Abstract Background Hypertension is a global public health concern. A synbiotic preparation containing Bifidobacterium lactis and Lactobacillus acidophilus has been used as adjunct therapy for hypertension. We sought to elucidate the antihypertensive activity of this preparation and explore the underlying mechanisms. Methods and results Blood pressure in rats was measured using the tail-cuff method. Colonization of the gastrointestinal tract by the two probiotics was determined by real-time quantitative polymerase chain reaction (qPCR). Mechanistic studies were performed by proteomic analyses based on liquid chromatography-mass spectrometry and STRING database and metabolomic analyses using the UHPLC-Q-TOF/MS platform and peroxisome proliferator-activated receptor (PPAR)β/γ antagonists. Although biochemical analysis of blood samples showed that the synbiotic preparation did not alter the levels of angiotensin II, aldosterone, or cortisol, it significantly lowered the systolic blood pressure in the treatment group. Moreover, the synbiotic preparation contributed to the localization of the two probiotics in the ileum and colon of the treatment group. Proteomics, immunochemistry, and real-time qPCR analyses showed that administration of the synbiotic preparation activated the PPAR signaling pathway in the ileum and significantly upregulated PPARβ and PPARγ. The antagonist studies further confirmed this finding. In addition, metabolomic analyses demonstrated that among the 27 metabolites that showed significant differences between the control and model groups, administration of the synbiotic preparation significantly upregulated lysophosphatidylethanolamine and phosphatidylcholine in the ileum of the treatment group. Conclusion The results of the study suggest that the novel synbiotic preparation reduces blood pressure by altering the composition of the intestinal microbiota, regulating PPAR signaling pathway, and activating the PPARβ and PPARγ cascade reactions in the ileum. Keywords: Synbiotics, Antihypertensive activity, PPAR pathway, Proteomics, Metabolomics, Spontaneous hypertensive rat Graphical abstract Image 1 [29]Open in a new tab Highlights * • The antihypertension of synbiotics was explored based on the clinical-evidences in our hospital. * • Synbiotics activate PPAR pathway to improve hypertension. * • Synbiotics just lower effectively the systolic pressure. * • LysoPE and PCs might be substances of hypertension. 1. Introduction The human gastrointestinal tract harbors approximately 1000 microbial species, which constitute approximately 90 % of all cells in the body. Human beings are considered “superorganisms” because of their close symbiotic association with the gut microbiota [[30]1]. The interaction of the gut microbiota with ingested food, host organs, and other gut microbiota makes the gastrointestinal tract a very complex system susceptible to the effects of environmental factors, diet, medication, and age [[31][2], [32][3], [33][4], [34][5]], and influences the health of the host; that is, dysbiosis of the gut microbiota can lead to the development of many diseases, such as infections, cardiovascular, and nervous system diseases. These diseases often improve when dysbiosis of the gut microbiota is corrected. Hypertension is a global public health concern that is regarded as the most prevalent modifiable cardiovascular disease risk factor. It contributes to many complications such as stroke, kidney failure, premature death, and disability. According to epidemiological data, 1.13 billion people and >3 % of children worldwide are afflicted with hypertension [[35]6]. The current clinical management approach for treating hypertension involves the administration of combined drugs that may induce potential adverse reactions. This often leads to poor compliance and adverse outcomes in children and older adults. Therefore, there is an urgent need to develop alternative and complementary therapies for the management of hypertension. A synbiotic preparation containing Lactobacillus acidophilus, Bifidobacterium lactis, and xylooligosaccharides has been certified by Institute for Drug Control, the General Logistics Department of PLA (Hou Zhizi [2006] F005081) as a novel in-hospital preparation to treat hypertension. It has been used as an adjuvant strategy for the treatment of hypertension in our hospital since 2006, and has shown significant efficacy in older adults. However, reports on the antihypertensive effects of L. acidophilus and B. lactis are limited. Therefore, in this study, we aimed to further elucidate the antihypertensive activity of this synbiotic preparation and explore the underlying mechanism to provide a scientific basis for its clinical use. 2. Materials and methods 2.1. Animals Male spontaneously hypertensive rats (SHRs) (n = 28) and Wistar Kyoto (WKY) rats (n = 14) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). The rats (body weight 160 ± 20 g, 6 weeks old) were housed at 21 ± 1 °C under 12-h light/12-h dark cycle (lights on at 07:00 and off at 19:00) with free access to a standard pellet diet and tap water. The rats were allowed to acclimatize to the housing facility for at least 1 week prior to the experiments. All experimental protocols were approved by the Animal Care and Use Committee of the General Hospital of Shenyang Military Area Command (No. 2016L09). Every effort was made to minimize the number of animals used and their suffering. 2.2. Experimental groups and administration of the synbiotic preparation All rats were randomly assigned to three groups as follows: WKY control (n = 14), SHR model (n = 14), and SHR treatment (n = 14) groups. The SHR treatment group was orally administered lyophilized synbiotic preparation containing 2.5 × 10^9 colony forming units (CFUs) of B. lactis, 2.5 × 10^9 CFUs of L. acidophilus, 0.3 g of xylooligosaccharides, and maltodextrin (BioGrowing Co., Ltd., Shanghai, China) at a dose of 2.5 g/kg twice daily for 7 weeks. The WKY control and SHR model groups were treated with maltodextrin at the same dose twice daily for 7 weeks. 2.3. Blood pressure measurement Blood pressure was measured using an automatic sphygmomanometer (Softron BP-98A; Softron, Tokyo, Japan) by the tail-cuff method [[36]7]. To avoid blood pressure fluctuations and to obtain reliable measurements, the rats were adapted to the blood pressure monitor prior to the experiment. According to the instructions of the non-invasive blood pressure monitor system manufacturer, the animals were kept in a chamber at 38 ± 0.5 °C for 10 min, and then placed in a rodent restrainer. A cuff with an infrared sensor was attached to the base of the tail as described previously. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured once the pulse stabilized. The blood pressure was measured every 3 min and averaged over three cycles. The blood pressure of each rat was measured once a week between 08:00 and 10:00 a.m. ([37]Fig. 1). Fig. 1. [38]Fig. 1 [39]Open in a new tab Experimental photo of blood pressure measurement. The rats were continuously fed the synbiotic preparation for 7 weeks, following which the preparation was withheld for 2 weeks to test for withdrawal effects. The rats in the WKY control (n = 14), SHR model (n = 14), and SHR-treatment groups (n = 14) were then orally administered the synbiotic preparation or maltodextrin at the same dose twice daily for 3 weeks, starting from the 10th week. During this period, the blood pressure was measured as described above. 