculum was the most abundant, representing 39.34% and 82.19% of OTUs in long-term HSD and NSD groups, respectively(Fig. 3 C). LEfSe analysis, with a threshold of an LDA score greater than 2, revealed six bacterial taxa at the phylum level showing differential abundance (Fig. 3 D). The long-term HSD group exhibited a lower abundance of Firmicutes compared to NSD group. Conversely, the abundance of Bacteroidetes , Proteobacteria , Candidatus - Saccharibacteria , Tenericutes , and Deferribacteres was higher in long-term HSD (Fig. 3 E). At the genus level, fifty-one bacteria showed differences in abundance, with the majority showing higher abundance in long-term HSD group, including Barnesiella , Christensenella , and Lactococcus (Fig. 3 F). In contrast, the abundance of Allobaculum , Lactobacillus , and Bifidobacterium was higher in NSD group. Functional profiles extrapolation using PICRUSt2 analysis indicated significant difference in metabolism-related pathways between the groups (Fig. 4 ). Xenobiotics biodegradation and metabolism, lipid metabolism, metabolism of cofactors and vitamins, and glycan biosynthesis and metabolism pathways were enriched in long-term HSD group, whereas nucleotide metabolism, carbohydrate metabolism, and metabolism of terpenoids and polyketides pathways were more prevalent in NSD group. Fecal metabolic profiling analysis of high salt intake In view of the potential disturbance in metabolic balance caused by long-term HSD, further analysis was conducted in fecal metabolism profiling. The overall PCA plot of all samples, shown in Fig. 5 A, demonstrated that all samples were divided into two clusters, and long-term HSD and NSD groups were clustered in distinct areas of PCA plot, indicating limited variation within per group and high differences between the groups. The R2 and Q2 values serve as the parameters for evaluating the interpretative and predictive abilities of OPLS-DA model, respectively. In the present study, R2X (cum) = 0.442, R2Y (cum) = 0.999, and Q2 = 0.874, suggesting that the OPLS-DA model exhibited excellent robustness and predictive ability. Based on untargeted ultrahigh-performance liquid chromatography–mass spectrometry, a total of 4686 metabolites were annotated. Fatty acyls, constituting 15.56% of the metabolites, were the most prominent, followed by glycerophospholipids at 13.06% (Fig. 5 B). According to the criteria (VIP > 1, |log2(Fold Change)| > 1 and p < 0.05), 644 differential metabolites were identified, with 326 metabolites up-regulated and 318 down-regulated in long-term HSD group (Fig. 5 C). Lipids, including fatty acyls, glycerophospholipids, prenol lipids, steroids lipids, and steroids and steroid derivatives, were the predominant differential metabolites, comprising over half of the differential metabolites quantities (Fig. 5 D). In comparison to NSD group, myriocin, norcholic acid, cerulenin, and 7-ketocholesterol levels increased, while prostaglandin B2 decreased in long-term HSD group (Fig. 5 E). Further pathway enrichment analysis revealed that three lipid metabolism-related pathways (linoleic acid metabolism, steroid hormone biosynthesis, and steroid biosynthesis) were significantly enriched in long-term HSD group (Fig. 5 F), suggesting that long-term HSD disrupts lipid metabolism balance. Correlation analysis of intestinal microbes, fecal metabolites, and altered indicator The relationships between intestinal microbes, fecal metabolites, and altered indicators were assessed through correlation analysis to investigate key gut microbes and fecal metabolites responsible for changes induced by long-term high salt intake (Fig. 6 ). Specifically, Christensenella displayed a negative correlation with WAT ( r = -0.805) and APN ( r = -0.812), but a positive correlation with FGF21 ( r = 0.831). Conversely, Firmicutes showed a positive correlation with body weight ( r = 0.744) and a negative correlation with FGF21 ( r = -0.942). In contrast, Barnesiella exhibited a negative correlation with body weight ( r = -0.750) and a positive correlation with FGF21 ( r = 0.773). Lactococcus was found to be negatively correlated with both WAT ( r = -0.806) and APN ( r = -0.833). Furthermore, several metabolites showed notable correlations: Myriocin, 7-ketocholesterol, norcholic acid, and cerulenin were negatively correlated with body weight ( r = -0.723 for myriocin, r = -0.877 for 7-ketocholesterol, r = -0.849 for cerulenin), and WAT( r = -0.660 for myriocin), as well as APN ( r = -0.794 for myriocin, r = -0.732 for 7-ketocholesterol, r = -0.726 for norcholic acid), but positively correlated with FGF21 ( r = 0.944 for myriocin, r = 0.762 for 7-ketocholesterol, r = 0.983 for norcholic acid, r = 0.798 for cerulenin). Additionally, prostaglandin B2 was positively correlated with WAT ( r = 0.708), and negatively correlated with FGF21 ( r = -0.899). Discussion Dietary salt is an essential micronutrient for maintaining life activity. High salt consumption, however, has been linked to adverse outcomes, including exacerbation of metabolism function and contribution to metabolic syndrome[ 21 , 22 ]. The long-term effects of high salt intake on organisms are not well-documented. In the present study, we developed a mouse model to explore the changes in organisms responsible for long-term HSD. We observed that long-term HSD impacted growth, metabolism homeostasis, and altered gut microbiota and metabolites profiling. Interestingly, mice fed with long-term HSD experienced lower weight gain and fat accumulation, which is opposite to previous results that high salt leads to obesity [ 23 , 24 ]. One reasonable explanation for this discrepancy is that high salt intake causes hyperphagia in mice, yet body weight remains stable under ad libitum dietary conditions due to a hypercatabolic state[ 25 ]. Our observation of stable body weight and reduced WAT in long-term HSD group after consuming high salt lend support to this hypothesis. Further investigation into the mechanisms revealed increased gut microbial diversity with long-term HSD, consistent with previous findings of decreased microbial diversity in obesity[ 26 ]. Notably, the abundance of Firmicutes and Bacteroidetes seems to be related to fat metabolism. The ratio of Firmicutes / Bacteroidetes is linked to obesity and metabolic disorders[ 27 ], and is considered as a vital index to measuring obesity[ 28 , 29 ]. Recent studies have indicated that individuals with obesity exhibited a higher relative abundance of Firmicutes and a lower relative abundance of Bacteroidetes[ 29 , 30 ]. Additionally, a reduction of Firmicutes / Bacteroidetes ratio showed a promising prospect for anti-obesity effect[ 31 , 32 ]. In our study, consumption of high salt altered the abundance of Bacteroidetes and Firmicutes and Firmicutes / Bacteroidetes ratio. Furthermore, the changes in abundance of Firmicutes were positively correlated with changes in body weight. These findings suggested that reduced abundance of Firmicutes may, at least partially, explain the changes in body weight. At the genus level, long-term high salt consumption decreased the abundance of Allobaculum , Lactobacillus , and Bifidobacterium , which was broadly in line with previous studies[ 22 , 33 ]. Moreover, long-term high salt intake increased the abundance of Barnesiella , Christensenella , and