Gut Microbes Are Associated with the Vascular Beneficial Effects of Dietary Strawberry on Metabolic Syndrome-Induced Vascular Inflammation

Abstract

Scope:

Metabolic syndrome (MetS) alters the gut microbial ecology and increases the risk of cardiovascular disease. This study investigates whether strawberry consumption reduces vascular complications in an animal model of MetS and identifies whether this effect is associated with changes in the composition of gut microbes.

Methods and results:

Seven-week-old male mice consume diets with 10% (C) or 60% kcal from fat (high-fat diet fed mice; HF) for 12 weeks and subgroups are fed a 2.35% freeze-dried strawberry supplemented diet (C+SB or HF+SB). This nutritional dose is equivalent to ≈160 g of strawberry. After 12 weeks treatment, vascular inflammation is enhanced in HF versus C mice as shown by an increased monocyte binding to vasculature, elevated serum chemokines, and increased mRNA expression of inflammatory molecules. However, strawberry supplementation suppresses vascular inflammation in HF+SB versus HF mice. Metabolic variables, blood pressure, and indices of vascular function were similar among the groups. Further, the abundance of opportunistic microbe is decreased in HF+SB. Importantly, circulating chemokines are positively associated with opportunistic microbes and negatively associated with the commensal microbes (Bifidobacterium and Facalibaculum).

Conclusion:

Dietary strawberry decreases the abundance of opportunistic microbe and this is associated with a decrease in vascular inflammation resulting from MetS.

Dietary supplementation of strawberry reduces vascular inflammation and alters the composition of gut microbiota in metabolic syndrome (MetS). Importantly, strawberry supplementation decreases the abundance of opportunistic microbe Oscillibacter, and the circulating inflammatory chemokines are negatively associated with the beneficial microbe Bifidobacterium. Strawberries might complement existing therapies to improve gut microbiota and thereby reduce vascular complications associated with MetS.

1. Introduction

Metabolic syndrome (MetS) greatly increases the risk of cardiovascular diseases such as atherosclerosis, heart disease, stroke, and type 2 diabetes and consists of a collection of conditions including hyperglycemia, excess fat accumulation, hypertension, and abnormal blood lipids.^[1] Nearly 35% of all US adults and 50% of those 60 years of age or older are estimated to have MetS.^[2] The treatment for MetS and comorbidities is costly to patients and a burden on the healthcare system. As such, in addition to pharmacological strategies to treat MetS, affordable, attainable, and straightforward lifestyle and nutritional intervention strategies are warranted.

High glucose, dyslipidemia, proinflammatory cytokines, and increased expression of adhesion molecules precipitate vascular inflammation and contribute significantly to the development of atherosclerotic cardiovascular disease in MetS.^[1] Dietary habits have been shown to directly affect many conditions associated with MetS, including blood pressure, inflammation, and vascular dysfunction.^[3] A westernized diet supplies a high content of proteins derived from high-fat meats, excess saturated fat, refined grains, alcohol, sugar, salt, and likely insufficient amount and variety of fruits and vegetables. This diet is associated with various health issues including inflammatory bowel disease, cardiovascular disease, diabetes, obesity, and MetS.^[4] In the context of vascular complications, high-fat diets (HFD) are commonly associated with increased endothelial inflammation which leads to the development of atherosclerotic vascular disease.^[5]

Evidence from epidemiological, preclinical, and clinical studies indicates that flavonoid-rich diets are vasculoprotective.^[6,7] Flavonoids are a large class of phenolic compounds found in high levels in fruits, vegetables, chocolate, soy, cocoa, red wine, and other plant-based foods. Anthocyanins are a subclass of flavonoids and are composed of a sugar moiety and anthocyanidin [aglycon component of anthocyanins such as pelargonidin, cyanidin, peonidin, delphinidin, petunidin, and malvidin].^[8] Anthocyanins are commonly found in berries and have shown positive vascular effects in human studies.^[6,7] Clinical and epidemiological studies provide evidence for the beneficial effects of berries including strawberries on cardiovascular risk factors such as blood pressure, endothelial function, and arterial stiffness.^[6,7,9] Strawberries are an exceptional source of anthocyanins, notably pelargonidin and cyanidin, and research focusing on the health benefits of strawberries is growing.^[8] A human study has demonstrated that consuming two to three servings of strawberries per week is associated with a reduced risk of myocardial infarction.^[10] Strawberry intake has also been shown to reduce the expression of vascular adhesion molecules in humans with cardiovascular risk factors.^[11,12]