2.4. Blood sample analysis Twenty-four hours after the 7th administration of the synbiotic preparation, the rats were anesthetized by intraperitoneal administration of chloral hydrate (0.3 mL/100 g) and then euthanized. Blood specimens were collected from the abdominal aorta into evacuated EDTA blood collection tubes for pretreatment from 08:00 to 10:00 a.m. Whole blood specimens (1 mL, containing 10 μL enzyme inhibitor) were centrifuged at 2200×g for 10 min at 4 °C to remove precipitated proteins and cell debris, and the clear supernatants were collected. The concentrations of angiotensin II (AII), aldosterone (ALD), and cortisol (COR) in the supernatants were analyzed using AII, ALD, and COR CLIA Microparticles Detection Kits (Autobio Diagnostics Co., Ltd., Zhengzhou, China), which are based on chemiluminescent magnetic microparticle immunoassay method. Briefly, 100 μL of the supernatant was mixed with 20 μL magnetic particle suspension, 50 μL antibody, and 50 μL enzyme combination and incubated for 34 min at 37 °C in a heating block according to the manufacturer's instructions. After rinsing five times with phosphate-buffered saline (PBS), the chemiluminescent substrate was added to the mixture. Chemiluminescent signals were measured using an AutoLumo A2000 analyzer (Autobio Diagnostics Co., Ltd.). Serial dilutions of the calibrators for AII (0, 10, 50, 250, 500, and 1000 ng/mL), ALD (0, 50, 100, 200, 500, and 1000 ng/mL), and COR (0, 2, 5, 10, 25, and 60 ng/mL) were used to generate the respective calibration curves. The concentrations of AII, ALD, and COR in the supernatants were calculated using the calibration curves [[40]8]. 2.5. Analysis of intestinal colonization by L. Acidophilus and B. Lactis 2.5.1. DNA extraction Twenty-four hours after the 7th administration of the synbiotic preparation, the rats were euthanized. After collection of blood specimens, the jejunum, ileum, and colon were separated in sterile PBS (0.1 mM/L, pH 7.2), and the ingesta were gently removed. Next, the intestinal segment and mucosal surface (100 mg each) were placed in cryopreserved tubes and frozen rapidly in liquid nitrogen [[41]9]. The samples were then stored at −80 °C. DNA was extracted from L. acidophilus and B. lactis (from 0.1 g of synbiotic preparation), and from the jejunum, ileum, and colon specimens using the TaKaRa MiniBEST Bacteria Genomic DNA Extraction Kit (Takara Bio, Inc., Kusatsu, Japan). Briefly, the frozen samples were lysed in lysozyme solution (200 μL), treated with Rnase A, and digested with proteinase K according to the manufacturer's instructions. Finally, the DNA was eluted with 50 μL of the elution solution. All DNA samples were stored at −20 °C. 2.5.2. Polymerase chain reaction (PCR) PCR amplification was performed in an Applied Biosystems 2720 Thermal Cycler (Applied Biosystems, Inc., Foster City, CA, USA) using L. acidophilus and B. lactis DNA extracted from the synbiotic preparation as template. The primer used for PCR are listed in [42]Table 1, and their sequences were as described previously [[43]10]. PCR was performed in 25-μL reaction volume containing 12.5 μL of 2 × Taq PCR MasterMix (Tiangen Biotech Co., Ltd., Beijing, China), 1 μL of forward primer (0.01 mM), 1 μL of reverse primer (0.01 mM), and 1 of μL DNA template. The reaction conditions were as follows: 94 °C for 4 min, followed by 30 cycles at 94 °C for 30 s, 56 °C for 30 s (L. acidophilus) or 58 °C for 30 s (B. lactis), and 72 °C for 1 min. An additional extension at 72 °C for 5 min was performed at the end. The amplified products (5 μL) were separated by agarose gel electrophoresis. The PCR products were purified using a DNA Fragment Purification Kit (Takara Bio Inc.). Table 1. Primers and probe sequences. Target bacterial group (amplicon size) Oligonucleotide sequence (5′–3′) L. acidophilus (86 bp) F: GAAAGAGCCCAAACCAAGTGATT R: CCCTTTCCACGGGTCCC 5’-(FAM)-TACCACTTTGCAGTCCTACA-(BHQ-X)-3′ B. lactis (195 bp) F: CCCTTTCCACGGGTCCC R: AAGGGAAACCGTGTCTCCAC 5’-(HEX)-AAATTGACGGGGGCCCGCACAAGC-(DABCYL)-3′ [44]Open in a new tab 2.5.3. DNA cloning and transformation Purified products were cloned into the pGEM-T Easy vector (Promega, Madison, WI, USA). Ligations were performed in a 10-μL reaction mixture containing 3 μL of PCR product, 1 μL of pGEM-T Easy vector, and 1 μL of T4 DNA ligase. The reactions were incubated at 37 °C overnight. The ligation mixtures were then transformed into Escherichia coli. Competent cells were prepared using the heat shock method [[45]11]. Briefly, the purified PCR products were added to a 50-μL aliquot of thawed competent cells. The mixture was placed on ice for 30 min, incubated in a water bath at 42 °C for 90 s for heat shock, and placed on ice for 5 min. Sterile SOC medium (500 μL) was added to the heat-shocked cells, followed by shaking at 37 °C for 1 h. Next, 100 μL of the bacterial solution was plated on LB solid medium containing 50 μg/mL ampicillin overnight at 37 °C. 2.5.4. Screening of recombinant E. coli The colonies on the plates were tested for the presence of the target fragment using the M13 universal primer pair (F: 5′-TGTAAAACGACGGCCAGT-3′, R: 5′-CAGGAAACAGCTATGACC-3′) by PCR. PCR was performed in a 25-μL reaction mixture containing 12.5 μL of 2 × GoTaq Colorless Master Mix (Promega), 1 μL of forward primer (0.01 mM), 1 μL of reverse primer (0.01 mM), and 1 μL of bacterial template. The reaction conditions were as follows: 94 °C for 5 min, followed by 30 cycles at 94 °C for 30 s, 56 °C for 30 s, and 72 °C for 1 min. An additional extension at 72 °C for 10 min was performed at the end. The amplified products (5 μL) were separated by agarose gel electrophoresis. The cloned sequences were confirmed by sequencing [[46]11]. 2.5.5. Real-time quantitative PCR (qPCR) Real-time qPCR assays were performed in an Applied Biosystems 7500 Fast Real-Time PCR system (Applied Biosystems) using DNA samples of the intestinal flora as template and genus-specific primer pairs ([47]Table 1). The reaction was performed in a 20-μL mixture containing 10 μL of 2 × Premix Ex Taq (Thermo Scientific, Waltham, MA), 0.4 μL of forward primer (0.01 mM), 0.4 μL of reverse primer (0.01 mM), 0.4 of μL probe (0.01 mM), and 2 μL of DNA template. The reaction conditions were as follows: 95 °C for 30 s, followed by 40 cycles at 95 °C for 5 s, and 60 °C for 34 s. The plasmid concentrations of L. acidophilus and B. lactis were 148 ng/μL and 135 ng/μL, respectively, as measured using a Nanodrop ND 1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA). The copy number was calculated using the following formula: (6.023 × 10^23 × plasmid amount [ng/μL])/(plasmid size [bp] × 10^9 × 650). Each standard curve was generated from at least six 10-fold dilutions in triplicates. Copy numbers of L. acidophilus and B. lactis in each intestinal segment were determined using the respective standard curve [[48]12]. 2.6. Metabonomic analyses The metabolomic analysis was performed using a method described previously [[49]13]. Briefly, 24 h after the 7th administration of the synbiotic preparation, all rats were euthanized and blood samples were collected. The ileum was separated in sterile PBS (0.1 mmol/L, pH 7.2) and the ingesta were gently removed. A section of the ileum (50 mg) was homogenized in 1 mL methanol for 2 min at 70 Hz. The mixture was centrifuged at 13,000 rpm for 15 min at 4 °C. The supernatant (200 μL) was added to the sample vial and metabonomic profiling was performed on the UHPLC-Q-TOF/MS platform. Liquid chromatography-mass spectrometry (LC-MS) analysis was performed using an Agilent 1290 Infinity LC system coupled to an Agilent 6538 Accurate-Mass Quadrupole Time-of-Flight (Q-TOF) mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Chromatographic separation was performed at 25 °C using a Waters Xselect® HSS T3 analytical column (2.1 × 100 mm, 2.5 μm; Waters, Milford, MA, USA). The mobile phases used were: (A) 0.1 % formic acid solution, and (B) I modified with 0.1 % formic acid. The flow rate was 0.4 mL/min, and the optimized gradient elution conditions were 5 % B at 0–2 min, 5–95 % B at 2–13 min, and 95 % B at 13–15 min. The MS was operated in both positive and negative ion modes. The parameters were optimized as follows: capillary voltage: 4 kV in the positive mode and 3.5 kV in the negative mode; drying gas flow: 10 L/min; gas temperature: 325 °C; nebulizer pressure: 20 psig; fragmentor voltage: 120 V; skimmer voltage: 45 V; mass range: m/z 100–1100; and reference ions in positive ion mode at m/z: 121.0509 and 922.0098, and in negative ion mode at m/z: 112.9856 and 1033.9881. Raw LC-MS data were converted to common data format (.mzdata) files using the Agilent MassHunter Qualitative software. The XCMS package ([50]http://bioconductor.org/biocLite.R) was used for peak extraction, alignment, and integration to generate a visual data matrix on the R platform. The data matrix was imported into the SIMCA-P program (version11.0; Umetrics, Umea, Sweden) for multivariate statistical analysis after weight normalization [[51]13], including unsupervised principal component analysis and supervised partial least squares-discriminant analysis (PLS-DA). Differentially expressed metabolites were first confirmed based on their extracted molecular weights using ion flow chromatography, and their extracted molecular weights were compared with those in common online databases including the Human Metabolome Database ([52]http://www.hmdb.ca/) and Metlin ([53]http://metlin.scripps.edu). 