Abstract
Excessive consumption of the simple sugar fructose, which induces
excessive hepatic lipogenesis and gut dysbiosis, is a risk factor for
cardiometabolic diseases. Here we show in male mice that the gut
microbiome, when adapted to dietary fibre inulin, catabolizes dietary
fructose and mitigates or reverses insulin resistance, hepatic
steatosis and fibrosis. Specifically, inulin supplementation, without
affecting the host’s small intestinal fructose catabolism, promotes the
small intestinal microbiome to break down incoming fructose, thereby
decreasing hepatic lipogenesis and fructose spillover to the colonic
microbiome. Inulin also activates hepatic de novo serine synthesis and
cystine uptake, augmenting glutathione production and protecting the
liver from fructose-induced lipid peroxidation. These multi-modal
effects of inulin are transmittable by the gut microbiome, where
Bacteroides acidifaciens acts as a key player. Thus, the gut
microbiome, adapted to use inulin (a fructose polymer), efficiently
catabolizes dietary monomeric fructose, thereby protecting the host.
These findings provide a mechanism for how fibre can facilitate the gut
microbiome to mitigate the host’s exposure to harmful nutrients and
disease progression.
Subject terms: Small intestine, Metabolomics, Metabolism, Liver,
Microbiota
__________________________________________________________________
The dietary fibre inulin is shown to promote fructose catabolism by the
small intestinal microbiome, thereby mitigating fructose-induced
hepatic lipogenesis and steatosis.
Main
Driven by the increased consumption of simple sugar and fat, the
prevalence of metabolic diseases, including obesity, diabetes and
metabolic dysfunction-associated steatotic liver disease (MASLD), has
risen alarmingly over the past few decades^[62]1. High-fat-containing
diets have been extensively used in animal models to study the
mechanism of obesity-associated MASLD^[63]2,[64]3. Importantly, ~25% of
patients with MASLD do not have obesity, but they show even higher
risks than patients with obesity of developing severe metabolic
dysfunction-associated steatohepatitis (MASH), cirrhosis and
hepatocellular carcinoma^[65]4 because they miss timely screening owing
to their normal body weights. Moreover, although patients with MASLD
exhibit a twofold higher risk of developing almost all types of
cancers, this risk is not observed in patients with MASLD who are not
obese^[66]5,[67]6, further underscoring the importance of studying
MASLD irrespective of obesity.
High fructose corn syrup (HFCS) consumption, especially in liquid form
(for example, soft drinks, juice), is an established risk factor for
lean MASLD, MASH, cirrhosis and hepatocellular carcinoma^[68]7–[69]10.
Prior studies with various nutritional interventions and genetic animal
models revealed many harmful effects of fructose on bodily health,
mainly through metabolic alterations in the liver, gastrointestinal
system and colonic gut microbiota, where most fructose catabolism
occurs^[70]11–[71]16. This toxic feature of fructose is associated with
its unique metabolism in mammals^[72]17. Unlike glycolysis, which
possesses several rate-limiting enzymes and product feedback
inhibitions, fructolysis is mediated by ketohexokinase, which is not
allosterically regulated, and triokinase^[73]18,[74]19. Therefore, when
tissues that express ketohexokinase encounter fructose, they rapidly
phosphorylate fructose to fructose 1-phosphate and deplete
intracellular ATP. Moreover, cleavage of fructose 1-phosphate generates
glyceraldehyde, a reactive metabolite that can induce oxidative stress
and damage DNA, RNA and proteins^[75]20,[76]21. In addition, compared
to glucose, fructose is a much more potent transcriptional activator of
lipogenesis in the liver, inducing hepatic steatosis and insulin
resistance^[77]22–[78]24. Finally, excessive fructose intake is linked
to intestinal abnormalities, including extended intestinal villi,
impaired barrier functions and gut dysbiosis^[79]25–[80]27.
In contrast to the monomeric sugar fructose, inulin, a fructose polymer
(see Fig. [81]1a), has been used as a prebiotic fibre that can improve
insulin sensitivity in patients with diabetes and reduce cholesterol
and triglyceride levels in individuals with obesity^[82]28–[83]30.
Consistently, mice fed inulin with a MASH-inducing diet exhibit less
severe steatohepatitis phenotypes than mice fed the MASH diet
alone^[84]31. Given that the gut microbiome, primarily in the large
intestine, is responsible for digesting inulin, many studies have
focused on changes in colonic microbiome composition induced by
inulin^[85]31,[86]32, which increases short-chain fatty acid (SCFA)
production and has beneficial effects^[87]33. However, SCFAs are also
copiously produced from many other nutrients, including proteins and
fructose^[88]15,[89]34, and excessive SCFAs can contribute to hepatic
lipogenesis and worsen metabolic disorders through the gut–brain
axis^[90]15,[91]35. Thus, the protective effects of inulin probably
involve further mechanisms in addition to colonic microbial changes and
SCFA production.
Fig. 1. Reversal of HFCS-induced metabolic dysfunctions by inulin
supplementation.
[92]Fig. 1
[93]Open in a new tab
a, Chemical structures of HFCS and inulin. G, glucose; F, fructose. b,
Experimental groups. Mice received a control (C) or inulin-supplemented
(I) diet with or without HFCS (F) in drinking water. For the CIF group,
mice first received a control diet with HFCS and then an inulin diet
with HFCS from week 16. c, Representative liver H&E staining and
quantitation of lipid accumulation. Scale bars, 200 μm (n = 4, 4 mice).
d,e, Body weight (d) and fat mass (e) (n = 8, 8, 8, 8, 9 mice). f–h,
Fasting insulin on weeks 14 and 26 (f) (n = 8, 7, 8 mice), fasting
glucose on week 26 (g) (n = 8, 8, 9 mice) and HOMA-IR on week 26 (h)
(n = 7, 7, 8 mice). i, Representative liver H&E staining and
quantitation of lipid accumulation. Staining was performed in three
mice per group, and lipid accumulation was quantified in four randomly
selected areas per liver. Scale bars, 50 μm. j, Liver lipidomics
(n = 7, 8, 9 mice). k, Abundances of the indicated hepatic lipid
species normalized to the CF group (n = 7, 8, 9 mice). Cer, ceramide;
SM, sphingomyelin; DG, diacylglycerol; TG, triacylglycerol. Numbers in
brackets denote total number of carbon atoms and number of double
bonds. l, Liver fibrosis marker gene expression (n = 8, 8, 7, 7, 9
mice). Data are means; error bars, s.e.m. P values determined by
one-way ANOVA with Tukey’s honestly significant difference (HSD) test
(e–l) or two-sided unpaired Student’s t-test (c). Illustrations in a
and b created with [94]BioRender.com.
[95]Source data
Using in vivo isotope tracing, metabolomics and transcriptomics
analysis in mice, we report that inulin induces the small intestinal
microbiome to clear dietary fructose, thereby reducing the detrimental
effects of fructose on the host. In addition, inulin redirects
fructose-derived carbons toward de novo serine and glutathione
synthesis in the liver to suppress lipid peroxidation induced by
fructose. Finally, through microbiome sequencing, antibiotic treatment,
gut microbiota transplantation and bacterial inoculation experiments,
we verified the essential role of the gut microbiome in these multiple
effects of inulin and identified Bacteroides acidifaciens as a key
mediator. Our findings thus provide a previously unrecognized mechanism
by which the fibre-modulated gut microbiome can eliminate deleterious
dietary nutrients and protect the host.
Results
Reversal of fructose-induced metabolic dysfunctions by inulin supplementation
To recapitulate the phenotype of lean patients with MASLD, we fed mice
HFCS-containing drinking water with standard chow. In parallel, to
determine the effect of inulin supplementation on HFCS-elicited
pathologies, we used an open-source diet, a control diet used in many
nutritional studies^[96]36–[97]38, and formulated an inulin-enriched
diet by replacing a small portion of corn starch (10% w/w) with inulin,
as in previous studies^[98]39–[99]41 (Supplementary Table [100]1). This
amount of inulin is higher than the ~4% w/w that is typically tolerated
by humans^[101]42, whereas rodent studies use 10% or even higher
(15%)^[102]43–[103]46, given that the metabolic rate and food
consumption are higher in rodents than humans^[104]47. We also sought
to test whether delayed inulin supplementation can reverse already
established MASLD (CIF group in Fig. [105]1b). To achieve this goal, we
first fed mice a control diet with HFCS-water for 16 weeks to induce
hepatic steatosis (Fig. [106]1c). We then switched the diet to the
inulin-supplemented diet with continual HFCS provision for an
additional 14 weeks.
Substituting corn starch for inulin resulted in ~7% fewer calories
(Supplementary Table [107]1). However, calculation of total calorie
intake based on the consumption of chow and HFCS (Extended Data Fig.
[108]1a,b) indicated no significant difference in total calorie intake
between the three groups that received HFCS (Extended Data Fig.
[109]1c). Consistent with previous reports^[110]16,[111]48, without a
high-fat diet provision, HFCS drinking alone did not increase body
weight substantially (Fig. [112]1d), although it increased the per cent
fat mass (Fig. [113]1e) and decreased per cent lean mass (Extended Data
Fig. [114]1d) compared to the control diet alone. Intriguingly, both
simultaneous and delayed inulin supplementation suppressed the
HFCS-induced fat–lean mass imbalance (Fig. [115]1e and Extended Data
Fig. [116]1d). However, this effect of inulin was not a result of
changes in locomotive activity, heat production, metabolic rates or
respiratory exchange ratio (Extended Data Fig. [117]1e–i).
Extended Data Fig. 1. Metabolic characterization of mice fed HFCS, inulin or
combinations.
[118]Extended Data Fig. 1
[119]Open in a new tab
a-c, Daily food, water, and calorie intake (n = 3,3,3,3,3 cages). d,
Lean mass normalized to body weight (n = 8,9,8,8,9 mice). e-i,
Locomotor activity, heat production, O[2] consumption, CO[2]
production, and respiratory exchange ratio (n = 8,9,8,8,9 mice). j,
Hepatic mtDNA contents (n = 5,7,7 mice). k, Representative liver
trichrome staining for fibrosis measurements from two independent
experiments. Scale bars, 50 μm (n = 3,3,3,3,3 mice). l, body water
enrichment measured 15 hours after administration of ^2H[2]O. m,
Circulating total saponified palmitate concentration (n = 12,9,10
mice). n, PCA plot of metabolites in cytosol and mitochondria fractions
of liver (n = 4,4 mice). o, Glucose-6-phosphate and α-ketoglutarate
levels in cytosolic vs mitochondrial fractions of liver (n = 4,4 mice).
p, ^13C-labeled circulating palmitate fraction over time after oral
provision of ^13C-palmitate (n = 4,3 mice). Data are mean±s.e.m.
P-values by one-way ANOVA with Tukey’s HSD test.