Gut microbes play a major role in the metabolism of anthocyanins. Approximately 90% of anthocyanins reach the large intestine and are metabolized by intestinal microbiota and digestive enzymes, suggesting that the biological effects of anthocyanins could be due to the circulating metabolites rather than the anthocyanins themselves.^[9,13] Furthermore, anthocyanins and intestinal microbiota display positive synergistic effects: intestinal microbiota contribute by metabolizing anthocyanins into bioavailable metabolites, while anthocyanins act as a prebiotic and promote intestinal colonization of microbiota.^[14,15] A recent study from our lab showed that dietary supplementation of strawberry reduces vascular inflammation, improves vascular dysfunction, and increases the abundance of beneficial gut bacteria in diabetic mice.^[8,16] However, the effect of dietary strawberries on vascular complications in MetS, and the associations among gut microbiota, strawberry intake, and vascular health are unknown. In the present study, we investigated whether dietary supplementation of strawberry reduces vascular complications in an animal model of MetS and identified whether this effect is associated with the changes in the composition of gut microbiota.

2. Results

2.1. Strawberry Supplementation does not Change Metabolic Parameters

We used HFD-fed C57BL/6J mice as an animal model of MetS which is one of the most commonly used diet-induced models of MetS.^[17] Further, HFD model shares many vascular phenotypes with human MetS including endothelial inflammation.^[5] Seven-week-old male C57BL/6J mice consumed rodent diets containing 10% (control diet fed mice; C) or 60% kcal from fat (HFD-fed mice; HF) for 12 weeks. Subgroups of C and HF consumed diet supplemented with 2.35% freeze-dried strawberry (SB) (C+SB or HF+SB). Compared to C mice, HF mice exhibited increased body mass, fat mass, fasting and non-fasting blood glucose, and serum lipids (cholesterol and triglycerides), together with reduced lean mass (Table 1). HF mice also exhibited a decreased VO₂, VCO₂, respiratory exchange ratio, and energy expenditure in both light and dark cycles (Figure 1A–D), as well as impaired glucose and insulin tolerance compared to C mice (Figure 2). Strawberry supplementation did not alter body mass, fat mass, lean mass, blood glucose, glucose tolerance, insulin tolerance, blood lipids, VO₂, VCO₂, respiratory exchange ratio, or energy expenditure in C+SB and HF+SB mice as compared to C and HF mice respectively (Table 1 and Figures 1 and 3). Total activity was similar among the groups (Figure 1E).

Table 1.

Body weight, food intake, blood glucose, and serum lipids in C, C+SB, HF, and HF+SB mice treated for 12 weeks.

Characteristicsa)	C	C+SB	HF	HF+SB
Body weight [g]	27.3 ± 0.6b	28.3 ± 0.8b	43.2 ± 1.1a	45.1 ± 0.8a
Food intake [g day−1]	2.06 ± 0.07	2.12 ± 0.03	2.28 ± 0.07	2.28 ± 0.07
Fasting blood glucose [mg dL−1]	90 ± 6b	91 ± 6b	128 ± 9a	139 ± 8a
Non-fasting blood glucose [mg dL−1]	164 ± 6b	154 ± 6b	205 ± 12a	237 ± 12a
Serum cholesterol [mg dL−1]	111 ± 25b	104 ± 24b	240 ± 12a	178 ± 25a
Serum triglycerides [mg dL−1]	102 ± 17	94 ± 14	102 ± 9	122±115
Body composition
Fat [%]	13.9 ± 2.8b	12.2 ± 1.1b	40.7 ± 1.5a	41.4 ± 1.4a
Lean [%]	71.2 ± 0.5a	71.4 ± 0.6a	54.9 ± 0.9b	54.6 ± 0.8b

^a)Values are mean ± SEM. Body weight, food intake, fasting blood glucose, non-fasting blood glucose, body composition, n = 14–15; and serum cholesterol and triglycerides, n = 5–8; and blood pressure, n = 6. Comparison among groups was made using one-way ANOVA and Tukey post hoc tests were performed when significant main effects were obtained. Labeled means in the row without a common letter differ, p < 0.05. C: control mice; C+SB: control mice treated with strawberry; HF: high-fat diet-fed mice; HF+SB: high-fat diet-fed mice treated with strawberry.

Figure 1.

VO₂ A), VCO₂ B), respiratory exchange ratio C), energy expenditure D), and total activity E) in day or night cycles of C, C+SB, HF, and HF+SB mice. C: control mice; C+SB: control mice treated with strawberry; HF: high-fat diet-fed mice; HF+SB: high-fat diet-fed mice treated with strawberry. Values are mean ± SEM (n = 5). Means without a common letter differ, p < 0.05.

Figure 2.

