Resistant starch can improve insulin sensitivity independently of the gut microbiota
Microbiome volume 5, Article number: 12 (2017)
Obesity-related diseases, including type 2 diabetes and cardiovascular disease, have reached epidemic proportions in industrialized nations, and dietary interventions for their prevention are therefore important. Resistant starches (RS) improve insulin sensitivity in clinical trials, but the mechanisms underlying this health benefit remain poorly understood. Because RS fermentation by the gut microbiota results in the formation of physiologically active metabolites, we chose to specifically determine the role of the gut microbiota in mediating the metabolic benefits of RS. To achieve this goal, we determined the effects of RS when added to a Western diet on host metabolism in mice with and without a microbiota.
RS feeding of conventionalized mice improved insulin sensitivity and redressed some of the Western diet-induced changes in microbiome composition. However, parallel experiments in germ-free littermates revealed that RS-mediated improvements in insulin levels also occurred in the absence of a microbiota. RS reduced gene expression of adipose tissue macrophage markers and altered cecal concentrations of several bile acids in both germ-free and conventionalized mice; these effects were strongly correlated with the metabolic benefits, providing a potential microbiota-independent mechanism to explain the physiological effects of RS.
This study demonstrated that some metabolic benefits exerted by dietary RS, especially improvements in insulin levels, occur independently of the microbiota and could involve alterations in the bile acid cycle and adipose tissue immune modulation. This work also sets a precedent for future mechanistic studies aimed at establishing the causative role of the gut microbiota in mediating the benefits of bioactive compounds and functional foods.
Obesity has become a public health crisis, with an estimated 39% of adults ages 18 and over worldwide considered overweight in 2014 and 13% deemed obese . Many obese individuals also experience a cluster of abnormalities (elevated blood triglycerides, fasting hyperglycemia, high blood pressure, and reduced HDL cholesterol), referred to as the metabolic syndrome, that places them at significant risk for the development of type 2 diabetes (T2D), hypertension, dyslipidemia, and cardiovascular disease (CVD) . Resistance to insulin is considered the earliest detectable metabolic aberrancy in persons who eventually develop T2D . The mechanisms underlying insulin resistance are multifactorial and include ectopic lipid accumulation in the muscle and liver as well as systemic inflammation, especially in adipose tissue . In particular, adipose tissue macrophages (ATMs) have been identified as immune cells driving the development of insulin resistance . Considering these underlying factors, successful therapeutic approaches and dietary interventions for treating metabolic syndrome should ideally provide both metabolic and immunological benefits to the host.
Dietary fibers, such as resistant starches (RS), have been studied extensively and are promising nutritional interventions for preventing metabolic diseases [5–7]. RS in particular are well documented to modulate insulin sensitivity in healthy and obese volunteers as well as in patients with metabolic syndrome [8–11]. Although these benefits likely arise from a multitude of mechanisms, the gut microbiota is increasingly considered one of the key factors underlying these health benefits [12–15]. Recent research concerning the beneficial effects of short-chain fatty acids (SCFA), which result from bacterial fermentation of non-digestible carbohydrates such as RS, on intestinal barrier function, gut peptide secretion [16–18], and immune function [19–21] further strengthen the concept that dietary fibers may exert some of their metabolic benefits via gut bacteria. However, despite the rationale for considering the microbiota in mediating the physiological effects of RS and fiber, very few studies have taken a mechanistic approach to determine the exact role of the microbiota such as feeding germ-free mice or performing microbiota transplants [22–25]. Rather, most studies have only assessed bacterial participation in a phenotype by correlations and not by methods that establish causality [26–30].
To systematically determine the gut microbiota’s contribution to the health benefits of dietary fiber, we compared the effects of feeding RS on host metabolism in Western diet-fed mice with and without a microbiota. The two types of RS preparations (RS type 2 and RS type 4) selected for this study have both been shown to significantly impact host metabolism [9, 11, 31, 32], yet each elicits very distinct effects on human gut microbiota composition . We therefore sought to evaluate how two types of RS capable of differentially influencing the gut microbiota impact insulin sensitivity. The findings reported here demonstrate that some metabolic benefits mediated by dietary RS, especially improvements in insulin levels, occur independently of the gut microbiota and could potentially involve alterations in the bile acid cycle as well as adipose tissue immune modulation in the form of macrophage recruitment and retention.
Germ-free C3H mice experienced similar gains in body weight as their conventionalized counterparts when fed a Western diet
To directly test the role of the gut microbiota in mediating the metabolic benefits of RS, we sought to compare the effects of feeding RS to mice with and without a gastrointestinal microbiota. However, C57BL/6 (B6) mice, the mouse strain traditionally used for diet-induced obesity (DIO) research, are resistant to the effects of feeding a Western diet (WD) when maintained germ-free [34–36]. In contrast, germ-free (GF) C3H/HeN (C3H) mice are generally not protected against DIO . To compare the role of the gut microbiota in the development of DIO in the two strains of mice, GF and conventionalized (CVZ) C3H and B6 mice were all fed the same WD for 8 weeks. GF B6 mice gained significantly less weight compared to their conventionalized counterparts (Fig. 1a). In contrast, GF C3H mice gained weight similarly to CVZ controls (Fig. 1b). Of note, CVZ C3H mice gained more weight and exhibited a higher energy intake than CVZ B6 mice (Fig. 1). These findings are consistent with previous reports [34–37] and indicate that GF C3H mice are a tractable model in which to study the role of the microbiota in the therapeutic effects of a dietary fiber in the context of obesity.
Because C3H mice fed a Western diet gained weight independently of the gut microbiota, we next sought to determine whether C3H mice respond to RS supplementation similarly to B6 mice (in which weight gain was gut microbiota-dependent) in the presence of a microbiota. C3H and B6 mice were fed either a control WD or a WD enriched with 10% RS for 8 weeks (Table 1). A portion of the corn starch in the purified diets was replaced with RS—an approach often applied in human nutritional intervention trials. In those interventions, participants are asked to consume either RS-enriched products [33, 38, 39] or RS supplements [9–11, 40, 41] with native or readily digestible starch or starch-based products serving as the placebo. The two types of RS fed (resistant starch type 2 (RS2) and resistant starch type 2 (RS4)) in this study were selected because they are known to improve host metabolism (including insulin resistance) yet elicit distinct effects on human gut microbiota composition [9, 11, 31–33]. In C3H mice, supplementation with RS4 improved insulin sensitivity despite having no impact on body weight, body weight gain, subcutaneous adipose tissue weight, and energy intake (Fig. 2). Similar results were obtained in B6 mice (Additional file 1: Fig. S1). These experiments revealed that, although the influence of the microbiota on weight gain was line-dependent, animals from both lines maintained the ability to respond to dietary modulation by RS in the presence of a microbiota.
