Abstract

   Obesity and type 2 diabetes are increasing in prevalence around the
   world, and there is a clear need for new and effective strategies to
   promote metabolic health. A low protein (LP) diet improves metabolic
   health in both rodents and humans, but the mechanisms that underlie
   this effect remain unknown. The gut microbiome has recently emerged as
   a potent regulator of host metabolism and the response to diet. Here,
   we demonstrate that a LP diet significantly alters the taxonomic
   composition of the gut microbiome at the phylum level, altering the
   relative abundance of Actinobacteria, Bacteroidetes, and Firmicutes.
   Transcriptional profiling suggested that any impact of the microbiome
   on liver metabolism was likely independent of the microbiome-farnesoid
   X receptor (FXR) axis. We therefore tested the ability of a LP diet to
   improve metabolic health following antibiotic ablation of the gut
   microbiota. We found that a LP diet promotes leanness, increases energy
   expenditure, and improves glycemic control equally well in mice treated
   with antibiotics as in untreated control animals. Our results
   demonstrate that the beneficial effects of a LP diet on glucose
   homeostasis, energy balance, and body composition are unlikely to be
   mediated by diet-induced changes in the taxonomic composition of the
   gut microbiome.

Introduction

   Around the world, approximately 425 million people have diabetes, and
   that number is expected to grow by 50% over the next three
   decades^[44]1. Beyond the direct effects of diabetes on mortality, its
   impact is amplified by its association with other causes of morbidity
   and mortality, such as cardiovascular disease^[45]2, cancer^[46]3, and
   Alzheimer’s disease^[47]4. Type 2 diabetes, which is associated with
   diet and obesity, accounts for the vast majority of diabetes cases, and
   the epidemic rise in obesity has fueled the development of this health
   crisis.

   Dietary interventions to control or prevent type 2 diabetes could be
   highly effective and affordable, but long-term reduced calorie diets
   have not proven to be sustainable for most people. Diets that alter the
   level of specific macronutrients without a decrease in caloric
   consumption may be more sustainable^[48]5; one variety of such diets
   are high protein, low carbohydrate diets such as the Atkins diet, which
   promise rapid weight loss without restricting calories^[49]6. Some
   clinical trials have observed that high protein diets can promote
   weight loss^[50]7–[51]9, at least in highly compliant subjects^[52]10.
   However, long-term prospective cohort studies have observed that high
   protein consumption is associated with increased insulin resistance,
   diabetes, cancer, and cardiovascular disease, and an overall increase
   in mortality^[53]11–[54]13.

   In agreement with these findings, recent long-term studies in
   Drosophila and mice, as well as a short-term randomized control trial
   conducted in humans, find that low protein (LP) diets are associated
   with improvements in health, survival, and insulin
   sensitivity^[55]13–[56]18. Reducing dietary protein largely blocks the
   effect of a high-fat diet on glucose tolerance^[57]18, and we recently
   showed that in a mouse model of pre-existing diet-induced obesity,
   reducing dietary protein rapidly restored metabolic health,
   dramatically reduced adiposity, and improved glucose tolerance and
   insulin sensitivity^[58]19. While some of these phenotypes are mediated
   in part by the insulin-sensitizing and energy expenditure promoting
   hormone fibroblast growth factor 21 (FGF21), it is likely that other
   mechanisms are also involved^[59]18–[60]24.

   Over the last decade, numerous studies have found that the composition
   of the gut microbiome plays an important role in regulating the
   metabolic health of both rodents and humans^[61]25,[62]26 by mediating
   the response to drugs, diet, and aging^[63]27–[64]33. One major pathway
   by which the gut microbiota regulates glycemic control is by altering
   bile acid metabolism and activating the farnesoid X receptor (FXR) –
   FGF15 signaling axis^[65]34,[66]35. Recent work suggests that at least
   in rodents, the major dietary factors that regulate the taxonomic
   composition of the gut microbiome are protein and carbohydrate
   intake^[67]36. However, the source of dietary protein – e.g. red meat,
   white meat, dairy, or plant protein – also has an important effect on
   the taxonomic composition of the gut microbiome^[68]37. It remains
   unknown if the effect of a LP diet on the composition and function of
   the gut microbiome plays a role in its beneficial metabolic effects.

