Marker combination for high-salt diet related renal injury from intestinal flora and application of marker combination

Abstract

The invention discloses a marker combination for high-salt diet related renal injury from intestinal flora and application. The marker combination is composed of dehydroepiandrosterone and Cyp1a1. The research finds that intestinal flora participates in a related marker combination (namely steroid hormone biosynthesis related metabolite dehydroepiandrosterone and Cyp1a1) of the high-salt diet related renal injury, and ROC curve analysis shows that the biomarkers have good discrimination efficiency in the high-salt diet related renal injury; the effect and application of the gene in the regulation and control of the kidney injury related to the high-salt diet by the intestinal renal axis are verified, and the gene is crucial for early diagnosis, personalized treatment and prognosis evaluation of patients with the kidney injury related to the high-salt diet.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to a combination of markers for kidney injury associated with a high-salt diet derived from gut microbiota and their application. Background Technology

[0002] Chronic kidney disease (CKD) is a chronic disorder caused by a variety of factors, resulting in structural and functional abnormalities of the kidneys. Decreased kidney function is its most common clinical manifestation. Approximately 10% of adults worldwide suffer from CKD, leading to 12 million deaths and 28 million disability-adjusted life years lost annually. It is projected that by 2040, CKD will become the fifth leading cause of death globally, with the highest rate of increase among all major causes of death.

[0003] The etiology of CKD is complex. Besides well-known causes such as diabetes, glomerulonephritis, and polycystic kidney disease, the cause remains unknown in some cases. The association between nutritional factors and chronic noncommunicable diseases (NCDs) has attracted significant attention. A global survey covering all-cause mortality in 191 countries (including an analysis of 46 high-income countries) showed that a high-salt diet (HSD) is a major risk factor for shortened life expectancy. Researchers such as Franz H. Messerli further confirmed that dietary salt intake is a risk factor for reduced life expectancy or premature death.

[0004] Long-term excessive salt intake damages organ function and induces diseases through mechanisms such as regulating blood pressure, activating the immune system, and exacerbating inflammatory responses, with the cardiovascular system and kidneys being most significantly affected. Sodium intake is closely related to hypertension and an increased risk of cardiovascular disease. A complex relationship exists between gut microbiota, kidney function, and hemolytic spondylitis (HSD). Multiple studies have confirmed the causal relationship between HSD and hypertension. The *Jinkui Yaolue* states, "Salty flavor nourishes the kidneys; excessive salt intake damages the kidneys."

[0005] High-salt diets (HSD) can alter the diversity, composition, and relative abundance of the gut microbiota, making the gut-kidney axis increasingly prominent in kidney diseases. Therefore, this study, based on transcriptomics, metabolomics analysis, and bioinformatics techniques, investigated and screened combinations of relevant markers from gut microbiota-derived HSD-related kidney injury, providing new insights for the prevention and treatment of HSD-related kidney diseases. Summary of the Invention

[0006] Therefore, embodiments of the present invention provide a combination of markers for kidney injury associated with a high-salt diet derived from gut microbiota and their application.

[0007] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0008] According to a first aspect of the present invention, the present invention provides a combination of markers for high-salt diet-related kidney injury derived from gut microbiota, said marker combination being dehydroepiandrosterone and Cyp1a1.

[0009] According to a second aspect of the present invention, the present invention provides the use of the combination of markers of high-salt diet-related kidney injury derived from gut microbiota as described above in the preparation of diagnostic and preventive products for high-salt diet-related kidney injury.

[0010] According to a third aspect of the present invention, the present invention provides a product for diagnosing and preventing kidney injury associated with a high-salt diet, the product comprising a reagent for detecting a marker, said marker being a combination of dehydroepiandrosterone and Cyp1a1.

[0011] Furthermore, the product also includes reagents for detecting the expression levels of dehydroepiandrosterone (DHEA) and Cyp1a1.

[0012] Furthermore, the product is a chip or a reagent kit.

[0013] According to a fourth aspect of the present invention, the present invention provides a method for screening combinations of markers as described above, the method comprising the following steps:

[0014] Experimental animal grouping and intervention: SPF and GF mice were divided into a normal diet group and a high-salt diet group. Mice in the high-salt diet group were fed a diet containing 8% NaCl for 4 weeks.