Feeding resistant starches substantially altered the gut microbiota composition
Supplementing a WD with RS2 or RS4 restored the microbial α-diversity reduced by feeding a WD to CVZ C3H mice (Fig. 3a). Principal Coordinate Analysis (PCoA) plot of β-diversity based on binary Jaccard distance revealed that gut communities clustered according to diet (Fig. 3b). Similar results were obtained via two other independent β-diversity metrics (Bray-Curtis distance and unweighted UniFrac distance, Additional file 2: Fig. S2a, b). Together, these findings indicate that RS induced global changes to the structure of the gut microbiota in mice. Accordingly, parametric analyses revealed that 124 species-like OTUs and 46 taxa were significantly affected by dietary treatment (Fig. 3c; Additional file 3: Tables S1, S2). Interestingly, RS corrected the proportions of seven taxa (Ruminococcaceae and Clostridiales Incertae Sedis XIII families and Clostridium IV, Anaerovorax, Oscillibacter, Elizabethkingia, and Flavonifractor genera) whose levels were altered upon feeding a WD. Consistent with observations from a previous RS feeding study of healthy humans, RS4 increased the Bacteroidetes phylum and decreased the Ruminococcaceae and Erysipelotrichaceae families . Non-parametric analysis of bacterial taxa using LEfSe confirmed most of these results and also highlighted that Proteobacteria-related taxa were specifically enriched in the WD-fed mice (Additional file 2: Fig. S2c, d). Altogether, these data indicate that supplementing a WD with RS caused substantial shifts in gut microbiota composition and was able to redress some of the aberrancies induced by feeding a WD.
Feeding resistant starches improved plasma insulin levels and the index of insulin resistance independently of the gut microbiota in C3H mice
To directly test the role of the gut microbiota in mediating the metabolic benefits of RS, both GF and CVZ C3H mice were fed either a control WD or a WD enriched with 10% RS for 8 weeks (Table 1). We choose to perform this experiment with C3H mice because, unlike the B6, GF C3H mice gained weight similarly to their CVZ littermates when fed a WD. Although WD feeding increased body weight, white adipose tissue weight, and plasma leptin levels in both GF and CVZ mice compared to low-fat diet (LFD) fed controls, RS feeding did not significantly impact these parameters (Additional file 4: Fig. S3a–c). No changes were observed in lean mass (as measured by tibialis muscle weights) for mice on any treatment (Additional file 4: Fig. S3d).
Both GF and CVZ C3H mice fed a control WD experienced a significant increase in fasting plasma insulin and glucose levels compared to the LFD-fed control animals (Fig 4a, b). Feeding a RS4-supplemented WD significantly decreased plasma insulin levels in CVZ mice (Fig. 4a). Moreover, these improvements were associated with substantial shifts in gut microbiota composition in CVZ mice (described above). Such associations are often considered an indication that the gut microbiota mediates the health effects of a dietary intervention [27, 42, 43]. However, when a WD supplemented with RS4 was fed to GF C3H mice, plasma insulin levels were significantly decreased to levels consistent with those observed in their CVZ counterparts, demonstrating that this RS-mediated metabolic improvement was not dependent on the presence of a gut microbiota (Fig. 4a). Both GF and CVZ mice fed an RS2-supplemented WD also experienced a similar trend toward decreased insulin levels, although it did not reach statistical significance (p = 0.07 for CVZ and p = 0.09 for GF, Dunnett’s post hoc tests). Hyperglycemia induced by the WD was not improved with RS feeding (Fig. 4b). Finally, feeding RS4 significantly improved the index of insulin resistance independently of the gut microbiota; feeding RS2 showed a similar trend (p = 0.07 for CVZ and p = 0.13 for GF, Dunnett’s post hoc tests) (Fig. 4c). Dietary supplementation with RS2 and RS4 also significantly improved circulating levels of C-peptide, a protein derived from proinsulin and a measure of insulin secretion, independently of microbial status (Fig. 5a).
Several reports indicate a role for the incretin glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) in bringing about the metabolic benefits of RS, especially reductions in body weights and fat mass accumulation . To that end, we measured the colonic messenger RNA (mRNA) expression and plasma levels of molecules related to the regulation of these incretins. Colonic expression of proglucagon, the precursor of GLP-1, was increased by RS4 feeding in a microbiota-dependent manner (Fig. 5b); however, no significant correlations (at the q value level) between proglucagon mRNA expression and any microbial taxa were found (data not shown). Proglucagon expression was increased in GF mice compared to CVZ littermates. In accordance with the increase in colonic proglucagon mRNA expression, active GLP-1 levels in plasma were also increased in GF mice (Fig. 5c) as previously described . However, the RS4-induced increase in proglucagon mRNA expression was not accompanied by a change in active GLP-1 plasma levels (Fig. 5c). This apparent discrepancy between proglucagon mRNA expression and plasma GLP-1 levels has been reported in other studies and could be explained by several factors, such as differences in the activity of the dipeptidyl peptidase 4 or the prohormone convertase 1/3 [44, 45]. Colonic expression and plasma levels of PYY were not affected by the RS diets (Fig. 5d, e). Of note, PYY levels were globally lower in GF mice compared to CVZ counterparts, consistent with previous reports . WD feeding decreased active ghrelin levels in both GF and CVZ animals compared to the LFD controls, but RS feeding had no additional impact (Fig. 5f). Neither plasma nor subcutaneous adipose tissue mRNA levels of adiponectin were affected by the dietary treatments, suggesting that adiponectin does not play a role in the modulation of insulin sensitivity by RS (Fig. 5g, h).
Reduced expression of adipose tissue macrophage markers in RS-fed mice was associated with improvements in the index of insulin resistance
To begin dissecting how RS could modulate host physiological and metabolic characteristics independently of the microbiota, we examined markers of adipose tissue macrophages (ATM) as these innate immune cells and their polarization phenotypes have been implicated in the control of insulin sensitivity [4, 47]. Feeding RS4 significantly reduced subcutaneous adipose tissue (SAT) mRNA expression of F4/80, a tissue macrophage marker, in both GF and CVZ mice compared to WD-fed controls (Fig. 6a). SAT expression of CD11c, a marker of antigen presenting cells and M1 macrophages, was reduced by RS2 and RS4 feeding in CVZ mice; a similar change (albeit lower and not statistically significant) was also observed in GF mice (Fig. 6b). F4/80 and CD11c mRNA expression levels in the visceral adipose tissue (VAT) were also decreased by the RS2 and RS4 treatments in both GF and CVZ mice, but these reductions did not consistently reach statistical significance (Fig. 6c, d). CD11c mRNA expression in SAT and VAT was strongly correlated with the index of insulin resistance for both CVZ and GF mice (Pearson r = 0.75 and 0.68 in SAT, Pearson r = 0.71 and 0.74 in VAT, p < 0.0001).
To confirm our observations that RS feeding is associated with a modulation of ATM, we isolated the stromal vascular fraction (SVF) from the SAT of CVZ mice. As expected, feeding a WD significantly increased the abundance of subcutaneous ATM as well as the ratio of M1 (F4/80+ CD11b+ CD11c+) to M2 (F4/80+ CD11b+ CD206+ or F4/80+ CD11b+ CD301+) macrophages (Fig. 6e–h; Additional file 5: Fig. S4a, b). Dietary supplementation with RS2 or RS4 significantly reduced the percentage of ATM in the SVF (Fig. 6e). Of interest, feeding RS2 and RS4 reduced both the M1 and M2 macrophage populations (Fig. 6f–h; Additional file 5: Fig. S4a, b). Plasma and SAT mRNA expression levels of monocyte chemoattractant protein 1 (MCP-1; also known as CCL2), a chemokine previously shown to affect macrophage infiltration into adipose tissues [48, 49], were not affected by RS feeding (Additional file 5: Fig S4c, d), suggesting that changes in this particular chemokine are not responsible for the decreased subcutaneous ATM accumulation observed when mice are fed RS. All together, these data demonstrate that RS-mediated improvements in insulin sensitivity are associated with a reduced number of both M1 and M2 macrophages.