Lactococcus , which were rarely reported to be associated with HSD. Barnesiella significantly correlated with hepatic lipid accumulation[ 34 ]. Christensenella had a high correlation with obesity and obesity-related metabolic disorders of the host[ 35 ]. Conversely, Lactococcus abundance had a negative correlation with obesity[ 36 ] and an alleviating effect on diet-induced obesity, fat accumulation, and adipose tissue metabolism[ 37 ]. It is widely demonstrated that dietary structure could affect the gut microbiome composition, which is accompanied by the alternation of intestinal metabolites, thereby contributing to several metabolic disorders. Similar to the results of intestinal microbes, long-term HSD also provoked significant changes in fecal metabolites, particularly lipid metabolites. Specifically, the levels of myriocin, cerulenin, norcholic acid, and 7-ketocholesterol were significantly increased, while the level of prostaglandins B2 was decreased after fed with long-term high salt. Myriocin has been shown to possess beneficial effects on attenuating body weight and lipid accumulation, as well as regulating fatty acid metabolism[ 38 , 39 ]. Moreover, myriocin inhibited the synthesis of ceramide[ 39 ] which was reported to stimulate lipid uptake and storage[ 40 ]. Similarly, cerulenin plays an important role in promoting weight loss and steatosis by blocking fatty acid synthase[ 41 ]. Norcholic acid is involved in bile acid metabolism and may serve as a key regulator for improving hyperlipidemia[ 42 ]. 7-ketocholesterol has been seen as a novel adipokine modulating the adipogenic potential of undifferentiated adipose precursor cells[ 43 ], and showed the abilities accelerating hepatic lipid accumulation and downregulating fatty acid oxidation[ 44 ]. Despite the link between prostaglandins E and obesity and lipolysis being widely documented[ 28 , 45 – 47 ], there was little focus on prostaglandins B2. Further pathway enrichment analysis results that three lipid metabolism-related pathways were significantly enriched in HSD group supported a viewpoint that lipid metabolism was disturbed by long-term high salt, which is partially evidenced by previous studies[ 48 ]. We also identified several potential interaction relationships between gut bacterias or metabolites and FGF21 and APN. The results indicated that Christensenella , Firmicutes , Barnesiella , Lactococcus , myriocin, cerulenin, norcholic acid, 7-ketocholesterol, and prostaglandins B2 were positively or negatively correlated with FGF21 or APN. Previous research has reported that Lactococcus could prevents the proliferation of adipogenic transcription factors APN and effectively alleviates diet-induced obesity and lipid deposition[ 37 ]. Moreover, myriocin is involved in lipid metabolism, which could reduce fat deposition by inhibiting the synthesis of ceramide[ 39 ]. Similarly, FGF21 could regulate the secretion of APN, and inhibit the level of ceramide, then form an FGF21-APN-ceramide axis to control energy metabolism and insulin action[ 49 ]. consequently, these interactions may provide a novel insight into understanding the complicated regulatory network of gut bacteria, fecal metabolites, and FGF21 or APN. Nonetheless, our research is not without limitations. Firstly, this study is a preliminary exploration, and the biological replicates in each group may be limited. Secondly, how the altered microbial communities and metabolites performed a function for lipid metabolism remain fully ambiguous. Thus, further studies are needed to validate our results. Consulsion In summary, our research revealed that long-term HSD stimulates FGF21 secretion in mice, alongside diminishing weight gain, white fat accumulation, and level of APN. Integrated 16S rRNA sequencing and fecal metabolomic analysis showed that long-term HSD impacts gut microbiota and metabolites profiles, including Firmicutes , Christensenella , Barnesiella , and Lactococcus , as well as myriocin, cerulenin, norcholic acid, 7-ketocholesterol, and prostaglandins B2. These gut bacteria and metabolites were significantly correlated with decreased body weight and white fat accumulation, and altered levels of FGF21 and APN, and potentially play a crucial role in lipid metabolism of organism. Declarations Author contributions D.L., M.P., and X.G. performed the experiments, analyzed the data, and drafted the manuscript. H.D., M.P., and X.G. supported executing in vivo experiments and data analysis. X.L., X.H, and Z.L. developed the concept, designed the study, and revised the manuscript. All authors have read, discussed the results, and approved the manuscript. Acknowledgements We thank Dr. Jing Gan, and Dr. Yuanrong Chen for their technical support related to the statistical analysis of data and revising our manuscript. Funding This research was supported by China National Funds for Distinguished Young Scientists (81925004). Data availability statement All data generated or analysed during this study are reasonably avaliable from the corresponding author. The raw data of 16S rRNA sequencing can be found in online repository with accession number: PRJNA1097326. Conflict of interest The authors declare that there are no conflicts of interest. Consent for publication All authors approved the version to be published. References Strazzullo P, Leclercq C (2014) Sodium. Adv Nutr 5 (2):188-190. doi:10.3945/an.113.005215 Brown IJ, Tzoulaki I, Candeias V, Elliott P (2009) Salt intakes around the world: implications for public health. 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J Clin Endocrinol Metab 101 (12):4974-4983. doi:10.1210/jc.2016-2918 Chang J, Koseki M, Saga A, Kanno K, Higo T, Okuzaki D, Okada T, Inui H, Tanaka K, Asaji M, Zhu Y, Kamada Y, Ono M, Saibara T, Ichi I, Ohama T, Nishida M, Yamashita S, Sakata Y (2020) Dietary Oxysterol, 7-Ketocholesterol Accelerates Hepatic Lipid Accumulation and Macrophage Infiltration in Obese Mice. Front Endocrinol (Lausanne) 11:614692. doi:10.3389/fendo.2020.614692 Curtis-Prior PB (1975) Prostaglandins and obesity. Lancet 1 (7912):897-899. doi:10.1016/s0140-6736(75)91690-6 Chatzipanteli K, Rudolph S, Axelrod L (1992) Coordinate control of lipolysis by prostaglandin E2 and prostacyclin in rat adipose tissue. Diabetes 41 (8):927-935. doi:10.2337/diab.41.8.927 Richelsen B (1988) Prostaglandin E2 action and binding in human adipocytes: effects of sex, age, and obesity. Metabolism 37 (3):268-275. doi:10.1016/0026-0495(88)90107-2 Yan X, Jin J, Su X, Yin X, Gao J, Wang X, Zhang S, Bu P, Wang M, Zhang Y, Wang Z, Zhang Q (2020) Intestinal Flora Modulates Blood Pressure by Regulating the Synthesis of Intestinal-Derived Corticosterone in High Salt-Induced Hypertension. Circ Res 126 (7):839-853. doi:10.1161/circresaha.119.316394 Holland WL, Adams AC, Brozinick JT, Bui HH, Miyauchi Y, Kusminski CM, Bauer SM, Wade M, Singhal E, Cheng CC, Volk K, Kuo MS, Gordillo R, Kharitonenkov A, Scherer PE (2013) An FGF21-adiponectin-ceramide axis controls energy expenditure and insulin action in mice. Cell Metab 17 (5):790-797. doi:10.1016/j.cmet.2013.03.019 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 May, 2025 Read the published version in BMC Gastroenterology → Version 1 posted Editorial decision: Revision requested 04 Jul, 2024 Editor assigned by journal 02 Jul, 2024 Submission checks