GM diversity and enterotype in pHTN and HTN

To identify whether gut microbial changes are associated with HTN, we performed shotgun metagenomic sequencing of fecal samples from a cohort of 196 Chinese individuals. The cohort consisted of 41 healthy controls, 56 subjects with pHTN, and 99 patients with primary HTN. All the participants were from a cohort study among employees of the Kailuan Group Corporation. The Kailuan study is a prospective cohort study focusing on the Kailuan community in Tangshan, a large modern city in northern China. All the subjects in the hypertension group were newly diagnosed hypertensive patients prior to antihypertensive treatment. Patients suffering from cancer, heart failure, renal failure, smoking, stroke, peripheral artery disease, and chronic inflammatory disease were all excluded. Drugs including statins, aspirin, insulin, metformin, nifedipine, and metoprolol were not used on the patients, and other drug consumption was not compared because the sample size was quite small. Individuals were also excluded if they had received antibiotics or probiotics within the last 8 weeks. Other than SBP and DBP, there was no significant difference in other clinical parameters among groups, except for fasting blood glucose level (FBG) (P = 0.026, C vs H; Kruskal-Wallis test, Additional file 1: Table S1). Bacterial DNA was extracted from stool samples, sequenced on the Illumina platform, and a total of 1211 Gb 125-bp paired-end reads were generated, with an average of 6.18 ± 1.43 (s.d.) million reads per sample (Additional file 2: Table S2). For each sample, a majority of high-quality sequencing reads (83.74–97.24%) were de novo assembled into long contigs or scaffolds, which were used for gene prediction, taxonomic classification, and functional annotation.

To characterize the bacterial richness, rarefaction analysis was performed by randomly sampling 100 times with replacement and estimating the total number of genes that could be identified from these samples. The curve in each group was near saturation, which suggested the sequencing data were great enough with very few new genes undetected. The rate of acquisition of new genes in control samples rapidly outpaced new gene acquisition in disease samples, suggesting lower levels of gene richness in the pHTN and HTN groups (Fig. 1a). The number of genes in both pHTN and HTN groups were significantly decreased as compared to the controls (P = 0.024, C vs P; P = 0.04, C vs H; Kruskal-Wallis test, Fig. 1b). Shannon index based on the genera profile was calculated to estimate the within-sample (α) diversity. Consistently, the α diversity at the genus level was much lower in pHTN and HTN groups (P = 0.023, C vs P; P = 0.016, C vs H; Kruskal-Wallis test, Fig. 1c). The reduced richness of genes and genera in the GM of pHTN and HTN groups is consistent with previous findings [19], suggesting possible deficiency of healthy microflora in hypertensive patients.

Fig. 1 Decreased diversity and shift of gut enterotypes in human adults with pHTN and HTN. a Rarefaction curves for gene number in control (n = 41), pHTN (n = 56), and HTN (n = 99) after 100 random sampling. The curve in each group is near smooth when the sequencing data are great enough with few new genes undetected. b, c Comparison of the microbial gene count and α diversity (as accessed by Shannon index) based on the genera profile in the three groups. C, control; P, pHTN; H, HTN. P = 0.024, C vs P; P = 0.04, C vs H; for gene count. P = 0.023, C vs P; P = 0.016, C vs H; for α diversity. P values are from Kruskal-Wallis test. d A total of 196 samples are clustered into enterotype 1 (blue) and enterotype 2 (red) by PCA of Jensen-Shannon divergence values at the genus level. The major contributor in the two enterotypes is Prevotella and Bacteroides, respectively. e Relative abundances of the top genera (Prevotella and Bacteroides) in each enterotype. P = 6.31e−31 and P = 2.09e−15, respectively; Wilcoxon rank sum test. f The percentage of control, pHTN and HTN samples distributed in two enterotypes. 26.83% normotensive controls, 48.21% pHTN, and 45.45% HTN are found in enterotype 1. P = 0.02, C vs P; P = 0.03, C vs H; Fisher’s exact test. Boxes represent the inter quartile ranges, the inside line or points represent the median, and circles are outliers Full size image