   In this study, we determined that an amino acid defined LP diet, which
   has similar metabolic benefits to a LP diet containing natural
   protein^[69]22, alters the taxonomic and functional composition of the
   gut microbiome. We found that a LP diet significantly alters the
   hepatic transcriptome, possibly reducing FXR-FGF15 signaling. Finally,
   we found that ablation of the gut microbiome with antibiotics does not
   significantly alter the metabolic response to a LP diet. Our data
   suggests that while dietary protein plays an important role in shaping
   the taxonomic and functional composition of the gut microbiome, these
   diet-induced changes do not mediate the beneficial metabolic effects of
   a LP diet in young, lean mice.

Materials and Methods

Animals and Treatments

   For all experiments, male C57BL/6J mice were purchased from The Jackson
   Laboratory and group housed in static microisolator cages in a
   specific-pathogen free animal facility. For experiments investigating
   the composition of the gut microbiome and transcriptional profiling of
   the liver, mice were purchased at 8 weeks of age, and diet changes
   occurred at 9 weeks of age. Approximately 4 months later, cecal
   contents and livers were collected from mice sacrificed in the morning
   following an overnight, approximately ~16 hr fast. For experiments in
   which the gut microbiome was ablated with antibiotics, mice were
   purchased at 5 weeks of age; starting at 6 weeks of age, mice were
   randomized at the cage level to receive water or water containing
   antibiotics as described below. Diet changes occurred at 9 weeks of
   age, and antibiotic treatment was continued for the duration of the
   experiment. All procedures involving animals were approved by the
   Institutional Animal Care and Use Committee of the William S. Middleton
   Memorial Veterans Hospital (Madison, WI), and all experiments were
   performed in accordance with relevant guidelines and regulations.

Diets

   Prior to 9 weeks of age, animals were fed the standard facility chow
   (Purina 5001; Purina Mills, Richmond, IN, USA). Amino acid defined
   animal diets (non-irradiated) were obtained from Envigo (formerly
   Harlan Laboratories). At 9 weeks of age, animals were switched to
   either a Control amino acid defined diet (TD.140711; 22.0% of calories
   derived from amino acids; 59.4% from carbohydrate; 18.6% from fat) or a
   Low Protein amino acid defined diet (TD.140712; 7.1% of calories
   derived from amino acids; 74.4% from carbohydrates; 18.5% from fat).
   The complete composition of these diets has been previously
   described^[70]22.

Antibiotic Treatment

   The gut microbiome was ablated using an antibiotic treatment protocol
   previously described to efficiently ablate the gut microbiome of
   mice^[71]38. Briefly, mice were provided with free access to autoclaved
   water containing 1 g/L ampicillin, 500 mg/L vancomycin, and 1 g/L
   neomycin; however, in contrast to the protocol followed in^[72]38,
   aspartame was omitted due to its negative effects on glucose
   homeostasis and body composition in mice^[73]39. The mice and the water
   bottles were weighed and changed biweekly to monitor water intake.
   Control mice were provided with autoclaved water not containing
   antibiotics. To verify the efficacy of the antibiotic treatment, fecal
   pellets were collected and total DNA was extracted using a modification
   of a previously described protocol^[74]40. Briefly, fecal pellets
   (~30–50 mg) were resuspended in a solution containing 500 µL of
   extraction buffer [200 mM Tris (pH 8.0), 200 mM NaCl, 20 mM EDTA],
   210 µL of 20% SDS, 500 µL phenol:chloroform:isoamyl alcohol (pH 7.9,
   25:24:1) and 500 µL of Fisher Scientific 1.4 mm diameter ceramic beads
   (Cat# 15340159). Following mechanical disruption using a FastPrep 24
   (M.P. Biomedicals), the solution was centrifuged at 8,000 rpm at 4 °C
   for three minutes. The aqueous layer was then sequentially precipitated
   using sodium acetate/isopropanol and sodium acetate/ethanol. DNA
   samples were then quantified using a Nanodrop 2000c.