[0015] Multi-omics analysis: Gene expression profiles of kidney and intestinal tissues were analyzed by RNA-seq sequencing, and metabolites of intestinal contents were detected by UPLC-MS / MS.

[0016] Differential biomarker screening: Combining transcriptomic and metabolomic data, metabolites and genes that are regulated by a high-salt diet and gut microbiota in conventional mice and are enriched in the steroid hormone biosynthesis pathway were screened.

[0017] Furthermore, the metabolite is dehydroepiandrosterone (DHEA), and the gene is Cyp1a1.

[0018] The embodiments of the present invention have the following advantages:

[0019] This study identified a combination of markers related to high-salt diet-related kidney injury (i.e., dehydroepiandrosterone and Cyp1a1, metabolites related to steroid hormone biosynthesis), validating their role and application in regulating high-salt diet-related kidney injury in the gut-kidney axis. This is crucial for the early diagnosis, personalized treatment, and prognostic assessment of patients with high-salt diet-related kidney injury. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0021] Figure 1 A high-salt diet promotes kidney damage in mice through a gut microbiota-dependent mechanism. Specifically:

[0022] (A) Experimental design for dietary intervention. (B) ELISA detection of changes in renal function indicators (S-Cr and BUN) in mice after 4 weeks of feeding in each group (n = 5-6 mice / group, mean ± SEM; *p<0.05, **p<0.01, ***p<0.001; no statistical difference in ns). (C) ELISA detection of changes in the levels of inflammatory factors (TNFα, IL-1β, IL-6, and IL-10) (n = 5-6 mice / group). (D) ELISA detection of changes in sIgA and NHE3 expression (n = 5-6 mice / group). (E) HE and Masson staining of renal tissue (scale bar 20 μm) and quantitative analysis of collagen fibers (n = 3 mice / group). ND: Normal diet group; HSD: High-salt diet group; GF: Germ-free mouse group.

[0023] Figure 2 This study investigated how a high-salt diet modulates renal gene expression profiles through gut microbiota. Specifically:

[0024] (A) PCA analysis showed that principal component 1 (PC1) and PC2 explained 66.89% and 6.77% of the variability, respectively, and the point distance reflected the differences in community structure. (B) Heatmap of differentially expressed genes in the kidney (ND vs HSD group). (C) Venn diagram showing changes in kidney genes caused by a high-salt diet. (D) Heatmap of the expression of the top 30 differentially expressed genes in kidney tissue. (E) KEGG pathway analysis: (1) Top 20 pathways enriched by upregulated genes; (2) Top 20 pathways enriched by downregulated genes; (3) Chord map of pathways with significant upregulated gene enrichment; (4) Chord map of pathways with significant downregulated gene enrichment (including steroid hormone biosynthesis pathway).

[0025] Figure 3 This study analyzed the changes in intestinal gene expression profiles and their functions induced by a high-salt diet. Among the findings:

[0026] (A) PCA analysis (PC1 and PC2 explained 40.15% and 15.83% of the variability, respectively). (B) Heatmap of differentially expressed genes in the gut. (C) Venn diagram showing changes in gut genes. (D) Heatmap of the top 30 differentially expressed genes in the gut. (E) KEGG analysis: (1) Top 20 enriched pathways of upregulated genes; (2) Top 20 enriched pathways of downregulated genes; (3) Pathways with significant enrichment of upregulated genes; (4) Pathways with significant enrichment of downregulated genes (including steroid hormone biosynthesis pathways).

[0027] Figure 4 This study aims to validate the changes in intestinal metabolic profiles and pathways induced by a high-salt diet. Specifically:

[0028] (A) PLS-DA analysis (components 1 and 2 explained 45% and 23.9% of the variability, respectively). (B) Heatmap of differentially expressed metabolites in the gut. (C) Venn diagram showing changes in gut metabolites. (D) Heatmap of the top 30 differentially expressed metabolites in the gut. (E) KEGG pathway analysis: (1) Top 20 enriched pathways of upregulated metabolites; (2) Top 20 enriched pathways of downregulated metabolites; (3) Validation of steroid hormone biosynthesis pathways.