[120]Source data
We next examined the effect of simultaneous and delayed inulin
supplementation on insulin resistance and hepatic steatosis elicited by
HFCS drinking. Compared to HFCS feeding alone, HFCS and inulin
co-feeding showed decreased trends in fasting insulin and glucose
levels (Fig. [121]1f,g). Switching the control diet to the
inulin-enriched diet (the CIF group), despite continued HFCS drinking,
also tended to decrease fasting insulin levels (Fig. [122]1f) without
affecting fasting glucose levels (Fig. [123]1g). Driven by the
decreased insulin levels, delayed inulin supplementation reduced the
homoeostatic model assessment of insulin resistance (HOMA-IR) as much
as simultaneous inulin supplementation did (Fig. [124]1h).
Next, we examined the effect of inulin on the liver. Simultaneous and
delayed inulin supplementation suppressed or reversed HFCS-induced
hepatic lipid accumulation (Fig. [125]1i). Liver lipidomics analysis
also revealed that inulin provision reduced hepatic lipid species,
including ceramide, sphingomyelin, diacylglycerol and triacylglycerol
(Fig. [126]1j,k). In patients with MASLD, mitochondrial DNA levels are
generally increased as a compensatory mechanism in response to
mitochondrial damage^[127]49,[128]50. Inulin supplementation decreased
mitochondrial DNA levels, suggesting low mitochondrial damage (Extended
Data Fig. [129]1j). Although HFCS feeding alone did not induce advanced
stages of fibrosis detectable by histological analysis (Extended Data
Fig. [130]1k), qPCR analysis indicated downregulated gene expression of
HFCS-induced liver fibrosis markers by simultaneous or delayed inulin
supplementation (Fig. [131]1l). Thus, even delayed inulin intake can
reverse HFCS-induced systemic metabolic dysfunctions and liver damage.
The effect of inulin on hepatic lipid synthesis and oxidation
In patients with MASLD, hepatic de novo lipogenesis (DNL) is a major
contributor to steatosis development, and fructose is a potent DNL
inducer^[132]22,[133]24,[134]51,[135]52. Therefore, we sought to
determine the effect of simultaneous and delayed inulin provision on
hepatic DNL using a deuterated water (^2H[2]O) tracer throughout the
diet interventions (Fig. [136]2a). We first measured circulating levels
of ^2H-labelled saponified fatty acids that reflect hepatic DNL. Mice
fed HFCS alone for 14 weeks showed significantly increased ^2H-labelled
fatty acids (Fig. [137]2b). Simultaneous or delayed inulin
supplementation suppressed or reversed such induction (Fig. [138]2b,c).
These results motivated us to quantitatively analyse hepatic DNL flux
by calculating the appearance rate of ^2H-labelled saponified palmitate
per cent in blood after normalization to body water enrichment^[139]53
(Extended Data Fig. [140]1l). This analysis revealed that both
simultaneous and delayed inulin supplementation significantly reduced
HFCS-induced DNL (Fig. [141]2d). We also noticed a significant
reduction both in circulating saponified palmitate following inulin
supplementation (Extended Data Fig. [142]1m) and ^2H-labelled palmitate
levels normalized to body water enrichment (Fig. [143]2e). Considering
that most circulating palmitate reflects triglycerides released from
the liver, these data suggest that inulin not only decreases hepatic
DNL but also affects other processes, such as hepatic secretion of
triglycerides or clearance of circulating triglycerides.
Fig. 2. Inulin supplementation suppresses hepatic lipogenesis and increases
FAO.
[144]Fig. 2
[145]Open in a new tab
a, Schematic of lipogenesis measurements using ^2H[2]O tracing. i.p.,
intraperitoneal b, ^2H-labelled fatty acids in circulating lipids on
week 14 (n = 9, 16, 9 mice). c, ^2H-labelled fatty acids in circulating
lipids before and after switching diets for the CIF group. Data are
fold changes relative to CF (n = 9, 9 mice). d, DNL rate measured by
^2H[2]O tracing after normalization to body water enrichment (n = 12,
9, 10 mice). See [146]Methods for more details. e, ^2H-labelled
palmitate concentration normalized to body water enrichment (n = 12,
10, 10 mice). f, Fructose catabolism and lipogenesis pathways. GA,
glyceraldehyde; GA3P, glyceraldehyde-3-phosphate. PEP,
phosphoenolpyruvate. g, Liver fructose catabolism gene expression
(n = 8, 7, 8, 9 mice). h, Liver lipogenesis gene expression (n = 8, 7,
8, 9 mice). i, FAO pathway. j, Schematic of ^13C-fructose tracing and
hepatic cytosol and mitochondria fractionation. k, ^13C-labelled C16:0
carnitine levels in cytosol versus mitochondria fraction of liver
(n = 4, 4 mice). l, Schematic of whole-body fatty acid oxidation
measurements using ^13C-acetate and ^13C-palmitate tracing. m, The
ratio of ^13CO[2] to unlabelled CO[2] over time after ^13C-acetate
administration (n = 8, 7 mice). n, The ratio of ^13CO[2] to unlabelled
CO[2] over time (left) and slope up to the maximum point (right) after
^13C-palmitate administration (n = 8, 7 mice). o, ^13CO[2] from
^13C-palmitate after normalization to circulating ^13C-palmitate
(n = 8, 7 mice). Data are means; error bars, s.e.m. P values determined
by one-way ANOVA with Tukey’s HSD test (b,d–h), two-way ANOVA with
Tukey’s HSD test (m,o) or two-sided unpaired Student’s t-test (c,k,n).
NS, not significant. Illustrations in a, j, and l created with
[147]BioRender.com.
[148]Source data
Excessive fructose catabolism in the liver has been shown to drive the
gene expression of DNL enzymes^[149]22,[150]23. We therefore measured
hepatic gene expression of enzymes for fructose catabolism and DNL
(Fig. [151]2f). As previously reported^[152]54, HFCS feeding strongly
induced the active isoform of Khk (Khk-c) (Fig. [153]2g). Simultaneous
and delayed inulin supplementation blocked such induction (Fig.
[154]2g). Intriguingly, Tkfc, an enzyme that converts fructose-derived
toxic glyceraldehyde to glyceraldehyde-3-phosphate (Fig. [155]2f), was
only induced when mice were fed both HFCS and inulin (Fig. [156]2g).
Given that glyceraldehyde is a reactive metabolite that generates
glycation products, which can damage DNA, RNA and proteins and induce
hepatocyte cell death^[157]55, the induction of Tkfc expression by
inulin may exert hepato-protective effects. In terms of DNL enzymes,
HFCS induced gene expression of Acly (ATP citrate lyase), Acss2
(acyl-CoA synthetase short-chain family member 2), Fasn (fatty acid
synthase) and Scd1 (stearoyl-coenzyme A desaturase 1) (Fig. [158]2h).
Simultaneous and delayed inulin supplementation suppressed gene
expression of Acss2 and Scd1 (Fig. [159]2h). Therefore, we conclude
that inulin-induced mitigation or reversal of hepatic pathologies is in
part attributed to the suppression of excessive hepatic fructose
catabolism and DNL.
Next, we investigated the effect of inulin on fatty acid oxidation
(FAO), which is normally suppressed by DNL through malonyl-CoA-mediated
carnitine palmitoyltransferase 1 (CPT1) inhibition (Fig. [160]2i).
Given the effect of inulin on suppressing DNL, we speculated that
inulin activates FAO, thereby contributing to the reversal of
HFCS-induced steatosis. During FAO, cytosolic fatty acids should be
first conjugated with carnitine, forming acylcarnitine for their import
into mitochondria (Fig. [161]2i). To measure newly synthesized
acylcarnitine from fructose, we orally provided mice with a
^13C-fructose tracer and fractionated the cytosol and mitochondria from
fresh liver (Fig. [162]2j and Extended Data Fig. [163]1n,o). We found
that inulin feeding significantly increased the levels of ^13C-labelled
acylcarnitine in the mitochondria but not in the cytosol (Fig.
[164]2k). These data suggest that compared to HFCS feeding alone, HFCS
with inulin feeding increased hepatic fructose carbon use to generate
mitochondrial acylcarnitine for FAO.
To more quantitatively assess systemic FAO, we compared the oxidation
of orally provided ^13C-palmitate versus ^13C-acetate (fully
oxidizable) into ^13CO[2] using indirect calorimetry (Fig. [165]2l).
This comparison is critical because CO[2] fixation reactions (by
pyruvate carboxylase, urea carboxylase and so forth) affect total
^13CO[2] exhalation^[166]56,[167]57. We did not observe significant
differences in ^13CO[2] production from ^13C-acetate (Fig. [168]2m). By
contrast, upon ^13C-palmitate administration, inulin-fed mice showed
accelerated exhalation of ^13CO[2] (Fig. [169]2n). Given that orally
provided ^13C-palmitate is mixed with unlabelled palmitate from
triglycerides in blood, we measured blood ^13C-palmitate enrichment and
normalized ^13CO[2] production to estimate the total circulating
palmitate oxidation (Extended Data Fig. [170]1p)^[171]58. Inulin-fed
mice showed significantly increased ^13CO[2] production (Fig. [172]2o).
These results suggest that inulin suppresses lipogenesis but boosts
FAO, which contributes to the prevention or reversal of HFCS-induced
hepatic steatosis.
The impact of inulin on preventing fructose spillover to liver and colon
Previous studies have shown that fructose catabolism in the small
intestine reduces fructose spillover to the liver and colonic
microbiome, thereby suppressing hepatic DNL, liver steatosis and gut
dysbiosis^[173]14,[174]15,[175]59. To determine how inulin affects
small intestinal fructose catabolism and fructose spillover, we orally
provided mice with a ^13C-fructose tracer (with unlabelled glucose)
(Fig. [176]3a). Inulin did not affect the production of labelled
fructose 1-phosphate (Extended Data Fig. [177]2a), the metabolite
marker of fructose catabolism (Fig. [178]2f), or the gene expression of
fructose transporters and catabolic enzymes in the small intestine
(Extended Data Fig. [179]2b). Moreover, inulin did not affect the
amount of fructose reaching the small intestine, as shown by similar
concentrations of ^13C-fructose in the jejunal and ileal contents
between groups (Fig. [180]3b). However, ^13C-fructose was nearly absent
in the caecum of inulin-fed mice (Fig. [181]3b). By comparison, glucose
levels were similar in the caecum between groups (Extended Data Fig.
[182]2c). These data suggest reduced fructose spillover to the colon in
inulin-fed mice.
Fig. 3. Inulin-fed small intestinal microbiome suppresses dietary fructose
spillover.