Mouse metabolic phenotyping

   Glucose and alanine tolerance tests (GTT and ATT) were performed by
   fasting the mice overnight for 16 hr and then injecting either glucose
   (1 g/kg) or alanine (2 g/kg) intraperitoneally as previously
   described^[75]41,[76]42. Glucose measurements were taken using a Bayer
   Contour blood glucose meter and test strips. Mouse body composition was
   determined using an EchoMRI 3-in-1 Body Composition Analyzer. For assay
   of multiple metabolic parameters (O[2], CO[2], food consumption) and
   activity tracking, mice were acclimated to housing in a Columbus
   Instruments Oxymax/CLAMS metabolic chamber system for ~24 hr, and data
   from a continuous 24-hr period was then recorded and analyzed.

Gut microbial community DNA preparation

   Approximately 20–50 mg of cecal matter was added to an autoclaved
   Sarstedt 2 m micro screw-cap tube (Ref# 72.693.005) containing
   approximately 500 μL of BioSpec Zirconia/Silica beads (Cat# 11079101z)
   and one large Bio Spec bead (Cat# 11079132ss). To this, 500 μL of
   200 mM Tris-HCl, pH 8.0/200 mM NaCl/20 mM EDTA was added, as well as
   210 μL 20% SDS. 500 μL of Phenol/Chloroform/isoamyl alcohol, pH 7.9,
   25:24:1, was added before bead beating using a FastPrep 24 (M.P.
   Biomedicals) until sample was fully homogenized in solution. Tubes were
   centrifuged at 8,000 rpm at 4 °C for three minutes. The aqueous layer,
   approximately 500 µL, was transferred to a new microcentrifuge tube
   (Axygen). 60 μL of 3 M NaAcetate was added, then 600 μL of isopropanol,
   then inverted to mix. The samples were placed on ice for one hour
   before centrifuging at 13,000 rpm at 4 °C for 20 minutes. Samples were
   decanted, and pellet was rinsed with 200 μL of 100% EtOH, then decanted
   and briefly dried. The pellet was dissolved in 100–200 μL of TE buffer.
   100 μL of DNA was cleaned using the Macherey-Nagel PCR Clean-up kit,
   using 2 NT3 washes and eluting with 50–100 μL of elution buffer.

Construction and Sequencing of v3-v4 16S Metagenomic libraries

   Purified genomic DNA was submitted to the University of
   Wisconsin-Madison Biotechnology Center. DNA concentration was verified
   fluorometrically using either the Qubit® dsDNA HS Assay Kit or
   Quant-iT™ PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific, Waltham,
   MA, USA). Samples were prepared in a similar process to the one
   described in Illumina’s 16 S Metagenomic Sequencing Library Preparation
   Protocol, Part # 15044223 Rev. B (Illumina Inc., San Diego, California,
   USA) with the following modifications: The 16 S rRNA gene V3/V4
   variable region was amplified with fusion primers (forward primer
   341 f:
   5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCT(N)[3/6]CCTACGGGNGGCWGCAG-3′,
   reverse primer 805r:
   5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT(N)[3/6]GACTACHVGGGTATCTAATCC-3′).
   Region specific primers were previously described (^[77]43; underlined
   sequences above), and were modified to add 3–6 random nucleotides
   ((N)[3/6]) and Illumina adapter overhang nucleotide sequences 5′ of the
   gene‐specific sequences. Following initial amplification, reactions
   were cleaned using a 0.7x volume of AxyPrep Mag PCR clean-up beads
   (Axygen Biosciences, Union City, CA). In a subsequent PCR, Illumina
   dual indexes and Sequencing adapters were added using the following
   primers (Forward primer:
   5′-AATGATACGGCGACCACCGAGATCTACAC[55555555]ACACTCTTTCCCTACACGACGCTCTTCCG
   ATCT-3′, Reverse Primer:
   5′-CAAGCAGAAGACGGCATACGAGAT[77777777]GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
   -3′, where bracketed sequences are equivalent to the Illumina Dual
   Index adapters D501-D508 and D701-D712,N716,N718-N724,N726-N729).
   Following PCR, reactions were cleaned using a 0.7x volume of AxyPrep
   Mag PCR clean-up beads (Axygen Biosciences). Quality and quantity of
   the finished libraries were assessed using an Agilent DNA 1000 kit
   (Agilent Technologies, Santa Clara, CA) and Qubit® dsDNA HS Assay Kit
   (ThermoFisher Scientific), respectively. Libraries were pooled in an
   equimolar fashion and appropriately diluted prior to sequencing. Paired
   end, 300 bp sequencing was performed using the Illumina MiSeq Sequencer
   and a MiSeq. 600 bp (v3) sequencing cartridge. Images were analyzed
   using the standard Illumina Pipeline, version 1.8.2. OTU assignments
   and diversity plots were created using QIIME analysis pipeline^[78]44.