[0029] Figure 5 This represents the interaction network between gut microbiota-related metabolites and host genes in mice on a high-salt diet. Among them:

[0030] (A) Correlation analysis of differentially expressed genes / metabolites in the gut and kidney (color represents the degree of correlation). (B) PPI network and Venn diagram of differentially expressed genes in the steroid hormone biosynthesis pathway; annotation scores of CYP1A1, HSD17B1, and SRD5A2 were 0.955 and 0.408, respectively. (C) ROC curve analysis. (D) Relative abundance of metabolites / genes related to the steroid hormone biosynthesis pathway (n = 5-6 animals / group). (E) RT-qPCR detection of Cyp1a1 expression in gut / kidney tissue (n = 3).

[0031] Figure 6 The gut microbiota-derived dehydroepiandrosterone (DHEA) and high salt content synergistically inhibit the expression of Cyp1a1 in the intestine and kidney. Among other things:

[0032] (A) Experimental design for dehydroepiandrosterone (DHEA) intervention. (B) Relative expression level of Cyp1a1 (n=3, **p<0.01). (C) Changes in inflammatory cytokine expression (n=8-10). (D) Changes in S-Cr and BUN levels (n=8-10). (E) HE and Masson staining (scale bar 20 μm) and collagen fiber quantification in kidney tissue (n=3, **p<0.01).

[0033] Figure 7 ROC curves for dehydroepiandrosterone and Cyp1a1. Detailed Implementation

[0034] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Example 1

[0036] 1. Method

[0037] (1) Laboratory animals and grouping

[0038] SPF-grade C57BL / 6J mice (male, 6-8 weeks old, provided by Beijing Huafukang Biotechnology Co., Ltd.) and GF-grade C57BL / 6J mice (male, 6-8 weeks old, provided by the Germ-Free Animal Research Platform of the First Affiliated Hospital of Sun Yat-sen University) were divided into a normal diet (ND) group and an HSD group, with 6 mice in each group. Animal experiments were approved by the Animal Experiment Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University and Jinan University (Ethics No.: IACUC-2021503-1). All mice were housed in a 12-hour light-dark cycle environment at 20-24℃ and 50% relative humidity.

[0039] (2) Experimental intervention

[0040] After 10 days of quarantine and environmental adaptation, the HSD group mice were fed a high-salt diet containing 8% NaCl for 4 weeks, while the ND group mice were fed a control diet containing 0.4% NaCl during the same period.

[0041] (3) Detection of renal function related indicators

[0042] Four weeks after HSD intervention, animals were anesthetized with isoflurane using a small animal ventilator (Shenzhen Ruiwode Life Science & Technology, model: f8821-010). Blood was collected via the orbital sinus into sterile blood collection tubes. After standing for 2-4 hours, the samples were centrifuged at 3000 rpm for 5 minutes, and the supernatant was aliquoted into sterile 1.5 mL centrifuge tubes. Species-specific ELISA kits (manufactured by China Meimian Company) were used to detect blood urea nitrogen (BUN) and serum creatinine (S-cr) levels, and the suitability of non-serum samples was verified. Kidney tissue was homogenized with 1% PMSF-NP40, centrifuged at 10000 rpm for 15 minutes at 4°C, and then ELISA kits were used to detect the levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β), interleukin-10 (IL-10), secretory immunoglobulin A (SIgA), and Na+ / H+ exchanger type 3 (NHE3) in the kidney tissue supernatant.

[0043] (4) Pathological observation of kidney tissue (HE and Masson staining)

[0044] Kidney tissue from one mouse was randomly selected from each group, fixed overnight at room temperature with 4% paraformaldehyde, and then routinely dehydrated, embedded, and prepared for HE and Masson staining. The tissue was observed under an optical microscope (Nikon Eclipse ci, imaging system: Nikon DS-FI2). Three fields of view were selected from each group of slides for photographing, and Image-Pro Plus 6.0 software was used to ensure consistent background lighting in all images.