Glucose tolerance test A) and insulin tolerance test B) in C, C+SB, HF, and HF+SB mice. C: control mice; C+SB: control mice treated with strawberry; HF: high-fat diet-fed mice; HF+SB: high-fat diet-fed mice treated with strawberry. Values are mean ± SEM (n = 8–10). Means without a common letter differ, p < 0.05.

Figure 3.

Blood pressure A), endothelium-dependent vasorelaxation B), endothelium-independent vasorelaxation C), receptor-mediated vasocontraction D), and nonreceptor-mediated vasocontraction E) in C, C+SB, HF, and HF+SB mice. control mice; CS: control treated with strawberry mice; HF: high-fat diet-fed mice; HF+SB: high-fat diet-fed mice treated with strawberry. Values are mean ± SEM (n = 7–9 for A; and 9–10 vessels for B–E, n = 18–19). Means without a common letter differ, p < 0.05.

2.2. Strawberry Supplementation does not Influence Blood Pressure or Reactivity of Resistance Arteries

Both systolic and diastolic blood pressure were similar among the groups (Figure 3A). No differences existed among groups concerning resting internal diameter at 0 mg tension or artery length (data not shown). Further, neither non-receptor mediated vasocontraction to KCl, receptor-mediated vasocontraction to phenylephrine (PE), endothelium-dependent vasorelaxation to acetyl choline (ACh), nor endothelium-independent vasorelaxation to sodium nitroprusside (SNP), were different among groups (Figure 3B–E).

2.3. Strawberry Supplementation Reduces MetS-Induced Vascular Inflammation

There was an increase in binding of mouse monocytic WEHI 78/24 cells to the aortic vessel isolated from HF mice compared with C mice, which is associated with increased serum inflammatory chemokines such as JE/monocyte chemotactic protein-1 (MCP-1) and KC/interleukin-8 (IL-8) (Figure 4A,B). Strawberry supplementation, however, decreased the binding of monocytes to endothelial surface of aortae and reduced serum inflammatory chemokines in HF+SB versus HF mice (Figure 4A,B). Aortic vessels isolated from HF mice exhibited a significant increase in mRNA expression of intercellular adhesion molecule-1 (ICAM1) and E-Selectin as compared with the aortic vessels from C mice (Figure 4C). Strawberry supplementation reduced the mRNA expression of E-Selectin in aortic vessels from HF+SB versus HF mice, with no alteration observed in the ICAM1 (Figure 4C). The mRNA expression of vascular cell adhesion molecule-1 (VCAM1) was similar among groups.

Figure 4.

Monocyte bindingto aortic vessels A), adhesion molecules in aortic vessels B), and serum MCP1/JE and KC/IL8 C) in C, C+SB, HF, and HF+SB mice. C: control mice; C+SB: control mice treated with strawberry; HF: high-fat diet-fed mice; HF+SB: high-fat diet-fed mice treated with strawberry. Values are mean ± SEM (n = 6–8). Means without a common letter differ, p < 0.05.

2.4. Strawberry Supplementation Alters the Composition of Gut Microbiota

2.4.1. α-Diversity and β-Diversity

Phylogenetic species richness and evenness within a sample was measured by the indices of α-diversity such as Faith’s phylogenic diversity and Shannon entropy. α-diversity indices were not significantly different among the groups (Figure 5A). The compositional differences between samples are represented by β-diversity, and it is a measure of global microbial composition. In the present study, β-diversity was significantly different between C versus HF and HF versus HF+SB (Figure 5B).

Figure 5.

α-Diversity indices A) and β-diversity of gut microbial communities B) in C, C+SB, HF, and HF+SB mice. C: control mice; C+SB: control mice treated with strawberry; HF: high-fat diet-fed mice; HF+SB: high-fat diet-fed mice treated with strawberry. Values are mean ± SEM (n = 12–15),.

2.4.2. Relative Abundance of Microbial Population

The qualitative measures of taxonomic abundance indicated differences in the microbial community at different taxonomic levels (phylum, family, and genus) among the groups (Figure 6A). Bacterial sequences were distributed among five bacterial phyla including: Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Verrucomicrobia. Several genera were significantly different between C versus HF and HF versus HF+SB (Supplementary Figure, Supporting Information). The relative abundance of selected genera was shown in Figure 6B. Bifidobacterium, Clostridium_sensu_stricto_1, Dubosiella, Faecalibaculum, A2, Blautia, Lachnoclostridium, two uncultured Lachnospiracea, Muribaculaceae, Colidextribacter, Intestinimonas, and Oscillibacter were significantly different between C versus HF (Figure 6B). Two uncultured Lachnospiracea and Oscillibacter were significantly different between HF versus HF+SB (Figure 6B).

Figure 6.