completed at journal 02 Jul, 2024 First submitted to journal 01 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4671182","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322668764,"identity":"92449ec9-b6b2-4c81-b330-b4bc865669b0","order_by":0,"name":"Dini Lin","email":"","orcid":"","institution":"the 3rd Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dini","middleName":"","lastName":"Lin","suffix":""},{"id":322668769,"identity":"28f0981c-c908-4a9e-a43f-2b6af27e19cd","order_by":1,"name":"Mengmeng Peng","email":"","orcid":"","institution":"the 3rd Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengmeng","middleName":"","lastName":"Peng","suffix":""},{"id":322668772,"identity":"f5dbbab6-69bb-4c6c-b262-ecb713daf183","order_by":2,"name":"Xiaotang Gao","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotang","middleName":"","lastName":"Gao","suffix":""},{"id":322668779,"identity":"6dffd639-80b1-4c2f-b21e-53d28f6b4d5d","order_by":3,"name":"Hanlin Du","email":"","orcid":"","institution":"the First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanlin","middleName":"","lastName":"Du","suffix":""},{"id":322668781,"identity":"ee0f7e3a-290e-4da3-aec1-7a6ec2f1e163","order_by":4,"name":"Zhuofeng Lin","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuofeng","middleName":"","lastName":"Lin","suffix":""},{"id":322668785,"identity":"a52028ed-2133-4c7a-8db6-484970cdc29d","order_by":5,"name":"Xianen Huang","email":"","orcid":"","institution":"the 3rd Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianen","middleName":"","lastName":"Huang","suffix":""},{"id":322668794,"identity":"a45a4d53-bba0-4826-ac15-673ef4b67052","order_by":6,"name":"Xuemian Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3PsQrCMBCA4ZNAXKJdI5b6CpWCk+CrJBS6CR0zODhIOqh09TEcHVOETufuqPgCunVT66qYujnkm+/n7gAc5w9RL7/uK3VnlJDiJNTMnnS5GV4Aid9t0zg8YWlPAhBR1NJkHHhs1DsvSIPDwCT9FOnzMJooOafgZUvxPSHzsr9RrP6lPMqdDxwPW8uWQnOG/LXlKJFCyKe2JKa8o8Nnwkap1KRJkpCoo8UrgWYJx9Z5g6Y+LOYCS2b9ZZCvwFyVmQzyfXGr1CzwsvX35A37bdxxHMf56AGizEJiDCsmTwAAAABJRU5ErkJggg==","orcid":"","institution":"the 3rd Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xuemian","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-07-02 03:47:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4671182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4671182/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12876-025-03929-5","type":"published","date":"2025-05-15T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61072926,"identity":"63d83247-6efd-4791-9fe8-62d9dd7bb1bf","added_by":"auto","created_at":"2024-07-25 08:51:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59952,"visible":true,"origin":"","legend":"\u003cp\u003eAltered indicators of mice fed with long-term HSD. (A) Body weight of mice. All the 8-week-old mice was fed with NSD or HSD for 22 weeks. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs NSD. (B) Tissue weight to body weight ratio of mice at 30 weeks. Systolic (C) and diastolic (D) blood pressure of mice at the initiation (8 weeks) and end (30 weeks). Serum level of FGF21 (E) and APN (F) of mice at 30 weeks. (G) glucose level of mice after injection glucose. (H) Fasting blood glucose level of mice at 30 weeks. (I) AUC value of GTT. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs NSD; **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 vs NSD; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 vs NSD; ns: no significance.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4671182/v1/3312d3422a8ed2da5416ba31.png"},{"id":61072257,"identity":"1fe12a53-5d86-49c2-a3de-8be7d6e14510","added_by":"auto","created_at":"2024-07-25 08:43:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66425,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha and beta diversity analysis. (A) Shannon rarefaction plot. Shannon(B), Simpson (C), ACE (D), and Chao1 (E) indices. (F) 3D plot of PCoA. (G) NMDS analysis. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs NSD; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 vs NSD Firmicutes/Bacteroidetes ratio.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4671182/v1/4f00e07a0ba889a69df959cb.png"},{"id":61072261,"identity":"ce02f826-6ba3-46f1-ac53-97a89dd53c01","added_by":"auto","created_at":"2024-07-25 08:43:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76865,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of gut microbial community. (A) Taxonomic proportion of gut microbiota at phylum. (B) the ratio of Firmicutes to Bacteroidetes. (C) Taxonomic proportion of gut microbiota at genus. (D) Histogram of LDA value distribution. Relative abundance of differential microbiotas at phylum (E) and genus (F). *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs NSD.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4671182/v1/bb27684d152d38d0090e639b.png"},{"id":61072927,"identity":"8ac90201-d24c-4997-8e6e-e23d2299aabb","added_by":"auto","created_at":"2024-07-25 08:51:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":28665,"visible":true,"origin":"","legend":"\u003cp\u003eSTAMP plot shows the differential pathways. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05 vs NSD; ns: no significance.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4671182/v1/efed038cae85685cd6ca1502.png"},{"id":61072270,"identity":"00e8192c-c23f-4eba-8569-c474d854a4aa","added_by":"auto","created_at":"2024-07-25 08:44:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145719,"visible":true,"origin":"","legend":"\u003cp\u003eFecal metabolomics analysis. (A) OPLS-DA analysis. (B) Top 10 class of total metabolites. (C) Volcano plot of differential metabolite analysis. (D) Top 10 class of differential metabolites. (E) Relative abundance of differential metabolites. (F) Pathway analysis showed that differential metabolites were enriched pathways, including linoleic acid metabolism, steroid hormone biosynthesis, and steroid biosynthesis. The rest are the involved pathways without significant differences.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4671182/v1/e555e05592b7cff4237105ed.png"},{"id":61072259,"identity":"a44209b5-06af-40c6-849e-2c860674e571","added_by":"auto","created_at":"2024-07-25 08:43:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135284,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis among gut microbiota, metabolite, and body weight, WAT, FGF21, and APN. Red and blue refer to significant positive and negative correlation, respectively. All \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05; \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 no showed.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4671182/v1/1674567bbe25a2338703b372.png"},{"id":83067897,"identity":"9d7a6dd1-3e6f-49b0-a12a-b2579d88589a","added_by":"auto","created_at":"2025-05-19 16:07:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1168762,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4671182/v1/28e79864-640c-4a62-a088-ce5c9b7dfce0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changes in microbial and metabolic profiles of mice fed with long-term high salt diet","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSodium is an essential nutrient that plays a vital role in maintaining normal cellular homeostasis and regulating fluid and electrolyte balance. It is equally crucial for maintaining extracellular fluid volume, muscle and nerve cell function, and the transmembrane transport of nutrients and substrates, attributed to its osmotic action[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While the daily minimum intake level required for physiological needs remains not well established, sodium deficiency is extremely unlikely in healthy individuals due to the presence of dietary salt in commonly consumed foods[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Conversely, an excess of salt intake, more than is physiologically necessary, is common in most populations worldwide.