2.7. Proteomics analyses Twenty-four hours after the 7th administration of the synbiotic preparation, all rats were euthanized and whole blood samples were collected. The ileum was separated in sterile PBS (0.1 mmol/L, pH 7.2) and the ingesta were removed gently. Proteins were extracted from the ileal samples from each group, purified, and then quantified using the bicinchoninic acid assay. Then, 120 μg (20 μg/sample) of pooled proteins from each group were reduced and alkylated. Following trypsin treatment at 37 °C overnight, each pooled sample was labeled with a different tag using the iTRAQ Reagent-8Plex Multiplex Kit according to the manufacturer's instructions. The peptide mixtures were fractionated by high-pH reverse-phase LC and analyzed by LC-MS/MS (EksigentNano LC-Ultra^TMsystem tandem TripleTOFTM5600; AB SCIEX, Foster City, CA, USA) to identify differentially expressed proteins. Differentially expressed proteins were identified using ProteinPilot 4.5 software and quantified using the different tags. Under the condition of a false discovery rate of <1 %, only proteins with at least one unique peptide were considered credible, and those that changed by 1.2-fold (up or down) and showed significant (P < 0.05) difference were filtered as differentially expressed proteins. The STRING database contains known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations stemming from computational predictions, knowledge transfer between organisms, and interactions taken from other primary databases. For proteomic analysis, all differentially expressed proteins were analyzed using STRING ([54]https://string-db.org/) to identify their annotation, protein-protein interactions, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment [[55]14], which provided invaluable information for further exploration. 2.8. Immunofluorescence analyses Twenty-four hours after the 7th administration of the synbiotic preparation, all rats were euthanized and whole blood samples were collected. After the ileum was separated in sterile PBS (0.1 mmol/L, pH 7.2) and the ingesta were removed gently, each ileum segment (approximately 5 mm) was embedded in OCT compound and sectioned transversely on a cryostat microtome. The sections (5 μm) were incubated overnight at room temperature in the dark with the following antibodies: anti-peroxisome proliferator-activated receptor (PPAR)α antibody (mouse anti-rat, 1:100; Thermo Fisher Scientific [MA1-822]), anti-PPARβ/δ antibody (rabbit anti-rat, 1:100; Abcam, Cambridge, MA, USA [ab23673]), and anti-PPARγ antibody (rabbit anti-rat, 1:100; Abcam [ab209350]). Subsequently, the sections were incubated for 30 min at room temperature in the dark with the corresponding secondary antibodies, including Alexa Fluor 555 (goat anti-rabbit, 1:100; Thermo Fisher Scientific [A21429]) and Alexa Fluor 488 (donkey anti-mouse, 1:100; Thermo Fisher Scientific [A21202]). Following counterstaining of the nuclei with 4′,6-diamino-2-phenylindole (DAPI; 1 mg/mL), the sections were mounted and examined using laser scanning confocal microscopy (FV1000; Olympus, Tokyo, Japan) [[56]15]. To confirm the antibody specificity, rabbit IgG (1:100; Abcam [ab172730]) and mouse IgG2b kappa (1:100; Thermo Fisher Scientific [11-4732-42]) isotypes were used as negative controls. 2.9. Real-time qPCR analysis of PPARs In brief, total RNA was extracted from ileal samples of the control, model, and treatment groups using an Eastep® Super RNA Extraction Kit (Promega, Beijing, China) according to the manufacturer's instructions, and stored at −80 °C. mRNA levels were analyzed using an Eppendorf BioPhotometer (Eppendorf, Hamburg, Germany). Prior to mRNA quantification, cDNA was reverse transcribed using a PrimeScript™ RT Reagent Kit (Takara Bio, Inc.) and iCycler™ 96-well Reaction Module (Bio-Rad Laboratories, Inc., Hercules, CA); reverse-transcription real-time qPCR was performed on the 7300 Fast Real-Time PCR System (Applied Biosystems) according to the manufacturer's instructions. The primer sequences used for the detection of PPARs α, β, and γ, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) are presented in [57]Table 2. GAPDH was used as an endogenous reference gene for normalization of the expression levels of PPARs α, β, and γ. To this end, the GAPDH Ct value was subtracted from the that of the target gene, generating a ΔCt value. ΔΔCt was calculated by subtracting the mean ΔCt value of the control group from the ΔCt of the model and treatment groups. Finally, fold changes in gene expression of PPARs α, β, and γ were calculated using the comparative 2^−ΔΔCt method based on the formula: 2^−ΔΔCt = ΔCt [model/treatment] − ΔCt [control]. Table 2. Sequences of Primers Used for qRT-PCR. Gene Forward Primer Reverse Primer PPARα CCCCACTTGAAGCAGATGACC CCCTAAGTACTGGTAGTCCGC PPARβ AATGCCTACCTGAAAAACTTCAAC TGCCTGCCACAGCGTCTCAAT PPARγ CGGAGTCCTCCCAGCTGTTCGCC GGCTCATATCTGTCTCCGTCTTC GAPDH TGTGAACGGATTTGGCCGTA GATGGTGATGGGTTTCCCGT [58]Open in a new tab 2.10. PPARβ and PPARγ antagonism tests All rats were randomly assigned to nine groups (seven rats/group) as follows: 1) WKY control, 2) WKY control + PPARβ antagonist, 3) WKY control + PPARγ antagonist, 4) SHR model, 5) SHR model + PPARβ antagonist, 6) SHR model + PPARγ antagonist, 7) SHR treatment, 8) SHR treatment + PPARβ antagonist, and 9) SHR treatment + PPARγ antagonist groups. The SHR treatment group was orally administered a lyophilized synbiotic preparation containing 2.5 × 10^9 CFUs of B. lactis, 2.5 × 10^9 CFUs of L. acidophilus, 0.3 g of xylooligosaccharides, and maltodextrin at a dose of 2.5 g/kg twice daily for 7 weeks. The WKY control and SHR model groups were treated with maltodextrin at the same dose twice daily for 7 weeks. From the 4th week to 7th week, the WKY control, SHR model, and SHR treatment groups were administered PPARβ antagonist (GSK3787, 500 μg/kg, i.p.) and PPARγ antagonist (GW9662, 50 μg/kg, i.p.) once a day. The SBP of each rat was measured according to the protocol described in Section [59]2.3. 2.11. Statistical analyses All data are presented as the mean ± standard error of the mean. The SPSS® Statistics (version 21; IBM Inc., Chicago, IL, USA) and SAS® (version 9.3; Statistical Analysis System, Inc., Raleigh, NC, USA) softwares were used for statistical analyses. The data from blood pressure measurements, blood sample analysis, analysis of the intestinal colonization by L. acidophilus and B. lactis, and real-time qPCR analysis of PPARs were subjected to analysis of variance (ANOVA) followed by Dunnett's multiple comparison test. Statistical significance was set at P < 0.05. 