Relative abundance of microbial population at the phylum level, family level, and genus level A), and the relative abundance of microbial population at genus level B) in C, C+SB, HF, and HF+SB mice. C: control mice; C+SB: control mice treated with strawberry; HF: high-fat diet-fed mice; HF+SB: high-fat diet-fed mice treated with strawberry. Values are mean ± SEM (n = 12–15), *p < 0.05 vs C and #p < 0.05 vs HF.

2.5. Beneficial Gut Bacteria are Negatively Associated with Serum Inflammatory Markers

Operational Taxonomic Unit functional associations were determined using Spearman’s correlation analysis. The present study indicates an association between selected gut microbiota (genus level) and serum inflammatory chemokines (JE/MCP-1 and KC/IL-8). Eleven genera were significantly associated with JE/MCP-1 and/or KC/IL-8 (Figure 7). The abundance of uncultured Lachnospiraceae, Blautia, uncultured Ruminococcaceae, Anaerotruncus, Lachnospiraceae_NK4A136_group, Lachnospiraceae_UGC.006, and Oscillibacter were positively correlated with both circulating inflammatory chemokines (JE/MCP-1 and KC/IL-8). In addition, Anaerotruncus and Lachnospiraceae_NK4A136_group were positively correlated with KC/IL-8. Commensal microbes Bifidobacterium and Facalibaculum were negatively associated with KC/IL-8. Clostridium_sensu_stricto-1 was negatively associated with both JE/MCP-1 and KC/IL-8.

Figure 7.

Bacterial taxa associated with serum chemokines (JE/MCP-1 and KC/IL-8). Spearman’s correlations between bacterial abundance data and serum chemokines using Shiny App (n = 8).

3. Discussion

Clinical and epidemiological studies support the cardiovascular benefits of strawberry consumption.^[10,18–20] We tested the hypothesis that a nutritional dose of strawberry for 12 weeks ameliorates vascular complications in an animal model of MetS and the vascular effects of strawberry is associated with specific gut microbes. In the present study, dietary supplementation of strawberry reduces MetS-induced vascular inflammation with a decrease in the abundance of beneficial gut microbes. Further, several bacterial taxa are associated with circulating inflammatory chemokines. Importantly, beneficial bacteria (Bifidobacterium and Facalibaculum) are negatively associated with the serum inflammatory marker KC/IL-8. Collectively, dietary supplementation of strawberry attenuates indices of vascular inflammation which is associated with the changes in microbial signatures.

MetS-induced vascular inflammation contributes to the pathogenesis of vascular disease. Vascular inflammation, which involves binding of monocytes to the aortic vessel followed by their transmigration into the subendothelial space, plays a major role in the development of vascular disease.^[21] In the present study, HF mice exhibited an enhanced vascular inflammation as shown by an increased WEHI 78/24 monocytic cells binding to the aortic vessel, increased circulating chemokines (JE/MCP1 and KC/IL8) and an enhanced expression of ICAM1 and E-Selectin compared with the C mice. Strawberry supplementation reduced circulating chemokines and suppressed mRNA expression of E-Selectin, which is associated with a reduced binding of monocytic WEHI78/24 cells to the vasculature in the HF+SB versus HF mice. The present study indicates that the effects of strawberry on inflammatory molecules may be regulated both at the transcriptional and translational levels. This is consistent with our previous study that showed the beneficial effect of strawberry on vascular inflammation in diabetic vasculature.^[8] Further, we demonstrated that serum obtained from strawberry fed mice (serum containing strawberry metabolites) reduced high glucose and palmitate induced endothelial inflammation in human aortic endothelial cells as shown by a reduced monocyte binding and KC/IL-8.^[8] This indicates the vascular beneficial effects of dietary strawberries reported in the present study could be possibly mediated through their circulating metabolites. Further, dietary strawberry was shown to reduce the serum levels of ICAM-1 and VCAM-1 in humans with cardiovascular risk factors.^[11,12]

A previous study showed that mice consuming HFD exhibited an impaired endothelium-dependent vasodilation in thoracic aortae.^[22] In the present study, endothelium-dependent vasorelaxation and endothelium-independent vasorelaxation were similar among the experimental groups. The differences in the study observations of vascular function in HFD-fed mice could be due to the difference in the type of artery used to assess the vascular function. We used mesenteric arteries based on evidence that resistance vessels are known to contribute importantly to peripheral resistance and blood pressure.^[23] Indeed, we recently showed that dietary strawberry improves endothelial-dependent vasorelaxation in diabetic db/db mice using mesenteric arteries.^[8]

We further studied the effect of strawberry on metabolic parameters to determine whether the improvement observed in vascular inflammation in mice supplemented with strawberry is due to a secondary effect related to metabolic parameters. In our study, HFD-fed mice (mice with MetS) showed an increased body weight, reduced lean mass, increased fat mass, increased blood glucose, increased blood lipids, and impaired glucose and insulin tolerance. But strawberry supplementation did not improve these metabolic parameters. We further observed a decrease in the respiratory exchange in the HFD-fed groups, indicating an increase in fat metabolism. However, strawberry supplementation did not alter energy expenditure in HF+SB versus HF mice. These results indicate that the observed vascular effects of strawberry are not mediated through improvement in metabolic parameters but are more likely influenced by a direct mechanism on the vasculature.