Changes in adipose tissue physiology and reduced liver and adipose tissue macrophage infiltration in obese mice have been associated with an improvement in gut permeability [50–52]. To that end, we investigated intestinal permeability by administering 4 kDa FITC-dextran to GF and CVZ mice but found that none of the dietary treatments significantly altered gut permeability during the fasting state (Fig. 7a). In accordance with this observation, ileal expression of ZO-1 and occludin were also not affected by any of the experimental diets (Fig. 7b, c). Altogether, these findings suggest that RS decrease expression of adipose tissue macrophage markers independently of the gut microbiota and changes in gut permeability.
Resistant starches redressed Western diet-induced changes in cecal bile acid profiles in both germ-free and conventionalized mice
Several studies report changes in the bile acid (BA) pool composition of individuals diagnosed as prediabetic or with type 2 diabetes [53–57]. BAs are capable of regulating both insulin sensitivity and glucose homeostasis as well as reducing the inflammatory activity of macrophages via nuclear farnesoid X receptor (FXR) and membrane-bound TGR5 signaling [45, 58, 59]. Moreover, RS feeding has been shown to alter BA levels in both humans and rodents [60–62]. These reports prompted us to analyze the cecal BA pool of GF and CVZ mice fed RS.
The cecal BA pool was markedly different between GF and CVZ mice, consistent with previous findings . Specifically, profiles from GF mice were dominated by taurine-conjugated primary bile acids (including tauro-β-muricholic acid) whereas the patterns from CVZ mice were more complex and included elevated levels of secondary BAs (Fig. 8, Additional file 3: Table S3, and Additional file 5: Figure S4e, f). WD feeding significantly increased the concentration of several BAs, such as taurocholic acid (TCA) and taurochenodeoxycholic acid (TCDCA), in both GF and CVZ mice while supplementing the WD with RS redressed these alterations in a microbiota-independent manner (Fig. 8). Interestingly, we observed strong correlations between TCA and TCDCA concentrations and plasma insulin levels, the index of insulin resistance, and ATM markers (q values below 0.0005 for insulin and index of insulin resistance, Additional file 3: Table S3). Together, these findings indicate that RS feeding impacts the cecal BA pool—especially taurine-conjugated BA—in a microbiota-independent manner.
An understanding of how dietary compounds such as RS can modify risk factors for complex diseases provides an important basis for the rational development of nutritional strategies for disease prevention and treatment. The gut microbiota is generally assumed to mediate RS-associated health benefits by fermenting RS to SCFA, which have numerous known effects on host physiology . Consistent with this view, studies in rodents implicate bacterial fermentation as a mediator of GLP-1 and PYY secretion in bringing about the metabolic benefits of RS . Indeed, GLP-1 and PYY are pleotropic, gut-derived hormones that, among others, affect glucose homeostasis and appetite . Because microbial responses to fiber and other prebiotics can be highly individualized [33, 65], determining how—and even if—the gut microbiota contributes to the physiological effects is an important step toward the development of efficient nutritional strategies. Although our findings confirm the pronounced effect of RS on gut microbiota composition and structure [33, 66], they do not support a causative role for these shifts in RS-induced metabolic improvements, including insulin sensitivity. Indeed, feeding RS2 and RS4 resulted in vastly different effects on the composition of the gut microbiota; however, both RS types induced physiological changes that were highly similar (with RS4 more consistently improving metabolic parameters as compared to RS2). Together, these findings further imply the importance of a microbiota-independent pathway in RS-mediated metabolic benefits.
Remarkably, the physiological characteristics of this microbiota-independent pathway in C3H mice, which include improvements in insulin sensitivity without effects on body weight, fat mass, and gut hormone levels, mirror those observed clinically in humans where improvement in insulin sensitivity is the primary beneficial outcome of feeding RS with little to no effect on body composition [8–11]. Moreover, these RS-mediated improvements in insulin sensitivity in humans are also observed without a concomitant increase in fasting GLP-1 levels . Clearly, we cannot exclude the possibility that the improvements in insulin sensitivity conferred by RS consumption occur via different mechanisms in mice versus humans. However, similar metabolic effects were observed both during human RS feeding studies and when RS was fed to mice in the absence of a microbiota. Together, these findings indicate that the most important clinical improvement induced by dietary RS in humans may occur without a contribution from the gut microbiota, bacterial fermentation, and gut hormones such as GLP-1 and PYY.
In contrast to our findings and those from human clinical trials, reductions in fat mass and/or body weight are often observed in rodent RS feeding studies in addition to the improvements in insulin resistance . These improvements in adiposity require bacterial fermentation in the gut and are connected with increases in gut hormones GLP-1 and PYY [32, 68, 69]. This discrepancy can potentially be explained by a RS dose-dependent effect. Rodent studies showing an effect of RS on body weight and fat mass provided doses of 30% to 55% (w/w) [32, 68, 70, 71], which are substantially higher than the doses used in the present study (10% RS) and in human clinical trials [8–11]. Feeding a lower dose of RS might account for the absence of an RS-mediated effect on GLP-1 and PYY levels and the lack of improvement in weight and adiposity. Indeed, secretion of GLP-1 and PYY into the intestine is stimulated by SCFA [17, 18], which are, as metabolic end products of bacterial fermentation, produced in a substrate-dependent manner. However, the doses of RS required to induce fat loss in rodents are not realistic for incorporation into human diets, leading one to question the clinical applicability of RS for controlling adiposity. Nonetheless, diets containing lower doses of RS represent an exciting opportunity to improve insulin sensitivity.
In addition to feeding lower RS doses to be more consistent with human studies, we also formulated mouse diets in such a way as to facilitate comparison between a diet with native starch to a diet where a portion of the native starch was replaced with RS. Although this approach does create differences in caloric content among the diets, it is utilized extensively in human nutrition and eliminates concerns over using so-called inert fibers (such as cellulose) to control for energy density that may actually exert their own effects on the host [14, 33, 38, 39, 72, 73]. As no significant effects of RS were observed on energy intake, body weight, body weight gain, or adipose tissue weights in the current study, the observed metabolic improvements likely occur independently of changes in energy intake and fat accumulation. We do acknowledge that a lack of caloric equivalence among diets can be considered a study limitation. Additionally, another limitation of our work is the absence of a functional assessment of glucose homeostasis in germ-free mice fed RS. In place of this assay, we instead measured a hallmark of insulin sensitivity (HOMA-IR), which also allowed us to simultaneously measure several additional metabolic parameters. Certainly, performing such functional experiments (e.g., insulin and oral glucose tolerance tests) would provide additional, valuable mechanistic insights.
Upon observing RS-induced improvements in insulin sensitivity in mice independently of the gut microbiota, we explored potential explanations for how the beneficial effects of RS may occur without a microbial contribution. One such possibility is that RS regulate innate immunity. Numerous studies have implicated CD11c+ ATMs in the development of insulin resistance , and RS-mediated improvements in insulin sensitivity in rats have been associated with reduced CD11c expression in adipose tissue . In our study, we demonstrate a strong correlation between ATM marker expression and the index of insulin resistance in both GF and CVZ animals. We recognize that evaluating immune and metabolic parameters in germ-free mice has its inherent limitations [35, 74–76], and we cannot exclude the possibility that the immature immune system present in germ-free mice was not fully restored by the conventionalization process. Nevertheless, we did observe decreases in ATM markers in both germ-free and conventionalized mice fed RS that were consistent with previous findings obtained in immunocompetent rats . Although the exact mechanism by which RS improves insulin levels and influences ATM abundance in the absence of a microbiota remains unclear, our findings suggest that RS-induced modulation of the BA pool may be involved. Indeed, connections among BA signaling, insulin sensitivity, and macrophages are emerging. Of note, deletion of the BA membrane receptor Tgr5 specifically in macrophages increased ATM accumulation and aggravated insulin resistance in obese animals . Considering our observations that feeding an RS-supplemented diet redressed the concentrations of BA elevated by a WD and the strong correlations between BAs, macrophage markers, and insulin metabolism, it is tempting to speculate that RS-induced modulation of the BA profile reduces macrophage migration, thereby contributing to improvements in insulin sensitivity. Clearly, future mechanistic studies are necessary to determine how RS improves insulin resistance independently of the gut microbiota.
