Structural modulation of gut microbiota in life-long calorie-restricted mice

Abstract

Calorie restriction has been regarded as the only experimental regimen that can effectively lengthen lifespan in various animal models, but the actual mechanism remains controversial. The gut microbiota has been shown to have a pivotal role in host health, and its structure is mostly shaped by diet. Here we show that life-long calorie restriction on both high-fat or low-fat diet, but not voluntary exercise, significantly changes the overall structure of the gut microbiota of C57BL/6 J mice. Calorie restriction enriches phylotypes positively correlated with lifespan, for example, the genus Lactobacillus on low-fat diet, and reduces phylotypes negatively correlated with lifespan. These calorie restriction-induced changes in the gut microbiota are concomitant with significantly reduced serum levels of lipopolysaccharide-binding protein, suggesting that animals under calorie restriction can establish a structurally balanced architecture of gut microbiota that may exert a health benefit to the host via reduction of antigen load from the gut.

ARTICLE

Received 1 Apr 2013 | Accepted 17 Jun 2013 | Published 16 Jul 2013

Chenhong Zhang1, Shoufeng Li2, Liu Yang2, Ping Huang2, Wenjun Li2, Shengyue Wang3, Guoping Zhao3, Menghui Zhang1, Xiaoyan Pang1, Zhen Yan4,5, Yong Liu2 & Liping Zhao1,6

Calorie restriction has been regarded as the only experimental regimen that can effectively lengthen lifespan in various animal models, but the actual mechanism remains controversial. The gut microbiota has been shown to have a pivotal role in host health, and its structure is mostly shaped by diet. Here we show that life-long calorie restriction on both high-fat or lowfat diet, but not voluntary exercise, signicantly changes the overall structure of the gut microbiota of C57BL/6 J mice. Calorie restriction enriches phylotypes positively correlated with lifespan, for example, the genus Lactobacillus on low-fat diet, and reduces phylotypes negatively correlated with lifespan. These calorie restriction-induced changes in the gut microbiota are concomitant with signicantly reduced serum levels of lipopolysaccharide-binding protein, suggesting that animals under calorie restriction can establish a structurally balanced architecture of gut microbiota that may exert a health benet to the host via reduction of antigen load from the gut.

DOI: 10.1038/ncomms3163 OPEN

Structural modulation of gut microbiota in life-long calorie-restricted mice

1 State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

2 Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences; Graduate School of the Chinese Academy of Sciences; Chinese Academy of Sciences, Shanghai 200031, China. 3 Shanghai-MOST Key Laboratory for Disease and Health Genomics, Chinese National Human Genome Centre, Shanghai 201203, China. 4 Departments of MedicineCardiovascular Medicine and Pharmacology, University of Virginia, Charlottesville, Virginia 22908, USA. 5 Center for Skeletal Muscle Research, Robert M. Berne Cardiovascular Research Centre, University of Virginia, Charlottesville, Virginia 22908, USA. 6 Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Centre for Systems Biomedicine, Shanghai 200240, China. Correspondence and requests for materials should be addressed to L.Z. (email: mailto:[email protected]

Web End [email protected] ) or to Y.L. (email: mailto:[email protected]

Web End [email protected] ).

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Since the initial study demonstrating the health-promoting and lifespan-extending effects of calorie restriction (CR) in mice1, benets related to alleviating the metabolic syndrome

have been observed in many mammals, including non-human primates and humans2,3. Despite various efforts, the actual mechanism remains controversial4,5.

Two recent, life-long metabonomic studies in dogs and monkeys revealed that CR was associated with changes in urinary bacterial metabolites, suggesting a potential connection among the gut microbiota, CR and aging6,7. Humans are considered supraorganisms with a vastly diverse and highly populated microbiota in the gut8,9, which can function as a metabolic organ to signicantly modulate nutrition, metabolism and the immunity of its host10. After the host digests and absorbs nutrients from the diet, the remaining part will reach the colon to maintain a highly diverse and populated chemostatic culture11,12. The composition and amount of the diet work as a dominant force in shaping the gut microbiota12,13.

Changes in the gut microbiota responding to different diets, such as a high-fat diet versus normal chow, may have a pivotal role in the development of obesity and related diseases13

18. Gut microbiota disrupted by a high-fat diet may produce higher amounts of endotoxin and increase gut permeability, leading to a higher plasma level of endotoxin, a higher level of inammation and eventually the development of metabolic disorders13,14,19,20. Our recent study showed that one endotoxin-producing strain isolated from the gut of an obese human caused obesity and insulin resistance in germ-free mice. These bacterium-induced obese mice had a signicantly elevated serum endotoxin load and increased systemic and local inammation, indicating a causative role of the endotoxin-producing members in the gut microbiota in metabolic syndrome21. However, it remains to be elucidated how far and to what direction the gut microbiota can be shifted by changing only the amount of food intake such as in CR treatment.