3. Results 3.1. Effect of the synbiotic preparation on blood pressure The SBP and DBP of the rats in the three groups was measured once a week ([60]Fig. 2A). Prior to treatment, both SBP and DBP of SHRs were significantly higher than those of WKY rats (152.4 ± 4.3 mmHg vs. 116.0 ± 11.5 mmHg [P < 0.05] and 118.3 ± 11.3 mmHg vs. 88.7 ± 9.6 mmHg [P < 0.05], respectively). During the seven-week treatment period, SBP was stable in the control group; however, it increased gradually in the model group and reached 183.3 ± 5.8 mmHg in the 7th week. Moreover, the synbiotic preparation significantly reduced SBP of SHRs in the treatment group from the 4th to 7th week compared with that of the model group, and reached 169.4 ± 11.4 mmHg and 168.9 ± 11.7 mmHg in the 6th and 7th weeks, respectively. However, the synbiotic preparation did not lower the DBP of SHRs, and there was no obvious difference between the DBP of SHRs in the treatment and model groups. During the treatment period, the DBP of SHRs in the model and treatment groups increased gradually and was significantly higher than that of WKY rats in the control group. Fig. 2. [61]Fig. 2 [62]Open in a new tab Effects of synbiotics on systolic blood pressure (A) and diastolic blood pressure (B). After SHR rats and WKY rats were administered synbiotics or maltodextrin continuously for 7 weeks, the withdrawal test was performed for 2 weeks, followed by readministered synbiotics or maltodextrin for three weeks from 10th week. The dots were presented as the mean ± standard error of the mean. Asterisks indicated statistical significance between the SHR-model or the SHR-treatment group and the WKY-control group on the same week, **p < 0.01. Symbol indicated statistical significance between the SHR-model and the SHR-treatment groups on the same week, #p < 0.05. After withdrawal of the synbiotic preparation from the 7th week, the SBP of SHRs rapidly recovered in the treatment group and was almost equivalent to that in the model group in the 9th week. However, after readministration of the synbiotic preparation in the 9th week, the SBP started to decrease gradually. In the 12th week, the SBP of SHRs in the treatment group was significantly different from that in the model group. However, during this period, there was no obvious difference between the DBP of SHRs in the treatment and model groups ([63]Fig. 2B). 3.2. Effect of the synbiotic preparation on plasma AII, ALD, and COR levels After seven weeks of treatment with the synbiotic preparation, the plasma levels of AII, ALD, and COR of SHRs in the model group were significantly higher than those of WKY rats in the control group ([64]Table 3), indicating that the renin-angiotensin-aldosterone system (RAAS) may be involved in the pathogenesis of spontaneous hypertension in SHRs. However, there was no significant difference in the plasma concentration of AII, ALD, and COR between the model and treatment groups, indicating that the antihypertensive mechanism of the synbiotic preparation was not related to the RAAS. Table 3. Effects of synbiotics on the levels of plasma AⅡ, ALD and COR after administered continuously for 7 Weeks. Index WKY-control (n = 7) SHR-model (n = 7) SHR-treatment (n = 7) AⅡ（pg/ml） 187.2 ± 37.43 234.1 ± 36.14* 230.7 ± 36.04* ALD（pg/ml） 278.9 ± 42.49 324.9 ± 52.57* 313.8 ± 35.72* COR（μg/dl） 6.291 ± 1.965 7.246 ± 1.043* 6.991 ± 0.986* [65]Open in a new tab [66]Table 3 After administered synbiotics for 7 weeks, the concentrations of AⅡ, ALD and COR in the plasma of SHRs in the both model and treatment groups exhibited significant higher than those of WKYs in the control group. However, there were not significant differences in the concentrations of AⅡ, ALD and COR in the plasma between the model and treatment groups. Results were expressed as the mean ± standard error of the mean. Asterisks indicated statistical significance between the SHR-model or the SHR-treatment group and the WKY-control group,*p < 0.05. AⅡ: AngiotensinⅡ; ALD: aldosterone; COR: cortisol. 3.3. Effect of the synbiotic preparation on the intestinal colonization by L. Acidophilus and B. Lactis To study the colonization of the intestinal segments by L. acidophilus and B. lactis, real-time qPCR assays were performed and standard curves were generated by regression analysis. The standard curves of L. acidophilus and B. lactis were y = −3.338x + 41.65 and y = −3.171x + 43.41 ([67]Fig. 3A and B); the coefficient correlations were 0.9997 and 0.9991, and the efficiencies were 99.3 % and 106.7 %, respectively. Fig. 3. [68]Fig. 3 [69]Open in a new tab (A) & (B): Standard curves of L. acidophilus and B. lactis, which were established from at least six 10-fold dilutions of plasmids measured by real-time qPCR assay. The standard curves of L. acidophilus and B. lactis were y = −3.338x + 41.65 and y = −3.171x + 43.41, respectively. The coefficient correlations were 0.9997 and 0.9991 and the efficiencies were 99.3 % and 106.7 %, respectively. (C) & (D): The quantities of L. acidophilus and B. lactis colonized in the jejunum (n = 7), ileum (n = 7) and colon (n = 7) after continuous administration of synbiotics for 7 weeks. Results were expressed as the mean ± standard error of the mean. Asterisks indicated statistical significance between the SHR-model or the SHR-treatment group and the WKY-control group, *p < 0.05. Symbol indicated statistical significance between the SHR-model and the SHR-treatment groups, #p < 0.05. Following 7 weeks of continuous administration of the synbiotic preparation, L. acidophilus and B. lactis predominantly colonized the ileum and colon; however, not the jejunum in the treatment group. In addition, the number of L. acidophilus that colonized the ileum and colon of SHRs in the treatment group were significantly higher than those of SHRs in the model group and WKY rats in the control group. The number of B. lactis that colonized the ileum of SHRs in the treatment group were significantly higher than those in the model group and WKY rats in the control group. However, the number of B. lactis in the colon of SHRs in the treatment group was almost equal to that in the control group and significantly higher than that in the model group. Moreover, the number of L. acidophilus colonizing the ileum in the model group was lower than that in the control group. These results indicated that the synbiotic preparation contributed to the colonization of the ileum and colon of SHRs by L. acidophilus and B. lactis and thus altered the composition of their gut microbiota ([70]Fig. 3C and D). 3.4. Metabonomic analysis of the antihypertensive effect of the synbiotic preparation in the ileum Gut microbiota produce thousands of metabolites through interactions with the host and partially digested food. These microbial metabolites are absorbed from the gut and play specific roles in pathological and physiological processes involved in an individual's susceptibility to disease and/or treatment outcomes. In the last decade, metabolomics has emerged as a systems biology approach to obtain valuable insights into intra- and extracellular regulatory processes involved in host-gut microbial metabolic interactions because of its ability to monitor metabolic variations and detect the adaptive multiparametric responses of an organism to exogenous stimuli in real time using MS. The PLS-DA model, a supervised multivariate statistical analysis method, was applied to visualize the trends in the control, model, and treatment groups. As illustrated by the PLS-DA score plots of the three groups in both the electrospray ionization-positive and -negative modes ([71]Fig. 4A and B), the control group was clearly separated from the model and treatment groups in the first principal component. The samples in the treatment group were located between the control and model groups in the first principal component, indicating that the metabolic profile of the model group was clearly altered compared with that of the control group and that the synbiotic preparation had a definite therapeutic effect on the model group. Supervised PLS-DA was applied to identify the differentially regulated metabolites between the model and control groups. Score plots from the PLS-DA model ([72]Fig. 4C and D) showed that the model group was clearly separated from the control group, indicating significant differences between the metabolic profiles of the model and control groups. When the two components were calculated, the cumulative R2X, R2Y, and Q2 values were 0.419, 0.937, and 0.693, respectively, in the positive mode, and 0.565, 0.942, and 0.826, respectively, in the negative mode. These values showed that the PLS-DA model had a good degree of fit and predictive ability to screen differential variables between the groups. Scatter plots of the PLS-DA model ([73]Fig. 4E and F) illustrate the contribution of different metabolite ions to the differences between the groups. Each dot on the plot