In conclusion, this study unequivocally demonstrates that the gut microbiota is not required to bring about all the metabolic benefits that arise from RS consumption in mice. Instead of relying on correlations between physiological parameters and specific microbial taxa, which is common practice but cannot assign causality, we experimentally determined the causative role of the gut microbiota using germ-free mice. We argue that similar gnotobiotic animal research could constitute an essential component to determining the prebiotic properties of a specific dietary compound . Based on our findings and those in the literature , we propose a model in which RS induces health benefits via both microbiota-dependent and microbiota-independent pathways. When RS are fed at higher doses, metabolites from bacterial fermentation are released at physiologically relevant levels, leading to a decrease in body weight and/or fat mass via a GLP1-PYY-dependent mechanism . When fed in clinically relevant doses, RS improves insulin resistance independently of the gut microbiota, possibly altering bile acid signaling as well as adipose tissue immune modulation. Although our findings are supported by observations in humans showing an impact of RS feeding on insulin sensitivity without any effect on fat mass or increase in fasting GLP-1 levels [8–10, 40, 67], additional studies are necessary to elucidate the exact mechanisms, dose-response relationships, and the role of microbiota variability in both humans and animal cohorts.
Germ-free (GF) C3H and C57BL/6 (B6) male mice were born and reared in flexible film isolators and maintained under gnotobiotic conditions at the University of Nebraska-Lincoln (UNL). Conventionalized (CVZ) mice were used for all experiments (unless specified otherwise) to limit genetic, maternal, and early-life-related confounding factors when comparing to GF animals.
Experimental diets were introduced to both GF and CVZ mice 9 and 21 days after conventionalization of C3H and B6 mice, respectively. Body weight was monitored weekly after conventionalization. Food intake was also monitored weekly after introduction of the experimental diets, but multiple measurements were hampered by spillage issues due to softness and crumbling of the Western diet. Feces were collected on the day of necropsy. The Institutional Animal Care and Use Committee at the University of Nebraska-Lincoln approved all procedures involving animals.
Germ-free (GF) status of all experimental mice was confirmed at least twice (prior to introducing the experimental diet and during the last week of the experiment) by analyzing fresh feces via PCR using universal bacterial primers targeting the 16S rRNA gene (30 cycles, primers 8F and 1391R)  and aerobic and anaerobic culture in brain heart infusion, Wilkins-Chalgren, and yeast mold broths for 7 days at 37 °C. Conventionally raised (CONV) B6 male mice were obtained from Jackson Laboratory (Bar Harbor, ME). Conventionalized (CVZ), CONV, and GF mice were housed in the same room on autoclaved bedding and fed the same autoclaved water and diet. All mice within an experiment were born within 5 to 9 days of one another. Three to ten mice were obtained per litter, and littermates were assigned to cages of two to three mice each. Mice were then randomly assigned to each treatment/intervention based on body weight at the time of conventionalization and diet introduction. No differences in body weight variance were observed among the treatment groups on the day of randomization. Conventionalization, insulin tolerance tests, and gut permeability assays are described in Additional file 6: supplemental methods.
Mice were fed an autoclavable chow diet (LabDiet 5K67, Purina Foods, St. Louis, MO) after weaning and during the period of time between conventionalization and introduction of the experimental diets. Experimental diets were prepared by Research Diets (New Brunswick, NJ) and sterilized by γ-irradiation (min 50 kGy, Neutron Products, Dickerson, MD). Irradiation efficacy was assessed by testing 50 spore strips of Bacillus pumillus (NAMSA, Northwood, OH) placed between bags of diet before irradiation and then incubated after irradiation in Soybean-Casein Digest broth at 33 °C for 7 days. Mice were fed a LFD (D12450K) and a customized WD (45% kcal from fat and 17% kcal from sucrose with low maltodextrine/high starch compared to D12451) where part of the corn starch was replaced by either 10% RS type 2 (RS2; HI-MAIZE® 260, Ingredion Incorporated, Westchester, IL) or 10% RS type 4 (RS4; Fibersym® RW, MGP Ingredients, Atchinson, KS). HI-MAIZE® 260 is derived from high amylose corn starch; it has a caloric content of 1.5 kcal/g and contains 62.50% of RS. Fibersym® RW is a chemically modified phosphorylated cross-linked RS4 prepared from wheat starch ; it has a caloric content of 0.44 kcal/g and contains 89.10% of RS. Diet composition is summarized in Table 1, with full details provided in Additional file 3: Table S4.
Blood and tissue analyses
Mice were euthanized via carbon dioxide asphyxiation. Blood was collected into EDTA-containing tubes and centrifuged (13,000×g, 3 min, 4 °C). At the time of collection, blood samples were treated with a DPPIV inhibitor (Millipore, Billerica, MA) and a protease inhibitor cocktail (Sigma, Saint-Louis, MO). Plasma insulin was measured using an ELISA-based assay (Mercodia, Uppsala, Sweden). The index of insulin resistance (IR), also called HOMA-IR, was calculated based on the original model from Matthews et al.: fasting glucose (mg/dL) × fasting insulin (mU/L)/405. For reference, HOMA-IR values in healthy humans are equal to 1, whereas IR values for healthy mice are greater than one [79–82]. Percent change values were calculated to assist the reader in understanding relative changes in the IR between each treatment compared to the WD control. Gut peptides, gut hormones, MCP-1, and adiponectin were measured using a Mouse Metabolic Magnetic Bead Panel or a Mouse Adiponectin Single Plex Magnetic Bead Kit (Milliplex, Millipore, Billerica, MA) and a MAGPIX instrument (Luminex Corporation, Austin, TX). Gene expression and flow cytometric analyses are described in Additional file 6: supplemental methods.
Microbial community analysis
Gut microbiota composition was assessed by 16S rRNA gene sequencing of fecal samples. Bacterial DNA was extracted from feces using the QIAamp DNA Stool Mini Kit (Qiagen, Valencia, CA) with a bead-beating step as previously described . Amplicon sequencing of the fecal microbiota was performed at the University of Minnesota Genomics Center as described in Additional file 6: supplemental methods.
Initial quality filtering of the reads was performed with the Illumina Software, yielding an average of 53,426 filter-passed reads per sample (accession numbers provided in supplemental methods). Quality scores were visualized with the FastQC software (http://www.bioinformatics.babraham.ac.uk/publications.html), and reads were trimmed to 250 bp (R1) and 230 bp (R2) with the FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit). Next, reads were merged with the merge-illumina-pairs application (with p value = 0.02, enforced Q30 check, perfect matching to primers which are removed by the software, and otherwise default settings including no ambiguous nucleotides allowed) . For samples with >20,000 merged reads, a subset of 20,000 reads was randomly selected using Mothur 1.32.1 centos 5.5 for Linux  to avoid large disparities in the number of sequences. Subsequently, the UPARSE pipeline implemented in USEARCH v7.0.1001  was used to further process the sequences. Putative chimeras were identified against the Gold reference database and removed. Clustering was performed with 98% similarity cutoff to designate operational taxonomic units (OTUs). Non-chimeric sequences were also subjected to taxonomic classification using the RDP MultiClassifier 1.1 from the Ribosomal Database Project  for phylum to genus characterization of the fecal microbiota. The phylotypes were computed as percent proportions based on the total number of sequences in each sample. Alpha and beta diversity indexes were calculated using QIIME . PCoA plot of the beta-diversity indexes were obtained using EMPeror . We used LEfSe to calculate and visualize the LDA Effect size  (http://huttenhower.sph.harvard.edu/galaxy/).