\u003c/p\u003e \u003cp\u003eSalt intake is an important worldwide public health issue[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The World Health Organization (WHO) recommends that the daily maximum intake of salt should not exceed 5 g in adults and should be adjusted downward for children. Data from a global 2010 study showed that the average daily intake of salt per person in the surveyed regions was 10.06 g[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Salt intake in Asia is among the highest in the world (14.01 g/d per capita), more than twice the recommended limit. Excessive intake of salt can lead to acute toxicity, with the possibility of a fatal outcome[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Chronic excessive salt intake can also lead to pathological changes in the body, although they may not be evident in the early stages. Evidence suggests that salt consumption could predict the future development of metabolic syndrome[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which, in turn, can serve as the foundation for various metabolism disorders including insulin resistance, abnormal lipid metabolism, and elevated blood pressure[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], contributing to the onset of diseases such as diabetes, non-alcoholic fatty liver disease, hypertension.\u003c/p\u003e \u003cp\u003eGut microbiota is considered a bridge between diet and host health[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The intestinal flora and their metabolites can directly impact physiological metabolism[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although salt is primarily absorbed in the upper intestine, a part of it reaches the colon[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and interacts with the gut microbiota. Previous studies have reported the relationships between high salt consumption, gut microbiota, and body metabolism[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, a relatively short feeding duration may not fully reveal the long-term effects of a high salt diet (HSD).\u003c/p\u003e \u003cp\u003eIn the present study, we established a model fed with a long-term HSD and conducted exploratory investigations into the effects of this HSD on growth performance, serum adipocytokines, and fecal microbiota composition and metabolites.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and treatment\u003c/h2\u003e \u003cp\u003e All animal experiments were conducted in accordance with the Animal Research Ethics Committee of Wenzhou Medical University (Ethical approval number: SYXK 2020-0014). Seven-week-old male C57BL/6J wild-type mice were purchased from Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China) and housed in a room with free access to water and diet, at a controlled temperature of 23\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, and subjected to a 12-hour/day light cycle. After 7 days of acclimatization, all the 8-week-old male mice were randomly divided into two groups: a high salt diet (HSD) group fed an 8% NaCl diet and a normal salt diet (NSD) group fed a 0.6% NaCl diet, for 22 weeks, with n\u0026thinsp;=\u0026thinsp;4 in each group. The HSD containing 8% NaCl was purchased from Dytes Co., Ltd. (Suzhou, China) and the NSD containing 0.6% NaCl was purchased from Cyagen Co., Ltd. (Guangzhou, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and index determination\u003c/h2\u003e \u003cp\u003eDuring the feeding period, the body weight of the mice was recorded every two weeks, and blood pressure was measured using tail-cuff systems (BP 2000, Visitech Systems, USA) at the start and the end of the experiment. Glucose tolerance test (GTT) was performed at the end of the experiment and glucose levels were detected and recorded from blood sample collected at 10, 20, 30, 45, 60, 75 and 90 minutes after glucose injection (0.01 mL/g administration). The blood glucose (BG) meter and BG test strips were purchased from Braun Co., Ltd. (Melsungen, Germany). The area under the curve (AUC) of GTT was calculated according to the formula: AUC\u0026thinsp;=\u0026thinsp;0.5 \u0026times; (BG0min\u0026thinsp;+\u0026thinsp;BG30min)/2\u0026thinsp;+\u0026thinsp;0.5 \u0026times; (BG30min\u0026thinsp;+\u0026thinsp;BG60min)/2\u0026thinsp;+\u0026thinsp;1 \u0026times; (BG60min\u0026thinsp;+\u0026thinsp;BG90min)/2. On the day of sacrifice, a blood sample was collected from the eyes of the mice under anesthesia (with sodium pentobarbital at 0.1mL/10g body weight). The heart, liver, kidney, and various adipose tissues (white, peri-renal, and brown) of the mice were weighed. The intestinal feces were collected under sterile conditions, and all tissues were stored at -80 ℃ after cataloging. The ELISA kits for the determination of serum fibroblast growth factor 21 (FGF21) and adiponectin (APN) were purchased from Antibody and Immunoassay Service Co., Ltd. (Hong Kong, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFecal metabonomics\u003c/h2\u003e \u003cp\u003eThe lyophilized feces samples were sent to Sangon Biotech (Shanghai) Co., Ltd (Shanghai, China) for metabonomics analysis. In brief, untargeted ultrahigh-performance liquid chromatography-mass spectrometry was performed using an AB Sciex Triple TOF 5600 mass spectrometer(Waters, UK) combined with ACQUITY UPLC H-Class system (Waters, UK). Extracted samples were injected into a chromatographic column (100mm \u003cb\u003e\u0026times;\u003c/b\u003e 2.1mm, 1.7um, ACQUITY UPLC BEH C-18, Waters, UK) at a flow rate of 0.4 mL/min, an injection volume of 5uL, and a column temperature of 45 ℃. The Mobile phase consisted of phase A (water with 0.1% formic acid) and phase B (methanol with 0.1% formic acid). The elution program was as follows: 0-2min, 5% mobile phase B; 2\u0026ndash;4 min, 20% mobile phase B; 4\u0026ndash;9 min, 25% mobile phase B; 9\u0026ndash;17 min, 60% mobile phase B; 17\u0026ndash;19 min,100% mobile phase B; 19-20.1min, 5%mobile phase B for equilibration. Mass spectrometry data were acquired by scanning in both positive and negative ion modes using an MS system equipped with an electrospray ionization source.\u003c/p\u003e \u003cp\u003eThe mass spectrometry raw data were acquired using the UNIFI Scientific Information System (Ver. 1.8.1.) and processed with Progenesis QI software(Ver.2.3, Nonlinear Dynamics, Newcastle, UK). The analytical parameters set were a precursor tolerance of 5 ppm, product tolerance of 10 ppm, and a product ion threshold of 5%. The raw abundance of metabolites was normalized as relative abundance for subsequent analysis. Metabolites annotation was performed using the Lipid metabolites and pathways strategy (Ver.2.3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lipidmaps.org\u003c/span\u003e\u003cspan address=\"http://www.lipidmaps.