represents an ion of a metabolite. The farther the dot from the origin point, the more significant its contribution to the differences between the groups, as indicated by larger corresponding variable importance (VIP) values. The VIP value, which was generated in the PLS-DA processing, represents the contribution of the ions in discriminating between the groups [[74]13]. Ions with VIP values > 1.0 were considered candidate differential ions. ANOVA and Tukey's post-hoc tests were performed to assess the significance of the differences among the three groups. Finally, as determined by P (M/C) values (P < 0.05), 27 metabolites that were significantly different between the control and model groups were identified ([75]Table 4). Moreover, the P (T/M) values (P < 0.05) revealed that among the 27 metabolites, lysophosphatidylethanolamine (lysoPE) and phosphatidylcholines (PCs) were significantly upregulated in the treatment group compared with that in the model group following administration of the synbiotic preparation. Fig. 4. [76]Fig. 4 [77]Open in a new tab Plots of multivariate statistical analysis based on variables of the control, model and treatment group. (A) PLS-DA scores plot of three groups in ESI positive mode; (B) PLS-DA scores plot of three groups in ESI negative mode; (C) PLS-DA scores plot of the control and model group in ESI positive mode; (D) PLS-DA scores plot of the control and model group in ESI negative mode; Each dot or diamond in the figure represents one sample in each group. (E) PLS-DA Scatter plot of the control and model group in ESI positive mode; (F) PLS-DA Scatter plot of the control and model group in ESI negative mode. Each dot represents an ion of metabolite. Table 4. The different metabolites in the control, model and treatment groups. No. m/z Rt (min) VIP FC(M/C) p (M/C) FC(T/M) p (T/M) Ion Formula Metabolites Related pathway 1 259.023 0.73 1.74 1.50 0.0051 0.97 0.9328 [M − H]- C6H13O9P D-Glucose 6-phosphate Inositol phosphate metabolism 2 167.02 0.88 2.41 0.79 0.0295 1.16 0.2239 [M − H]- C5H4N4O3 Uric acid Purine metabolism 169.035 1.00 1.40 0.74 0.0120 1.05 0.8666 [M+H]+ C5H4N4O3 Uric acid Purine metabolism 3 133.032 1.00 1.49 1.27 0.0439 1.04 0.8597 [M+H]+ C5H8O2S THTC – 4 346.055 1.02 1.02 2.35 0.0015 1.15 0.5178 [M − H]- C10H14N5O7P Adenosine monophosphate cAMP signaling pathway 5 153.04 1.03 2.01 3.90 0.0318 0.53 0.2117 [M+H]+ C5H4N4O2 Xanthine Purine metabolism 6 318.3 9.31 1.17 1.95 0.0491 0.56 0.0763 [M+H]+ C18H39NO3 Phytosphingosine Sphingolipid metabolism 7 526.294 10.16 1.03 1.41 0.0022 0.89 0.2746 [M+H]+ C27H44NO7P LysoPE (22:6/0:0) Glycerophospholipid metabolism 8 502.293 10.17 1.79 1.39 0.0013 0.81 0.0228 [M+H]+ C25H44NO7P LysoPE (20:4/0:0) Glycerophospholipid metabolism 9 506.36 10.55 1.07 1.44 0.0419 0.98 0.9866 [M+H]+ C26H52NO6P C-8 Ceramide-1-phosphate Sphingolipid metabolism 10 526.351 10.87 3.93 0.46 0.0014 1.00 1.0000 [M + FA-H]- C24H52NO6P Lyso-PAF C-16 Glycerophospholipid metabolism 11 917.628 10.89 1.72 0.42 0.0009 1.30 0.5811 [M − H]- C49H91O13P PI(18:0/22:2) Glycerophospholipid metabolism 12 873.574 10.93 2.10 0.66 0.0313 1.05 0.9599 [M − H]- C50H83O10P PG (22:6)/22:2) Glycerophospholipid metabolism 13 524.336 10.96 3.24 1.98 0.0121 1.17 0.5210 [M + FA-H]- C24H50NO6P LysoPC(P-16:0) Glycerophospholipid metabolism 14 450.299 11.55 1.33 0.82 0.0366 1.06 0.7314 [M − H]- C22H46NO6P PC(O-14:1/0:0) Glycerophospholipid metabolism 15 494.36 12.40 1.10 2.05 0.0277 0.85 0.6780 [M+H]+ C25H52NO6P PE (P-20:0/0:0) Glycerophospholipid metabolism 16 536.407 12.49 2.18 1.55 0.0032 0.88 0.3748 [M+H]+ C28H58NO6P PC(P-20:0/0:0) Glycerophospholipid metabolism 17 280.263 12.68 1.19 1.61 0.0267 0.71 0.1011 [M+H]+ C18H33NO Linoleamide – 18 327.233 13.22 3.81 1.28 0.0041 0.93 0.4296 [M − H]- C22H32O2 Docosahexaenoic acid Biosynthesis of unsaturated fatty acids 19 835.664 13.39 1.92 2.47 0.0130 0.86 0.7194 [M+Na]+ C47H93N2O6P SM(d18:2/24:0) Sphingolipid metabolism 20 833.631 13.39 2.93 0.29 0.0004 1.28 0.8284 [M+H]+ C46H89O10P PG (18:0/22:1) Glycerophospholipid metabolism 21 303.232 13.40 6.54 1.20 0.0111 0.97 0.8438 [M − H]- C20H32O2 Arachidonic Acid Arachidonic acid metabolism 22 804.552 13.57 5.58 2.09 0.0038 0.61 0.0265 [M+Na]+ C44H80NO8P PC(16:0/20:4) Glycerophospholipid metabolism 23 494.361 13.75 1.35 1.36 0.0335 0.92 0.6982 [M − H]- C25H54NO6P PE (O-20:0/0:0) Glycerophospholipid metabolism 24 305.248 13.82 1.42 1.39 0.0081 0.84 0.1581 [M − H]- C20H34O2 Eicosatrienoic Acid Linoleic acid metabolism 25 538.423 13.94 3.21 1.53 0.0267 0.88 0.5969 [M+H]+ C28H60NO6P PC(O-20:0/0:0) Glycerophospholipid metabolism 26 331.264 14.09 3.92 1.32 0.0171 0.96 0.8416 [M − H]- C22H36O2 Docosatetraenoic Acid Biosynthesis of unsaturated fatty acids 27 281.248 14.26 3.10 1.31 0.0027 0.93 0.4364 [M − H]- C18H34O2 Oleic Acid Fatty acid biosynthesis [78]Open in a new tab [79]Table 4 Determined by P (M/C) values (P < 0.05), 27 metabolites with significant differences between the control and model group were identified by the method of metabonomics analysis. Moreover, among 27 metabolites, lysophosphatidylethanolamine (LysoPE) and phosphatidylcholines (PC) could be significantly reregulated in the treatment group after the administration of synbiotics, compared with the model group, which was determined by P (T/M) values (P < 0.05). m/z: mass charge ratio; Rt: rational time; VIP: variable importance; FC: fold change; M/C: model/control; T/M: treatment/model. 3.5. Proteomic analysis of the antihypertensive effect of the synbiotic preparation in the ileum STRING analysis revealed the interaction network between the differentially expressed proteins and enriched the pathways according to the KEGG database. In this study, 71 proteins were differentially expressed in the ileum between the model and control groups (P (M/C) < 0.05), among which 38 were upregulated (fold change (M/C) > 1.20) and 33 were downregulated (fold change (M/C) < 0.80) ([80]Table 5). Pathway enrichment analysis of these 71 differentially expressed proteins revealed that the PPAR signaling pathway exhibited the highest score ([81]Fig. 5), with six of the differentially expressed proteins were related to this pathway ([82]Table 6). In addition, STRING network analysis of the differentially expressed proteins confirmed the interactions among the proteins related to the PPAR signaling pathway ([83]Fig. 6). Table 5. The differentially expressed proteins in the control, model and treatment groups. No. Accession Name Gene Peptides (95 %) Fold Change (M/C) P (M/C) Fold Change (T/M) P (T/M) 1 [84]Q62812 Myosin-9 MYH9 192 0.7311 0.0001 1.1272 0.2136 2 [85]D3ZHA0 Filamin-C FLNC 103 0.5495 0.0001 1.7701 0.0202 3 [86]P07335 Creatine kinase B-type KCRB 92 0.5546 0.0022 0.6982 0.8360 4 [87]P48037 Annexin A6 ANXA6 48 0.5200 0.0013 1.6293 0.0081 5 [88]Q63270 Cytoplasmic aconitate hydratase ACOC 32 0.6668 0.0478 1.3552 0.3422 6 [89]P22985 Xanthine dehydrogenase/oxidase XDH 31 0.7447 0.0479 1.1376 0.2121 7 [90]Q62635 Mucin-2 (Fragment) MUC2 29 0.3802 0.0007 1.0000 0.8674 8 [91]Q5RKI0 WD repeat-containing protein 1 WDR1 34 0.7447 0.0361 1.3677 0.0731 9 [92]Q9Z1X1 Extended synaptotagmin-1 ESYT1 17 0.6607 0.0285 1.4060 0.1628 10 [93]P98089 Intestinal mucin-like protein (Fragment) MUC2L 21 0.3767 0.0092 1.2942 0.8671 11 [94]Q08163 Adenylyl cyclase-associated protein 1 CAP1 27 0.6427 0.0155 0.9036 0.9856 12 [95]Q62952 Dihydropyrimidinase-related protein 3 DPYL3 23 0.6607 0.0356 0.6427 0.6558 13 [96]F1LQ48 Heterogeneous nuclear ribonucleoprotein L HNRPL 25 0.5649 0.0152 0.8710 0.6664 14 [97]Q5XI07 Lipoma-preferred partner homolog LPP 17 0.4325 0.0420 1.1272 0.8142 15 [98]Q497B0 Omega-amidase NIT2 NIT2 11 0.4831 0.0090 0.8241 0.7662 16 [99]Q9Z1Z9 PDZ and LIM domain protein 7 PDLI7 15 0.6081 0.0461 0.9727 0.5253 17 [100]P07150 Annexin A1 OS=Rattus norvegicus ANXA1 15 0.3281 0.0189 2.9923 0.0057 18 [101]P14046 Alpha-1-inhibitor 3 A1I3 38 0.4699 0.0453 1.7701 0.0171 19 [102]P54290 Voltage-dependent calcium channel subunit α-2/Δ-1 CA2D1 9 0.3192 0.0019 2.4889 0.0766 20 [103]Q6IFW6 Keratin, type I cytoskeletal 10 K1C10 13 0.3733 0.0090 2.3988 0.0732 21 [104]Q10728 Protein