Bile acid analysis
Forty-six bile acids were quantified in cecal contents by ultrahigh performance liquid chromatography—multiple-reaction monitoring mass spectrometry (UPLC-MRM-MS) at the University of Victoria Genome British Columbia Proteomics Centre using a protocol adapted from Han et al.  (see Additional file 6: supplemental methods).
A sample size of n = 8 was calculated for the four treatment groups after considering a power of 80%, a significance level of 0.05, an effect size of 0.7, and a 10% attrition rate using G*Power 220.127.116.11 . Data were analyzed using Prism 5.0 (GraphPad Software, San Diego, CA) via a one-way ANOVA followed by Dunnett’s pairwise comparison post hoc test with the WD group as control. The global effects of microbial status (presence or absence of the gut microbiota) and dietary intervention were assessed using two-way ANOVA. All data were checked for normality using tests available in Prism 5.0 (Kolmogorov-Smirnov, D’Agostino and Pearson, and Shapiro-Wilk normality tests). Data determined to be non-normal even after log-transformation were analyzed using a Kruskal-Wallis test and Dunn’s post-tests as indicated in the legend. Body weight evolution was analyzed by two-way ANOVA with repeated measures followed by a Bonferroni post hoc test (Fig. 1). For parametric analyses of the gut microbiota, the p value of the one-way ANOVA was adjusted to control the false discovery rate for multiple tests according to the Benjamini and Hochberg procedure . Robust (Huber) estimation was used to weigh down outliers, as implemented in JMP Pro 11 (SAS Institute, Cary, NC). R software (R Foundation for Statistical Computing, Vienna, Austria)  was used for multiple correlation analyses and p value adjustments according to the Benjamini and Hochberg procedure. p < 0.05 was considered statistically significant.
Adipose tissue macrophage
Resistant starch type 2
Resistant starch type 4
Subcutaneous adipose tissue
Short-chain fatty acids
Stromal vascular fraction
Visceral adipose tissue
World Health Organization. Obesity and overweight. Fact sheet N°311, available at http://www.who.int/mediacentre/factsheets/fs311/en/. 2015. Accessed 28 June 2015.
Alberti KGMM, Zimmet P, Shaw J. Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med. 2006;23:469–80.
DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al. Type 2 diabetes mellitus. Nat Rev Dis Primers. 2015;1:15019.
Patsouris D, Li PP, Thapar D, Chapman J, Olefsky JM, Neels JG. Ablation of CD11c-positive cells normalizes insulin sensitivity in obese insulin resistant animals. Cell Metab. 2008;8:301–9.
Kaczmarczyk MM, Miller MJ, Freund GG. The health benefits of dietary fiber: beyond the usual suspects of type 2 diabetes mellitus, cardiovascular disease and colon cancer. Metabolism. 2012;61:1058–66.
Jakobsdottir G, Nyman M, Fak F. Designing future prebiotic fiber to target metabolic syndrome. Nutrition. 2014;30:497–502.
Slavin J. Fiber and prebiotics: mechanisms and health benefits. Nutrients. 2013;5:1417–35.
Robertson MD, Bickerton AS, Dennis AL, Vidal H, Frayn KN. Insulin-sensitizing effects of dietary resistant starch and effects on skeletal muscle and adipose tissue metabolism. Am J Clin Nutr. 2005;82:559–67.
Robertson MD, Wright JW, Loizon E, Debard C, Vidal H, Shojaee-Moradie F, et al. Insulin-sensitizing effects on muscle and adipose tissue after dietary fiber intake in men and women with metabolic syndrome. J Clin Endocrinol Metab. 2012;97:3326–32.
Johnston KL, Thomas EL, Bell JD, Frost GS, Robertson MD. Resistant starch improves insulin sensitivity in metabolic syndrome. Diabet Med. 2010;27:391–7.
Maki KC, Pelkman CL, Finocchiaro ET, Kelley KM, Lawless AL, Schild AL, et al. Resistant starch from high-amylose maize increases insulin sensitivity in overweight and obese men. J Nutr. 2012;142:717–23.
Higgins JA. Resistant starch and energy balance: impact on weight loss and maintenance. Crit Rev Food Sci Nutr. 2014;54:1158–66.
Birt DF, Boylston T, Hendrich S, Jane JL, Hollis J, Li L, et al. Resistant starch: promise for improving human health. Adv Nutr. 2013;4:587–601.
Keenan MJ, Zhou J, Hegsted M, Pelkman C, Durham HA, Coulon DB, et al. Role of resistant starch in improving gut health, adiposity, and insulin resistance. Adv Nutr. 2015;6:198–205.
Bindels LB, Walter J, Ramer-Tait AE. Resistant starches for the management of metabolic diseases. Curr Opin Clin Nutr Metab Care. 2015;18:559–65.
Lin HV, Frassetto A, Kowalik Jr EJ, Nawrocki AR, Lu MM, Kosinski JR, et al. Butyrate and propionate protect against diet-induced obesity and regulate gut hormones via free fatty acid receptor 3-independent mechanisms. PLoS One. 2012;7:e35240.
Tolhurst G, Heffron H, Lam YS, Parker HE, Habib AM, Diakogiannaki E, et al. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes. 2012;61:364–71.
Bindels LB, Dewulf EM, Delzenne NM. GPR43/FFA2: physiopathological relevance and therapeutic prospects. Trends Pharmacol Sci. 2013;34:226–32.
Smith PM, Howitt MR, Panikov N, Michaud M, Gallini CA, Bohlooly Y, et al. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science. 2013;341:569–73.
Arpaia N, Campbell C, Fan X, Dikiy S, van der Veeken J, de Roos P, et al. Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature. 2013;504:451–5.
Trompette A, Gollwitzer ES, Yadava K, Sichelstiel AK, Sprenger N, Ngom-Bru C, et al. Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis. Nat Med. 2014;20:159–66.
Bindels LB, Delzenne NM, Cani PD, Walter J. Towards a more comprehensive concept for prebiotics. Nat Rev Gastroenterol Hepatol. 2015;12:303–10.
Delzenne NM, Bindels LB. Gut microbiota: Ganoderma lucidum, a new prebiotic agent to treat obesity? Nat Rev Gastroenterol Hepatol. 2015;12:553–4.
Chang CJ, Lin CS, Lu CC, Martel J, Ko YF, Ojcius DM, et al. Ganoderma lucidum reduces obesity in mice by modulating the composition of the gut microbiota. Nat Commun. 2015;6:7489.
Chassaing B, Miles-Brown J, Pellizzon M, Ulman E, Ricci M, Zhang L, et al. Lack of soluble fiber drives diet-induced adiposity in mice. Am J Physiol Gastrointest Liver Physiol. 2015;309:G528–41.
Martinez I, Wallace G, Zhang C, Legge R, Benson AK, Carr TP, et al. Diet-induced metabolic improvements in a hamster model of hypercholesterolemia are strongly linked to alterations of the gut microbiota. Appl Environ Microbiol. 2009;75:4175–84.