Microbiota analysis using QIIME

   Microbiota analysis to obtain OTU assignments and diversity plots were
   performed using Quantitative Insights Into Microbial Ecology
   (QIIME)^[79]44 version 1.9.1. Illumina sequencing reads were adapter
   and quality trimmed using the Skewer^[80]45 trimming program to remove
   low quality (<Q25) bases and sequencing adapters. Reads shorter than
   100 nucleotides after trimming were discarded. Flash^[81]46 was used to
   merge paired end reads into amplicon sequences using a minimum overlap
   of 10 nucleotides. Amplicons were then PCR primer trimmed and quality
   filtered. Sequences were then clustered in OTUs using an open-reference
   OTU picking protocol based on 97% identity using UCLUST^[82]46 against
   the Greengenes reference database^[83]47. Representative sequences
   (most abundant sequence in OTUs) were picked, aligned to
   GreenGenes^[84]47 Core reference alignment using PyNAST^[85]48.
   Taxonomic assignments were associated with OTUs based on the taxonomy
   associated with the Greengenes reference sequence defining each OTU.
   UniFrac distances between samples were calculated using the Greengenes
   reference tree
   (ftp://greengenes.microbio.me/greengenes_release/gg_13_5/gg_13_8_otus.t
   ar.gz). The resulting biom-formatted OTU table was filtered to remove
   singletons and OTUs that could not be aligned using PyNAST. Alpha
   rarefaction curves were calculated for all samples with a rarefaction
   upper limit of (median depth/sample count). Samples were removed from
   further characterization if they did not contain sufficient reads at a
   depth where the Good’s coverage value for most samples was greater than
   0.9. Beta diversity was calculated using weighted and unweighted
   unifrac on OTU data leveled according to the lowest sample depth. An
   alternative normalization by CSS^[86]49 is also provided for additional
   downstream analysis.

Liver mRNA preparation and transcriptional profiling

   Total liver RNA was extracted with Trireagent (Sigma) as previously
   described^[87]19. Concentration and purity of RNA was initially
   determined using a Nanodrop 2000c, and then submitted to the University
   of Wisconsin- Madison Biotechnology Center Gene Expression Center & DNA
   Sequencing Facility. RNA quality was then assayed using an Agilent RNA
   NanoChip, and stranded mRNA libraries with polyA enrichment were
   prepared as described in the Illumina TruSeq Stranded mRNA Sample
   Preparation Guide Rev. E. DNA sequencing was performed using an
   Illumina HiSeq. 2500 1 × 100 (TruSeq v3) full flowcell. Pathway
   enrichment was performed using the “edgeR”^[88]50 and
   “org.Mm.eg.db”^[89]51 packages in R version 3.4.4^[90]52.

Gene expression (qPCR)

   Total RNA was isolated from liver with Tri-Reagent, and cDNA was
   generated as previously described^[91]24. Oligo dT primers and primers
   for real-time PCR were obtained from Integrated DNA Technologies
   (Coralville, IA,USA). Primer sequences used for qPCR were as follows:
   Cyp8b1: F: GTTTCTGGGTCCTCTTATTCCTG, R: TGGGAGTGAAAGTGAACGAC; Zfp36l1:
   F: CACACCAGATCCTAGTCCTTG, R: CTGGGAGTGCTGTAGTTGAG; Cyp7a1: F:
   AACGATACACTCTCCACCTTT, R: CTGCTTTCATTGCTTCAGGG; Ppp2cb: F:
   ATGGAAGGATATAACTGGTGCC, R: AGGTGCTGGGTCAAACTG; Map2k1: F:
   CGTACATCGTGGGCTTCTAC, R: CAGAACTTGATCCAAGGACCC; Shp: F:
   CTACCCTCAAGAACATTCCAGG, R: CACCAGACTCCATTCCACG; Abcb11: F:
   CCTCATACGGAAACCCAAGATC, R: CTGACTGTTGATAGGCGATGG; Actb: F:
   ACCTTCTACAATGAGCTGCG, R: CTGGATGGCTACGTACATGG. Reactions were run on an
   Applied Biosystems StepOnePlus Real-Time PCR System (Thermo Fisher
   Scientific) with Invitrogen SYBR Green PCR Master Mix (ThermoFisher
   Scientific). Actin (Actb) was used to normalize the results from
   gene-specific reactions.