phosphatase 1 regulatory subunit 12A MYPT1 13 0.5546 0.0203 1.2246 0.5933 22 [105]O35413 Sorbin and SH3 domain-containing protein 2 SRBS2 8 0.4786 0.0462 0.9120 0.4625 23 [106]Q9WUH4 Four and a half LIM domains protein 1 FHL1 11 0.5649 0.0127 1.2474 0.6657 24 [107]Q66H12 Alpha-N-acetylgalactosaminidase NAGAB 9 0.7379 0.0419 0.7870 0.8217 25 [108]P42123 L-lactate dehydrogenase B chain LDHB 9 0.4207 0.0263 1.4191 0.2011 26 [109]P18163 Long-chain-fatty-acid--CoA ligase 1 ACSL1 10 0.5970 0.0188 0.9817 0.8839 27 [110]P14141 Carbonic anhydrase 3 CAH3 6 0.5248 0.0420 1.3932 0.4506 28 [111]Q07969 Platelet glycoprotein 4 CD36 6 0.2992 0.0094 0.8241 0.6546 29 [112]Q02765 Cathepsin S CATS 8 0.4831 0.0369 1.6293 0.1728 30 [113]P58775 Tropomyosin beta chain TPM2 29 0.4613 0.0447 0.6252 0.4235 31 [114]O08837 Cell division cycle 5-like protein CDC5L 3 0.6855 0.0481 1.1482 0.2812 32 [115]Q63010 Liver carboxylesterase B-1 EST5 4 0.4966 0.0047 2.5119 0.0050 33 [116]P16086 Spectrin alpha chain, non-erythrocytic 1 SPTN1 100 1.2942 0.0282 1.1695 0.2841 34 [117]P02770 Serum albumin ALBU 138 1.8880 0.0269 1.3804 0.0000 35 [118]P07756 Carbamoyl-phosphate synthase, mitochondrial CPSM 46 1.4859 0.0035 0.8551 0.4272 36 [119]P01026 Complement C3 CO3_ 43 1.3428 0.0373 1.1066 0.1617 37 [120]P06685 Sodium/potassium-transporting ATPase subunit α-1 AT1A1 80 1.6904 0.0196 0.8472 0.3237 38 [121]P55281 Cadherin-17 CAD17 27 2.0893 0.0082 0.9120 0.0814 39 [122]Q63041 Alpha-1-macroglobulin A1M 29 2.0137 0.0147 1.3804 0.0660 40 [123]P28037 Cytosolic 10-formyltetrahydrofolate dehydrogenase AL1L1 24 1.8707 0.0006 1.0471 0.9649 41 [124]P15684 Aminopeptidase N AMPN 41 1.9588 0.0051 1.0765 0.2186 42 [125]P55260 Annexin A4 ANXA4 37 1.4588 0.0339 0.7870 0.9124 43 [126]P50123 Glutamyl aminopeptidase AMPE 23 1.5276 0.0198 0.9817 0.2151 44 [127]P82808 Glutamine--fructose-6-phosphate aminotransferase 1 GFPT1 30 1.7865 0.0153 0.5200 0.0525 45 [128]O54728 Phospholipase B1, membrane-associated PLB1 16 2.1677 0.0206 1.1272 0.9222 46 [129]P07872 Peroxisomal acyl-coenzyme A oxidase 1 ACOX1 21 1.9231 0.0014 1.0280 0.5170 47 [130]Q5SGE0 Leucine-rich PPR motif-containing protein LPPRC 16 1.8707 0.0067 0.7516 0.1874 48 [131]Q68FS4 Cytosol aminopeptidase AMPL 20 1.4997 0.0356 0.8790 0.2671 49 [132]Q6Q0N1 Cytosolic non-specific dipeptidase CNDP2 24 1.2942 0.0067 1.0765 0.7875 50 [133]Q5XHZ0 Heat shock protein 75 kDa, mitochondrial TRAP1 18 1.6596 0.0305 0.7447 0.1331 51 [134]P25409 Alanine aminotransferase 1 ALAT1 17 1.4191 0.0291 0.8710 0.2152 52 [135]O54697 N-acetylated-α-linked acidic dipeptidase-like protein NALDL 24 1.6904 0.0103 0.8551 0.6989 53 [136]Q9QZA2 Programmed cell death 6-interacting protein PDC6I 15 1.4859 0.0488 0.8790 0.5099 54 [137]Q62774 Unconventional myosin-Ia MYO1A 25 1.2359 0.0166 1.0666 0.9077 55 [138]P18886 Carnitine O-palmitoyltransferase 2, mitochondrial CPT2 12 1.6444 0.0420 0.9908 0.4667 56 [139]P28826 Meprin A subunit beta MEP1B 12 1.7701 0.0048 1.2474 0.7813 57 [140]P51538 Cytochrome P450 3A9 CP3A9 11 5.7016 0.0190 0.9036 0.4332 58 [141]P06399 Fibrinogen alpha chain FIBA 11 1.9588 0.0498 0.9908 0.6370 59 [142]P43245 Multidrug resistance protein 1 MDR1 13 2.2699 0.0008 1.0093 0.2770 60 [143]P04639 Apolipoprotein A-I APOA1 14 2.2080 0.0244 0.7943 0.4474 61 [144]Q63269 Inositol 1,4,5-trisphosphate receptor type 3 ITPR3 7 1.9953 0.0090 0.5916 0.4216 62 [145]P51574 Solute carrier family 15 member 1 S15A1 6 2.7542 0.0213 1.3062 0.7479 63 [146]P51870 Cytochrome P450 4F5 CP4F5 7 3.6644 0.0138 1.0186 0.9775 64 [147]P16446 Phosphatidylinositol transfer protein alpha isoform PIPNA 5 1.6749 0.0206 1.0765 0.2475 65 [148]P62994 Growth factor receptor-bound protein 2 GRB2 5 2.8840 0.0407 1.0666 0.8967 66 [149]P05982 NAD(P)H dehydrogenase [quinone] 1 NQO1 7 6.3680 0.0105 0.6855 0.4866 67 [150]P02651 Apolipoprotein A-IV APOA4 4 2.2699 0.0157 0.3802 0.0317 68 [151]P01836 Ig kappa chain C region, A allele KACA 5 3.6644 0.0272 0.9638 0.7284 69 [152]P43244 Matrin-3 MATR3 4 2.9648 0.0352 0.5152 0.5355 70 [153]Q6TA48 Mucosal pentraxin MPTX 4 3.6308 0.0247 1.3932 0.5832 71 [154]P49743 Retinoic acid receptor RXR-beta RXRB 2 0.6026 0.0414 1.5417 0.0454 [155]Open in a new tab [156]Table 5 71 differentially expressed proteins in the ileum between the model and control group (P(M/C) < 0.05) were determined by the method of proteomics analysis, among which 38 were upregulated (Fold Change(M/C) > 1.20) and 33 were downregulated (Fold Change(M/C) < 0.80). After administration of synbiotics, 26 of 71 proteins were reregulated in the ileum of the treatment group [(Fold Change(T/M) < 0.80 and Fold Change(M/C) > 1.20) or (Fold Change(T/M) > 1.20 and Fold Change(M/C) < 0.80)]. M/C: model/control; T/M: treatment/model. Fig. 5. [157]Fig. 5 [158]Open in a new tab Kegg pathway enrichment analysis. Table 6. The differentially expressed proteins related to PPAR signaling pathway. Uniport Accession Name Molecular function [159]Q07969 CD36 molecule (Cd36) lipoprotein particle binding, lipid binding, fatty acid binding [160]P07872 acyl-CoA oxidase 1(Acox1) fatty-acyl-CoA binding, acyl-CoA oxidase activity, fatty acid binding [161]P18163 acyl-CoA synthetase long-chain family member 1(Acsl1) acetate-CoA ligase activity, long-chain fatty acid-CoA ligase activity [162]P04639 apolipoprotein A1 (Apoa1) lipid transporter activity, phospholipid binding, lipid binding [163]P18886 carnitine palmitoyltransferase 2(Cpt2) Carnitine O-palmitoyltransferase activity [164]P49743 Retinoic acid receptor RXR-beta ligand-activated sequence-specific DNA binding, steroid hormone receptor activity [165]Open in a new tab [166]Table 6 Compared with the WKYs, six differentially expressed proteins involved in PPAR signaling pathway were determined in the ileum segment of SHRs by the method of proteomics analysis. Fig. 6. [167]Fig. 6 [168]Open in a new tab Network Analysis of differentially expressed proteins. Among these proteins, the six red balls represented the proteins related to PPAR signaling pathway (Rxrb, Acsl1, Cpt2, Acox1, CD36 and Apoa1). The lines between balls represented as follows. Following administration of the synbiotic preparation, 26 out of the 71 differentially expressed proteins were reregulated ([fold change (T/M) < 0.80, fold change (M/C) > 1.20] or [fold change (T/M) > 1.20 and fold change (M/C) < 0.80]) in the ileum of SHRs ([169]Table 5). Notably, among the six differentially expressed proteins involved in the PPAR signaling pathway, retinoic acid receptor beta (RXRβ), a key protein, was significantly reregulated (P (T/M) < 0.05) following administration of the synbiotic preparation ([170]Table 5). 3.6. Immunofluorescence analysis To confirm the results of the proteomics analysis and to identify the PPAR subtype that plays an important role in the antihypertensive mechanism, the expression of PPARs α, β, and γ was determined by immunohistochemistry. The data showed that following 7 weeks of continuous administration of the synbiotic preparation, the expression of PPARα in the ileum was significantly downregulated in the SHR model and SHR treatment groups compared with that in the WKY group ([171]Fig. 7A), which indicated that the synbiotic preparation had no effect on PPARα expression in SHRs. However, the expression of PPARβ and γ in the ileum was significantly upregulated in the SHR treatment group compared with that in the SHR model group, and their levels were almost the same as that in the WKY group ([172]Fig. 7B and C). These data indicated that the synbiotic preparation upregulates the expression of PPARβ and γ in the ileum of the SHR treatment group to attenuate blood pressure. Fig. 7. [173]Fig. 7 [174]Fig. 7 [175]Fig. 7 [176]Fig. 7 [177]Open in a new tab (Ã C) The expressions of PPARα, β and γ in the ileum segment of WKY, SHR-model and SHR-treatment groups. In the left column, the sections of ileum in WKY, SHR-model and SHR-treatment groups were stained with PPARα, β and γ antibody. PPARα exhibited green and PPARβ and γ exhibited red. In the middle column, the sections of ileum in WKY, SHR-model and SHR-treatment groups were counterstained with DAPI, and the nuclei in the ileum segment