[0045] (5) RNA-seq sequencing

[0046] RNA extraction: using Total RNA was extracted from kidney and colon tissues using reagents (Invitrogen, USA), and genomic DNA was removed using DNase I (Takara). RNA quality was assessed using a 2100 Bioanalyzer (Agilent), and quantification was performed using an ND-2000 (NanoDrop Technologies). Libraries were constructed using only high-quality RNA samples (OD260 / 280 = 1.8–2.2, OD260 / 230 ≥ 2.0, RIN ≥ 6.5, 28S:18S ≥ 1.0, > 1 μg).

[0047] Library preparation and Illumina sequencing: RNA-seq transcriptome libraries were constructed using the Illumina TruSeq™ RNA Sample Preparation Kit (San Diego, USA). After quantification with a TBS380, paired-end sequencing (2×150bp reads) was performed using an Illumina HiSeq xten / NovaSeq 6000 sequencer.

[0048] Differential expression analysis and functional enrichment: Transcript expression levels were calculated using the TPM method, and gene quantification was performed using RSEM (http: / / deweylab.biostat.wisc.edu / rsem / ). Metabolic pathways with significantly enriched differentially expressed genes (DEGs) were screened using KEGG pathway analysis (Goatools and KOBAS tools).

[0049] (6) UPLC-MS / MS analysis

[0050] Metabolite Extraction and Quality Control: Intestinal contents samples were accurately weighed, and metabolites were extracted using 400 μL of methanol:water (4:1, v / v) solution. After standing at -20℃, the samples were homogenized using a Wonbio-96c high-throughput tissue homogenizer (Shanghai Wanbo Biotechnology) at 50 Hz for 6 minutes, vortexed for 30 seconds, and sonicated at 5℃ and 40 kHz for 30 minutes. After precipitating proteins by standing at -20℃ for 30 minutes, the samples were centrifuged at 13000×g for 15 minutes at 4℃, and the supernatant was used for LC-MS / MS analysis. Quality control samples (QC) were prepared by mixing equal volumes of all samples and injected periodically to monitor analytical stability.

[0051] Chromatographic conditions: ExionLC TM The AD system (AB Sciex) is equipped with an ACQUITY UPLC. Mobile phase A is a 0.1% formic acid aqueous solution, and mobile phase B is acetonitrile:isopropanol (1:1, v / v) containing 0.1% formic acid. The gradient elution program is: 0-3 min 95% A → 80% A; 3-9 min 80% A → 5% A; 9-13 min maintenance at 5% A; 13.1-16 min equilibration at 95% A. Injection volume is 20 μL, flow rate is 0.4 mL / min, and column temperature is 40 °C.

[0052] Mass spectrometry conditions: TripleTOF TM The AB Sciex 5600+ mass spectrometer is equipped with an ESI ion source and supports switching between positive and negative ion modes. Parameter settings: ion source temperature 500℃; curtain gas (CUR) 30psi; ion source GS1 / GS2 50psi; ion spray voltage -4000V (negative mode) / 5000V (positive mode); declustering voltage 80V; collision energy 20-60V; mass scan range 50-1000m / z.

[0053] Data preprocessing and annotation

[0054] Raw data underwent peak detection and alignment using Progenesis QI 2.3 (Waters), retaining over 80% of the detected metabolic features. Metabolite identification was performed by comparing the exact quality and MS / MS fragmentation against the HMDB (http: / / www.hmdb.ca / ) and Metlin (https: / / metlin.scripps.edu / ) databases. Metabolic features with an RSD > 30% in QC samples were removed.

[0055] Multivariate statistical analysis

[0056] PCA and OPLS-DA analyses were performed using ropls software (Version 1.6.2) on the Majorbio cloud platform (https: / / cloud.majorbio.com). Metabolites with a variable importance projection (VIP) > 1 and p < 0.05 were considered significantly different metabolites. Pathway enrichment analysis was performed using the KEGG database (http: / / www.genome.jp / kegg / ), and Fisher's exact test was used to identify significantly enriched pathways.

[0057] (7) Real-time quantitative PCR (RT-qPCR)

[0058] use Total RNA was extracted from tissues using reagents (Invitrogen, USA), and DNase I (Invitrogen, USA) was used to remove genomic DNA. Each group had three replicates. ABI was used. Gene expression was detected using the 7500 system in conjunction with the SYBR Green qPCR SuperMixKit (Promega), with GAPDH as an internal control. Primer sequences are shown in Table 1.