Evidence indicates a link between the gut microbiome and metabolic events which contribute to the MetS.^[24] A high-fat, low-fiber diet can induce dysbiosis resulting in an increased abundance of opportunistic microbes and decreased commensal microbes.^[25] Dysbiosis will affect the production of microbial metabolites that leads to the complications associated with MetS such as cardiovascular disease.^[24] Clinical studies have shown changes in gut microbiota during the shift between a low-fat diet and HFD. A low-fat diet in humans is associated with an increased abundance in commensal microbes such as Faecalibacterium, while a HFD is associated with an increase in opportunistic microbes (Alistipes and Bacteroides) and a decrease in commensal microbe Faecalibacterium.^[25] HFD-fed mice have also shown a reduction in α-diversity, changes in β-diversity, and alteration of gut microbes at different taxa levels compared to mice fed standard chow.^[26,27] In the present study, there are no significant differences among the groups in a-diversity indices. However, β-diversity was significantly different between HF versus C and HF versus HF+SB mice indicating MetS-induced dysbiosis could be improved with dietary strawberry.

Bacterial community profiling indicates significant differences in the relative abundance at different taxa levels (phylum, family, and genus) among the groups. At the genus level, many of the bacterial genera were altered in HF versus C mice and HF+SB versus HF mice. The abundance of commensal microbes such as Bifidobacterium and Faecalibaculum was significantly decreased in HF versus C mice. Bifidobacterium is considered to be a beneficial or commensal genus and is associated with positive effects for the host in the large intestine. Evidence shows that the etiology and development of MetS are closely associated with changes in the gut microbiota, including a decrease in the abundance of Bifidobacterium.^[28] Bifidobacterium modulate lipid and glucose metabolism, improve the gut barrier function, improve insulin resistance, reduce low grade inflammation, and stimulate the host immune system.^[28,29] Faecalibaculum is a lactic acid-producing bacteria and exhibits an anti-obesity effect.^[30] Faecalibaculum decreased with HFD and its presence was shown to reduce the risk of metabolic diseases.^[31] Consistent with previous studies, the abundance of opportunistic microbes (Blautia, Muribaculaceae, and Oscillibacter) were increased in HF versus C mice.^[31,32] Blautia and Oscillibacter are positively associated with metabolic diseases such as obesity.^[31,32] Oscillibacter is enriched in mice with colorectal cancer as well as positively associated with obese and diabetic phenotypes.^[33] Oscillibacter in HFD-fed mice was shown to trigger inflammation by activating the mTOR complex 1.^[34] Further, the abundance of Oscillibacter is diet-responsive in obese individuals.^[35] In the present study, strawberry supplementation decreased the abundance of Oscillibacter in HF+SB versus HF mice. Similarly, in a previous study supplementation of anthocyanins reduced gut inflammation and decreased the abundance of Oscillibacter in colitis induced mice.^[36] Our study suggests the anti-inflammatory effects of strawberry observed in HF+SB versus HF could be possibly due to the decreased abundance of Oscillibacter. In the present study, the abundance of 15 genera belong to the family Lachnospiraceae were altered (nine increased and six decreased) in HF versus C mice. Further, strawberry supplementation altered the abundance of eight genera belonging to Lachnospiraceae (five increased and three decreased) in HF+SB versus HF mice. Lachnospiraceae are short-chain fatty acids producers but different taxa of Lachnospiraceae are associated with several diseases.^[37] For example, some taxa belong to the family Lachnospiraceae showed a beneficial anti-inflammatory association with an improvement in colorectal cancer and liver cirrhosis.^[37] However, human studies also reported an increase in the abundance of some taxa of Lachnospiraceae in diabetes, metabolic diseases, and inflammatory bowel disease.^[37] In the present study, strawberry supplementation increased the abundance of two uncultured Lachnospiraceae in HF+SB versus HF mice which are decreased in HFD versus C mice. Due to their controversial role, the biological relevance of Lachnospiraceae in the present study remains unclear.