[183]Fig. 3
[184]Open in a new tab
a, Schematic of dietary fructose catabolism by the host organs and gut
microbiome. The small intestine first catabolizes fructose, and the
leftover fructose spills over to the liver or colon and induces
lipogenesis and gut dysbiosis. b, ^13C-fructose levels in various
intestinal contents 30 min after oral provision of HFCS with
^13C-labelled fructose (n = 8, 8, 9 mice). c, ^13C-labelled SCFAs in
caecal contents (n = 8, 8, 8 mice). d, Comparison of labelled
metabolite abundances in caecal contents between CF versus IF (left)
(n = 8, 8 mice) or CF versus CIF (right) (n = 8, 8 mice). e,
^13C-labelled metabolites in jejunal contents (n = 7, 7, 7 mice). f,
Dietary intervention groups. XIF, antibiotics-treated group. g, Faecal
16S rDNA copy number (n = 3, 3, 3, 6 mice). h, ^13C-labelled
circulating saponified fatty acids normalized to hepatic ^13C-acetyl
CoA fraction, 1 h after provision of HFCS with ^13C-labelled fructose
(n = 7, 8, 8, 6 mice). i, Schematic of small intestinal microbiome
transplantation experiments from donors (CF or IF) to recipients (CF
after antibiotics). j,k, ^13C-labelled acetate fraction in jejunal
content (j) and liver lipogenesis gene expression (k) in recipient
mice, 1 h after provision of HFCS with ^13C-labelled fructose (n = 9, 9
mice). l, Schematic of faecal transplantation experiments. m,n,
^13C-labelled butyrate in faeces (m) and ^13C-labelled circulating
saponified fatty acids (n) in recipient mice 1 h after provision of
HFCS with ^13C-labelled fructose (n = 8, 8 mice). Data are means; error
bars, s.e.m. P values determined by one-way ANOVA with Tukey’s HSD test
(b,c,e,g,h), two-sided unpaired Student’s t-test (d) or one-sided
unpaired Student’s t-test (j,k,m,n). Illustrations in a, i and j
created with [185]BioRender.com.
[186]Source data
Extended Data Fig. 2. Inulin does not affect fructose catabolism by the host
small intestine.
[187]Extended Data Fig. 2
[188]Open in a new tab
a, ^13C-labeled F1P abundances in small intestine, 30 min after oral
provision of HFCS with fructose ^13C-labeled (n = 8,8,9 mice). b, Small
intestinal fructose transporter and catabolism gene expression
(n = 8,8,9 mice). c, Glucose levels in caecal content, 30 min after
oral provision of HFCS (n = 8,8,9 mice). d-e, Total and ^13C-labeled
SCFAs concentrations in portal blood, 1 h after oral provision of HFCS
with fructose ^13C-labeled (n = 7,7,7 mice, left), (n = 12,8,12 mice,
right). Data are mean±s.e.m. P-values by one-way ANOVA with Tukey’s HSD
test. ns=not significant.
[189]Source data
The absence of fructose in the caecum of inulin-fed mice can also
reflect faster removal of fructose by the gut microbiome (thus,
fructose disappeared quickly). To examine this possibility, we measured
caecal labelled SCFAs, the most abundant metabolic products of
microbial fructose catabolism^[190]15. Labelled SCFAs in caecal
contents displayed similar depletion in inulin-fed mice (Fig. [191]3c),
excluding the possibility of fast fructose catabolism by the colonic
microbiome. Further supporting this notion, global untargeted
metabolomics also revealed overall diminished fructose-derived labelled
metabolites in the caecum of mice fed fructose and inulin (Fig.
[192]3d). Therefore, we conclude that inulin supplementation blocks
fructose spillover to the colon.
Interestingly, although fructose-derived SCFA production was reduced in
the caecum of mice fed fructose and inulin (Fig. [193]3c), it was
increased in the jejunal contents of the same mice, especially for
acetate and butyrate (Fig. [194]3e). By comparison, neither labelled
SCFAs nor total SCFAs were increased in the portal blood of mice fed
both fructose and inulin from two independent mouse cohorts (Extended
Data Fig. [195]2d,e). SCFAs such as butyrate have been suggested as
host-beneficial products of microbial nutrient catabolism^[196]60.
Although previous studies have shown an increase in portal SCFA levels
after inulin feeding^[197]61–[198]63, our data suggest that
simultaneous feeding of fructose, which alters the host organs and
intestinal microbial community^[199]64,[200]65, appears to counteract
this inulin effect. Together, these data suggest that inulin
supplementation enhances fructose catabolism by the microbiome of the
small intestine, without affecting the host’s small intestine fructose
catabolism. This can lead to reduced fructose spillover to liver and
colon. Given that inulin is a fructose polymer, gut microbiome using
inulin as a carbon source may also boost fructose catabolism^[201]66,
lowering the host’s exposure to dietary fructose.
Gut microbiome mediates inulin’s effects on reducing fructose spillover
To determine whether the gut microbiome is critical for the effects of
inulin on reducing fructose spillover, we treated mice fed both inulin
and fructose with an antibiotic cocktail (XIF group) (Fig. [202]3f).
Faecal 16S rDNA copy numbers confirmed successful microbiome depletion
by antibiotics treatment (Fig. [203]3g), without affecting body weight
gain, food, water or total calorie intake (Extended Data Fig.
[204]3a–d). We first aimed to determine whether antibiotic treatment
affects hepatic fructose carbon usage for fatty acid synthesis. After
oral ^13C-fructose provision, ^13C enrichment of hepatic acetyl CoA,
the primary precursor for fatty acid synthesis, was similar between the
groups (Extended Data Fig. [205]3e). However, circulating ^13C-labelled
fatty acids normalized to hepatic acetyl CoA ^13C enrichment revealed
that antibiotics abolished inulin’s suppressive effect on fatty acid
synthesis using fructose carbons (Fig. [206]3h and Extended Data Fig.
[207]3f). Liver lipidomics and qPCR analysis also indicated that
antibiotic treatment reversed the impact of inulin on decreasing liver
lipid contents and gene expression of DNL enzymes and fibrosis markers
(Extended Data Fig. [208]3g–i). These data suggest that the gut
microbiome mediates the advantageous impacts of inulin.
Extended Data Fig. 3. Antibiotics reverse inulin’s effects.
[209]Extended Data Fig. 3
[210]Open in a new tab
a, Body weight (n = 7,8,7,6 mice). b-d, Daily food, water, and calorie
intake (n = 3,3,3,2 cages). e, ^13C-labeled acetyl CoA in liver
(n = 7,8,7,6 mice), 1 hr after provision of HFCS with fructose
^13C-labeled in mice fed the indicated diet for 16 weeks (n = 7,8,8,6
mice). f, ^13C-labeled circulating saponified fatty acids. g,
Abundances of the indicated hepatic lipid species normalized to the C
group (n = 8,7,6 mice), h-i, Liver lipogenesis (h) and fibrosis marker
(i) gene expression (n = 7,8,7,6 mice). j, NMDS plot showing microbial
diversity among donor and recipient mice of jejunal microbiome
transplant experiment (n = 5-7 mice/group). Data are mean±s.e.m.
P-values determined by one-way ANOVA with Tukey’s HSD test.
[211]Source data
We next conducted gut microbiota transplantation experiments to
determine whether inulin-mediated effects are transmittable. Donor mice
were fed HFCS alone (CF) or with inulin (IF) for 1 month to ensure the
adaptation of the microbiome to each diet. In the meantime, recipient
mice were fed only HFCS for 1 month to induce basal DNL. Then, after
treatment of antibiotics to recipient mice for 1 week, the jejunal
microbiome was transplanted from each donor group to recipients every
4 days for 13 days (Fig. [212]3i). After the fourth transplantation, we
performed 16S rRNA sequencing in the small intestinal contents of the
donor versus recipient mice to compare their gut microbiome profiles.
Beta diversity comparison by non-metric multidimensional scaling (NMDS)
based on the Bray-Curtis index showed that CF and IF recipients
resemble their respective donors on NMDS2. On the other hand, NMDS1
separates donors from recipients, probably reflecting the antibiotics
pre-treatment effects on recipients (Extended Data Fig. [213]3j). These
microbiome analysis data suggest that our jejunal microbiome transplant
was successful.
Importantly, ^13C-fructose tracing revealed that IF recipients
exhibited higher fructose catabolism in the jejunum than CF recipients
(Fig. [214]3j). IF recipients also showed a trend of decreased
lipogenic gene expression in the liver (Fig. [215]3k). Given that
faecal transplant is more feasible for human applications, we also
examined whether the effect of inulin is transmittable by faecal
microbiome. We transplanted faecal microbiome from each donor group to
recipients weekly for 5 weeks, followed by oral ^13C-fructose tracing
in recipient mice at the terminal endpoint (Fig. [216]3l). IF
recipients exhibited substantially lowered fructose spillover to the
colon compared to CF recipients (Fig. [217]3m). Concomitantly, IF
recipients displayed lowered production of labelled fatty acids from
fructose compared to CF recipients (Fig. [218]3n). Therefore, the
effect of inulin on suppressing colonic fructose spillover and DNL
(reflecting hepatic fructose spillover) is transferable by the gut
microbiome.
Hepatic metabolic rewiring by inulin under HFCS consumption
Given that inulin-fed mice showed less fructose carbon usage for fatty
acid synthesis^[219]23 (Fig. [220]3h), we were curious about the fate
of fructose carbons in their livers. To answer this question, we
provided ^13C-fructose (with unlabelled glucose) and performed
metabolomics-based unbiased analysis in the liver to survey metabolic
products derived from fructose (Fig. [221]4a). Unexpectedly, mice fed
HFCS with inulin exhibited high labelling of glycine, serine and
several serine-containing metabolites from ^13C-fructose (Fig. [222]4b
and Supplementary Table [223]2). Likewise, delayed inulin
supplementation also exhibited an increased trend of hepatic serine and
glycine synthesis from fructose (Fig. [224]4c). The proportions of
newly synthesized serine and glycine made from fructose in inulin-fed
mouse livers were substantial, at ~30% and ~15%, respectively (Fig.
[225]4d).
Fig. 4. Inulin rewires hepatic fructose carbon use toward serine and GSH
biosynthesis.
[226]Fig. 4
[227]Open in a new tab
a, Schematic of ^13C-fructose tracing and untargeted metabolomics in
liver. b, Comparison of hepatic ^13C-labelling (%) of metabolites
between IF and CF, 30 min after oral provision of HFCS with
^13C-labelled fructose, by Student’s t-test followed by false discovery
rate correction. Different colours indicate metabolite categories. CE,
cholesteryl ester; FA, fatty acid; MG, monoacylglycerol (n = 8, 8
mice). c,d, ^13C-labelled abundances (c) and labelling fractions (d) of
serine and glycine in liver (n = 8, 8, 9 mice). M, isotopologue. e,
Correlation coefficient (r) and P values between ^13C-labelled serine
ion count and the indicated labelled metabolite ion counts. f,
^13C-labelled fractions of the indicated metabolites in liver. GSSG,
oxidized GSH (n = 8, 8, 9 mice). g, Comparison of hepatic gene
expression between CF and IF (left) (n = 5, 5 mice) or CF and CIF
(right) (n = 5, 4 mice). h, Comparison of ^13C-labelled ion counts of
the indicated metabolites between CF and IF in liver, 30 min after oral
provision of ^13C-Cys. *P < 0.05, **P < 0.01 (n = 8, 9 mice). FC, fold
change. i, Immunofluorescence staining and quantitation of liver 4-HNE,
a lipid peroxidation marker. Scale bars, 20 μm (n = 6, 6 mice). j,
Hepatic malondialdehyde levels measured by TBARS assay (n = 8, 9 mice).
k, Schematic of the GSH synthesis pathway. Red arrows indicate that
metabolites and Slc7a11 gene (which encodes xCT) are significantly
upregulated in IF compared to CF. Hcy, homocysteine; Cth,
cystathionine. Data are means; error bars, s.e.m. P values determined
by one-way ANOVA with Tukey’s HSD test (c), Student’s t-test followed
by false discovery rate correction (b,g) or two-sided unpaired
Student’s t-test (h–j). Illustrations in a and h were created with
[228]BioRender.com.