To explore the difference between the microbial communities at different stages of HTN, enterotypes were identified based on the abundance of genera using Partitioning Around Medoid (PAM) clustering method. The optimal number of enterotypes was two as indicated by Calinski-Harabasz (CH) index (Additional file 3: Figure S1). Then Principal Coordinate Analysis (PCoA) using Jensen-Shannon distance was performed to cluster the 196 samples into two distinct enterotypes (Fig. 1d). Prevotella was the most enriched genus in enterotype 1; Bacteroides was the most enriched genus in enterotype 2 (P = 6.31e−31 and P = 2.09e−15, respectively; Wilcoxon rank sum test, Fig. 1e). Both contributors in the two enterotypes have been reported in European and Chinese populations before [2, 3]. There was a higher percentage of pre-hypertensive and hypertensive patients distributed in enterotype 1 (48.21% for pHTN, and 45.45% for HTN), while more healthy controls (73.17%) were found in enterotype 2 (P = 0.02, C vs P; P = 0.03, C vs H; Fisher’s exact test; Fig. 1f). These findings suggest that enterotype 2 may represent a GM community structure associated with healthy control, while enterotype 1 may be associated with pHTN and HTN.

Considering the higher percentage of HTN patients in enterotype 1, we clustered the genera in this enterotype and further explored the microbial co-occurrence network by Spearman’s correlation. There was a positively interacted network constituted by 12 genera, which were negatively correlated with Prevotella, the core genus in this enterotype (Additional file 4: Figure S2a). All these genera were decreased in enterotype 1 as compared with enterotype 2 (Additional file 4: Figure S2b). Eight out of them were directly linked to Prevotella, while the other four, including Oscillibacter, Faecalibacterium, Butyrivibrio, and Roseburia, were indirectly linked to Prevotella. These findings highlighted the possibility of Prevotella as a key genus associated with pHTN and HTN. The difference in gut enterotype distribution revealed profound changes of the intestinal microbiome structure in both pHTN and HTN, implying the significance of gut microbes in the development of HTN.

pHTN and HTN-associated genera in GM

Genes were aligned to the NR database and annotated to taxonomic groups. The relative abundance of gut microbes was calculated by summing the abundance of genes as listed in Additional file 2: Table S3–S4. P values were tested by Wilcoxon rank sum test and corrected for multiple testing with Benjamin & Hochberg method as others previously did [4, 25]. It is worth mentioning that 44 genera were differentially enriched in control, pHTN, and HTN (P < 0.1, Wilcoxon rank sum test, Fig. 2a and Additional file 2: Table S5). Fifteen of them were further shown in Fig. 2b. Genera such as Prevotella and Klebsiella were overrepresented in individuals with pHTN or HTN (Fig. 2b). Prevotella, originated from mouth and vagina, was abundant in the microbiome of our study cohort. The pathogenesis of periodontal diseases and rheumatoid arthritis are thought to be attributed to Prevotella [3, 26]. A wide range of infectious diseases are known to be attributed to Klebsiella [27, 28]. Porphyromonas and Actinomyces, which were also elevated in the HTN group, are morbific oral bacteria that cause infections and periodontal diseases [29].

Fig. 2 Genera strikingly different across groups. a Relative abundance of the top 44 most different genera across groups at the criteria of P value <0.1 by Wilcoxon rank sum test. C, control; P, pHTN; H, HTN. The abundance profiles are transformed into Z scores by subtracting the average abundance and dividing the standard deviation of all samples. Z score is negative (shown in blue) when the row abundance is lower than the mean. Genera at P value <0.01 are marked with dark green star, P value <0.05 with light green star, and P value ≥0.05 with gray circle. b The box plot shows the relative abundance of four genera enriched in pHTN and HTN patients, and 11 genera abundant in control. Genera are colored according to the phylum. Boxes represent the inter quartile ranges, lines inside the boxes denote medians, and circles are outliers Full size image

By contrast, Faecalibacterium, Oscillibacter, Roseburia, Bifidobacterium, Coprococcus, and Butyrivibrio, which were enriched in healthy controls, declined in pHTN and HTN patients (Fig. 2b). Our observations were consistent with the genera negatively correlated with Prevotella in the network of enterotype 1 (Additional file 4: Figure S2), and these bacteria are known to be essential for healthy status. For example, reduced levels of Faecalibacterium and Roseburia in the intestines are associated with Crohn’s disease and ulcerative colitis [30, 31]. Both bacteria are crucial for butyric acid production [30, 32]. Moreover, Bifidobacterium is an important probiotic necessary to intestinal microbial homeostasis, gut barrier, and lipopolysaccharide (LPS) reduction [33].