Statistics

   Statistics were carried out in Prism 7 (Graphpad Prism). For each
   measured parameter, we conducted a two‐factor ANOVA which included an
   effect of drug treatment (vehicle or antibiotics), an effect of diet
   (Control or Low AA), and an interaction between diet and treatment. A
   Sidak’s post-test was performed to determine the significance of
   factors identified as significant in the two-factor ANOVA. PCA analysis
   was performed using Clustvis^[92]53.

Results

A Low Amino Acid Diet Alters the Taxonomic Composition of the Gut Microbiome

   To determine if the taxonomic composition of the microbiome is altered
   by consuming reduced dietary protein, we fed mice amino acid (AA)
   defined diets modeled on the AA profiles of naturally sourced control
   and low protein diets. The Control diet is modeled on a naturally
   sourced 21% protein diet, while the Low AA diet is based on a naturally
   sourced 7% protein diet, and we have previously shown that low protein
   and Low AA diets are comparable in their effect on glycemic control and
   body composition^[93]22. As shown in Fig. [94]1, mice fed a Low AA diet
   for four months have improved glucose tolerance (Fig. [95]1A) and
   reduced weight and fat mass gain relative to mice fed the Control diet
   (Fig. [96]1B).

Figure 1.

   [97]Figure 1
   [98]Open in a new tab

   A low protein diet promotes metabolic health and alters the taxonomic
   composition of the cecal microbiome. (A) Glucose tolerance test on male
   C57BL/6J mice fed a Control (22% of calories from amino acids) or Low
   AA (7% of calories from amino acids) diet for 4 months (n = 8–10/group;
   *p < 0.05, t-test). (B) Weight and body composition were measured
   immediately prior to diet start and after 10 weeks on the indicated
   diets (n = 8–10/group; *p < 0.05, = t-test). (C) Bar plot of average
   relative abundance at the phylum taxonomic level. Top 6 phyla are
   shown. (D) Principle component analysis of demonstrating the effect of
   diet on taxonomic composition. (E,F) Bacterial phyla differentially
   represented in cecal contents from mice fed the specified diets for 4
   months (n = 7–12/group; Sidak’s test following ANOVA, *p < 0.05). Error
   bars represent SEM.

   We sacrificed mice fed the Control and Low AA diets after four months,
   collecting cecal contents and the liver. We prepared DNA from the
   contents of the cecum, and performed 16 S ribosomal RNA (rRNA)
   sequencing in order to identify alterations in the microbial
   composition of the gut microbiome. We utilized QIIME to determine
   relative taxonomic composition of each sample at the phylum and family
   levels (Figs [99]1C and [100]S1A). While the alpha diversity did not
   significantly differ between Control and Low AA diet-fed mice
   (Fig. [101]S1B), we observed a major shift in the taxonomic composition
   of the gut microbiome. Utilizing principal component analysis, we
   determined that the first two principle components explained a majority
   of the variability in the taxonomic composition of the gut microbiome,
   and individuals clustered by diet (Fig. [102]1D). We observed major
   differences at the phylum level, with an increase in the relative
   abundance of Firmicutes (Fig. [103]1E), and a decrease in the relative
   abundance of Bacteroidetes and Actinobacteria (Fig. [104]1F). At the
   family level, we found that the increased relative abundance of
   Firmicutes was primarily driven by an increase in the order
   Clostridiales (Fig. [105]S1c); we also observed a decrease in the
   abundance of the Bacteroidetes families S24–7 and Odoribacteraceae, and
   the Actinobacteria families Bifidobacteriaceae and Coriobacteriaceae
   (Fig. [106]S1D).