[229]Source data
Next, we sought to determine the biological implications of activated
serine and glycine synthesis in the livers of inulin-fed mice. To this
end, we performed a correlation-based unbiased analysis to identify
metabolites most significantly associated with increased serine
synthesis. This analysis identified several metabolites in the
glutathione (GSH) synthesis pathway (Fig. [230]4e). Indeed, liver
cystathionine, GSH and oxidized GSH showed higher labelling from
fructose in mice fed inulin with HFCS compared to mice fed HFCS alone
(Fig. [231]4f).
Serine and glycine can be de novo synthesized from the glycolytic
intermediate, 3-phosphoglycerate, through Phgdh (3-phosphoglycerate
dehydrogenase) and Psat1 (phosphoserine aminotransferase 1)^[232]67
(Extended Data Fig. [233]4a). GSH, a tripeptide composed of glycine,
glutamate and cysteine, requires both glycine synthesis and cystine
uptake as the rate-limiting steps^[234]68,[235]69. RNA sequencing
(RNA-seq) revealed a drastic induction of cystine transporter Slc7a11
(which encodes xCT) (~41-fold), Phgdh (~tenfold) and Psat1 (~ninefold)
in mice fed HFCS and inulin compared to mice fed HFCS alone (Fig.
[236]4g and Supplementary Table [237]3). Delayed inulin supplementation
exerted even more profound induction of Slc7a11 (~53-fold), Phgdh
(~22-fold) and Psat1 (~11-fold) (Fig. [238]4g and Supplementary Table
[239]3). Consistently, unsupervised pathway analysis captured glycine,
serine and cysteine metabolism as one of the top upregulated pathways
in mice fed HFCS with simultaneous or delayed inulin supplementation
(Extended Data Fig. [240]4b,c). By contrast, MASLD-linked genes
(including Acss2 and Scd1) and Cidea (cell death-inducing DNA
fragmentation factor) were decreased by inulin supplementation (Fig.
[241]4g and Supplementary Table [242]3).
Extended Data Fig. 4. Inulin activates hepatic serine and GSH synthesis.
[243]Extended Data Fig. 4
[244]Open in a new tab
a, Schematic of serine synthesis pathway from ^13C-fructose. Red
circles indicate labeled carbons. b-c, Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathway enrichment of IF(b) and CIF(c) compared CF
(n = 5,5,4 mice for CF, IF and CIF groups). P-values by Fisher’s exact
test. d, Hepatic malondialdehyde abundances measured by chemical
derivatization and LC-MS measurement (n = 8,9 mice). e,
Immunofluorescence staining and quantitation of liver dihydroethidium
to measure ROS. Each dot indicates a mean of quadruplicate values from
two independent experiments. Scale bars, 20 μm (n = 6,6 mice). f,
^13C-labeled serine in liver of intestine-specific Khk-C transgenic
mice, 1 hr after provision of HFCS with fructose ^13C-labeled (n = 3, 3
mice). Data are mean±s.e.m. P-values by two-sided unpaired Student’s
t-test.
[245]Source data
To directly examine GSH synthesis, we performed oral ^13C-cysteine
tracing experiments. The resulting data revealed enhanced hepatic
synthesis of GSH and associated metabolites from cysteine in mice fed
both inulin and HFCS compared to mice fed HFCS alone (Fig. [246]4h).
GSH is critical for resolving oxidative stress and lipid
peroxidation-mediated ferroptosis. Indeed, inulin-fed mice showed
decreased hepatic lipid peroxidation markers, including
4-hydroxynonenal (4-HNE), malondialdehyde^[247]70 (Fig. [248]4i,j and
Extended Data Fig. [249]4d) and dihydroethidium staining (Extended Data
Fig. [250]4e). Therefore, we concluded that inulin supplementation
activates both serine and glycine synthesis and cystine uptake in liver
to augment GSH synthesis and mitigate HFCS-elicited hepatic lipid
peroxidation (Fig. [251]4k).
Inulin induces liver serine synthesis via the gut microbiome
We next sought to determine whether the effect of inulin on inducing
liver serine synthesis is mediated by the gut microbiome. We performed
^13C-fructose tracing after antibiotics treatment and measured liver
serine synthesis (Fig. [252]5a). Inulin supplementation to HFCS-fed
mice increased liver synthesis of serine and glycine using fructose
carbons, but the effect was abolished by antibiotics (Fig. [253]5b,c).
Moreover, antibiotics also blocked the inulin-elicited induction of
serine biosynthesis genes Phgdh and Psat1 (Fig. [254]5d).
Fig. 5. Inulin induces liver serine synthesis via gut microbiome.
[255]Fig. 5
[256]Open in a new tab
a, Experimental groups including the antibiotics-treated group (XIF).
b,c, ^13C-labelled abundances of serine and glycine in serum on week 4
(b) and liver on week 16 (c), 1 h after oral provision of HFCS with
^13C-labelled fructose (n = 7, 8, 7, 6 mice). d, Serine synthesis gene
expression in liver (n = 7, 8, 7, 6 mice). e, Schematic of jejunal
microbiome transplantation experiments from donors (CF or IF) to
recipients (CF after antibiotics). Abx, antibiotics. f, ^13C-labelled
serine abundances in liver of recipient mice (n = 8, 8 mice). Data are
means; error bars, s.e.m. P values were determined by one-way ANOVA
with Tukey’s HSD test (b–d) or one-sided unpaired Student’s t-test (f).
Illustrations in a and e created with [257]BioRender.com.
[258]Source data
To further determine whether the effect of inulin on liver serine
synthesis is transmittable via the microbiome, we conducted small
intestinal microbiota transplant experiments from donors fed HFCS with
or without inulin to recipients fed HFCS alone following antibiotics
treatment (Fig. [259]5e). ^13C-fructose tracing at the end of
transplantation cycles revealed that mice that received gut microbiome
from the donor mice fed HFCS with inulin showed significantly higher
serine synthesis than mice that received gut microbiome from the donor
mice fed HFCS alone (Fig. [260]5f). Therefore, the gut microbiome is
both necessary and sufficient for the inulin-induced serine synthesis.
We were curious whether inulin-mediated lipogenesis reduction and
serine production are mechanistically linked. To test this idea, we
implemented our previously reported intestine-specific Khk-C transgenic
mice, which exhibit reduced hepatic lipogenesis owing to enhanced
intestinal clearance of dietary fructose^[261]16. However, we found no
increase in serine synthesis in these mice (Extended Data Fig.
[262]4f), suggesting that these two biological processes (lipogenesis
suppression and serine production induction by inulin) are driven
through distinct mechanisms (for example, different gut microbiome
species).
B.acidifaciens contributes to inulin effects
Finally, to identify gut microbiota species that mediate inulin’s
effects, we performed 16S rRNA sequencing of the contents of both the
small and large intestines of mice fed control water, HFCS alone or
HFCS with inulin. Linear discriminant analysis effect size revealed
marked differences in the microbial composition at each taxonomic level
across the groups, with a maximum depth to genus level (Fig.
[263]6a,b). As previously reported, inulin supplementation enriched
Bacteroidetes in the large intestine, which is known to be the primary
degrader of polysaccharides^[264]71,[265]72. Interestingly, consumption
of either HFCS alone or with inulin increased total bacterial contents
in the small intestine (Extended Data Fig. [266]5a). Alpha diversity or
the Firmicutes to Bacteroidetes ratio was not changed (Extended Data
Fig. [267]5a). In the large intestine, compared to mice fed HFCS alone,
mice fed both HFCS and inulin showed increased total bacterial
contents, with no change in alpha diversity but a decreased Firmicutes
to Bacteroidetes ratio (Extended Data Fig. [268]5b). This suggests
improved gut microbiome health by inulin, given that an increased
Firmicutes to Bacteroidetes ratio is a marker of microbiome dysbiosis
in patients with metabolic disease^[269]73–[270]75. Therefore, reduced
fructose spillover by inulin-adapted small intestinal gut microbiome
may contribute to a healthy microbiome in the large intestine.
Fig. 6. B. acidifaciens contributes to inulin’s effects on lipogenesis
suppression and fructose catabolism in the small intestine.
[271]Fig. 6
[272]Open in a new tab
a,b, Linear discriminant analysis effect size analysis of jejunal (a)
and caecal (b) microbial taxa. The cladogram shows the taxa with
significant differences in abundance (from phylum to genus level)
(n = 8, 8, 8 mice). c–f, Pearson correlation analysis between bacterial
abundances and hepatic lipogenesis or serine synthesis. g, Relative
abundance of Bacteroides spp. in jejunal contents (n = 6, 7 mice) and
B. acidifaciens in caecal contents (n = 8, 8, 8 mice). h, Schematic of
single-bacteria inoculation experiments. Recipient mice were fed HFCS
alone for 4 weeks, followed by antibiotic treatment for 1 week.
Anaerobically cultured bacteria were orally delivered to the recipient
mice every day for 2 weeks while the mice received HFCS with inulin to
promote bacteria survival and inulin usage. i, ^13C-labelled
circulating saponified fatty acids normalized to hepatic ^13C-acetyl
CoA fraction in the recipient mice, 1 h after oral provision of HFCS
with ^13C-labelled fructose (n = 12, 12, 12 mice). j, ^13C-labelled
concentrations (left two) and carbon fractions (right two) of the
indicated SCFAs in the jejunal contents of the recipient mice, 1 h
after oral provision of HFCS with ^13C-labelled fructose (n = 12, 11,
12 mice). k, Proposed model of inulin’s multi-modal effects on
stimulating small intestinal microbial breakdown of dietary fructose,
reducing fructose spillover to colon, reducing hepatic lipogenesis and
augmenting hepatic serine and GSH synthesis. Data are means; error
bars, s.e.m. P values determined by one-way ANOVA with Tukey’s HSD test
(g, right panel, i,j) or by one-sided unpaired Student’s t-test (g,
left panel). Illustrations in k were created with [273]BioRender.com.
[274]Source data
Extended Data Fig. 5. Characterization of gut microbiome in mice fed HFCS
with or without inulin.