Martinez I, Perdicaro DJ, Brown AW, Hammons S, Carden TJ, Carr TP, et al. Diet-induced alterations of host cholesterol metabolism are likely to affect the gut microbiota composition in hamsters. Appl Environ Microbiol. 2013;79:516–24.
Bindels LB, Beck R, Schakman O, Martin JC, De Backer FC, Sohet FM, et al. Restoring specific Lactobacilli levels decreases inflammation and muscle atrophy markers in an acute leukemia mouse model. PLoS One. 2012;7:e37971.
Yang J, Bindels LB, Segura Munoz RR, Martinez I, Walter J, Ramer-Tait AE, et al. disparate metabolic responses in mice fed a high-fat diet supplemented with maize-derived non-digestible feruloylated oligo- and polysaccharides are linked to changes in the gut microbiota. PLoS One. 2016;11:e0146144.
Neyrinck AM, Possemiers S, Druart C, Van de Wiele T, De BF, Cani PD, et al. Prebiotic effects of wheat arabinoxylan related to the increase in bifidobacteria, roseburia and Bacteroides/Prevotella in diet-induced obese mice. PLoS One. 2011;6:e20944.
Nichenametla SN, Weidauer LA, Wey HE, Beare TM, Specker BL, Dey M. Resistant starch type 4-enriched diet lowered blood cholesterols and improved body composition in a double blind controlled cross-over intervention. Mol Nutr Food Res. 2014;58:1365–9.
Zhou J, Martin RJ, Raggio AM, Shen L, McCutcheon K, Keenan MJ. The importance of GLP-1 and PYY in resistant starch’s effect on body fat in mice. Mol Nutr Food Res. 2015;59:1000–3.
Martinez I, Kim J, Duffy PR, Schlegel VL, Walter J. Resistant starches types 2 and 4 have differential effects on the composition of the fecal microbiota in human subjects. PLoS One. 2010;5:e15046.
Ding S, Chi MM, Scull BP, Rigby R, Schwerbrock NM, Magness S, et al. High-fat diet: bacteria interactions promote intestinal inflammation which precedes and correlates with obesity and insulin resistance in mouse. PLoS One. 2010;5:e12191.
Backhed F, Manchester JK, Semenkovich CF, Gordon JI. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc Natl Acad Sci U S A. 2007;104:979–84.
Rabot S, Membrez M, Bruneau A, Gerard P, Harach T, Moser M, et al. Germ-free C57BL/6J mice are resistant to high-fat-diet-induced insulin resistance and have altered cholesterol metabolism. FASEB J. 2010;24:4948–59.
Fleissner CK, Huebel N, Abd El-Bary MM, Loh G, Klaus S, Blaut M. Absence of intestinal microbiota does not protect mice from diet-induced obesity. Br J Nutr. 2010;104:919–29.
Dodevska MS, Sobajic SS, Djordjevic PB, Dimitrijevic-Sreckovic VS, Spasojevic-Kalimanovska VV, Djordjevic BI. Effects of total fibre or resistant starch-rich diets within lifestyle intervention in obese prediabetic adults. Eur J Nutr. 2016;55:127–37.
Gentile CL, Ward E, Holst JJ, Astrup A, Ormsbee MJ, Connelly S, et al. Resistant starch and protein intake enhances fat oxidation and feelings of fullness in lean and overweight/obese women. Nutr J. 2015;14:113.
Bodinham CL, Smith L, Thomas EL, Bell JD, Swann JR, Costabile A, et al. Efficacy of increased resistant starch consumption in human type 2 diabetes. Endocr Connect. 2014;3:75–84.
Bodinham CL, Smith L, Wright J, Frost GS, Robertson MD. Dietary fibre improves first-phase insulin secretion in overweight individuals. PLoS One. 2012;7:e40834.
Cotillard A, Kennedy SP, Kong LC, Prifti E, Pons N, Le CE, et al. Dietary intervention impact on gut microbial gene richness. Nature. 2013;500:585–8.
Martinez I, Lattimer JM, Hubach KL, Case JA, Yang J, Weber CG, et al. Gut microbiome composition is linked to whole grain-induced immunological improvements. ISME J. 2013;7:269–80.
Wichmann A, Allahyar A, Greiner TU, Plovier H, Lunden GO, Larsson T, et al. Microbial modulation of energy availability in the colon regulates intestinal transit. Cell Host Microbe. 2013;14:582–90.
Prawitt J, Abdelkarim M, Stroeve JH, Popescu I, Duez H, Velagapudi VR, et al. Farnesoid X receptor deficiency improves glucose homeostasis in mouse models of obesity. Diabetes. 2011;60:1861–71.
Samuel BS, Shaito A, Motoike T, Rey FE, Backhed F, Manchester JK, et al. Effects of the gut microbiota on host adiposity are modulated by the short-chain fatty-acid binding G protein-coupled receptor, Gpr41. Proc Natl Acad Sci U S A. 2008;105:16767–72.
Lumeng CN, Bodzin JL, Saltiel AR. Obesity induces a phenotypic switch in adipose tissue macrophage polarization. J Clin Invest. 2007;117:175–84.
Weisberg SP, Hunter D, Huber R, Lemieux J, Slaymaker S, Vaddi K, et al. CCR2 modulates inflammatory and metabolic effects of high-fat feeding. J Clin Invest. 2006;116:115–24.
Kanda H, Tateya S, Tamori Y, Kotani K, Hiasa K, Kitazawa R, et al. MCP-1 contributes to macrophage infiltration into adipose tissue, insulin resistance, and hepatic steatosis in obesity. J Clin Invest. 2006;116:1494–505.
Cani PD, Possemiers S, Van de Wiele T, Guiot Y, Everard A, Rottier O, et al. Changes in gut microbiota control inflammation in obese mice through a mechanism involving GLP-2-driven improvement of gut permeability. Gut. 2009;58:1091–103.
Muccioli GG, Naslain D, Backhed F, Reigstad CS, Lambert DM, Delzenne NM, et al. The endocannabinoid system links gut microbiota to adipogenesis. Mol Syst Biol. 2010;6:392.
Everard A, Belzer C, Geurts L, Ouwerkerk JP, Druart C, Bindels LB, et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc Natl Acad Sci U S A. 2013;110:9066–71.
Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010;5:e13953.
Zhao X, Fritsche J, Wang J, Chen J, Rittig K, Schmitt-Kopplin P, et al. Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics. 2010;6:362–74.
Vincent RP, Omar S, Ghozlan S, Taylor DR, Cross G, Sherwood RA, et al. Higher circulating bile acid concentrations in obese patients with type 2 diabetes. Ann Clin Biochem. 2013;50:360–4.
Wewalka M, Patti ME, Barbato C, Houten SM, Goldfine AB. Fasting serum taurine-conjugated bile acids are elevated in type 2 diabetes and do not change with intensification of insulin. J Clin Endocrinol Metab. 2014;99:1442–51.
Haeusler RA, Astiarraga B, Camastra S, Accili D, Ferrannini E. Human insulin resistance is associated with increased plasma levels of 12 alpha-hydroxylated bile acids. Diabetes. 2013;62:4184–91.
Thomas C, Gioiello A, Noriega L, Strehle A, Oury J, Rizzo G, et al. TGR5-mediated bile acid sensing controls glucose homeostasis. Cell Metab. 2009;10:167–77.
Perino A, Pols TW, Nomura M, Stein S, Pellicciari R, Schoonjans K. TGR5 reduces macrophage migration through mTOR-induced C/EBPbeta differential translation. J Clin Invest. 2014;124:5424–36.