The divergence of GM composition in each sample was assessed to explore the correlation of microbial abundance with body mass index (BMI), age, and gender (Additional file 5: Figure S3). Although the gender ratio is discrepant among groups (Additional file 1: Table S1), we found no remarkable regularity of bacterial abundance based on BMI, age or gender.

To further validate the bacterial alterations in HTN, an independent metagenomic analysis was performed using the sequencing data generated from a previous study of type 2 diabetes [2]. From a total of 174 non-diabetic controls in the study, normotensive controls with SBP ≤125 mmHg or DBP ≤80 mmHg were enrolled, and HTN were elected with the inclusion criteria of SBP ≥150 mmHg or DBP ≥100 mmHg. The FBG levels between normotensive controls and HTN were similar. Finally, six subjects (HTNs, n = 3; normotensive controls, n = 3) were included in our analysis (Additional file 2: Table S6). As expected, the microbial diversity was decreased in HTN (Additional file 6: Figure S4a), and there were at least 20 genera showing consistent trends with our findings, including decreased Butyrivibrio, Clostridium, Faecalibacterium, Enterococcus, Roseburia, Blautia, Oscillbacter, and elevated Klebsiella, Prevotella, and Desulfovibrio (Additional file 6: Figure S4b, Additional file 2: Table S7).

Collectively, these results supported our hypothesis that bacteria associated with healthy status were reduced in patients with HTN. This phenomenon together with the overgrowth of bacteria such as Prevotella and Klebsiella may play important role in the pathology of HTN.

Co-abundance groups enriched in pHTN and HTN

Firstly, for each gene, an OR score was calculated according to the abundance of each gene. Then, for the comparative analysis between control and HTN samples, the HTN-associated genes were classified as HTN-enriched (OR >2) or HTN-depleted (OR <0.5) as previously described [34]. When calculating HTN-associated ORs, samples of pHTN were excluded, and samples labeled as HTN were excluded as well when calculating pHTN-associated ORs. A total of 1,120,526 genes significantly different in relative abundance across groups were identified (Additional file 7: Table S8). Secondly, we clustered genes by a rather high threshold (Spearman’s correlation coefficient ≥0.7) according to previous methods [4, 35]. Spearman’s correlation coefficient was analyzed by R. The cluster groups with at least 50 genes were defined as co-abundance groups (CAGs) [4], and used for further analysis [35]. One thousand ninety-nine distinct CAGs were obtained (Additional file 2: Table S9–S11 and Additional file 8: Figure S5a). Seven hundred fourteen CAGs were assigned to known bacterial genera based on the tracer genes, with at least 80% of the genes mapped to the reference genome at an identity higher than 85% (Additional file 8: Figure S5b).

CAGs were further clustered by Spearman’s correlation based on the abundance. Compared with the controls, there were 316 CAGs and 372 CAGs enriched in pHTN and HTN, respectively (Additional file 2: Table S12). In the control group, Firmicutes and Roseburia were more abundant (Fig. 3a, b). Most CAGs enriched in pre-hypertensive samples were originated from Enterobacter, a disease-causing bacteria linked to obesity. Klebsiella, causally implicated in various infections, was also overrepresented in pre-hypertensive and hypertensive patients [27]. Additionally, most recent studies revealed that Fusobacterium was enriched in the fecal samples of patients with liver cirrhosis, colorectal carcinoma, or ulcerative colitis [4, 36, 37]. We also detected several clusters of CAGs assigned to Fusobacterium enriched in pHTN and HTN groups. About 200 CAGs were different in pHTN and HTN. Most of them in pHTN were from Enterobacter and Klebsiella, while Prevotella and Fusobacterium were more abundant in HTN.