became blue. In the right column, here were merge images of PPARα, β and γ and DAPI in the ileum of WKY, SHR-model and SHR-treatment groups. (D) The negative control of PPARα, β and γ. Bar: 20 μm. 3.7. Real-time qPCR analysis of PPARs Real-time qPCR demonstrated that PPARα expression was significantly downregulated in the model and treatment groups compared with that in the control group. Moreover, expression of PPARβ and γ was significantly downregulated in the model group compared with that in the control group; however, the expression of PPARβ and γ was markedly upregulated in the treatment group compared with that in the model group after the administration of the synbiotic preparation. In addition, the expression of PPARβ; however, not PPARγ, was markedly downregulated after the administration of the synbiotic preparation, compared with that in the control group ([178]Fig. 8). Fig. 8. [179]Fig. 8 [180]Open in a new tab The expressions of PPARα, β and γ in the ileum segments of the control, model and treatment groups were determined by the method of qPCR. All data were presented as the mean ± standard error of the mean. Asterisks indicated statistical significance between the SHR-model or the SHR-treatment group and the WKY-control group, *p < 0.05, **p < 0.01, ***p < 0.001. Symbol indicated statistical significance between the SHR-model and the SHR-treatment groups, #p < 0.05. 3.8. Effects of PPARβ and PPARγ antagonists In the first 4 weeks, the SBP showed no significant difference between the SHR model and SHR-treatment groups; however, there were marked differences between the SHR and WKY control groups. However, from week 4 to week 7, the SBP of the SHR-treatment group was lower than that of the SHR model group. When the rats were administered the PPARβ (GSK3787) or PPARγ (GW9662) antagonists, the antihypertensive effect of synbiotic preparation in the SHR treatment group was significantly inhibited from week 4 to week 7. The SBP of the SHR-treatment group administered the antagonist showed no difference compared to that of the SHR model group with or without antagonists; however, the SBP was significantly different compared to that of the SHR-treatment group without antagonist. The SBP in the two SHR model groups showed no significant difference with or without the antagonist. Furthermore, the SBP was not significantly different between WKY control and WKY control + PPARβ antagonist or PPARγ antagonist groups ([181]Fig. 9A and B). Fig. 9. [182]Fig. 9 [183]Open in a new tab (A) The effects of GSK3787 and synbiotics on SBP in the SHRs. In the first 4 weeks, there were no difference in SBP between SHR-model and SHR-treatment group, both of which showed the significant difference in comparison with WKY group. From Week 4 to Week 7, there were obviously difference in SBP between SHR-model and SHR-treatment group. However, after administered with GSK3787, the level of SBP of SHR-treatment group rebounded, which showed the significant difference, compared with that of SHR-treatment group without GSK3787. GSK3787 had no effects on the SHR-model and WKY group. The dots were presented as the mean ± standard error of the mean. Asterisks indicated statistical significance between the SHR-model or the SHR-treatment group and the WKY-control group on the same week, **p < 0.01. Symbol indicated statistical significance between the SHR-model and the SHR-treatment groups on the same week, #p < 0.05. Letter indicated statistical significance between the SHR-treatment without and with GSK3787, a p < 0.05. (B) The effects of GW9662 and synbiotics on SBP in the SHRs. In the first 4 weeks, there were no difference in SBP between SHR-model and SHR-treatment group, both of which showed the significant difference in comparison with WKY group. From Week 4 to Week 7, there were obviously difference in SBP between SHR-model and SHR-treatment group. However, after administered with GW9662, the level of SBP of SHR-treatment group rebounded, which showed the significant difference, compared with that of SHR-treatment group without GW9662. GW9662 had no effects on the SHR-model and WKY group. The dots were presented as the mean ± standard error of the mean. Asterisks indicated statistical significance between the SHR-model or the SHR-treatment group and the WKY-control group on the same week, **p < 0.01. Symbol indicated statistical significance between the SHR-model and the SHR-treatment groups on the same week, #p < 0.05. Letter indicated statistical significance between the SHR-treatment without and with GW9662, a p < 0.05. 4. Discussion SHRs are inbred genetic models of experimental hypertension. Their blood pressure rapidly increases to >180 mmHg between 6th and 12th week after birth, reaching a maximum value of 240 mmHg in the 24th week, accompanied by heart, brain, and kidney damage. Given that the progression of hypertension in SHRs is similar to that of human primary hypertension in many aspects [[184]16], and that many potential antihypertensive agents have been identified using SHRs, this animal model was selected for the present study. Blood pressure measurements showed that following administration of the synbiotic preparation, the SBP of SHRs in the treatment group decreased significantly in the 6th week compared with that of SHRs in the model group. After withdrawal of the synbiotic preparation, the SBP of the SHRs in the treatment group rapidly recovered within 2 weeks to almost the same level as that in the model group. Following readministration of the synbiotic preparation, the SBP of SHRs in the treatment group improved. These data indicate that the synbiotic preparation may exert an antihypertensive effect in SHRs. However, the DBP of SHRs in the treatment group did not change significantly compared to that of SHRs in the model group. Moreover, plasma levels of AII, COR, and ALD were not significantly different between the treatment and model groups. SBP constitutes the highest arterial blood pressure in the cardiac cycle and occurs immediately after systole of the left ventricle of the heart, representing the capability of cardiac output that is dependent on myocardial systole and aortic distention; whereas DBP constitutes the lowest arterial blood pressure of the cardiac cycle and occurs during diastole of the heart, reflecting the extent of peripheral vascular resistance [[185]17]. AII strongly contracts the blood vessels and increases peripheral vascular resistance, leading to elevated blood pressure. AII also stimulates the adrenal cortical zona cells to synthesize and release ALD, which increases blood volume and blood pressure [[186]18]. COR, a glucocorticoid, is secreted by the adrenal cortex band cells and regulates glucose metabolism and electrolyte distribution. Overproduction of COR leads to hypertension [[187]19]. Therefore, SBP and DBP are regulated by the RAAS. However, notably, our data indicate that the antihypertensive mechanism of the synbiotic preparation does not involve the RAAS. Because gut microbiota-mediated antihypertensive effects may involve attenuation of systemic dysfunction in the host via interactions of the local microbiota in the gastrointestinal tract, we examined the colonization of the gastrointestinal tract by L. acidophilus and B. lactis from the synbiotic preparation and the underlying antihypertensive mechanism using molecular biological techniques, proteomics, metabolomics, and immunohistochemistry. Following 7 weeks of continuous administration of SHRs with the synbiotic preparation, both L. acidophilus and B. lactis predominately colonized the ileum and colon, and the quantities of both L. acidophilus and B. lactis were significantly higher in the ileum and colon than that in the jejunum. The entire intestinal mucosal surface is covered with a thick layer of mucus gel (>100 μm) secreted by goblet cells that protects the epithelial lining from luminal sheer forces, adhesion, and invasion by microorganisms, dietary toxins, and other antigens present in the intestinal lumen. The intestinal mucus consists of two layers with similar protein composition. The inner layer is firmly