Ebihara K, Shiraishi R, Okuma K. Hydroxypropyl-modified potato starch increases fecal bile acid excretion in rats. J Nutr. 1998;128:848–54.
Trautwein EA, Forgbert K, Rieckhoff D, Erbersdobler HF. Impact of beta-cyclodextrin and resistant starch on bile acid metabolism and fecal steroid excretion in regard to their hypolipidemic action in hamsters. Biochim Biophys Acta. 1999;1437:1–12.
van Munster IP, Tangerman A, Nagengast FM. Effect of resistant starch on colonic fermentation, bile acid metabolism, and mucosal proliferation. Dig Dis Sci. 1994;39:834–42.
Sayin SI, Wahlstrom A, Felin J, Jantti S, Marschall HU, Bamberg K, et al. Gut microbiota regulates bile acid metabolism by reducing the levels of tauro-beta-muricholic acid, a naturally occurring FXR antagonist. Cell Metab. 2013;17:225–35.
Canfora EE, Jocken JW, Blaak EE. Short-chain fatty acids in control of body weight and insulin sensitivity. Nat Rev Endocrinol. 2015;11:577–91.
Salonen A, Lahti L, Salojarvi J, Holtrop G, Korpela K, Duncan SH, et al. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. ISME J. 2014;8:2218–30.
Tachon S, Zhou J, Keenan M, Martin R, Marco ML. The intestinal microbiota in aged mice is modulated by dietary resistant starch and correlated with improvements in host responses. FEMS Microbiol Ecol. 2013;83:299–309.
Bodinham CL, Al-Mana NM, Smith L, Robertson MD. Endogenous plasma glucagon-like peptide-1 following acute dietary fibre consumption. Br J Nutr. 2013;110:1429–33.
Zhou J, Martin RJ, Tulley RT, Raggio AM, McCutcheon KL, Shen L, et al. Dietary resistant starch upregulates total GLP-1 and PYY in a sustained day-long manner through fermentation in rodents. Am J Physiol Endocrinol Metab. 2008;295:E1160–6.
Belobrajdic DP, King RA, Christophersen CT, Bird AR. Dietary resistant starch dose-dependently reduces adiposity in obesity-prone and obesity-resistant male rats. Nutr Metab (Lond). 2012;9:93.
Harazaki T, Inoue S, Imai C, Mochizuki K, Goda T. Resistant starch improves insulin resistance and reduces adipose tissue weight and CD11c expression in rat OLETF adipose tissue. Nutrition. 2014;30:590–5.
Keenan MJ, Janes M, Robert J, Martin RJ, Raggio AM, McCutcheon KL, et al. Resistant starch from high amylose maize (HAM-RS2) reduces body fat and increases gut bacteria in ovariectomized (OVX) rats. Obesity (Silver Spring). 2013;21:981–4.
Lu H, Gui Y, Guo T, Wang Q, Liu X. Effect of the particle size of cellulose from sweet potato residues on lipid metabolism and cecal conditions in ovariectomized rats. Food Funct. 2015;6:1185–93.
Chen HL, Lin YM, Wang YC. Comparative effects of cellulose and soluble fibers (pectin, konjac glucomannan, inulin) on fecal water toxicity toward Caco-2 cells, fecal bacteria enzymes, bile acid, and short-chain fatty acids. J Agric Food Chem. 2010;58:10277–81.
Claus SP, Ellero SL, Berger B, Krause L, Bruttin A, Molina J, et al. Colonization-induced host-gut microbial metabolic interaction. MBio. 2011;2:e00271–10.
Swann JR, Want EJ, Geier FM, Spagou K, Wilson ID, Sidaway JE, et al. Systemic gut microbial modulation of bile acid metabolism in host tissue compartments. Proc Natl Acad Sci U S A. 2011;108 Suppl 1:4523–30.
Belkaid Y, Hand TW. Role of the microbiota in immunity and inflammation. Cell. 2014;157:121–41.
Lane DJ. 16S/23S rRNA sequencing. In: Stackebrandt E, Goodfellow M, editors. Nucleic acid techniques in bacterial systematics. New York: John Wiley & Sons, Inc; 1991.
Woo KS, Seib PS. Cross-linked resistant starch: preparation and properties. Cereal Chem. 2002;79:819–25.
Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294:E15–26.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9.
Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27:1487–95.
Lee S, Muniyappa R, Yan X, Chen H, Yue LQ, Hong EG, et al. Comparison between surrogate indexes of insulin sensitivity and resistance and hyperinsulinemic euglycemic clamp estimates in mice. Am J Physiol Endocrinol Metab. 2008;294:E261–70.
Eren AM, Vineis JH, Morrison HG, Sogin ML. A filtering method to generate high quality short reads using illumina paired-end technology. PLoS One. 2013;8:e66643.
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.
Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–42.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
Vazquez-Baeza Y, Pirrung M, Gonzalez A, Knight R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience. 2013;2:16.
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.
Han J, Liu Y, Wang R, Yang J, Ling V, Borchers CH. Metabolic profiling of bile acids in human and mouse blood by LC-MS/MS in combination with phospholipid-depletion solid-phase extraction. Anal Chem. 2015;87:1127–36.
Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175–91.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289–300.
R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2012.http://www.R-project.org/. Accessed July 2015.
We thank Ody Maningat and Liming Cai (MGP Ingredients) for providing the RS, Dr. Jennifer Clarke (University of Nebraska-Lincoln) for expert advice on statistical analysis, Dr. Shawn Rigby (Iowa State University) for assistance with flow cytometry, Drs. Jun Han and Christoph H. Borchers (University of Victoria Genome British Columbia Proteomics Centre) for assistance with bile acids analysis, Dr. Junyi Yang and Maria Ximena Maldonaldo Gomez (University of Nebraska-Lincoln) for assistance with tissue processing, and Dr. Michael Pellizzon (Research Diets, Inc.) for assistance with diet formulations. ART and LBB would like to thank Drs. Andy Benson and Devin Rose (University of Nebraska-Lincoln) and Dr. Nathalie Delzenne (Université catholique de Louvain) for their helpful discussions and critical reading of the manuscript. We are all especially grateful for the technical expertise and skillful animal husbandry provided by Brandon White and the staff at the UNL Gnotobiotic Mouse Facility.
This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health (1P20GM104320), the Nebraska Corn Board, and start-up funding from the University of Nebraska-Lincoln to ART. LBB was supported by a complementary post-doctoral grant awarded by the “Fonds Spécial de Recherche, Université catholique de Louvain.” VM is a research fellow of the “Fonds pour la recherche dans l’industrie et l’agriculture” (FRIA, Belgium). NS benefits from a Clarín postdoctoral contract (Marie Curie European CoFund Program) co-financed by Plan Regional de Investigación del Principado de Asturias, Spain. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
The 16S rRNA gene sequencing information can be found in the MG-RAST database, with the following accession numbers: 4603180.3, 4603181.3, 4603182.3, 4603183.3, 4603184.3, 4603185.3, 4603186.3, 4603187.3, 4603188.3, 4603189.3, 4603190.3, 4603191.3, 4603192.3, 4603193.3, 4603194.3, 4603195.3, 4618881.3, 4618882.3, 4618883.3, 4618884.3, 4618885.3, 4618886.3, 4618887.3, 4618888.3, 4618889.3, 4618890.3, 4618891.3, 4618892.3, 4618893.3, 4618894.3, 4618895.3, and 4618896.3. A list of OTUs significantly affected by dietary intervention is included in Additional file 3: Table S1, a list of bacterial taxa significantly affected by dietary intervention is included in Additional file 3: Table S2, a bile acid dataset summary table, including correlations between bile acids and selected host physiological parameters, is provided in Additional file 3: Table S3, and the composition of the rodent diets is provided in Additional file 3: Table S4.