[0059] Table 1

[0060]

[0061] (8) Animal verification experiments

[0062] Twelve SPF-grade C57BL / 6J mice (male, 6-8 weeks old, provided by Beijing Huafukang Biotechnology) were divided into a control group and a dehydroepiandrosterone (DHEA) group at the Animal Experiment Center of Jiangnan University (ethics number: JN.No20220930c0101224

[378] ). DHEA (Sigma-Aldrich) was dissolved in sterile corn oil at a concentration of 12 mg / mL and administered intraperitoneally for 2 weeks at a dose of 60 (low dose) or 120 (high dose) mg / kg / day. The control group received an equal volume of corn oil. Kidney and intestinal tissues were analyzed by HE, Masson staining, ELISA, and RT-PCR, using the same methods as described above.

[0063] Statistical analysis

[0064] Data are expressed as mean ± standard error (SEM). GraphPad Prism software was used for t-tests and graphing. A p-value < 0.05 was considered statistically significant.

[0065] 2. Results

[0066] (1) A high-salt diet promotes kidney damage in mice through a gut microbiota-dependent mechanism.

[0067] To investigate the differences in renal injury assessment indicators between conventional and germ-free (GF) mice under high-salt diet (HSD) conditions, we conducted the following experiment. The levels of renal function-related indicators (S-cr, BUN, TNF-α, IL-6, IL-1β, IL-10, NHE3, and SIgA) in conventional and GF mice after 4 weeks of HSD or normal diet (ND) feeding were measured. Figure 1 A). In conventional mice, HSD significantly increased renal S-cr and BUN levels; conversely, HSD-treated GF mice showed significantly decreased S-cr and BUN expression levels. Figure 1 B). Inflammatory factor detection showed that HSD significantly increased TNF-α, IL-6, and IL-1β levels in the kidney tissue of conventional mice, while significantly decreasing IL-10 (P<0.01), whereas no such changes were observed in GF mice. Figure 1 C). SIgA is an important antibody involved in intestinal mucosal immunity, while NHE3 mediates sodium absorption and proton secretion

[16] . HSD significantly increased NHE3 levels and decreased SIgA levels in the kidneys of conventional mice, but had no significant effect on GF mice ( Figure 1 D). HE staining of kidney tissue showed ( Figure 1 E), HSD induced inflammatory cell infiltration around the renal arterioles and renal interstitium in conventional mice, and eosinophil aggregation was observed in Bowman's capsule, while no significant changes were observed in GF mice (P>0.05). Masson staining results showed that HSD significantly increased collagen fibers in the renal tissue of conventional mice (P<0.001), but had no significant effect on GF mice. Quantitative analysis of collagen fibers ( Figure 1 E) Further confirmation that HSD significantly increased the collagen fiber content in the kidney tissue of conventional mice (P<0.001), while the change in collagen fiber in the kidney tissue of GF mice was not significant (P>0.05). These findings indicate that under HSD conditions, kidney tissue damage in conventional mice was significantly aggravated, while kidney damage in GF mice was relatively mild.

[0068] (2) Gut microbiota regulation of high salt-related kidney injury and altered renal gene expression profile