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Human Metabolome Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hmdb.ca/\u003c/span\u003e\u003cspan address=\"https://hmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Metabolite Link database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metlin.scripps.edu\u003c/span\u003e\u003cspan address=\"https://metlin.scripps.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo identify potential differential metabolites, mutilvariable analysis was applied for metabolite profiling. The orthogonal partial least square discriminant analysis (OPLS-DA) was implemented using \u0026ldquo;ropls\u0026rdquo; R package[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] (Ver. 1.32.0). Variable importance in the projection (VIP) was used to evaluate the importance of each metabolite in OPLS-DA model. The \u003cem\u003ep\u003c/em\u003e-values from the Student\u0026rsquo;s t-test and VIP values were used to screen differential metabolites. Metabolite with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2(Fold Change)| \u0026gt; 1, and the value of VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 was considered a differential metabolite and was extracted for further analysis. KEGG pathway enrichment analysis of differential metabolites was conducted using MetaboAnalyst software (Ver.5.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA gene sequencing and data analysis\u003c/h2\u003e \u003cp\u003eThe lyophilized feces samples were sent to Sangon Biotech (Shanghai) Co., Ltd (Shanghai, China) for 16S rRNA gene sequencing. Briefly, total genomic DNA was extracted, and the V3 and V4 regions of bacterial 16S rRNA gene were amplified using dual-indexed primers: 341F: (5\u0026rsquo;-CCTACGGGNGGCWGCAG-3\u0026rsquo;) and 805R: (5\u0026rsquo;-GACTACHVGGGTATCTAATCC-3\u0026rsquo;). The quality-qualified library was used for high-throughput sequencing on the Illumina Hiseq 2500 system (Illumina, San Diego, USA). Raw data generated from the Illumina platform were merged into sequences using PEAR[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] software ( Ver.0.9.6) based on the overlapping relationship of paired-end reads. These sequences were then divided into individual samples based on their barcode sequences. Subsequently, the data from each sample were processed to remove low-quality data and retain high-quality data using QIIME[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] software (Ver.1.8.0). Non-specific amplification and Chimeras were also eliminated using Usearch[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] software (Ver.5.2.236), and sequences with 97% similarity were clustered into one operational taxonomic unit (OTU). The representative sequences of each OTU were classified into different taxonomic levels (phylum, class, order, family, and genus) using the Ribosomal Database Project (RDP) classifier[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] (Ver.2.12) with a threshold of 0.8. Additionally, the relative abundance of species was quantified at each taxonomic level.\u003c/p\u003e \u003cp\u003eAlpha diversity was evaluated by calculating Shannon, Simpson, Ace, and Chao1 indices with \u0026ldquo;vegan\u0026rdquo; R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/vegandevs/vegan\u003c/span\u003e\u003cspan address=\"https://github.com/vegandevs/vegan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Ver.2.6-4). Using Bray-Curtis distance, principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) analysis were performed to assess beta diversity. Species differences between groups were identified using Linear discriminate analysis effect size (LEfSe) based on \u0026ldquo;microeco\u0026rdquo; R package[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] (Ver.1.1.0). The KEGG functional profiles of the microbial community were predicted by using PICRUSt2[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA double-blind experimental analysis mode was used, and all statistical analyses were performed using R software (Ver.4.3.1). The normality of data distribution was assessed with the Shapiro-Wilk test. Groups comparisons were performed using either Student\u0026rsquo;s t-test or the Wilcoxon test. The results of the statistical analysis were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). A level of \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLong-term HSD altered fat accumulation, glucose homeostasis, and levels of FGF21 and APN\u003c/h2\u003e \u003cp\u003eDuring the feeding period, the body weight of mice fed with NSD increased gradually, while mice fed with HSD experienced a lower weight gain that did not differ significantly from their initial weight until the end of the treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Moreover, the body weight of HSD-fed mice was lower than that of NSD-fed mice at each stage. Organ and tissue weights also changed after long-term HSD treatment. The heart and kidney weight-to-body weight ratio was elevated in long-term HSD group, while the weights of white, peri-renal, and brown adipose tissue relative to body weight decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In the terms of blood pressure, both systolic and diastolic blood pressure underwent a significant elevation after long-term HSD treatment compared with the initial blood pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). At the end of the treatment, systolic blood pressure was higher in long-term HSD group than that in NSD group, but diastolic blood pressure did not significantly differ between the groups. Additionally, several metabolic indicators underwent significantly changes. The serum level of adipocytokines FGF21 increased in response to long-term HSD treatment, while the level of APN decreased dramatically (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F). The glucose level and AUC value of GTT were higher in long-term HSD mice than in NSD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, H \u003cb\u003eand I\u003c/b\u003e). To sum up, long-term HSD alters growth performance and metabolism homeostasis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOverview of gut microbial communities in response to long-term HSD\u003c/h2\u003e \u003cp\u003eThe impact of intestinal microbiota on obesity and metabolic disease is widely doucumented[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, we explored the changes in gut microbial communities in response to long-term HSD. In the present study, we acquired 749783 high-quality reads, from which 2357 representative OTUs were identified at a similarity level of 97%. The Shannon curve was plateaued with an increase in the sequences count, indicating adequate sequencing depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Alpha diversity was assessed using several indices. The Shannon and Simpson indices in HSD group were greater compared to those in NSD group, whereas the ACE and Chao1 indices showed the opposite trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C, D \u003cb\u003eand E\u003c/b\u003e), indicating that gut microbial community richness and diversity were altered in response to long-term HSD. Considering species composition and abundance, we performed PCoA and NMDS analyses to assess beta diversity at the OTU level. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF and G, long-term HSD and NSD groups were divided into two compartments in the 3D PCoA plot, and a similar pattern was observed in the NMDS score plot. Adonis (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.644, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) and ANOSIM (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.865, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) analyses further confirmed the significant differences between the groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecies annotation information for representative OTUs was identified using the RDP classifier. At the phylum level, \u003cem\u003eFirmicutes\u003c/em\u003e emerged as the most predominant phylum, constituting 65.99% and 94.10% of OTUs in long-term HSD and NSD groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The ratio of \u003cem\u003eFirmicutes\u003c/em\u003e to \u003cem\u003eBacteroidetes\u003c/em\u003e was decreased in long-term HSD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Moreover, \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eCandidatus Saccharibacteria\u003c/em\u003e, \u003cem\u003eTenericutes\u003c/em\u003e, and \u003cem\u003eDeferribacteres\u003c/em\u003e were more enriched in long-term HSD group compared to NSD group. At the genus level, among the total 257 annotated genera, \u003cem\u003eAllobaculum\u003c/em\u003e was the most abundant, representing 39.34% and 82.19% of OTUs in long-term HSD and NSD groups, respectively(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). LEfSe analysis, with a threshold of an LDA score greater than 2, revealed six bacterial taxa at the phylum level showing differential abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The long-term HSD group exhibited a lower abundance of \u003cem\u003eFirmicutes\u003c/em\u003e compared to NSD group. Conversely, the abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eCandidatus\u003c/em\u003e-\u003cem\u003eSaccharibacteria\u003c/em\u003e, \u003cem\u003eTenericutes\u003c/em\u003e, and \u003cem\u003eDeferribacteres\u003c/em\u003e was higher in long-term HSD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). At the genus level, fifty-one bacteria showed differences in abundance, with the majority showing higher abundance in long-term HSD group, including \u003cem\u003eBarnesiella\u003c/em\u003e, \u003cem\u003eChristensenella\u003c/em\u003e, and \u003cem\u003eLactococcus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). In contrast, the abundance of \u003cem\u003eAllobaculum\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e was higher in NSD group. Functional profiles extrapolation using PICRUSt2 analysis indicated significant difference in metabolism-related pathways between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Xenobiotics biodegradation and metabolism, lipid metabolism, metabolism of cofactors and vitamins, and glycan biosynthesis and metabolism pathways were enriched in long-term HSD group, whereas nucleotide metabolism, carbohydrate metabolism, and metabolism of terpenoids and polyketides pathways were more prevalent in NSD group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFecal metabolic profiling analysis of high salt intake\u003c/h2\u003e \u003cp\u003eIn view of the potential disturbance in metabolic balance caused by long-term HSD, further analysis was conducted in fecal metabolism profiling. The overall PCA plot of all samples, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, demonstrated that all samples were divided into two clusters, and long-term HSD and NSD groups were clustered in distinct areas of PCA plot, indicating limited variation within per group and high differences between the groups. The R2 and Q2 values serve as the parameters for evaluating the interpretative and predictive abilities of OPLS-DA model, respectively. In the present study, R2X (cum)\u0026thinsp;=\u0026thinsp;0.442, R2Y (cum)\u0026thinsp;=\u0026thinsp;0.999, and Q2\u0026thinsp;=\u0026thinsp;0.874, suggesting that the OPLS-DA model exhibited excellent robustness and predictive ability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on untargeted ultrahigh-performance liquid chromatography\u0026ndash;mass spectrometry, a total of 4686 metabolites were annotated. Fatty acyls, constituting 15.56% of the metabolites, were the most prominent, followed by glycerophospholipids at 13.06% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). According to the criteria (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, |log2(Fold Change)| \u0026gt; 1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 644 differential metabolites were identified, with 326 metabolites up-regulated and 318 down-regulated in long-term HSD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Lipids, including fatty acyls, glycerophospholipids, prenol lipids, steroids lipids, and steroids and steroid derivatives, were the predominant differential metabolites, comprising over half of the differential metabolites quantities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In comparison to NSD group, myriocin, norcholic acid, cerulenin, and 7-ketocholesterol levels increased, while prostaglandin B2 decreased in long-term HSD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Further pathway enrichment analysis revealed that three lipid metabolism-related pathways (linoleic acid metabolism, steroid hormone biosynthesis, and steroid biosynthesis) were significantly enriched in long-term HSD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), suggesting that long-term HSD disrupts lipid metabolism balance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of intestinal microbes, fecal metabolites, and altered indicator\u003c/h2\u003e \u003cp\u003eThe relationships between intestinal microbes, fecal metabolites, and altered indicators were assessed through correlation analysis to investigate key gut microbes and fecal metabolites responsible for changes induced by long-term high salt intake (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Specifically, \u003cem\u003eChristensenella\u003c/em\u003e displayed a negative correlation with WAT (\u003cem\u003er\u003c/em\u003e = -0.805) and APN (\u003cem\u003er\u003c/em\u003e = -0.812), but a positive correlation with FGF21 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.831). Conversely, \u003cem\u003eFirmicutes\u003c/em\u003e showed a positive correlation with body weight (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.744) and a negative correlation with FGF21 (\u003cem\u003er\u003c/em\u003e = -0.942). In contrast, \u003cem\u003eBarnesiella\u003c/em\u003e exhibited a negative correlation with body weight (\u003cem\u003er\u003c/em\u003e = -0.750) and a positive correlation with FGF21 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.773). \u003cem\u003eLactococcus\u003c/em\u003e was found to be negatively correlated with both WAT (\u003cem\u003er\u003c/em\u003e = -0.806) and APN (\u003cem\u003er\u003c/em\u003e = -0.833). Furthermore, several metabolites showed notable correlations: Myriocin, 7-ketocholesterol, norcholic acid, and cerulenin were negatively correlated with body weight (\u003cem\u003er\u003c/em\u003e = -0.723 for myriocin, \u003cem\u003er\u003c/em\u003e = -0.877 for 7-ketocholesterol, \u003cem\u003er\u003c/em\u003e = -0.849 for cerulenin), and WAT(\u003cem\u003er\u003c/em\u003e = -0.660 for myriocin), as well as APN (\u003cem\u003er\u003c/em\u003e = -0.794 for myriocin, r = -0.732 for 7-ketocholesterol, \u003cem\u003er\u003c/em\u003e = -0.726 for norcholic acid), but positively correlated with FGF21 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.944 for myriocin, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.762 for 7-ketocholesterol, r\u0026thinsp;=\u0026thinsp;0.983 for norcholic acid, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.798 for cerulenin). Additionally, prostaglandin B2 was positively correlated with WAT (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.708), and negatively correlated with FGF21 (\u003cem\u003er\u003c/em\u003e = -0.899).