LBB, JW, and ART conceived the project, contributed to the experimental designs, performed the experiments, interpreted the results, generated the figures, and wrote the manuscript. RSM, JCGN, EAC, MIQV, and HK performed the experiments. IM provided guidance for gut microbiota analysis. NS, CGRG, VM, and GGM performed the experiments and interpreted the results. RJS performed the experiments and contributed to the experimental design. All authors discussed the results and approved the manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The Institutional Animal Care and Use Committee at the University of Nebraska-Lincoln approved all procedures involving animals (Protocols 817 and 1215).
Resistant starches improved insulin sensitivity in B6 mice in the presence of a microbiota. Glycemic response after insulin injection in conventional 16 week-old B6 mice fed experimental diets for 7 weeks (a, b) (LFD, low fat diet; WD, Western diet; RS2, WD with resistant starch 2; RS4, WD with resistant starch 4). Body weights at necropsy (c), body weight gains over 8 weeks of dietary intervention (d), subcutaneous adipose tissue (SAT) weights at necropsy (e) and energy intake (as recorded over a week at week 4) (f) in conventional B6 mice fed experimental diets for 8 weeks. Although RS4 feeding tended to reduce the body and subcutaneous adipose tissue weights of B6 mice, these differences were not statistically significant (p = 0.18 and p = 0.51, respectively, RS4 versus WD according to Dunnett’s post-hoc tests). #p=0.08, *p<0.05, ***p<0.001 versus WD (one-way ANOVA with Dunnett’s post-hoc tests). Mean ± SEM. (a, b) N=5-9; (c-e) N=8; (f) N=4 cages. (PDF 30 kb)
Feeding resistant starches changed the gut microbiota composition. (a) Principal Coordinate Analysis plot of β-diversity based on Bray-Curtis distance. (b) Principal Coordinate Analysis plot of β-diversity based on Unweighted Unifrac distance. (c) LEfSe cladogram highlighting the bacterial taxa specifically enriched in each dietary group. (d) LEfSe results highlighting the bacterial taxa specifically enriched in each dietary group. LFD, low fat diet; WD, Western diet; RS2, WD with resistant starch 2; RS4, WD with resistant starch 4. N=8. (PDF 1390 kb)
Supplemental Tables. List of OTUs significantly affected by dietary intervention. Each OTU is identified at the species level and the percentage of homology is indicated (%ID). R_FDR_p indicates the p-value of the one-way ANOVA adjusted to control the false discovery rate for multiple tests according to the Benjamini and Hochberg procedure and with Robust (Huber) estimation to down weight outliers. LFD, WD, RS2, RS4 represent the mean relative abundance of each OTU (as a fraction of one) for each group; SEM are indicated in the last 4 columns. *p<0.05 versus WD (one-way ANOVA with Dunnett’s post-hoc tests). N=8. Table S2. List of bacterial taxa significantly affected by dietary intervention. R_FDR_p indicates the p-value of the one-way ANOVA adjusted to control the false discovery rate for multiple tests according to the Benjamini and Hochberg procedure and with Robust (Huber) estimation to down weight outliers. LFD, WD, RS2, RS4 represent the mean relative abundance of each bacterial taxon (as a fraction of one) for each group; SEM are indicated in the last 4 columns. *p<0.05 versus WD (one-way ANOVA with Dunnett’s post-tests). N=8. Table S3. Bile acid dataset summary table, including correlations between bile acids and selected host physiological parameters. Mean concentrations (in nmole/g of cecal contents) with SEM for each group along with fold changes. Pearson r, and p- and q-values for the Pearson correlation tests are presented. Only bile acids with mean concentrations above 1nmole/g for at least one experimental treatment were included in the multiple correlation analyses. Correlations with a p-value <0.05 are highlighted in pink and correlations with a p- and a q-value <0.05 are highlighted in red. N=6-8/group. Table S4. Diet composition. Ingredient quantity, energy density and macronutrient repartition for all the experimental diets. LFD, low fat diet; WD, Western diet; RS2, WD with resistant starch 2; RS4, WD with resistant starch 4. References to the standard diet formulations from Research Diets are also included. Mice were fed a low fat diet (LFD, D12450K) and a customized Western diet (WD, 45% kcal from fat and 17% kcal from sucrose with low maltodextrine/high starch compared to D12451) where a portion of the corn starch was replaced by either 10% RS type 2 (RS2; HI-MAIZE® 260, Ingredion Incorporated, Westchester, IL) or 10% RS type 4 (RS4; Fibersym® RW, MGP Ingredients, Atchinson, KS). HI-MAIZE® 260 is derived from high amylose corn starch; it has a caloric content of 1.5 kcal/g and it contains 62.5% RS. Fibersym® RW is a chemically modified phosphorylated cross-linked RS4 prepared from wheat starch; it has a caloric content of 0.44 kcal/g and contains 89.1% RS. (PDF 260 kb)
Resistant starches did not impact body weights, white adipose tissue weights, tibialis muscle weights and plasma leptin levels in germ-free (GF) or conventionalized (CVZ) C3H mice fed experimental diets for 8 weeks. (a, b, d) Body, adipose tissue and tibialis weights. (c) Plasma leptin levels. *p<0.05 versus WD fed mice of the same microbial status (one-way ANOVA with Dunnett’s post-hoc tests). For C, GF-RS4 data were determined to be non-normal even after log-transformation and were analyzed using a Kruskal-Wallis test then Dunn’s post-tests. LFD, low fat diet; WD, Western diet; RS2, WD with resistant starch 2; RS4, WD with resistant starch 4. Mean ± SEM. N=7-8. (PDF 29 kb)
Resistant starches modulated subcutaneous adipose tissue macrophage accumulation and cecal bile acid profiles in C3H mice fed experimental diets for 8 weeks. Two different markers were used to identify M2 macrophages in C3H mice, CD206 and CD301. Both markers provided similar results, as shown in Fig. 5 for CD206 and Fig S5 for CD301. (a) Percentage of M2 (CD301+) macrophages in the stromal vascular fraction (SVF) of the SAT of C3H mice. (b) Ratio of M1/M2, using CD301 as a M2 marker. (c) Plasma monocyte-chemoattractant protein-1 (MCP-1) levels. (d) MCP-1 expression in subcutaneous adipose tissue (SAT). (e) Principal Component Analysis plot of the bile acid profiles by microbial status. (f) Principal Component Analysis plot of the bile acid profiles by microbial status and dietary treatment. CVZ: conventionalized. GF: germ-free. LFD, low fat diet; WD, Western diet; RS2, WD with resistant starch 2; RS4, WD with resistant starch 4. #p=0.07, *p<0.05 vs WD fed mice of the same microbial status (one-way ANOVA with Dunnett’s post-hoc tests). Microbial status significantly impacted MCP-1 mRNA expression in the SAT (analysis of all 8 treatments using two-way ANOVA). (a-d) Mean ± SEM. (a, b) N=14-15 except for the LFD where N=6. (c, d) N=6-8. (e, f) N=7-8. (PDF 98 kb)
Supplemental materials and methods. (PDF 208 kb)
About this article
Cite this article
Bindels, L.B., Segura Munoz, R.R., Gomes-Neto, J.C. et al. Resistant starch can improve insulin sensitivity independently of the gut microbiota. Microbiome 5, 12 (2017). https://doi.org/10.1186/s40168-017-0230-5