Fig. 3 Comparative analysis of GM enrichment across groups based on CAGs. a CAGs are defined as a minimum of 50 linked genes, and the correlation network of CAGs differentially enriched in pHTN and the control group is performed by Spearman’s correlation based on the abundance. b The network of CAGs enriched in HTN is compared to controls. CAGs are colored according to the taxonomic assignment as labeled, and the node size is scaled with the number of genes within the CAG. Edges between nodes denote Spearman correlation >0.8 (red) or between 0.7 and 0.8 (gray) Full size image

To further examine the relationship between clinical indices and CAGs of GM, physiological parameters of SBP, DBP, BMI, FBG, total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL) were included in a Spearman’s correlation analysis. We observed that SBP and DBP could negatively influence the CAGs enriched in the control group, such as Firmicutes and Roseburia, and positively interacted with Prevotella and Desulfovibrio, which were abundant in pHTN and HTN (Additional file 9: Figure S6). Whereas, both TC and TG were negatively correlated with Enterobacter, that was enriched in pHTN and HTN groups. Altogether, these results indicated that the bacterial communities in individuals with pHTN and HTN are similar, and the collective effect of these bacteria may account for intestinal dysbiosis in HTN.

Functional alteration in GM of pHTN and HTN

Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Carbohydrate-Active EnZymes (CAZy) [38] database, we evaluated gut microbial functions across groups in our study cohort. All the genes were aligned to the KEGG database and CAZy database, and proteins were assigned to the KEGG orthology and CAZy families (Additional file 2: Table S13–S15). Principal component analysis (PCA) based on KEGG orthology revealed striking differences in microbial functions at the first principal component (PC1) between controls and patients (P < 0.001, Wilcoxon rank sum test, Fig. 4a). Nearly all the KEGG modules and CAZy families displayed a similar discrepancy in pHTN and HTN when compared with the controls (Fig. 4b, c), illustrating the common functional features in pHTN and HTN. Sixty-five (n = 65) KEGG modules were differentially enriched among the three groups (adjusted P value <0.05, Wilcoxon rank sum test, Additional file 2: Table S12). The thirty-nine (n = 39) modules decreased in pHTN and HTN groups were involved in branched-chain amino acid biosynthesis and transport, ketone body biosynthesis, two-component regulatory system, and degradation of methionine and purine (Fig. 4b). These metabolic functions are essential for the host and have been observed in healthy populations [4, 5, 39, 40]. Although previous studies have found that iron, phosphate, and amino acid transport system, GABA biosynthesis, and methanogenesis were enriched in the patients subjected to colorectal cancer or liver cirrhosis [4, 39], these metabolic functions were not enriched in our patient cohort. We observed seventeen (n = 17) modules elevated in pHTN and HTN, including LPS biosynthesis and export, phospholipid transport, phosphotransferase system (PTS), biosynthesis of phenylalanine and phosphatidylethanolamine, and secretion system (Fig. 4b). The capacity to synthesize and export LPS of the gut microbiome in patients with colorectal carcinoma has been suggested to represent an important mechanism whereby inflammation contributes to tumor progression [5, 41, 42]. PTS system, phosphatidylethanolamine biosynthesis, secretion system, and transport of phospholipid, which were overrepresented in pHTN and HTN, are also linked to diabetes, liver cirrhosis, and rheumatoid arthritis [2, 4]. Additionally, the metagenome of patients were enriched in genes associated with cellulose-binding domains but depleted in host glycan-utilizing enzymes (Fig. 4c). These gut microbial functions in hypertensive patients are commonly associated with other diseases. Although the functional annotation analyses are predictive, it indicated that impairment of GM may evoke a disease-linked state through interference of physiological metabolic functions.

Fig. 4 Microbial gene functions annotation in pHTN and HTN. a PCA based on the relative abundance of KEGG orthology groups in 196 samples. Significant differences across groups are established at the first principal component (PC1) values, and shown in the box plots above. **P value <0.001, Wilcoxon rank sum test. b The average abundance of KEGG modules differentially enriched in control, pHTN, and HTN gut microbiome. Twenty nine modules enriched in control, and 11 modules overrepresented in both pHTN and HTN are shown in green and pink, respectively. The functional potential of KEGG modules are demonstrated on the right. c Heat map showing the abundance of 11 most significantly altered CAZy family in pHTN or HTN as compared to control Full size image