[275]Extended Data Fig. 5
[276]Open in a new tab
a, Total bacterial amounts, Alpha-diversity, and Firmicutes to
Bacteroidetes ratio in jejunum (n = 6,5,7 mice). b, Total bacterial
amounts, Alpha-diversity, and Firmicutes to Bacteroidetes ratio in
caecum (n = 8,8,8 mice). c, Relative abundance of B. pseudolongum in
jejunal (n = 6,5,7 mice) and caecal content (n = 8,8,8 mice). d,
^13C-labeled acetyl CoA enrichment (%) in liver, 1 hr after provision
of HFCS with fructose ^13C-labeled (n = 12,12,12 mice). e, ^13C-labeled
circulating saponified fatty acids (n = 12,12,12 mice). f, Relative
abundances of the indicated hepatic lipid species (n = 12,12 mice). g,
Gene expression of fibrosis markers in liver (n = 12,12 mice). h,
Hepatic ^13C-labeled serine and glycine abundances in mice that
received vehicle or the indicated bacteria species, 1 hr after
provision of HFCS with fructose ^13C-labeled (n = 12,12,12 mice). i,
Growth changes of B. acidifaciens in response to the addition of inulin
or glucose (n = 3). j, Changes in ^13C-fructose consumption and labeled
short-chain fatty acid production of B. acidifaciens over incubation
time (n = 3). Data are mean±s.e.m. P-values by one-way ANOVA with
Tukey’s HSD test (a-e, k) or two-way ANOVA with Tukey’s HSD test (i-j).
[277]Source data
From this global sequencing analysis, we next sought to identify the
bacterial species contributing to inulin-induced metabolic changes in
the liver. We performed a correlation-based unbiased analysis between
the abundance of each bacterial species and hepatic DNL (Fig.
[278]6c,d) or serine production (Fig. [279]6e,f). Although most
bacterial species showed positive correlations with hepatic DNL (Fig.
[280]6c), B. acidifaciens and Bifidobacterium pseudolongum exhibited a
strong negative correlation with hepatic DNL (Fig. [281]6c,d), and B.
acidifaciens showed a significant positive correlation with liver
serine synthesis (Fig. [282]6e, f). Moreover, the abundance of
B. acidifaciens was increased in both small and large intestines of
mice fed HFCS and inulin (Fig. [283]6g). B. pseudolongum did not show a
clear pattern (Extended Data Fig. [284]5c).
To determine whether these candidate gut bacteria can recapitulate
inulin’s effects, we performed single-bacteria inoculation experiments.
Recipient mice were fed HFCS alone for 4 weeks to induce basal DNL,
followed by antibiotics treatment for 1 week (Fig. [285]6h). Then, each
anaerobically cultured bacteria or vehicle-alone control was orally
delivered to the recipient mice every day for 2 weeks while the diet
was switched to HFCS with inulin to promote not only the survival of
the inoculated bacteria but also their inulin usage. On the last day,
we performed ^13C-fructose tracing. Measurements of circulating
^13C-labelled fatty acids normalized to hepatic ^13C enrichment of
acetyl CoA (Extended Data Fig. [286]5d,e) revealed that treatment with
B. acidifaciens, but not B. pseudolongum or vehicle treatment,
significantly decreased fatty acid synthesis from fructose (Fig.
[287]6i). Similarly, treatment with B. acidifaciens, but not
B. pseudolongum or vehicle treatment, increased ^13C-labelled acetate
and butyrate in the contents of the small intestine (Fig. [288]6j),
suggesting enhanced fructose catabolism by B. acidifaciens in the small
intestine. However, B. acidifaciens treatment did not affect hepatic
lipid accumulation, fibrosis or serine production (Extended Data Fig.
[289]5f–h), suggesting that other bacterial species are involved or
that the treatment duration was not sufficiently long.
Lastly, we investigated whether inulin supports the growth of
B. acidifaciens. Indeed, inulin provision increased the growth of
B. acidifaciens more than glucose (Extended Data Fig. [290]5i).
Additionally, B. acidifaciens grown in inulin-containing media showed
enhanced fructose catabolism and subsequent production of SCFAs
(Extended Data Fig. [291]5j). Thus, inulin-adapted B. acidifaciens
boosts fructose catabolism, which can mitigate the host’s exposure to
excessive fructose. Altogether, our results support the conclusion that
B. acidifaciens mediates, at least in part, inulin’s effect in
suppressing HFCS-induced hepatic DNL by breaking down dietary fructose
in the small intestine (Fig. [292]6k).
Discussion
One of the clinical concerns of HFCS-induced pathologies is lean MASLD,
which poses diagnostic challenges owing to the absence of significant
weight gain. Lean patients with MASLD, therefore, have high risks of
disease progression to MASH, cirrhosis and HCC without timely disease
management. The causal mechanisms between excessive fructose
consumption and lean MASLD have been extensively studied in animal
models^[293]16,[294]48,[295]76. By contrast, studies on protective
factors such as dietary fibres like inulin have been primarily
performed in epidemiology or focused on the microbiome, specifically in
the colon or faeces^[296]77–[297]79. Upon investigating the
interactions between these two common dietary components (fructose and
inulin), we identified an unexpected microbe–host interaction mechanism
by which inulin enhances fructose breakdown by the small intestinal
microbiota (Fig. [298]6k). Even after hepatic steatosis has developed
owing to HFCS through increased DNL and suppressed FAO via CPT1 (ref.
^[299]80), delayed inulin supplementation is sufficient to reverse it.
Further human studies will be critical to determine the optimal amount
of inulin in the diet and consumption durations that exhibit these
protective effects.
Chemically, inulin is a soluble dietary fibre composed of one glucose
molecule and numerous (~20–100) fructose molecules. Accordingly, when
bacteria use inulin as a carbon source, they must activate enzyme
machinery to break down fructose after cleaving inulin into monomeric
fructose molecules. Indeed, in vitro cultured gut bacteria grown with
inulin increase enzymes required for fructose degradation, such as
β-fructofuranosidase, sucrose-6-phosphatase and
phosphofructokinase^[300]66,[301]81. Similarly, our in vivo and in
vitro data suggest that when inulin-adapted gut microbiota encounter
monomeric fructose, they effectively catabolize it, thereby reducing
the host’s fructose exposure and mitigating metabolic consequences.
Through gut microbiome sequencing, in vivo isotope tracing and a
correlation-based, unbiased approach, we identified B. acidifaciens as
one of the contributors to these inulin-mediated effects. The abundance
of B. acidifaciens was shown to be increased in the faeces of an
inulin-fed MASH mouse model^[302]31. Our data suggest that
B. acidifaciens also increases in both the small and large intestines
after consumption of inulin and HFCS. This may indicate that
B. acidifaciens can outgrow in the inulin-enriched intestinal
microenvironment, even in the presence of incoming dietary HFCS.
Intriguingly, B. acidifaciens has been found to prevent obesity in mice
by promoting various hormones, including glucagon-like peptide 1
(GLP-1)^[303]82 and to alleviate concanavalin A-induced liver injury by
blocking CD95 signalling^[304]83, although these studies have been
performed in the absence of HFCS feeding. Our study suggests a
mechanism by which inulin-adapted B. acidifaciens eliminates dietary
fructose, thereby suppressing metabolic dysfunctions, regardless of
obesity or exposure to hepatotoxic drugs.
In addition to fructose catabolism in the intestine, inulin
supplementation also changes the fate of fructose carbons in the liver
by redirecting the usage of fructose carbons to serine, glycine and GSH
synthesis. Previous studies have observed lower glycine levels in
individuals with MASLD^[305]84,[306]85. In addition, depletion of
glycine results in GSH deficiency, increasing susceptibility to
oxidative stress and hepatic steatosis^[307]86. Given that hepatic
glycine homoeostasis is regulated by Shmt2 (serine
hydroxymethyltransferase 2)^[308]87, these and other studies have
focused on the role of Shmt2 in MASLD^[309]86–[310]88. Intriguingly,
our RNA-seq data indicated that inulin supplementation does not affect
the gene expression of Shmt2. Instead, inulin supplementation, in a
microbiome-dependent manner, drastically increases Phgdh and Psat1, the
key de novo serine synthesis pathway enzymes. Furthermore, inulin also
activates transcription of Slc7a11, the cystine transporter, which can
enhance GSH synthesis and block hepatic lipid peroxidation.
We then asked what mechanism might underlie serine biosynthesis induced
by inulin. Decreased fructose spillover to the liver or lipogenesis is
less likely to be the mechanism because intestine-specific Khk-C
transgenic mice that exhibit lower fructose spillover and lipogenesis
do not show increased serine synthesis (Extended Data Fig.
[311]4f)^[312]16. Notably, antibiotics block inulin-induced serine
synthesis, while B. acidifaciens suppresses lipogenesis without
inducing serine synthesis. Therefore, other inulin-dependent microbiome
species may signal to the liver to boost serine synthesis. Another
remaining question surrounds the relative contribution of reduced
lipogenesis and enhanced serine synthesis to inulin-mediated protective
effects. Determining the contributions of each pathway would require
future studies using genetically modified mouse models such as
hepatocyte-specific Phgdh knockout mice.
Ultimately, it will be crucial to determine whether long-term provision
of B. acidifaciens is sufficient to reverse hepatic steatosis and
fibrosis and whether sexual dimorphism exists in inulin action, as our
study used only males. To summarize, our findings on the effects of
inulin in shifting fructose catabolism away from host organs and toward
the small intestinal microbiome pave the way for protecting the host’s
health by allowing the gut microbiome to consume toxic dietary
nutrients.
Methods
Mouse studies
Animal studies followed protocols approved by the Institutional Animal
Care and Use Committee of the University of California, Irvine
(AUP-22-121). Male C57BL/6 mice (8 weeks old) were purchased from
Jackson Laboratory. The duration of each experiment (for example, diet
feeding) is indicated in the figure legends. In this study, only males
were used because MASLD is more prevalent in men than women. Generation
of Khk-C transgenic mice was previously reported in ref. ^[313]16. Mice
were group-housed on a normal light–dark cycle (07:00–19:00 h) with
free access to chow and water. We modified the open-source diet
(Research Diets, D11112201) to reduce dextrose in the diet and replace
corn starch with inulin. Mice were fed either a control (Research
Diets, D21050401) or 10% (gm%) inulin diet (Research Diets, D21050402).
The composition of both diets is shown in Supplementary Table [314]1.
For HFCS provision, mice were provided either normal drinking water or
15% (weight/weight) fructose and 15% glucose mixture in drinking water.
Animals were randomly assigned to experimental groups, and no specific
method of randomization was used. No statistical methods were used to
pre-determine sample sizes, but our sample sizes are similar to those
reported in previous publications^[315]15,[316]16. Unless otherwise
indicated, experiments were replicated independently at least twice.