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDietary salt is an essential micronutrient for maintaining life activity. High salt consumption, however, has been linked to adverse outcomes, including exacerbation of metabolism function and contribution to metabolic syndrome[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The long-term effects of high salt intake on organisms are not well-documented. In the present study, we developed a mouse model to explore the changes in organisms responsible for long-term HSD. We observed that long-term HSD impacted growth, metabolism homeostasis, and altered gut microbiota and metabolites profiling. Interestingly, mice fed with long-term HSD experienced lower weight gain and fat accumulation, which is opposite to previous results that high salt leads to obesity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. One reasonable explanation for this discrepancy is that high salt intake causes hyperphagia in mice, yet body weight remains stable under ad libitum dietary conditions due to a hypercatabolic state[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our observation of stable body weight and reduced WAT in long-term HSD group after consuming high salt lend support to this hypothesis.\u003c/p\u003e \u003cp\u003eFurther investigation into the mechanisms revealed increased gut microbial diversity with long-term HSD, consistent with previous findings of decreased microbial diversity in obesity[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Notably, the abundance of \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e seems to be related to fat metabolism. The ratio of \u003cem\u003eFirmicutes\u003c/em\u003e/\u003cem\u003eBacteroidetes\u003c/em\u003e is linked to obesity and metabolic disorders[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and is considered as a vital index to measuring obesity[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Recent studies have indicated that individuals with obesity exhibited a higher relative abundance of Firmicutes and a lower relative abundance of Bacteroidetes[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, a reduction of \u003cem\u003eFirmicutes\u003c/em\u003e/\u003cem\u003eBacteroidetes\u003c/em\u003e ratio showed a promising prospect for anti-obesity effect[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In our study, consumption of high salt altered the abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e/\u003cem\u003eBacteroidetes\u003c/em\u003e ratio. Furthermore, the changes in abundance of \u003cem\u003eFirmicutes\u003c/em\u003e were positively correlated with changes in body weight. These findings suggested that reduced abundance of \u003cem\u003eFirmicutes\u003c/em\u003e may, at least partially, explain the changes in body weight. At the genus level, long-term high salt consumption decreased the abundance of \u003cem\u003eAllobaculum\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e, which was broadly in line with previous studies[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, long-term high salt intake increased the abundance of \u003cem\u003eBarnesiella\u003c/em\u003e, \u003cem\u003eChristensenella\u003c/em\u003e, and \u003cem\u003eLactococcus\u003c/em\u003e, which were rarely reported to be associated with HSD. \u003cem\u003eBarnesiella\u003c/em\u003e significantly correlated with hepatic lipid accumulation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. \u003cem\u003eChristensenella\u003c/em\u003e had a high correlation with obesity and obesity-related metabolic disorders of the host[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Conversely, \u003cem\u003eLactococcus\u003c/em\u003e abundance had a negative correlation with obesity[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and an alleviating effect on diet-induced obesity, fat accumulation, and adipose tissue metabolism[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is widely demonstrated that dietary structure could affect the gut microbiome composition, which is accompanied by the alternation of intestinal metabolites, thereby contributing to several metabolic disorders. Similar to the results of intestinal microbes, long-term HSD also provoked significant changes in fecal metabolites, particularly lipid metabolites. Specifically, the levels of myriocin, cerulenin, norcholic acid, and 7-ketocholesterol were significantly increased, while the level of prostaglandins B2 was decreased after fed with long-term high salt. Myriocin has been shown to possess beneficial effects on attenuating body weight and lipid accumulation, as well as regulating fatty acid metabolism[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, myriocin inhibited the synthesis of ceramide[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] which was reported to stimulate lipid uptake and storage[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, cerulenin plays an important role in promoting weight loss and steatosis by blocking fatty acid synthase[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Norcholic acid is involved in bile acid metabolism and may serve as a key regulator for improving hyperlipidemia[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. 7-ketocholesterol has been seen as a novel adipokine modulating the adipogenic potential of undifferentiated adipose precursor cells[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and showed the abilities accelerating hepatic lipid accumulation and downregulating fatty acid oxidation[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Despite the link between prostaglandins E and obesity and lipolysis being widely documented[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], there was little focus on prostaglandins B2. Further pathway enrichment analysis results that three lipid metabolism-related pathways were significantly enriched in HSD group supported a viewpoint that lipid metabolism was disturbed by long-term high salt, which is partially evidenced by previous studies[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also identified several potential interaction relationships between gut bacterias or metabolites and FGF21 and APN. The results indicated that \u003cem\u003eChristensenella\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eBarnesiella\u003c/em\u003e, \u003cem\u003eLactococcus\u003c/em\u003e, myriocin, cerulenin, norcholic acid, 7-ketocholesterol, and prostaglandins B2 were positively or negatively correlated with FGF21 or APN. Previous research has reported that \u003cem\u003eLactococcus\u003c/em\u003e could prevents the proliferation of adipogenic transcription factors APN and effectively alleviates diet-induced obesity and lipid deposition[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, myriocin is involved in lipid metabolism, which could reduce fat deposition by inhibiting the synthesis of ceramide[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Similarly, FGF21 could regulate the secretion of APN, and inhibit the level of ceramide, then form an FGF21-APN-ceramide axis to control energy metabolism and insulin action[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. consequently, these interactions may provide a novel insight into understanding the complicated regulatory network of gut bacteria, fecal metabolites, and FGF21 or APN.