Metabolic profiling of GM in pHTN and HTN

Considering the aberrant function profiles of gut microbes in disease subjects, we wondered the microbe-host interactions in HTN. As some end products of fermentation by the GM could enter the bloodstream and exert important influences on the physiology of the hosts, we explored the host metabolic profiling in fasting serum of a subset of 124 subjects by high-throughput liquid chromatography-mass spectrometry (LC/MS) and examined the relationship between GM and metabolites in the circulation. Thirty healthy controls, 31 pHTNs, and 63 patients of HTN from our previous cohort were randomly enrolled. The serum samples were subjected to LC/MS analysis in both positive ion mode (ES+) and negative ion mode (ES−). After eliminating the impurity peaks and duplicate identifications, we identified a total of 1290 chromatographic peaks in ES+ and 2289 variables in ES− for further analyses. To discriminate the metabolic profiles across groups, we performed clustering analyses based on partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The serum samples from distinct groups were largely separated according to the PLS-DA plots (Fig. 5a). The scatter plots in pHTN group were closer to those in HTN, suggesting a similar metabolic mode. Furthermore, individuals in either pHTN or HTN groups were separated from the controls as further evidenced by the OPLS-DA score scatter plots (Fig. 5b).

Fig. 5 Aberrant metabolic patterns in pHTN and HTN. a PLS-DA score plots based on the metabolic profiles in serum samples from control, pHTN, and HTN group in ES+ and ES−. n = 30 for control, n = 31 for pHTN, and n = 63 for HTN. b Score scatter plots of OPLS-DA comparing the metabolic differences identify the separation between pHTN and control, HTN and control, respectively. c Metabolites significantly changed in pHTN or HTN as compared to control at VIP >1.5 and P value (t test) <0.05 are identified. Venn diagrams demonstrate the number of altered metabolites shared between pHTN (green) and HTN (red) by the overlap. d The relative amount of 26 endogenous compounds concurrently varied in both pHTN and HTN groups is transformed into Z scores in the heat map. There are six metabolites failed to be identified. e The relationship between 26 endogenous metabolites and the 44 top altered genera (Fig. 2a) in pHTN and HTN is estimated by Spearman’s correlation analysis. And those with low correlated (|r| <0.4) are not shown. Genera and metabolites are distinguished as abundant in control (green) or HTN (pink) Full size image

The compositional changes in patients involved 167 analytes that were significantly different between pHTN and control, and 215 analytes altered in HTN (Fig. 5c). There were 26 metabolites which were obviously different in both pHTN and HTN groups as compared to the control (Additional file 2: Table S16). Notably, these metabolites exhibited statistically analogous profiles of alterations in pHTN and HTN, which was consistent with our findings based on gut microbiome (Fig. 5d). Endogenous compounds whose levels significantly decreased in pHTN and HTN include phosphatidylserine (PS), 3,4,5-trimethoxycinnamic acid, lysophosphatidylcholine (LysoPC), S-carboxymethyl-l-cysteine, and lysophosphatidylethanolamine (LysoPE). 3,4,5-Trimethoxycinnamic acid is capable to protect against inflammatory diseases through suppressing cell adhesion molecules in vascular endothelial cells [43]. Also S-Carboxymethyl-l-cysteine exerts anti-inflammatory properties [44]. These observed downregulations could promote the inflammatory environment associated with HTN. On the other hand, endogenous compounds whose levels significantly increased in pHTN and HTN include metabolites such as Nα-acetyl-l-arginine, stearic acid, phosphatidic acid (PA), and glucoside. Elevated levels of Nα-acetyl-l-arginine and stearic acid have been previously observed in uremia and spontaneously hypertensive rats [45, 46]. These compounds may represent possible markers for the development of HTN and might be derived from gut microflora or their fermented products. To explore this idea, the relationship between 26 representative metabolites and the 44 most different genera was examined by correlation analysis (Fig. 5e). Control-enriched trichloroethanol glucuronide was positively correlated with Bifidobacterium and Akkermansia, but negatively linked to Prevotella. Conversely, there was a positive association between 9,10-dichloro-octadecanoic acid (stearic acid) and microflora including Klebsiella, Prevotella, and Enterbacter, which were all overrepresented in HTN. It was accordant that both Bifidobacterium and Roseburia negatively interacted with 9,10-dichloro-octadecanoic acid, which was hence considered as an important GM-influenced metabolic product in HTN. Thus the distinguished metabolic profiling in HTN was closely connected to intestinal microflora variation, although whether these metabolic products were directly metabolized by the intestinal microorganisms remained to be explored.