[0069] To investigate the differences in gene expression in the kidney tissues of conventionally fed mice and GF mice under HSD conditions, and to identify the pathway distribution of specifically upregulated genes related to kidney tissue damage in conventionally fed mice, we performed transcriptome sequencing on kidney tissues from HSD or ND-fed mice. PCA analysis showed that HSD altered the gene expression profiles in the kidneys of both conventionally fed and GF mice. Figure 2 A). Differentially expressed genes in the kidney showed significant upregulation or downregulation between the ND and HSD groups. Figure 2B). Among the conventional mouse HSD and ND groups, 312 differentially expressed genes were identified (criteria: |log2fc|>1 and P<0.05), including 121 upregulated and 191 downregulated genes; 988 differentially expressed genes were identified in the GF group, including 325 upregulated and 663 downregulated genes. Figure 2 C). A total of 34 common differentially expressed genes were screened from the two groups. Figure 2 C). Considering that the intersection of the two sets of differentially expressed genes may include genes influenced by both HSD and gut microbiota, we constructed a high-salt-microbiota-related differentially expressed gene set containing 284 genes, of which 278 are differentially expressed genes specific to conventional mice. Figure 2 C). Based on this, we propose that these 284 genes are specific genes closely related to kidney injury. KEGG analysis shows ( Figure 2 D), 107 upregulated differentially expressed genes were mainly enriched in the synthesis, secretion and action of parathyroid hormone, viral protein-cytokine receptor interaction and TGF-β signaling pathway (D). Figure 2 E1). String diagram analysis further revealed a significant enrichment of these genes in the parathyroid hormone synthesis pathway. Figure 2 E3). The 177 downregulated differentially expressed genes were mainly enriched in basal cell carcinoma, the Wnt signaling pathway, and ECM-receptor interaction pathways. Figure 2 E2). String plot analysis showed that these genes were significantly enriched in the first 20 pathways, including lipid metabolism-related pathways—steroid hormone biosynthesis (E2). Figure 2 E4). The results showed that there were differences in gene expression between conventional mice and GF mice under HSD conditions, and the genes specifically upregulated by kidney injury in conventional mice were distributed in the steroid hormone biosynthesis pathway.

[0070] (3) Analysis of changes and functions in gut microbiota regulation of gut gene expression profiles in high-salt-related kidney injury

[0071] To investigate gene expression differences in intestinal tissues, we performed transcriptome sequencing on intestinal tissues. PCA analysis showed that HSD altered the intestinal gene expression profiles of the two groups of mice. Figure 3 A). 650 differentially expressed genes were identified between the HSD and ND groups in conventional mice, including 410 upregulated and 240 downregulated genes; 3583 differentially expressed genes were identified in the GF group, including 607 upregulated and 2976 downregulated genes. Figure 3 C). By constructing a high-salt-microbial community-related differentially expressed gene set, 426 genes were obtained, of which 385 were differentially expressed genes specific to conventional mice. Figure 3 C). Heatmap analysis showed that the gut microbiota is involved in regulating the expression changes of specific kidney genes under HSD conditions. Figure 3 D). KEGG enrichment analysis showed that ( Figure 3E), 286 upregulated differentially expressed genes were mainly enriched in pathways such as primary immunodeficiency, African trypanosomiasis, and mineral uptake. Figure 3 E1). String diagram analysis showed the enrichment of these genes in the first 20 pathways. Figure 3 E3). The 144 downregulated differentially expressed genes were mainly enriched in pathways such as herpes simplex virus infection, ECM-receptor interaction, and glycosphingolipid biosynthesis. Figure 3 E2). Notably, the steroid hormone biosynthesis pathway reappeared in the significantly enriched lipid metabolism-related pathways (E2). Figure 3 E4). Combining Figure 2 The E4 results showed that differentially regulated gene sets in both intestinal and kidney tissues, regulated by high-salt microbiota interactions, were consistently and significantly enriched in the steroid hormone biosynthesis pathway. This indicates that genes specifically upregulated in the kidney tissue of conventional mice and those specifically upregulated in the intestinal tissue share common pathway distribution characteristics.

[0072] (4) Validation of the metabolic profile and common pathways related to high-salt diet-related kidney injury regulated by gut microbiota

[0073] UPLC-TOF / MS analysis of changes in colonic contents metabolites revealed that the PLS-DA model showed that HSD altered the metabolic profiles of both groups of mice. Figure 4 A). In the conventional mouse HSD group, 354 significantly differentially regulated metabolites were identified, including 236 upregulated and 118 downregulated; in the GF group, 503 differentially regulated metabolites were identified. Figure 4 C). The constructed high-salt-microbial-associated differential metabolite pool contained 197 metabolites, of which 158 were specific to conventional mice. Figure 4 C). Heatmap analysis confirmed that the gut microbiota is involved in regulating the expression changes of specific metabolites in gut contents under HSD conditions. Figure 4 D). KEGG pathway analysis showed ( Figure 4 E) 82 upregulated differentially regulated metabolites were significantly involved in pathways such as thiamine metabolism, steroid hormone biosynthesis, and toluene degradation; 115 downregulated differentially regulated metabolites were mainly involved in the mTOR signaling pathway, cancer-centric carbon metabolism, and arginine / proline metabolism. These results corroborate the findings of gut and kidney KEGG analysis, confirming that gut microbiota-related steroid hormone biosynthesis pathways play a crucial role in HSD-related kidney injury.