Daily water and food intake were determined by measuring the total
consumption in a cage divided by the number of mice in the cage. For
antibiotic treatment, a cocktail of antibiotics, including 0.5 g l^−1
ampicillin, neomycin, metronidazole and 0.25 g l^−1 vancomycin, was
dissolved in HFCS-water. For gut bacteria transplantation, faecal or
small intestinal contents (200 mg) were freshly collected from donor
mice at 09:00–10:00 h using sterilized utensils and microcentrifuge
tubes. The samples were immediately dissolved in 2 ml of sterilized
anaerobic PBS containing 0.1 g l^−1 of l-cysteine. The materials were
then homogenized using sterilized pellet pestles and centrifuged at
500g for 3 min at 20 °C to remove particulate matter, based on studies
by others that performed gut bacteria transplantation^[317]89–[318]91.
The resulting bacterial supernatant was supplemented with 10%
sterilized glycerol and then transferred to antibiotic-treated
recipient mice by oral gavage (200 µl per mouse) with a plastic feeding
tube (Instech Laboratories) on the same day. The remaining solution was
stored at −80 °C until use for oral gavage. Gut bacteria
transplantation was performed weekly for 5 weeks for faeces (five
times) and every 4 days for 13 days for jejunal contents (four times).
For measurement of circulating levels of ^2H-labelled fatty acid using
^2H[2]O, mice received ^2H[2]O dissolved in 0.9% NaCl by
intraperitoneal injection (30 µl g^−1) at 10:00 h. Mice were
transferred to new cages without food. At 16:00 h, serum was collected
by tail snip. For measurement of DNL flux, mice received ^2H[2]O
dissolved in 0.9% NaCl by intraperitoneal injection (30 µl g^−1) at
18:00 h and serum was collected via tail snip at 09:00 h the following
morning. For ^13C-fructose tracing, mice received a solution of
unlabelled glucose and ^13C-fructose (2 g kg^−1 body weight each) by
oral gavage (10 µl g^−1 body weight) at 09:00 h. For ^13C-cysteine
tracing, mice received a solution of 0.2 M ^13C-cysteine by oral gavage
(5 µl g^−1 body weight) at 09:00 h. At 10:00 h, tail blood and tissues
were collected. Intestine-specific Khk-C transgenic mice received a
solution of unlabelled glucose and ^13C-fructose (2 g kg^−1 body weight
each) by oral gavage (10 µl g^−1 body weight) at 09:00 h, and liver
tissues were collected at 10:00 h. Tail blood was collected by tail
snip to measure circulating metabolites after different durations.
Tissues were quickly dissected and snap-frozen in liquid nitrogen with
a pre-cooled Wollenberger clamp. Multiple cohorts were used, and data
were combined if there was no statistical difference between cohorts.
For fasting glucose and insulin measurements, serum was collected after
10 h of fasting (08:00–18:00 h) by tail snip, glucose was measured
using liquid chromatography–mass spectrometry (LC–MS) and insulin was
measured using an Ultra Sensitive Mouse Insulin ELISA Kit (cat. no.
90080; Crystal Chem).
Bacterial culture
B. acidifaciens (DSM 15896) was purchased from a German collection of
microorganisms and cell cultures. B. pseudolongum (ATCC 25526) was
obtained from American Type Culture Collection. B. acidifaciens was
cultured on Columbia CNA agar medium containing sheep blood (Thermo
Scientific) and chopped meat medium (CMM; Fisher Scientific) at 37 °C
in the EZ Anaerobe Container System (BD). B. pseudolongum was cultured
on modified BHI agar and broth (BD) at 37 °C in the EZ Anaerobe
Container System (BD). To measure the growth of B. acidifaciens in
response to inulin, PBS, 0.4 g l^−1 glucose (Sigma-Aldrich) or
0.4 g l^−1 inulin (Sigma-Aldrich) was added to 60% v/v CMM.
B. acidifaciens was inoculated, and optical density at 600 nm was
measured using a microplate reader (Victor). To measure the fructose
catabolic activity of B. acidifaciens, the bacterium was cultured in
CMM + glucose or CMM + inulin until the early stationary phase, washed
with PBS and then the bacterial pellets were resuspended to the same
optical density in CMM + glucose or CMM + inulin supplemented with
0.1 g l^−1 ^13C-fructose for a 3 day culture. To prepare a bacterial
solution for oral delivery to mice, bacterial cultures were centrifuged
and washed with PBS after 72 h of incubation. The bacterial pellets
were resuspended in 25% (v/v) glycerol and stored at −80 °C before oral
gavage to mice; 25% glycerol without any bacterium was used for the
control. After HFCS provision and antibiotics treatment as described
above, 200 µl of bacteria solution and glycerol were transferred to
recipient mice by oral gavage (200 µl per mouse) with a plastic feeding
tube (Instech Laboratories) every day for 2 weeks.
Histology
Freshly collected liver tissues were fixed in 4% paraformaldehyde
overnight, embedded in paraffin, sectioned and stained with
haematoxylin and eosin (H&E). Tissues were submitted to the
Experimental Tissue Shared Resource Facility at the University of
California Irvine. For trichrome staining, Gomori’s Trichrome Stain Kit
(Polysciences) was used. Images were captured with a high-resolution
image scanner (Ventana DP200, Roche). Hepatic lipid accumulation was
quantified by analysing the digital slides using QuPath
(v.0.4.4)^[319]92. A pixel classifier was trained from representative
images among the groups. This pixel classifier was then applied to
annotations of the same size within each slide. These regions were
measured, and the area values (in μm^2) for the regions classified as
lipids were used.
Indirect calorimetry, ^13C FAO analysis and echo magnetic resonance imaging
O[2] consumption, CO[2] release, respiratory exchange ratio, locomotor
activity and heat production were monitored for individually housed
mice using Phenomaster metabolic cages (TSE Systems). The climate
chamber was set to 21 °C and 50% humidity, with a 12–12 h light–dark
cycle (07:00–19:00 h) as the home-cage environment. Animals were
entrained for 24 h in the metabolic cages before the start of each
experiment to allow for environmental acclimation. Data were collected
at 40 min intervals, and each cage was recorded for 3.25 min before
time point collection. For measuring ^13CO[2], environmental levels of
^13CO[2] and total CO[2] in the sealed cages were calibrated to ±1.1%
^13CO[2], as the natural abundance of ^13C. Body composition was
measured using an EchoMRI Whole Body Composition Analyzer, which
provides whole-body fat and lean mass measurements. To account for
potential changes in ^13CO[2] loss caused by CO[2] fixation reactions
(for example, those catalysed by pyruvate carboxylase or urea
carboxylase)^[320]56,[321]57, ^13CO[2] production was measured using
indirect calorimetry after ^13C-acetate administration. In brief, mice
were fasted for 6 h and administered ^13C-acetate by oral gavage
(0.3 mg g^−1 body weight), and ^13CO[2] recovery was measured for
individually housed mice using Phenomaster metabolic cages (TSE
Systems). Under the same conditions, we performed oral ^13C-palmitate
administration and ^13CO[2] measurements. To estimate the oxidation of
the total circulating palmitate pool from circulating triglycerides and
exogenous ^13C-palmitate, we measured circulating un-esterified
^13C-palmitate enrichment (%) in serial time points and used these
values to normalize ^13CO[2] (ref. ^[322]58).
Quantitative PCR with reverse transcription
RNA samples were prepared using TRIzol Reagent (Invitrogen) according
to the manufacturer’s instructions. RNA was reverse-transcribed to cDNA
using the iScript kit (Bio-Rad). The resulting cDNA was analysed by
qPCR with reverse transcription using SYBR green master mix (Life
Technologies) on a QuantStudio6 Real-Time PCR system (Life
Technologies). Relative mRNA expression was calculated from the
comparative threshold cycle values relative to housekeeping genes
Actin, 36b4 and Tbp. Primer sequences were: Tgfb1 (forward,
CTCCCGTGGCTTCTAGTGC; reverse, GCCTTAGTTTGGACAGGATCTG), Acta2 (forward,
ATGCTCCCAGGGCTGTTTTCCCAT; reverse, GTGGTGCCAGATCTTTTCCATGTCG), Vim
(forward, TTTCTCTGCCTCTGCCAAC; reverse, TCTCATTGATCACCTGTCCATC), Col1a1
(forward, GCTCCTCTTAGGGGCCACT; reverse, CCACGTCTCACCATTGGGG), Mmp13
(forward, CTTCTTCTTGTTGAGCTGGACTC; reverse, CTGTGGAGGTCACTGTAGACT),
Glut5 (forward, TCTTTGTGGTAGAGCTTTGGG; reverse,
GACAATGACACAGACAATGCTG), Khk-a (forward, TTGCCGATTTTGTCCTGGAT; reverse,
CCTCGGTCTGAAGGACCACAT), Khk-c (forward, TGGCAGAGCCAGGGAGAT; reverse,
ATCTGGCAGGTTCGTGTCGTA), Aldob (forward, CACCGATTTCCAGCCCTC; reverse,
GTTCTCCACCTTTATCCTTTGC), Tkfc (forward, GCATCTCAGAGCAGAAGTGTG; reverse,
CAAGTCAGGGTTAGAGGCTAC), Acly (forward, CAGCCAAGGCAATTTCAGAGC; reverse,
CTCGACGTTTGATTAACTGGTCT), Acss2 (forward, ATGGGCGGAATGGTCTCTTTC;
reverse, TGGGGACCTTGTCTTCATCAT), Fasn (forward, GGAGGTGGTGATAGCCGGTAT;
reverse, TGGGTAATCCATAGAGCCCAG), Scd1 (forward,
TTCAGAAACACATGCTGATCCTCATAATTCCC; reverse,
ATTAAGCACCACAGCATATCGCAAGAAAGT), Tbp (forward,
CCCTATCACTCCTGCCACACCAGC; reverse, GTGCAATGGTCTTTAGGTCAAGTTTAC), Actin
(forward, CCCTGTATGCTCTGGTCGTACCAC; reverse, GCCAGCCAGGTCCAGACGCAGGATG)
and 36b4 (forward, GGAGCCAGCGAGGCCACACTGCTG; reverse,
CTGGCCACGTTGCGGACACCCTCC).
Mitochondria and cytosolic fractionation
To isolate liver mitochondria^[323]93, freshly isolated mouse liver was
weighed and rapidly extracted and homogenized in ice-cold liver
homogenization buffer (1 ml per 200 mg of liver; 200 mM sucrose, 5 mM
Tris, 1 mM EGTA, 100 μg ml^−1 digitonin pH 7.4), followed by
centrifugation at 1000g for 1 min at 4 °C. The resulting 200 μl
supernatants were combined with 500 μl spin buffer (150 mM sucrose,
5 mM Tris, 1 mM EGTA, 25 mM ammonium bicarbonate pH 7.4) to reduce
suspension density. Each 700 μl mixture was carefully layered above
300 μl liver oil mix (60:40 silicone oil to dioctyl phthalate; density,
1.066 g ml^−1 at 20–23.5 °C) and 100 μl of 23% glycerol in pre-mixed
tubes, then centrifuged at 9,727g for 1 min at 4 °C. The cytosolic
layer and most of the oil were aspirated, and the remaining oil was
removed after freezing the lower layer in a dry ice–ethanol bath and
washing with dry ice–cold hexane. The mitochondrial pellets were pooled
to obtain the final sample.