We further determined the association between the microbes at the genera level and inflammatory chemokines. Spearman’s correlation analysis revealed 11 genera that are significantly associated with serum inflammatory chemokines JE/MCP-1 and KC/IL-8. Importantly, the commensal microbes Bifidobacterium and Faecalibaculum are negatively associated with the inflammatory chemokine KC/IL-8. Further, opportunistic microbes Blautia and Oscillibacter which increased in HF versus C mice are positively associated with both JE/MCP-1 and KC/IL-8. Strawberry supplementation reduced Oscillibacter and circulating inflammatory chemokines (JE/MCP-1 and KC/IL-8) in HF+SB versus HF mice suggesting the possible link between the anti-inflammatory effect of dietary strawberry and reduced Oscillibacter. Taken together, it is possible that improvements in vascular inflammation observed in HF+SB versus HF mice could be due to an improvement in gut dysbiosis. However, more research is needed to identify the functional roles of these genera as well as their connection with strawberry supplementation and vascular inflammation risk.

To our knowledge, the present study is the first to show the association between strawberry, intestinal microbiota, and inflammatory chemokines. However, our study has a few limitations. First, we used only male mice in the present study. The hormonal changes in female mice could affect the outcomes of the experimental results and hence we used male mice to understand the accurate response to dietary strawberries. We are planning to use female mice in our ongoing studies to see if the same results can be extended to females. Second, we did not analyze the chemical composition of the freeze-dried strawberry powder and serum metabolites of strawberries. This analysis could help to explain the contribution of strawberry-derived metabolites and gut microbes in mediating the vascular effects of dietary strawberries.

4. Concluding Remarks

Our findings indicate that dietary supplementation of strawberry reduces vascular inflammation and alters the composition of gut microbiota in a mouse model of MetS. Further, we provide promising evidence that dietary strawberry decreases the abundance of the opportunistic microbe Oscillibacter, and that circulating inflammatory chemokines are negatively associated with the beneficial microbe Bifidobacterium. Our implementation of a study design that includes investigation of the role of gut microbiota to ameliorate the effects of MetS is also a step toward future research in this new and exciting area of metabolic health. Future studies employing gain or loss of function approaches to manipulate gut microbiota are warranted given that the changes in microbiota suggest a possible microbiota dependent mechanism for improving inflammation. In conclusion, our study provides strong proof of concept for further considering strawberry as an adjunct therapy to improve intestinal microbiota and thereby prevent, delay, or reduce vascular complications associated with the development of MetS.

5. Experimental Section

Experimental Animals:

Six-week-old male C57BL/6J mice (Stock number: 000664) were obtained from the Jackson Laboratory (Bar Harbor, ME, USA). Mice were housed five per cage and maintained in a 12-h light/dark cycle, 23 ± 1 °C and 45 ± 5% humidity under artificial light. The Institutional Animal Care and Use Committee at the University of Utah approved all protocols (Protocol Number: 18–10005). These protocols conformed to the Guide for the Care and Use of Laboratory Animals published by the US National Institute of Health. Mice were housed at the University of Utah Comparative Medicine Center Vivarium under humane conditions. Protocols were initated after 1-week acclimation period. At 7-weeks, C57BL/6J mice were divided randomly into four groups (5 animals/cage × 3 cages × 4 groups = 60 animals), and received rodent diets with 10% kcal from fat (control diet fed mice; C) or 60% kcal from fat (HFD-fed mice; HF) for 12 weeks. Subgroups of C and HF mice were fed a 2.35% freeze-dried SB supplemented diet (C+SB or HF+SB). HF model shared many vascular phenotypes with human MetS including endothelial inflammation.^[5]

Standard Chow and Strawberry Supplemented Chow:

The freeze-dried strawberry powder was provided by FutureCeuticals (Momence, IL, USA). The customized pelleted diets including: standard diet, HFD, standard diet supplemented with 2.35% freeze-dried strawberry powder, and HFD- supplemented with 2.35% freeze-dried strawberry powder (Table 2) were supplied by Research Diets (New Brunswick, NJ, USA). Adjustments were made to the strawberry-supplemented diet to compensate for the fiber and additional sugars provided by the freeze-dried strawberry powder. The concentration of freeze-dried strawberry powder used in this study was determined based on the Food and Drug Administration recommendation for the extrapolation of doses from humans to animals by normalization to body surface area. The average human consumption of strawberries was used to determine the nutritional dose of freeze-dried strawberry powder. Therefore, the concentration of 2.35% freeze-dried strawberry powder used in the mouse diet was designed to be equivalent to two human servings of fresh strawberries (~160 g strawberries).^[8]

Table 2.

Composition of standard diet, high fat diet, and strawberry-supplemented diet.