attached to the epithelium and functions as a barrier to prevent bacterial invasion, whereas the outer layer consists of a loose matrix that is generally colonized by bacteria. Several studies have demonstrated that the thickness of the mucus layer varies along the intestine and is thickest in the colon and thinnest in the jejunum. The rate of mucus secretion and extent of mucus accumulation provide a suitable environment for endogenous microflora [[188]20,[189]21] and contribute to L. acidophilus and B. lactis colonization of the ileum and colon. Moreover, a symbiotic relationship exists between L. acidophilus and B. lactis; namely, vitamin B and folic acid released by B. lactis promotes L. acidophilus proliferation, which in turn produces nutrients for B. lactis, all of which are conducive to the co-colonization of both microorganisms in the ileum and colon. In addition, xylooligosaccharides in the synbiotic preparation facilitate the co-colonization of B. lactis and L. acidophilus in the gastrointestinal tract [[190]22]. Proteomics is an powerful tool to study hypertensive conditions and related mechanisms, and has been widely used to study hypertension since 2001 [[191]23,[192]24]. In the present study, KEGG pathway enrichment analysis of the 71 differentially expressed proteins in the ileum indicated that the mechanism underlying hypertension in SHRs may involve the PPAR signaling pathway. Six proteins related to this pathway were upregulated or downregulated in the model group (acyl-CoA oxidase, apolipoprotein A1, and carnitine palmitoyltransferase 2 expression were upregulated, whereas CD36, acyl-CoA synthetase long chain family member, and RXRβ expression were downregulated). Moreover, these proteins were modulated by the synbiotic preparation. These results further suggest that the synbiotic preparation may regulate the PPAR signaling pathway to improve the blood pressure of SHRs by facilitating the interaction of the two colonizing microorganisms with the cells of the small intestine. Based on the above results, it is conceivable that in the lumen of the small intestine of a healthy host, fatty acids and their metabolites, the major bacterial fermentation products of carbohydrates, lipids, and proteins, are transported across the cell membrane by binding to fatty acid translocase (FAT/CD36) on the membrane, and are then transported into the nucleus by binding to fatty acid-binding protein (FABP) in the cytoplasm. After entering the nucleus, the complex consisting of FABP and the ligand bound to the PPAR receptor forms a heterodimer with RXR, which activates the transcriptional machinery through the recruitment of coregulators. The transcriptional machinery regulates gene expression by binding to specific DNA sequence elements, termed PPAR response elements, which activate the biochemical cascade reactions of PPARs. However, hypertension, a chronic disease, may cause dysbiosis of the endogenous microflora community in the gastrointestinal tract of the host and lead to the loss of probiotics. This disruption may decrease the amount and absorption of fatty acids and their metabolites produced by probiotics by the cells of the small intestine, which may in turn attenuate the PPAR signaling pathway to exacerbate hypertension. Following administration of the synbiotic preparation, L. acidophilus and B. lactis colonize the gastrointestinal tract of SHRs and restore the balance of endogenous microflora, which leads to restoration of the PPAR signaling pathway to improve hypertension. Notably, among the six PPAR pathway-related proteins identified in the present study, RXRβ, a key nodal protein in this pathway, was significantly reregulated following administration of the synbiotic preparation. The reason for the specific and significant reregulation of RXRβ by the symbiotic preparation compared to the other PPAR pathway-related proteins may be attributed to the inadequate intervention (in terms of the duration and dosage of the preparation) or inadequate numbers of samples analyzed, all of which merit further exploration. PPARs belong to the nuclear hormone receptor superfamily of transcription factors, and comprise of three distinct subtypes PPAR α, β/δ, and γ, all of which are expressed in the small intestine [[193]25]. We sought to determine which PPAR subtypes played a role in improving blood pressure. The results of immunohistochemistry revealed that the expression of PPARβ and γ was significantly upregulated in the ileum of the treatment group compared with that of the model group, which further confirmed the proteomics results. Consistent with these results, administration of the PPARβ and γ antagonists GSK3787 and GW9662, significantly increased SBP that was lowered by the synbiotic preparation in the SHR treatment group. Several lines of evidence have shown that activation of PPARβ and γ improves eNOS-mediated vasodilatation by increasing antioxidant activity and inhibits the inflammatory response in the vascular wall by repressing NF-κB signaling, which, in turn, restores vascular structure and function to reduce blood pressure [[194][26], [195][27], [196][28]]. Activation of PPARβ was previously shown to cause a progressive reduction in SBP; however, not in DBP, in SHRs [[197]28], which is consistent with the blood pressure data in the present study. Additionally, the metabolomic analysis indicated that the levels of lysoPE and PCs in the ileum were significantly higher in the treatment group than that in the model group. Several studies have reported that lysoPE and PCs released from intestinal bacterial fermentation and their metabolites, such as free fatty acids, specifically activate the PPAR pathway and induce PPAR expression [[198][29], [199][30], [200][31], [201][32], [202][33]]. Therefore, the synbiotic preparation may improve the blood pressure of SHRs by altering the composition of their intestinal microbiota, regulating PPAR signaling pathway, and activating the PPARβ and γ cascade in the ileum. Although the synbiotic preparation contributed to the localization of L. acidophilus and B. lactis both in the ileum and colon of SHRs in the treatment group, the gut microbiota inhabiting the colon segment only ferments carbohydrates and proteins that escape absorption in the small intestine during digestion. The small intestine serves as the primary digestive and absorptive organ for a wide range of metabolites, such as short-chain fatty acids produced from the interaction of the microbiota with dietary components. These metabolites are effectively absorbed in the small intestine, and activate relevant biochemical cascade reactions, leading to changes in the physiology and pathology of the host. Given that the colon has a lower absorption efficacy than the ileum, it is unlikely to be the most important part of the gastrointestinal tract contributing to the synbiotic preparation-mediated improvement of the blood pressure of SHRs. However, this warrants further investigation. 5. Conclusions In conclusion, the novel synbiotic preparation improves blood pressure by altering the composition of the intestinal microbiota, regulating PPAR signaling pathway, and activating the PPARβ and γ cascade reactions in the ileum. Funding The work was supported by grants from project of Natural Science Foundation of Liaoning Province (20170540927) and Shenyang Science and Technology Plan Project (22-321-33-33). Data availability Data will be made available on request. CRediT authorship contribution statement Ying Huang: Writing – review & editing, Validation, Conceptualization. Fang Wang: Conceptualization. Wei Gong: Writing – original draft, Formal analysis. Yufeng Chen: Writing – review & editing. Declaration of competing interest It is the first time to submit the research article to Heliyon. This manuscript is based on the original research results and has never been published elsewhere. We warrant that all of the authors have contributed substantially to the manuscript and approved the final submission. If accepted, it will not be published elsewhere in the same form, in English or in any other language. Acknowledgements