[0074] (5) Interaction network between gut-kidney axis microbiota-associated metabolites and host genes

[0075] Spearman correlation analysis elucidated the interactions between differentially expressed genes (kidney: Cyp1a1, Cyp2b10, Hsd17b1; gut: Srd5a2, Cyp3a44, Cyp1a1) and differentially expressed metabolites (3α,21-dihydroxy-5β-pregnane-11,20-dione, dehydroepiandrosterone) related to steroid hormone biosynthesis pathways in the gut and kidney. Figure 5 A). PPI network display built from the STRING database ( Figure 5 B), the annotation scores for CYP1A1, HSD17B1, and SRD5A2 were 0.955 and 0.408, respectively, suggesting that Cyp1a1 may be the most critical core gene among the five genes. ROC curve analysis showed ( Figure 5 C), these biomarkers showed good discriminative power (AUC>0.9) in HSD-induced conventional mice. Relative expression analysis showed ( Figure 5 D), compared with the ND group, the relative abundance of 3α,21-dihydroxy-5β-pregnane-11,20-dione and dehydroepiandrosterone was increased in HSD-fed conventional mice, while the expression of Cyp1a1, Cyp2b10, Hsd17b1 in the kidneys and Srd5a2, Cyp3a44, and Cyp1a1 in the intestines was decreased, while GF mice showed no such changes. RT-qPCR validation experiments confirmed ( Figure 5 E), HSD significantly downregulated Cyp1a1 expression in the kidneys and intestines of conventional mice, while this phenomenon was not observed in GF mice. This indicates that dehydroepiandrosterone (DHEA) can induce kidney injury by regulating the Cyp1a1 gene, and Cyp1a1 is a key gene for activating the gut-kidney axis through gut microbiota. DHEA intervention experiments showed ( Figure 6 A) Compared with the control group, mice injected with dehydroepiandrosterone for two weeks showed significantly lower levels of Cyp1a1 in the kidney and intestinal tissues (P<0.05). Figure 6 B). Different doses of dehydroepiandrosterone (DHEA) all significantly increased the levels of IL-6, TNF-α, IL-1β, S-cr, and BUN in renal tissue (P<0.0001), while significantly decreasing the level of IL-10 (BUN). Figure 6 CD). HE staining showed ( Figure 6E) Dehydroepiandrosterone (DHEA) caused structural abnormalities in kidney tissue: severe dilation of some renal tubules filled with inflammatory cells (red arrows), glomerular mesangial cell proliferation (black arrows), and increased basophilia of renal tubular epithelial cells with a small amount of inflammatory cell infiltration (yellow arrows). Masson staining confirmed that DHEA led to a significant increase in renal tissue collagen fibers. These results validated the association between core differentially metabolites and genes in the steroid hormone biosynthesis pathway and HSD-related kidney injury. Finally, a multivariate ROC was constructed by combining markers related to gut microbiota involvement in high-salt diet-related kidney injury (i.e., the steroid hormone biosynthesis-related metabolites DHEA and Cyp1a1), which showed good predictive power. Figure 7 ).

[0076] In summary, this study identified a combination of markers related to high-salt diet-related kidney injury (i.e., dehydroepiandrosterone and Cyp1a1, metabolites related to steroid hormone biosynthesis), validating their role and application in regulating high-salt diet-related kidney injury through the gut-kidney axis.

[0077] References:

[0078] [1].Kalantar-Zadeh,K.,et al.,Chronic kidney disease.Lancet, 2021.398(10302):p.786-802.

[0079] [2]. Godeau, D., et al., Return-to-work, disabilities and occupational health in the age of COVID-19. Scand JWork Environ Health, 2021.47(5):p.408-409.

[0080] [3].