	Standard diet	Strawberry supplemented standard diet (2.35% in diet)	High-fat diet	Strawberry supplemented high-fat diet (2.35% in diet)
Ingredient	g kg−1	g kg−1	g kg−1	g kg−1
Casein, high nitrogen	200	198.1	200	198.6
L-Cystine	3	3	3	3
Corn starch	502.85	506	0	0
Maltodextrin	115.5	115.5	112.1	114.5
Sucrose	68.8	63.2	68.8	64.7
Dextrose	5.6	0	5.6	1.5
Fructose	6.8	0	6.8	1.8
Cellulose, BW200	48.9	44.7	48.9	45.8
Inulin	1.3	0	1.3	0.3
Corn oil	25	25	25	25
Lard	20	19.2	245	244.4
Mineral Mix, S10026	10	10	10	10
DiCalcium phosphate	13	13	13	13
Calcium carbonate	5.5	5.5	5.5	5.5
Potassium citrate	16.5	16.5	16.5	16.5
Vitamin Mix, V10001	10	10	10	10
Choline bitartrate	2	2	2	2
Freeze-dried strawberry	0	24.83	0	18.2
	1054.75	1056.53	773.5	774.8

Measurement of Metabolic Parameters:

blood glucose concentrations in tail vein blood samples were measured using Bayer Contour Next One blood glucose monitoring system (Parsippany, NJ, USA). Intraperitoneal glucose tolerance tests (IPGTT) were performed by fasting the mice overnight, followed by an injection with a single bolus of glucose (2 g kg⁻¹ body weight). Measurements of blood glucose concentrations at 0, 15, 30, 60, and 120 min after glucose administration were taken.^[8] Intraperitoneal insulin tolerance tests (IPITT) were performed after the mice were fasted for 4 h and then injected with insulin (0.75 units kg⁻¹ body weight). Blood glucose concentrations were measured at 0, 15, 30, 60, and 120 min after insulin injection.^[8] Serum cholesterol and triglycerides were quantified using commercially-available quantitation kits according to the manufacturer’s instruction (Abcam, Cambridge, MA, USA). Total body fat was measured by TD-NMR using the LF50 body composition mice analyzer (Minispec; Bruker, Germany). Whole body metabolism and activity were measured by indirect colorimetry using Comprehensive Laboratory Animal Monitoring System (CLAMS). Columbus instruments CLAMS (Columbus, OH, USA) mouse metabolic chamber directly measured oxygen consumption, carbon dioxide production, food intake, water intake, and activity continuously over a 72-h period.

Blood Pressure Measurement and Collection of Tissue Samples:

Blood pressure was measured in conscious mice using a noninvasive, computerized blood pressure tail cuff system (Kent Scientific Blood Pressure system, Torrington, CT, USA) at week 10, as described previously.^[8] Terminal experiments were performed at 12 weeks wherein mice were anesthetized using 2–5% isoflurane, the chest cavity was opened, and a blood sample was collected via cardiac puncture. The aorta was isolated and used to assess vascular inflammation. Mesenteric arteries were used to evaluate vascular function.

Assessment of Vascular Function:

Isometric tension techniques were used to assess vascular function in two mesenteric artery segments per mouse as was described previously.^[8,38,39] After vessels were mounted and the myograph chamber warmed from room temperature to 37° over ≈30 min, a series of internal circumference-active tension curves were produced to evaluate the vessel diameter that evoked the greatest tension development (L_max) to 100 mM potassium chloride (KCl). Non-receptor and receptor mediated vasocontractile responses to KCl (20–100 mM) and phenylephrine (PE, 10⁻⁸–10⁻⁵ M), respectively, then were assessed. After arteries were precontracted to ≈65% of maximal PE-induced contraction and tension was stable, responses to acetylcholine (ACh, 10⁻⁸–10⁻⁶ M; to determine endothelium-dependent vasorelaxation) and sodium nitroprusside (SNP, 10⁻⁹–10⁻⁴ M; to determine endothelium-independent vasorelaxation), were completed. An interval of 20–30 min separated each of the five experimental protocols. An analog-to-digital interface card (Biopac Systems Inc., Santa Barbara, CA, USA) that allows for subsequent off-line quantitative analyses was used to continuously record all tension data. An average was calculated from the results of the two vessel segments for each mouse.