Mitochondrial DNA analysis
For mtDNA copy number analysis, 50 ng of DNA was extracted with the
Purelink Genomic DNA Mini (Invitrogen), and qPCR was performed with a
pair of primers for mtDNA (MT-16S rRNA-ND1) with 18S for normalization.
The following primers were used: MT-16S-ND1 (forward,
CACCCAAGAACAGGGTTTGT; reverse, TGGCCATGGGTATGTTGTTAA) and 18S (forward,
TAGAGGGACAAGTGGCGTTC; reverse, CGCTGAGCCAGTCAGTGT).
RNA-seq analysis
RNA samples were prepared using TRIzol Reagent (Invitrogen) according
to the manufacturer’s instructions. RNA concentration was quantified
using fluorimetry (Qubit 2.0 fluorometer; Life Technologies), and
quality was assessed using an Agilent BioAnalyzer 2100 (Agilent
Technologies). Ribosomal RNA depletion and sample library preparation
were performed using the Illumina TruSeq Stranded Total RNA with
RiboZero. The libraries were then sequenced on a NovaSeq 6000
(Illumina) using paired-end sequencing. The quality of the raw
sequencing data was assessed using FastQC, and all samples passed the
quality control analysis. Raw sequencing reads were aligned to the
mouse reference genome mm10 (GRCm38) using STAR, and RNA-seq counts
were obtained using featureCounts. These raw counts were further
analysed using DESeq2 for differential gene expression analysis. To
elucidate the biological pathways involved in our study, we used Kyoto
Encyclopedia of Genes and Genomes pathway enrichment analysis using
Fisher’s exact test. For gene-set enrichment analysis, gene sets from
the Molecular Signatures Database (v.2023.2) were used.
Metabolite measurements using LC–MS
For aqueous metabolites extraction, serum (5 µl) was mixed with 150 µl
of extraction solvent (40:40:20 methanol:acetonitrile:water, v:v:v) at
−20 °C, vortexed and immediately centrifuged at 16,000g for 10 min at
4 °C. The supernatant (70 µl) was collected for LC–MS analysis. Frozen
tissue samples were ground at liquid nitrogen temperature with a
CryoMill (Retsch). The resulting tissue powder (approximately 20 mg)
was weighed and then mixed with −20 °C extraction solvent containing
0.5% formic acid (40 µl per mg tissue), vortexed and neutralized with
15% NH[4]HCO[3] (3.5 µl per mg tissue). Following vortexing and
centrifugation at 16,000g for 10 min at 4 °C, the supernatant (70 µl)
was loaded into LC–MS vials. Metabolites were analysed by a
quadrupole–orbitrap mass spectrometer (Q-Exactive Plus Hybrid
Quadrupole–Orbitrap, Thermo Fisher) coupled to hydrophilic interaction
chromatography by heated electrospray ionization. LC separation was
performed on an Xbridge BEH amide column (2.1 mm × 150 mm, 2.5 µm
particle size, 130 Å pore size; Waters) at 25 °C using a gradient of
solvent A (5% acetonitrile in water with 20 mM ammonium acetate and
20 mM ammonium hydroxide) and solvent B (100% acetonitrile). The flow
rate was 150 µl min^−1. The LC gradient was: 0 min, 90% B; 2 min, 90%
B; 3 min, 75% B; 7 min, 75% B; 8 min, 70% B; 9 min, 70% B; 10 min, 50%
B; 12 min, 50% B; 13 min, 25% B; 14 min, 20% B; 15 min, 20% B; 16 min,
0% B; 20.5 min, 0% B; 21 min, 90% B; and 25 min, 90% B. Autosampler
temperature was set at 4 °C and the injection volume of the sample was
3 μl. MS analysis was acquired in negative and positive ion modes with
Full MS scan mode from m/z 70 to 830 and 140,000 resolution with the
following operational parameters: AGC target, 3 × 10^6; maximum IT,
500 ms; sheath gas flow rate, 40; aux gas flow rate, 10; sweep gas flow
rate, 2; spray voltage, +3.8 kV and −3.5 kV; spray current, 33 μA;
capillary temperature, 300 °C; s-lens RF level, 50; aux gas heater
temperature, 360 °C. MS2 analysis was acquired in negative and positive
ion modes with Full MS/dd-MS2 from m/z 70 to 830. For full MS, the
parameters were: resolution, 70,000; AGC target, 1 × 10^6; maximum IT,
200 ms. For MS/dd-MS2, the parameters were: resolution, 17,500; AGC
target, 1 × 10^5; maximum IT, 50 ms; loop count, 15; isolation window,
1.2 m/z; stepped CE, ±20 and 50 eV with the following operational
parameters: sheath gas flow rate, 40; aux gas flow rate, 10; sweep gas
flow rate, 2; spray voltage, +3.8 kV and −3.5 kV; spray current, 33 μA;
capillary temperature, 300 °C; s-lens RF level, 50; aux gas heater
temperature, 360 °C. Data were analysed using the EI-MAVEN software and
Compound Discoverer software (Thermofisher Scientific). The identity of
metabolites was confirmed based on the retention time and accurate m/z
of authentic synthesized chemical standards from Sigma-Aldrich, as well
as MS2 fragmentation patterns available in HMDB ([324]https://hmdb.ca)
and mzCloud database ([325]https://www.mzcloud.org). Natural isotope
correction was performed with AccuCor2 R code
([326]https://github.com/wangyujue23/AccuCor2). Labelled ion counts
refer to the sum of all labelled forms, in which each form is weighted
by the fraction of carbon atoms labelled. It is used to calculate
fractional carbon labelling (%) by normalizing against the ion count of
the total pool. The concentrations of selected metabolites were
determined by calibration curves using authentic synthesized standards.
The metabolite concentrations in tissues or intestinal contents were
calculated using the following equation: concentration
(μmol g^−1) = concentration of extracted sample (μM) × volume of
extraction solution (μl) / tissue weight (mg).
Lipid measurements using LC–MS
For lipid extraction, samples were mixed with −20 °C isopropanol
(150 µl per 5 µl serum and 40 µl per mg tissue), vortexed and
immediately centrifuged at 16,000g for 10 min at 4 °C^[327]15. The
supernatant (70 µl) was loaded into LC–MS vials. Lipids were analysed
by a quadrupole–orbitrap mass spectrometer (Q-Exactive Plus Hybrid
Quadrupole–Orbitrap) coupled to reverse-phase chromatography with
electrospray ionization. LC separation was on an Atlantis T3 Column
(2.1 mm × 150 mm, 3 µm particle size, 100 Å pore size; Waters) at 45 °C
using a gradient of solvent A (1 mM ammonium acetate, 35 mM acetic acid
in 90:10 water:methanol) and solvent B (1 mM ammonium acetate, 35 mM
acetic acid in 98:2 isopropanol:methanol). The flow rate was
150 µl min^−1. The LC gradient was: 0 min, 25% B; 2 min, 25% B;
5.5 min, 65% B; 12.5 min, 100% B; 16.5 min, 100% B; 17 min, 25% B; and
30 min, 25% B. MS analysis was acquired in positive ion mode with Full
MS and MS/dd-MS2 scan mode from m/z 290 to 1,200. The MS operational
parameters and data analysis are the same as for the metabolite
analysis.
Saponified fatty acid measurement using LC–MS
Serum (5 µl) or liver powder (20 mg) was incubated with 0.5 ml of 0.3 M
KOH in 90% methanol at 80 °C for 1 h in a 2 ml glass vial. Then, formic
acid (50 µl) was added for neutralization. The saponified fatty acids
were extracted by adding 500 µl of hexane and vortexing. After 5 min
for separation of the layers, 250 µl of the top hexane layer was
transferred to a new glass vial. Samples were then dried under a
nitrogen gas stream and redissolved in 100 µl (for serum) or 500 µl
(for liver) of 1:1 isopropanol:methanol for LC–MS analysis^[328]15.
Fatty acids were analysed by a quadrupole–orbitrap mass spectrometer
(Q-Exactive Plus Hybrid Quadrupole–Orbitrap) coupled with reverse-phase
chromatography with electrospray ionization. LC separation was
performed on an Atlantis T3 Column (2.1 mm × 150 mm, 3 µm particle
size, 100 Å pore size; Waters) at 45 °C using a gradient of solvent A
(1 mM ammonium acetate, 35 mM acetic acid in 90:10 water:methanol) and
solvent B (1 mM ammonium acetate, 35 mM acetic acid in 98:2
isopropanol:methanol). The flow rate was 150 µl min^−1. The LC gradient
was: 0 min, 25% B; 2 min, 65% B; 5.5 min, 100% B; 16.5 min, 100% B;
16.5 min, 25% B with a flow rate of 200 µl min^−1; 19 min, 25% B with a
flow rate of 200 µl min^−1; 19.1 min, 25% B with a flow rate of
150 µl min^−1; and 20 min, 25% B. MS analysis was acquired in negative
ion mode with Full MS scan mode from m/z 200 to 530. The MS operational
parameters and data analysis are the same as for the metabolite
analysis
Body water enrichment and DNL calculation
To quantify body water enrichment, 5 μl of serum, 5 μl of water, 4 μl
of 1 M sodium hydroxide and 10 μl of acetone were mixed in a glass vial
and incubated overnight at room temperature to promote ^2H exchange
between ^2H[2]O in serum and acetone^[329]94. The resulting ^2H-acetone
was derivatized to 2,4-dinitrophenylhydrazine (2,4-DNPH)^[330]95. The
2,4-DNPH solution was prepared by dissolving 20 mg of 2,4-DNPH in 10 ml
of ethanol with 100 μl of H[2]SO[4] and 150 μl of water. The
precipitate formed upon mixing was then isolated by filtration (cat.
no. 09-790-D; Fisher Scientific). The filtrate was treated with 1 ml of
H[2]SO[4], and 5 μl of this solution was mixed with 20 μl of the sample
solution. After a 2 h incubation at room temperature, 200 μl of ethanol
was added, and the samples were transferred to 1.5 ml tubes for
centrifugation at 4 °C for 20 min. The clear supernatant was
transferred into a glass vial for LC–MS analysis. ^2H[1] acetone and
unlabelled acetone were measured by LC–MS analysis, using the same
method as for SCFA analysis (see below), and were used to calculate the
fraction of ^2H[1] acetone. Calibration standards of known ^2H fraction
water were prepared by mixing naturally labelled water and 99.9%
^2H[2]O. The ^2H[1] acetone fraction of ^2H[2]O serial dilution in
naturally labelled water was used to generate a standard curve. Then,
the ^2H[1] acetone fraction in the serum samples was substituted into
the standard curve equation to calculate body water enrichment, as
previously described^[331]53,[332]96. The contribution of fatty acid
synthesis was determined using equation ([333]1).