A Low Amino Acid Diet Alters the Hepatic Transcriptome, but does not activate
FXR-FGF15 signaling

   A low protein diet improves glucose tolerance in part by improving
   hepatic insulin sensitivity^[107]22,[108]54. While induction of the
   insulin-sensitizing hormone FGF21 has been shown to play a role in this
   response^[109]20,[110]21, possibly via inhibition of hepatic mTORC1
   (mechanistic Target Of Rapamycin Complex 1)^[111]55, we recently
   observed that dietary methionine restriction, which mimics many of the
   effects of a low protein diet, can improve glucose tolerance
   independently of changes in FGF21 and hepatic mTORC1^[112]24.

   Over the past decade, it has become clear that the composition and
   function of the gut microbiome can regulate host metabolism, including
   glycemic control^[113]56,[114]57. One recently characterized pathway by
   which the microbiome can regulate host glucose metabolism is through
   bile acids; alterations in the amount and type of secondary bile acids
   can regulate glycemic control by activating or repressing the FXR –
   FGF15 signaling axis^[115]34,[116]35.

   We used RNA-Seq to identify gene expression changes induced by a low
   protein diet. We identified several differentially expressed metabolic
   pathways when mice were fed a Low AA diet (Fig. [117]2A,B). In
   particular, we observed altered expression of many genes involved in
   xenobiotic or drug metabolism as well as steroid hormone biosynthesis.
   However, our analysis did not identify bile acid signaling as
   significantly altered, and we did not observe significantly altered
   expression of Shp, Cyp7a1, or several other genes that have been shown
   to be regulated by the FXR-FGF15 signaling axis
   (Fig. [118]2C)^[119]58–[120]60. Targeted qPCR analysis of mRNA from a
   larger, additional cohort of mice confirmed that a Low AA diet did not
   significantly alter the expression of any of one of these genes
   (Fig. [121]S2). However, we observed an overall effect of diet
   consistent with reduced FXR-FGF15 signaling (Fig. [122]S2).

Figure 2.

   [123]Figure 2
   [124]Open in a new tab

   A low protein diet alters the hepatic transcriptome and shows distinct
   changes in biological pathways. (A–C) RNA-Seq was performed on the
   livers for mice fed a Control diet or a Low AA for four months. (A) A
   heatmap indicating the relative expression of genes involved in the
   most significantly enriched biological KEGG (Kyoto Encyclopedia of
   Genes and Genomes) pathways based on genes differentially expressed in
   the livers of Control and Low AA fed mice (q < 0.05, FDR). Genes in
   more than one significantly enriched KEGG pathway are listed only once,
   and assigned to the most significantly affected pathway. (B) Pathway
   enrichment analysis was performed using g:Profiler (g:GOSt)^[125]73,
   and the p-values of KEGG pathways significantly up- and downregulated
   by Low AA diet feeding were determined. Colors are matched to that of
   pathways in (A). (C) Heatmap representing the relative expression of
   liver genes known to be altered by FXR-FGF15 bile acid signaling.

The metabolic effects of protein restriction are not mediated by the gut
microbiota

   In order to directly assess if the altered taxonomic composition of the
   gut microbiome contributes to the metabolic effects of a low protein
   diets, we pretreated mice with either vehicle or antibiotics (ABX) for
   three weeks; mice were then randomized to either Control or Low AA
   diets. Antibiotic-treated mice continued to receive antibiotics
   throughout the course of the experiment (Fig. [126]3A). As expected,
   mice treated with antibiotics had significantly reduced fecal DNA
   content (Fig. [127]3B). Over the course of the experiment, we tracked
   weight and body composition of mice in each group (Fig. [128]3C–F). As
   expected based on our previous studies, mice fed the Low AA diet gained
   less weight, less fat mass, and less lean mass than mice fed a Control
   diet. While antibiotic administration increased weight gain and lean
   mass gain compared to vehicle treated mice, the Low AA diet had similar
   effects on weight and body composition in the presence and absence of
   antibiotics.

Figure 3.

   [129]Figure 3
   [130]Open in a new tab

   A low protein diet alters body composition similarly in vehicle and
   antibiotic-treated mice. (A) Schematic representation of the
   experimental plan; mice were pretreated with antibiotics or vehicle for
   three weeks, and then randomized to either a Control or Low AA diet.
   (B) Fecal DNA content was determined following 3 weeks of antibiotic
   treatment (n = 8/group; *p < 0.05, t-test). (C) Weight of the mice in
   each group was tracked following randomization to each diet. (D–F)
   Weight and body composition were determined immediately prior to diet
   start and after 6 weeks on the indicated diets, and the change in (D)
   weight, (E) fat mass, and (F) lean mass was determined (n = 12/group;
   statistics for the overall effects of diet, antibiotic (ABX) treatment,
   and the interaction represent the p-value from a two-way ANOVA;
   *p < 0.05 from a Sidak’s post-test examining the effect of parameters
   identified as significant in the two-way ANOVA). Error bars represent
   SEM.