\u003c/p\u003e \u003cp\u003eNonetheless, our research is not without limitations. Firstly, this study is a preliminary exploration, and the biological replicates in each group may be limited. Secondly, how the altered microbial communities and metabolites performed a function for lipid metabolism remain fully ambiguous. Thus, further studies are needed to validate our results.\u003c/p\u003e"},{"header":"Consulsion","content":"\u003cp\u003eIn summary, our research revealed that long-term HSD stimulates FGF21 secretion in mice, alongside diminishing weight gain, white fat accumulation, and level of APN.\u003c/p\u003e \u003cp\u003eIntegrated 16S rRNA sequencing and fecal metabolomic analysis showed that long-term HSD impacts gut microbiota and metabolites profiles, including \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eChristensenella\u003c/em\u003e, \u003cem\u003eBarnesiella\u003c/em\u003e, and \u003cem\u003eLactococcus\u003c/em\u003e, as well as myriocin, cerulenin, norcholic acid, 7-ketocholesterol, and prostaglandins B2. These gut bacteria and metabolites were significantly correlated with decreased body weight and white fat accumulation, and altered levels of FGF21 and APN, and potentially play a crucial role in lipid metabolism of organism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.L., M.P., and X.G. performed the experiments, analyzed the data, and drafted the manuscript. H.D., M.P., and X.G. supported executing in vivo experiments and data analysis. X.L., X.H, and Z.L. developed the concept, designed the study, and revised the manuscript. All authors have read, discussed the results, and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Jing Gan, and Dr. Yuanrong Chen for their technical support related to the statistical analysis of data and revising our manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by China National Funds for Distinguished Young Scientists (81925004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are reasonably avaliable from the corresponding author. The raw data of 16S rRNA sequencing can be found in online repository with accession number: PRJNA1097326.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the version to be published.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStrazzullo P, Leclercq C (2014) Sodium. Adv Nutr 5 (2):188-190. doi:10.3945/an.113.005215\u003c/li\u003e\n\u003cli\u003eBrown IJ, Tzoulaki I, Candeias V, Elliott P (2009) Salt intakes around the world: implications for public health. 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Cell Metab 17 (5):790-797. doi:10.1016/j.cmet.2013.03.019\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"long-term high salt diet, gut microbiota, fecal metabolomics, lipid metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4671182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4671182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: High salt diet (HSD) has been considered as a risk factor for the development of metabolic disorders. However, less is known about long-term implications of HSD. Therefore, the aim of this study was to conduct a preliminary investigation into the effects of mice feeding with long-term HSD on gut microbial and metabolic profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: In this study, C57BL/6J mice were fed with HSD for 22 weeks, after which fat and gut feces were collected. The composition of fecal microbiota was determined using 16S rRNA gene sequencing. Fecal metabolic profiling of mice was identified through untargeted ultrahigh-performance liquid chromatography-mass spectrometry. In addition, the serum levels of adipocytokines, including fibroblast growth factor 21 (FGF21) and adiponectin (APN), were measured.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Long-term HSD disrupted the growth performance of mice. Compared to those fed a normal salt diet, mice on a long-term HSD showed slower weight gain, as well as lower fat accumulation and serum levels of APN, while experiencing elevated blood pressure and levels of serum FGF21 and glucose. The 16S rRNA sequencing revealed changes in community richness and diversity, with long-term HSD affecting the abundance of certain gut microbiota, including \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eChristensenella\u003c/em\u003e, \u003cem\u003eBarnesiella\u003c/em\u003e, and \u003cem\u003eLactococcus\u003c/em\u003e. Fecal metabolomic analysis also uncovered alterations in metabolites, such as myriocin, cerulenin, norcholic acid, 7-ketocholesterol, and prostaglandins B2. Further analysis indicated that these gut and microbiota and metabolites are predominantly involved in the lipid metabolism of the organism. Importantly, variations in these gut metabolites and microbiota were significantly correlated with body weight, fat accumulation, and the levels of FGF21 and APN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Long-term HSD affects physiological traits, alters gut metabolites profiles, and impacts the composition and function of gut microbiota, thus causes a certain impact on lipid metabolism.\u003c/p\u003e","manuscriptTitle":"Changes in microbial and metabolic profiles of mice fed with long-term high salt diet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 08:43:49","doi":"10.21203/rs.3.rs-4671182/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-04T10:51:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-02T15:05:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-02T15:03:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2024-07-02T03:46:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2fab6c28-7621-4c4f-8f61-2c0e75cbd38d","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:03:01+00:00","versionOfRecord":{"articleIdentity":"rs-4671182","link":"https://doi.org/10.1186/s12876-025-03929-5","journal":{"identity":"bmc-gastroenterology","isVorOnly":false,"title":"BMC Gastroenterology"},"publishedOn":"2025-05-15 15:58:06","publishedOnDateReadable":"May 15th, 2025"},"versionCreatedAt":"2024-07-25 08:43:49","video":"","vorDoi":"10.1186/s12876-025-03929-5","vorDoiUrl":"https://doi.org/10.1186/s12876-025-03929-5","workflowStages":[]},"version":"v1","identity":"rs-4671182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4671182","identity":"rs-4671182","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}