[MATH: DNL=2H-labeled palmitate
enrichmentbody water
enrichment×number of
exchangeable hydrogens :MATH]
1
^2H-labelled palmitate enrichment was calculated using equation
([334]2), where ^2H[1], ^2H[2] and ^2H[3] indicate the ^2H-labelled
fraction of each isotopologue.
[MATH: 2Henrichment=2H1+2H2×2+2H3×3 :MATH]
2
The number of exchangeable hydrogens (n) was calculated using the
fractions of ^2H[1] and ^2H[2] palmitate as in equation ([335]3). The
rate of DNL per hour was determined by dividing the time elapsed since
^2H[2]O administration.
[MATH: 2H2
2H1
=(n−1)2×bodywaterfraction(1−bodywaterfraction) :MATH]
3
SCFA measurement using LC–MS
Serum (5 µl) or intestinal content (1 mg) was mixed with derivatizing
reagent (100 µl) and incubated for 1 h at 4 °C. The derivatizing
reagent was prepared by mixing 12 mM
N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide, 25 mM
3-nitrophenylhydrazine and pyridine (4% v/v) in 40:40:20
methanol:acetonitrile:water (v/v/v). Samples were centrifuged at
16,000g for 10 min at 4 °C, and 10 µl of supernatant was mixed with
90 µl of the quenching reagent (0.5 mM β-mercaptoethanol in water).
After centrifugation at 16,000g for 10 min at 4 °C, the supernatants
were collected for LC–MS analysis. SCFAs were analysed using a
quadrupole–orbitrap mass spectrometer (Q-Exactive Plus Hybrid
Quadrupole–Orbitrap) coupled with reverse-phase chromatography with
electrospray ionization. LC separation was performed on an Atlantis T3
Column (2.1 mm × 50 mm, 3 µm particle size, 100 Å pore size; Waters)
using a gradient of solvent A (water) and solvent B (methanol) at
60 °C. The flow rate was 300 µl min^−1. The LC gradient was: 0 min, 10%
B; 2.3 min, 80% B; 3.6 min, 80% B; 3.7 min, 10% B; and 5 min, 10% B. MS
analysis was acquired in negative ion mode with Full MS scan mode from
m/z 100 to 300. The MS operational parameters and data analysis are the
same as for the metabolite analysis.
Malondialdehyde measurement using LC–MS
Malondialdehyde in liver tissue (10 mg) was mixed with 20 µl of
butylated hydroxytoluene solution (1 g l^−1 in ethanol) and 220 µl of
50% ethanol solution^[336]97. Samples were centrifuged at 16,000g for
10 min at 4 °C, and 200 µl of supernatant was mixed with 200 µl of
2,4-dinitrophenyl-hydrazine solution (0.05 M in acetonitrile:acetic
acid 9:1 (v/v)). After incubating samples for 2 h at 60 °C, the samples
were mixed with 530 µl of water and 1 ml of hexane. Samples were
vortexed and incubated for 10 min at 25 °C to separate the layers.
Then, 500 µl of the top hexane layer was transferred to a new glass
vial. Samples were then dried under a nitrogen gas stream and
redissolved in 200 µl of acetic acid solution (0.03% in
acetonitrile:water 4:6 (v/v)). After centrifugation at 16,000g for
10 min at 4 °C, 150 µl of the supernatant was collected for LC–MS
analysis. Malondialdehyde was analysed using a quadrupole–orbitrap mass
spectrometer (Q-Exactive Plus Hybrid Quadrupole–Orbitrap) coupled to
reverse-phase chromatography with electrospray ionization. LC
separation was performed on an Atlantis T3 Column (2.1 mm × 150 mm,
3 µm particle size, 100 Å pore size; Waters) at 45 °C using a gradient
of solvent A (1 mM ammonium acetate, 35 mM acetic acid in 90:10
water:methanol) and solvent B (1 mM ammonium acetate, 35 mM acetic acid
in 98:2 isopropanol:methanol). The flow rate was 150 µl min^−1. The LC
gradient was: 0 min, 25% B; 2 min, 100% B; 5 min, 100% B; 11.5 min,
100% B; 11.6 min, 25% B; and 15 min, 25% B. MS analysis was acquired in
negative ion mode with Full MS scan mode from m/z 200 to 400. The MS
operational parameters and data analysis are the same as for the
metabolite analysis. Malondialdehyde was also quantified using a
thiobarbituric acid reactive substances (TBARS) assay kit (10009055,
Cayman Chemical).
Immunofluorescence imaging
Tissue samples were fixed in 4% buffered paraformaldehyde for 1 h at
room temperature. After fixation, tissues were dehydrated with 30%
sucrose in PBS overnight, frozen and embedded in Frozen Section Media
(Leica) and cut into 20 μm-thick sections using a Cryocut Microtome
(Leica). The samples were then blocked in protein block serum (Agilent)
with 0.3% Triton X-100 (Thermo Fisher) for 1 h. For 4-HNE staining, the
sections were incubated overnight at 4 °C with anti-4-HNE antibody
(clone 12F7; Invitrogen, 1:200) in antibody diluent (Agilent), then
with anti-mouse secondary antibody conjugated to Alexa Fluor 488
(1:1,000; Jackson ImmunoResearch) for 2 h at room temperature. For
reactive oxygen species staining, the sections were incubated with 5 µM
dihydroethidium (309800, Sigma-Aldrich) for 30 min at 37 °C. After
staining, the samples were washed three times for 10 m each in PBS with
0.3% Triton X-100. Finally, tissue sections were mounted with
Vectashield plus DAPI (4′,6-diamidino-2-phenylindole) (Vector Labs) and
imaged with an LSM 980 confocal microscope (Zeiss).
Bacteria 16S rDNA quantification
Bacterial DNA was extracted from faecal pellets (10–20 mg) using
Quick-DNA Faecal/Soil Microbe Kits (Zymo Research) according to the
manufacturer’s instructions. Purified DNA was amplified by qPCR using
SYBR green master mix (Life Technologies) on a QuantStudio6 Real-Time
PCR system (Life Technologies). DNA from the E. coli DH5a strain was
used as a standard for determining the copy number of the 16S rDNA gene
of universal bacteria by qPCR. Primer pairs targeting the bacterial
universal 16S rRNA gene, Bacteroides spp. and B. pseudolongum were
selected from previous studies^[337]98,[338]99 as follows: bacterial
universal 16S rRNA gene (forward, GTGGTGCACGGCTGTCGTCA; reverse,
ACGTCATCCACACCTTCCTC), B. pseudolongum (forward,
CRATYGTCAAGGAACTYGTGGCCT; reverse, GCTGCGAMGAKACCTTGCCGCT) and
Bacteroides spp. (forward, CTGAACCAGCCAAGTAGCG; reverse,
CCGCAAACTTTCACAACTGACTTA). Relative bacterial abundances of Bacteroides
spp. and B. pseudolongum were calculated from threshold cycle values
relative to universal bacterial abundance in each sample.
16S rRNA gene amplicon sequencing and analysis
The ZymoBIOMICS-96 MagBead DNA Kit (Zymo Research) was used to extract
DNA from mouse jejunal or caecal contents. Bacterial 16S ribosomal RNA
gene-targeted sequencing was performed using the Quick-16S NGS Library
Prep Kit (Zymo Research). The bacterial 16S primers amplified the V3–V4
region of the 16S rRNA gene. The sequencing library was prepared using
an innovative library preparation process in which PCR reactions were
performed in real-time PCR machines to control cycles and, therefore,
limit PCR chimera formation. The final PCR products were quantified
with qPCR fluorescence readings and pooled together based on equal
molarity. The final pooled library was cleaned with the Select-a-Size
DNA Clean & Concentrator (Zymo Research), then quantified with
TapeStation (Agilent Technologies) and Qubit (Thermo Fisher
Scientific). The final library was sequenced on Illumina MiSeq with a
v3 reagent kit (600 cycles). The sequencing was performed with 10% PhiX
spike-in. Unique amplicon sequence variants were inferred from raw
reads using the DADA2 pipeline. Taxonomy assignment was performed using
Uclust from Qiime (v.1.9.1) with the Zymo Research 16S reference
database. Composition visualization, alpha diversity and beta diversity
analyses were performed with Qiime (v.1.9.1). Taxonomy that has
significant abundance among different groups was identified by linear
discriminant analysis effect size using default settings. To compare
microbiome diversity among donor and recipient mice of the jejunal
content transplant experiment, NMDS analysis based on Bray-Curtis
dissimilarities was performed using Microbiome Analyst (v.2.0).
Amplicon sequence variants were used to plot for NMDS analysis in
Extended Data Fig. [339]3j.
Statistical analysis
Data collection and analysis were not performed blind to the conditions
of the experiments. Data distribution was assumed to be normal, but
this was not formally tested. Tukey’s HSD test and two-sided or
one-sided Student’s t-test were used to calculate P values, with
P < 0.05 considered significant. False discovery rate correction was
performed for metabolomics with the Benjamini and Hochberg method.
Outliers were defined as values more than 1.5 times the interquartile
range below quartile 1 or above quartile 3 (ref. ^[340]100).
Reporting summary
Further information on research design is available in the [341]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[342]Reporting Summary^ (1.9MB, pdf)
[343]Supplementary Table 1^ (21.6KB, xlsx)
Composition of control and inulin-supplemented diets. Inulin accounts
for 10% w/w. Note that the inulin diet contains ~7% less kcal g^−1
owing to the replacement of a small portion of corn starch with inulin.
[344]Supplementary Table 2^ (257KB, xlsx)
LC–MS analysis results of metabolites labelled from ^13C-fructose in
liver from CF and IF. P values by Student’s t-test followed by false
discovery rate correction.
[345]Supplementary Table 3^ (26.6KB, xlsx)
List of enriched genes in IF and CIF relative to CF (fold change > 2,
P < 0.05) and RNA-seq results. Genes mentioned in the Article are
indicated in bold. P value by Student’s t-test followed by false
discovery rate correction.
Source data
[346]Source Data Fig. 1^ (162.3KB, xlsx)
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[347]Source Data Fig. 2^ (35.2KB, xlsx)
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[348]Source Data Fig. 3^ (43.6KB, xlsx)
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[349]Source Data Fig. 4^ (2.5MB, xlsx)
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[350]Source Data Fig. 5^ (20.9KB, xlsx)
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[351]Source Data Fig. 6^ (143.1KB, xlsx)
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[352]Source Data Extended Data Fig. 1^ (113.5KB, xlsx)
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[353]Source Data Extended Data Fig. 2^ (28.5KB, xlsx)
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[354]Source Data Extended Data Fig. 3^ (34.8KB, xlsx)
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[355]Source Data Extended Data Fig. 4^ (17.7KB, xlsx)
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[356]Source Data Extended Data Fig. 5^ (33.4KB, xlsx)
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Acknowledgements