   As we determined previously, mice fed a Low AA diet had improved
   glucose tolerance compared to mice fed a Control diet (Fig. [131]4A).
   We also specifically assessed gluconeogenesis in the liver by
   performing an alanine tolerance test; as in our previous studies
   utilizing pyruvate, we observed a decrease in the area under the curve
   (AUC) in mice fed a Low AA diet, indicating improved suppression of
   gluconeogenesis (Fig. [132]4B). We saw equivalent reductions in AUC in
   response to a Low AA diet in both vehicle and antibiotic fed mice; and
   we did not observe any differences in AUC resulting from antibiotic
   treatment.

Figure 4.

   [133]Figure 4
   [134]Open in a new tab

   A low protein diet improves glucose homeostasis similarly in vehicle
   and antibiotic-treated mice. (A) Glucose and (B) alanine tolerance
   tests were conducted in mice fed the indicated diets for 8 weeks and 4
   weeks, respectively (n = 12/group; statistics for the overall effects
   of diet, antibiotic (ABX) treatment, and the interaction represent the
   p-value from a two-way ANOVA; *p < 0.05 from a Sidak’s post-test
   examining the effect of parameters identified as significant in the
   two-way ANOVA). Error bars represent SEM.

   Rodents fed a low protein diet have increased food consumption and
   increased energy expenditure^[135]15,[136]18,[137]20–[138]22,[139]61.
   We examined the effect of antibiotics on these phenotypes by placing
   mice in metabolic chambers and assessing food consumption, spontaneous
   activity, respiratory exchange ratio (RER), and energy expenditure
   (Fig. [140]5A–F). In agreement with our previous results, we observed
   increased food consumption (Fig. [141]5A,B) and energy expenditure
   (Fig. [142]5E,F) in mice fed a Low AA diet, with equivalent effects in
   both vehicle and antibiotic treated mice. Surprisingly, while a Low AA
   diet increased both the daytime and nighttime RER of vehicle-treated
   mice, mice treated with antibiotics did not have increased daytime RER
   when fed a Low AA diet, and had a reduced increase in nighttime RER
   (Fig. [143]5C). The effects of diet on spontaneous activity were
   relatively small, with increased activity in Low AA fed mice during the
   day (Fig. [144]5D).

Figure 5.

   [145]Figure 5
   [146]Open in a new tab

   A low protein diet increases food consumption and energy expenditure
   similarly in vehicle and antibiotic-treated mice. (A–F) Metabolic
   chambers were used to assess (A,B) food consumption, (C) spontaneous
   activity, (D) respiratory exchange ratio (RER), and (E,F) energy
   expenditure in mice fed the indicated diets for approximately two
   months. (n = 5–7/group; statistics for the overall effects of diet,
   antibiotic (ABX) treatment, and the interaction represent the p-value
   from a two-way ANOVA; *p < 0.05 from a Sidak’s post-test examining the
   effect of parameters identified as significant in the two-way ANOVA).
   Error bars represent SEM.

Discussion

   Understanding the mechanisms by which dietary choices impact metabolic
   health is an area of significant research interest due to the
   increasing prevalence of diabetes and obesity in the population.
   Recently, we and others have shown that reducing dietary protein can
   promote metabolic health in both humans and rodents, but the molecular
   mechanisms that mediate these effects are unclear.

   Here, we examine the effect of a low protein diet on the gut
   microbiome. In general agreement with the results of Holmes and
   colleagues^[147]36, we find that reducing dietary amino acid levels
   (and increasing dietary carbohydrates) results in an increased
   Firmicutes-to-Bacteroidetes ratio. These findings demonstrate the
   robustness of this intervention on the gut microbiome, as the same
   effect can be observed in two different laboratories on different
   continents, using either naturally sourced or defined diets. These
   results are somewhat surprising, as some previous studies have
   suggested a link between obesity and an increased prevalence of
   Firmicutes^[148]62. However, they are consistent with the model of
   Holmes and colleagues that increased caloric intake promotes Firmicutes
   abundance^[149]36.