[0081] [4].Foreman,K.J.,et al.,Forecasting life expectancy,years of lifelost,and all-cause and cause-specific mortality for 250causes of death:reference and alternative scenarios for 2016-40for 195countries andterritories.Lancet,2018.392(10159):p.2052-2090.

[0082] [5].Messerli,F.H.,et al.,Sodiumintake,life expectancy,and all-causemortality.Eur Heart J,2021.42(21):p.2103-2112.

[0083] [6].Jobin,K.,et al.,Sodium and its manifold impact on our immunesystem.Trends Immunol,2021.42(6):p.469-479.

[0084] [7].Xu,A.,et al.,Association of a Province-Wide Intervention WithSalt Intake and Hypertension in Shandong Province,China,2011-2016.JAMAInternMed,2020.180(6):p.877-886.

[0085] [8].Ding,R.,et al.,Calcitriol ameliorates damage in high-salt diet-induced hypertension:Evidence of communication with the gut-kidney axis.ExpBiol Med(Maywood),2022.247(8):p.624-640.

[0086] [9].Nouri,Z.,et al.,The microbiota-gut-kidney axis mediates hostosmoregulation in a small desert mammal.NPJ biofilms and microbiomes,2022.8(1).

[0087]

[10] .Ding,R.,et al.,Calcitriol ameliorates damage in high-salt diet-induced hypertension:Evidence of communication with the gut-kidney axis.ExpBiol Med(Maywood),2022.247(8):p.624-640.

[0088]

[11] .Liu,T.,et al.,Gut microbial evidence chain in high-salt dietexacerbates intestinal aging process.Front Nutr,2022.9:p.1046833.

[0089]

[12] .Wan,C.,et al.,Serum Untargeted Metabolism Reveals the Mechanismof L.plantarum ZDY2013 in Alleviating Kidney Injury Induced by High-SaltDiet.Nutrients,2021.13(11).

[0090]

[13] .Stavropoulou,E.,et al.,Focus on the Gut-Kidney Axis in Healthand Disease.Front Med(Lausanne),2020.7:p.620102.

[0091]

[14] . Liu, T., et al., Gut Microbiota-Related Evidence Provides NewInsights Into the Association Between Activating Transcription Factor 4andDevelopment of Salt-Induced Hypertension in Mice. Front Cell Dev Biol, 2020.8: p.585995.

[0092]

[15] . Kigel, A., et al., Maternal immunization during the second trimester with BNT162b2 mRNA vaccine induces a robust IgA response in HumanMilk: A prospective cohort study. Am J Clin Nutr, 2023.

[0093] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A marker panel of a gut microbiota-derived high-salt diet-associated kidney injury, characterized in that, The marker combination is dehydroepiandrosterone and Cyp1a1.

2. Use of the marker combination of the high-salt diet-related renal injury of intestinal flora origin in claim 1 in the preparation of a high-salt diet-related renal injury diagnosis and prevention product.

3. A product for the diagnosis and prophylaxis of high salt diet associated kidney injury, characterized by, The product comprises a reagent for detecting the marker, and the marker is a combination of dehydroepiandrosterone and Cyp1a1.

4. The product for the diagnosis and prevention of high salt diet-related kidney injury according to claim 3, characterized by, The product further comprises a reagent for detecting the expression levels of dehydroepiandrosterone and Cyp1a1.

5. The product for diagnosis and prevention of high salt diet-related kidney injury according to claim 3, characterized by, The product is a chip or a kit.

6. The marker combination screening method according to claim 1, characterized by, The method comprises the following steps: Grouping and intervention of experimental animals: SPF and GF mice are divided into a normal diet group and a high-salt diet group, and the mice in the high-salt diet group are fed with 8% NaCl feed for 4 weeks; Multi-omics analysis: RNA-seq sequencing is used to analyze the gene expression profiles of kidney and intestinal tissues, and UPLC-MS / MS is used to detect intestinal content metabolites; Differential marker screening: combined with transcriptome and metabolome data, metabolites and genes regulated by high-salt diet and intestinal flora in conventional mice and enriched in steroid hormone biosynthesis pathways are screened.

7. The screening method of the marker combination according to claim 6, wherein, The metabolite is dehydroepiandrosterone, and the gene is Cyp1a1.

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