Identification of pHTN and HTN basing on gut microbiome

To illustrate the microbial and metabolic signature of pHTN and HTN, and further exploit the potential of gut microbiome and metabolites in HTN identification, random forest disease classifier using explanatory variables of CAGs, metabolites, and species abundances were performed. Tenfold cross-validation was repeated for five times and the receiver operating characteristic (ROC) curves for classifying pHTN and HTN patients from controls were developed.

We could detect HTN individuals accurately based on the gut CAGs + metabolites, as indicated by the area under the receiver operating curve (AUC) of up to 0.91, and 95% confidence interval (CI) of 0.75–1 (Fig. 6a). Similarly, comparing to the other variables, the variable of CAGs + metabolites was more effective to classify pHTN samples from controls, showing an AUC of 0.89, and 95% CI of 0.65–1 (Fig. 6b). Thus, we conducted a testing set consisted of 13 randomly chosen subjects based on CAGs + metabolites. In this assessment analysis, both pHTN and HTN patients possess remarkable features in gut microbiome and metabolites as compared to the controls (Fig. 6c). However, we observed poor performance on the test set when discriminating between pHTN and HTN by lower specificity and sensitivity (AUC, 0.57; 95% CI, 0.21–0.93; Fig. 6c). This further validated the similarity of pHTN and HTN in our previous findings. Among the most discriminatory CAGs to distinguish pHTN or HTN from control, there were some CAGs similarly enriched in both pHTN and HTN subjects, including CAG-172 (Prevotella), CAG-197 (Prevotella), CAG-759 (Faecalibacterium), CAG-765 (Faecalibacterium), and CAG-793 (Faecalibacterium) (Fig. 6d). These CAG markers were the common microbial characteristics of pHTN and HTN and contributed a lot to the identification of pHTN and HTN.

Fig. 6 A classification to identify pHTN and HTN patients from controls. a, b Random forest models are constructed using explanatory variables of CAGs + species (red curve), CAGs + metabolites (green curve), metabolites (yellow curve), CAGs (blue curve), and species (purple curve). The AUC shows the classification of control versus HTN, or control versus pHTN as the numbers of variables increase. The CAGs + metabolites-based classification is more efficient as indicated by a higher AUC. c ROC of the random forest classifier using CAGs + metabolites based on the 1000 most important variables by ranking the variables by importance. AUC = 0.91 for control versus pHTN (n = 12, red curve), AUC = 0.89 for control versus HTN (n = 12, green curve), and AUC = 0.57 for pHTN versus HTN (n = 13, blue curve). d The top 50 different CAGs distinguish HTN from control based on the random forest model using explanatory variables of CAGs + metabolites. e The top 50 CAGs discriminate between pHTN and control using explanatory variables of CAGs + metabolites. The lengths of bar in the histogram represent mean decrease accuracy, which indicates the importance of the CAG for classification. The color denote the enrichment of CAG in control (blue), in HTN or pHTN (red) according to OR score Full size image

We also investigated the utility of the classifier based on microbial CAGs + species. Consistently, the AUC for identifying pHTN and HTN from the controls was 0.67 (95% CI, 0.39–0.95) and 0.81 (95% CI, 0.53–1), respectively, and the performance on pHTN and HTN individuals was not as satisfactory (AUC, 0.47; 95% CI, 0.19–0.75; Additional file 10: Figure S7a). For HTN classification, CAGs and species taxonomic annotated to Prevotella, including Prevotella sp. CAG:5226.CAG-377, Prevotella bivia, and CAG-184 were typically important (Additional file 10: Figure S7b). Overall, the pHTN- and HTN-associated microbial and metabolic features captured by the classifier offered further evidence for dysbiotic gut microbiome and highlighted great potential ability for detection of pHTN and HTN populations by GM and metabolites.