   Since the gut microbiome has proven to be a potent regulator of
   metabolic health, obesity, and glycemic control, we tested the
   hypothesis that diet-induced changes in the gut microbiome mediate the
   beneficial effects of a low protein diet on metabolism. We first used a
   targeted approach, examining the response of the liver, which is
   responsive to microbiome-mediated alterations in the amount and type of
   secondary bile acids via the FXR-FGF15 signaling axis^[150]34,[151]35,
   at the transcriptional level. While we observed altered expression of
   many genes in response to reduced dietary protein, we found no evidence
   of altered bile acid signaling and no evidence of increased signaling
   by the FXR-FGF15 signaling axis. However, a follow-up qPCR analysis of
   a targeted panel of genes regulated by FXR-FGF15 signaling in a second
   cohort of mice suggested that a low protein diet might reduce hepatic
   FXR-FGF15 signaling.

   To directly test the role of gut microbiome, we then took an unbiased
   approach, testing the requirement for an intact gut microbiome in the
   metabolic response to reduced dietary protein. Following three weeks of
   high-dose antibiotic treatment, a regimen previously shown to ablate
   the gut microbiome and dramatically reduce fecal DNA content, we placed
   mice on either Control or Low AA diets. As anticipated based on
   previous studies in mice and many other mammals^[152]63–[153]66,
   antibiotics had an overall positive effect on growth and lean mass.
   However, we observed that protein restriction had very similar effects
   on weight, body composition, glucose tolerance, and energy expenditure
   in both the presence and absence of antibiotics. The one major
   difference we observed was that antibiotic treated, Low AA-fed mice had
   a lower RER relative to their vehicle treated counterparts, suggesting
   increased utilization of lipids and decreased utilization of
   carbohydrates as a fuel source.

   A significant caveat of our studies is that they were conducted in
   young, lean C57BL/6 J mice, which have relatively low intestinal
   permeability; this may limit the ability of the gut microbiome
   composition to affect host metabolism. The microbiome could play a
   larger role in the metabolic response of obese or older animals, which
   have increased gut permeability^[154]67,[155]68; therefore,
   investigating the role of the gut microbiome in response to reduced
   dietary protein in aged or obese animals might be an important area for
   future study. In addition, we did not examine the taxonomic composition
   of the gut microbiome of antibiotic treated mice, which could help
   clarify if there are any antibiotic-resistant microbes which might
   mediate the metabolic effects of a low protein diet. Finally, we did
   not examine the metabolic effects of protein restriction in germ-free
   mice; examination in these animals, which completely lack all bacteria,
   could conceivably reveal subtle effects of the gut microbiome on the
   response to dietary protein that were not detectable using our
   antibiotic-ablation model.

   Our study also examined only a limited number of metabolic phenotypes
   associated with a low protein diet, over a relatively short period of
   time; other phenotypes associated with reduced protein consumption,
   including increased longevity and healthspan^[156]69, may be more
   directly linked to composition of the gut microbiota. A more detailed
   investigation of the transcriptional response to protein restriction in
   antibiotic-treated or germ-free mice may provide valuable clues to
   identify specific phenotypes that are dependent upon changes in the gut
   microbiome. There is also growing understanding that the specific amino
   acid composition of the diet mediates metabolic health^[157]70–[158]72,
   and there may be a role for the microbiome in the metabolic response to
   other diets with unusual amino acid profiles or from particular dietary
   sources.

   Our findings highlight that dietary macronutrient composition plays an
   important role in determining the taxonomic composition of the gut
   microbiome. Yet, while the effects of a low protein diet on the gut
   microbiome are dramatic, at least in the short term, an intact gut
   microbiome is not required to realize the metabolic benefits of a low
   protein diet on glucose homeostasis, body composition, or energy
   balance. Identifying the physiological and molecular mechanisms by
   which reducing dietary protein can promote metabolic health remains
   critical to developing drugs which can take advantage of these pathways
   to combat obesity and diabetes.

Supplementary information

   [159]Supplementary Information^ (403.7KB, pdf)

Acknowledgements