Assessment of Vascular Inflammation:

The binding of monocytes to the vasculature, inflammatory markers, and adhesion molecules were assessed as was described previously.^[8,38,40] Briefly, segments of abdominal aorta proximal to the iliac bifurcation was used to assess the binding of monocyte to the endothelial layer of aorta. Aortae were longitudinally opened, pinned to 4% plated agar and incubated with EBM-medium containing 1% heat-inactivated FBS for 10 min at 37 °C. Then WEHI78/24 mouse monocytic cells labeled with calcein-AM were added to the endothelium exposed aortic segments. After 30 min incubation, the aortic segments were gently washed with PBS to remove unbound monocytes. Binding of monocytes to aortic vessels were visualized, captured, and counted using Olympus IX73 fluorescence microscope. Five fields per aortic vessel was used to quantify the number of monocytes bound to aortic vessel. Circulating inflammatory markers such as JE/monocyte chemotactic protein-1 (MCP-1) and KC/interleukin-8 (IL-8) were measured by ELISA kits according to manufacturer’s instructions (R&D Systems, Minneapolis, MN, USA). The mRNA expression of adhesion molecules intercellular adhesion molecule-1 (ICAM1), vascular cell adhesion molecule-1 (VCAM1), and E-Selectin were measured using RT-PCR as described previously.^[8,38,40] Total RNA was isolated from aortic vessels using Qiazol (Qiagen, CA, USA). cDNA was synthesized using an RT-PCR kit (Qiagen, CA, USA), and the expression of adhesion molecules was measured with qPCR using SYBR green (Qiagen, CA, USA).

Microbial Community Profiling:

Cecum contents were used to extract genomic DNA using the DNeasy PowerSoil Kit (Qiagen, MD, USA). For amplification of the V4 variable region of the 16S rRNA gene using 515F/806R primers, 50 ng of genomic DNA were utilized.^[41] Library preparation was performed using Nextera XT DNA Library Preparation Kit (Illumina, Cat# FC-131-1096). Nextera XT indices were used to label DNA segments with individual barcodes to accommodate multiplexing. Of pooled amplicons, paired-end sequencing (2 × 250 bp) was completed using an Illumina MiSeq with ≈30% PhiX DNA, as described previously.^[8]

Data Analysis:

Data were analyzed with the use of Prism (Version 7.0; GraphPad) or SPSS (Version 25; IBM). One-way ANOVA was used to compare groups at a single time point. To analyze vascular function, comparison of multiple time points among groups was made using two-way repeated-measures ANOVA. When a p value (p < 0.05) indicated significance, the Tukey post hoc test was used to identify the location of differences among groups. Processing and quality filtering of reads were performed by using scripts in QIIME 2. The duel-indexed barcodes from I1 and I2 fastq files were concatenated using the merge_bcs_reads.py (https://gist.github.com/walterst/7326543), and the same script used to concatenate the combined indices into the R1 read. Data were imported into QIIME 2 as a MultiplexedPairedEndBarcodeInSequence type.^[42] Any commands prefixed by “q2” are QIIME 2 plugins. Paired-end reads were demultiplexed, and primers trimmed, using q2-cutadapt. Primer trimmed reads were merged using q2-vsearch and subsequently quality filtered with q2-quality-filter with min-quality 10. Merged reads were denoised with q2-deblur using trim-length 250.^[43,44] A RESCRIPt prepared Naïve Bayes classifier for the V4 hyper-variable region was constructed from the SILVA 138.1 reference database and used to classify the resulting exact sequence variants (ESVs)/ features with q2-feature-classifier.^[45–50] Taxonomy-based filtering was performed with q2-taxa filter-table to remove any ESVs that were classified as “chloroplast,” “mitochondria,” “eukaryota,” “unclassified,” any features that did not have at least a phylum-level classification were also removed. ESVs that did not have at least 90% identity and query alignment to the SILVA reference database were removed with q2-quality-control. ESVs were inserted into a SILVA reference tree using q2-fragment-insertion.^[51,52] Prior to downstream analyses, the feature-table was filtered using feature-table filter-features-conditionally by removing any features that did not appear in at least 40% of all samples and accounted for less than a 0.1% total abundance within the feature-table. This avoids spurious issues with log-ratios for compositional based diversity and differential abundance analyses. Compositional beta diversity analyses and unsupervised differential logratio analyses were carried out using q2-deicode.^[53] Supervised differential log-ratio analyses was performed with q2-songbird using differentialprior 0.5 summary interval 1.^[54] These data were subsequently visualized using q2-qurro and q2-emperor.^[55,56] For differential abundance analyses, the main feature-table was split into two sub-tables, one that contains samples from C & HF, and another that contains samples HF & HF+SB. Differential abundance analyses were performed with q2-aldex2 on each table as ALDEx2 has been shown to provide robust and consistent results.^[57] Spearman’s correlations were performed using the Shiny App between bacterial abundance data and serum inflammatory chemokines (JE/MCP-1 and KC/IL-8). All data were considered statistically significant where p < 0.05, and were expressed as mean ± SEM, where appropriate.

Data Availability Statement

Raw sequencing reads for all samples described in this project have been deposited in the NCBI Sequence Read Archive under accession number: PRJNA784995. https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP348694. Further information are available from the corresponding author on reasonable request.