High BP is transferrable by fecal transplant

Previous studies have revealed that antibiotics and probiotics are potential treatment modalities for BP in both animal models and clinical trials [19, 21, 22, 47]. We speculated that the alterations in GM under pro/antibiotic use may be associated with BP changes. There is evidence that Dahl salt-sensitive rats transplanted with salt-resistant rat microbiota have further exacerbated BP, which indicate that the microbiota resident within the cecum of the Dahl salt-sensitive rat, but not the salt-resistant rat, are in a symbiotic relationship with the host [20]. Thus the differences between Dahl salt-sensitive rats and the salt-resistant rats are highlighted. Investigators have also proved that transplantation of cecal contents from hypertensive obstructive sleep apnea rats on high-fat diet into the same obstructive sleep apnea recipient rats on normal chow diet lead to higher BP similar to the donors [23]. In this study, it seems that a major contributor to the gut dysbiosis of HTN is a high-fat diet. Therefore, direct studies testing if microbial transplantation can transmit changes in BP from hypertensive donors to recipients are still lacking. To further demonstrate whether alterations of GM are a causal factor for the progression of HTN in vivo, fecal bacteria from hypertensive patients were transplanted to GF mice in the present work.

The donors for microbiota transplantation consisted of two patients of HTN and one normotensive control (Additional file 11: Table S17). They were strictly selected, and fresh fecal samples from donors were inoculated to recipient mice as soon as possible. Male GF mice at 8–10 weeks were divided into groups and orally inoculated with stool samples two times at 1-day interval (Fig. 7a). The fecal samples of recipient mice post-transplantation were investigated by 16S V4 region amplicon sequencing (Additional file 2: Table S18). The sequences were de novo clustered at 97% sequence identity and annotated to genera. From HTN patients, 128 genera were successfully colonized in the intestine of HTN mice, and 110 genera were transferred to control-mice from the control donor (Fig. 7b). Shannon index based on the genera profile showed reduced bacterial diversity in HTN mice (P = 0.048; t test, Fig. 7c). As expected, PCoA at the genus level clustered HTN patients and mice colonized with hypertensive GM into one group, but control and control mice into a separated group (Fig. 7d). Moreover, at the genus level, Anaerotruncus, Coprococcus, Ruminococcus, Clostridium, Roseburia, Blautia, and Bifidobasterium were confirmed to be deficient, while Coprobacillus and Prevotella were shown to be more abundant in HTN mice, which was in agreement with our previous observations in the metagenomic analyses (Additional file 2: Table S19, Fig. 7e).

Fig. 7 Post-transplanted intestinal microbial profiles and BP of recipient mice. a Schematic representation of fecal microbiota transplantation. GF mice (n = 5 for control, n = 10 for HTN) are orally inoculated with prepared fecal contents from two patients of HTN and one normotensive control, respectively. The gut microbial profiles are analyzed at 7 days, and BP is measured at 10 weeks post-transplantation. C, control; H, HTN. b Venn diagram comparing the shared genera number in gut microbiome of human donors (n = 1 for control, n = 2 for HTN) and recipient mice (n = 3 for control, n = 6 for HTN). c Shannon index of recipient mice at the genus level demonstrate significantly reduced α diversity in HTN group. P = 0.048 from t test. Boxes represent the inter quartile ranges, lines inside the boxes denote medians, and circles are outliers. d PCoA plots of human donors and recipient mice based on microbial genera separate HTN group from the controls. e Heat map comparing the abundance of altered genera between control and HTN mice. Red, more abundant; blue, less abundant. Genera present consistent trend with the metagenomic analysis are marked with green points, while inconsistent with gray points. f SBP, DBP, MBP, and HR of the recipient mice (n = 5 for control, n = 10 for HTN) are measured by tail-cuff method. Data are presented as mean ± s.e.m. P = 0.018, SBP; P = 0.019, DBP; P = 0.014, MBP; P = 0.11, HR; t test Full size image

At 10 weeks post-transplantation, BP of recipient mice in HTN and control group was measured by the tail-cuff method. Notably, the HTN mice exhibited significantly higher SBP, DBP, and mean blood pressure (MBP) as compared to controls (P < 0.05), as well as elevated heart rate (P = 0.11) (Fig. 7f). Early studies have shown that when compared to conventional controls, GF rats possess significantly lower cardiac output, relatively diminished regional blood flow, lower level of systemic BP response after blood loss, and hypotonic microvasculature [48], which might lead to a low systolic BP in the recipient mice. These findings provided novel and direct evidence that GM could influence the BP of host directly. Therefore, changes in the GM might be the mechanism underlying the effect of antibiotics and probiotics on BP control. As the number of donors for transplantation is limited, larger number of fecal transplants from hypertensive, pre-hypertensive, and normotensive control participants should be carried out in the future to further establish the magnitude of BP changes.