Recently, Zhou et al.22 identied genetic modulators of aging by analysing mid-life gene expression in the liver of a mouse model with life-long dietary and exercise interventions. In that study, male C57BL/6 J mice were subjected to either a low-fat diet (10% fat, D12450B, Research Diets) or a high-fat diet (60% fat, D12492, Research Diets). For each type of diet, animals were divided into three groups: (1) fed ad libitum with sedentary activity in the cage (LFD or HFD), (2) fed 70% of the ad libitum (LFD CR or HFD CR) or (3) fed ad libitum with voluntary

wheel-running exercise (LFD Ex or HFD Ex). Each group

had 30 individually caged animals, and the entire trial lasted almost 4 years until all animals died22. The longest living and healthiest animals were in the LFD CR group. Relative to the

LFD group, their median lifespan (153 weeks) and maximum lifespan (185.51.6 weeks) increased by approximately 20% and 25%, respectively. The LFD CR group also exhibited the lowest

and most stable body weight and fat content, as well as the best metabolic phenotypes, such as glucose homoeostasis and serum lipid prole, at the different indicated ages throughout their lifespan. The HFD group had the shortest lifespan and the worst metabolic phenotypes. Compared with the HFD control group, restricted high-fat diet intake (HFD CR) resulted in dramatic

extensions of the median and maximum lifespans (both by B36% from 101 to 137 weeks and from 118.81.5 to 161.91.5 weeks, respectively), which became similar to those of the LFD (127 and 148.73.1 weeks, respectively) and LFD Ex (131 and

159.63.7 weeks, respectively) groups. In addition, similar metabolic phenotypes were observed among these groups. Voluntary running exercise resulted in a signicant increase in the median and maximum lifespan (by B13%, from 101 to 114 weeks, and by B18%, from 118.81.5 to 139.71.9 weeks,

respectively) when animals were fed ad libitum on high-fat diet but not on low-fat diet. Together, these data demonstrate that obesity-related metabolic syndrome is highly associated with accelerated aging and reduced lifespan. CR can more effectively alleviate diet-associated metabolic disorders and attenuate aging than voluntary exercise, leading to a prolonged healthy lifespan, consistent with early studies in rats22.

In the current study, we use faecal and serum samples from the same animal trial as Zhou et al.22 to investigate the impact of lifelong CR and voluntary exercise on the endotoxin load and architecture of gut microbiota and pinpoint the association between a specic combination of populations in the gut microbiota and the variations in healthy phenotype and lifespan of their hosts. Our ndings suggest that an improved architecture of gut microbiota may be a critical element in mediating the health-promoting actions of CR, highlighting the potential of modulation for gut microbiota in developing effective anti-aging dietary interventions.

ResultsOverall structural changes of gut microbiota in life-long CR. To determine whether the CR-mediated protection of mice against obesity-associated metabolic syndrome and promotion of healthy aging are associated with alteration of gut microbiota structure, we rst proled the overall structural changes of gut microbiota from all available animals at 62, 83 and 141 weeks of age by bar-coded pyrosequencing of the V3 region of 16S rRNA genes. Of 293,557 valid reads from 288 samples with an average of 1,019 reads per sample (205 s.d.), 4,613 species-level operational taxonomic units (OTUs) were delineated using 97% as a homology cut-off value (Supplementary Fig. S1). b-Diversity analysis can indicate the extent of similarity between microbial communities by measuring the degree to which membership or structure is shared between communities23. Based on the data matrix of the weighted UniFrac distance, unweighted pair-group method using arithmetic averages and principal coordinate analysis showed both age-dependent and diet-responsive structural rearrangement of gut microbiota (Fig. 1 and Supplementary Fig. S2). Although no signicant age-related shift of gut microbiota was observed around mid-life ages (between 62 and 83 weeks), the gut microbiota from all groups of mice alive at the late-life age of 141 weeks displayed the same trend of moving into an aging space. Conversely, separated microbiota clusters were observed in high-fat diet-fed mice relative to low-fat diet-fed mice at the two mid-life ages. Moreover, in parallel with its profound effects on health improvement and longevity, CR showed more prominent impact on the overall architecture of gut microbiota than exercise, particularly with unique microbiota clusters detected in the LFD CR group both at mid-life and late-life ages. The

differences between the gut microbiota of animals with or without voluntary exercise were not signicant in the present study. These results suggest a possible correlation of the clustering pattern of gut microbiota with the health conditions in response to life-long nutritional intervention.

Specic phylotypes modulated by life-long CR. As an algorithm to robustly identify features that are statistically different among biological classes, linear discriminant analysis (LDA) effect size (LEfSe)24 was employed to identify specic phylotypes responding to life-long CR at both mid-life (62 weeks of age) and late life (141 weeks of age). We did not analyse data at 83 weeks of age because there is no signicant age-related shift of gut microbiota between 62 and 83 weeks.

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a Actinobacteria Bacteroidetes Firmicutes Proteobacteria TM7Others

b

HFD+CR

(62 wk)

HFD+Ex

(62 wk)

HFD+Ex

(83 wk)

HFD+CR

(83 wk)

62 weeks LFD

HFD

LFD+Ex

LFD+CR

LFD

(141 wk)

LFD+Ex

(141 wk)

LFD+CR

(141 wk)

0.2

0.1

0.1

0.2

0.3

0.4

0

HFD

(83 wk)

HFD+Ex

HFD+CR

HFD

(62 wk)

PC3 (11.6%)

HFD+CR

(141 wk)

HFD+Ex

(141 wk)

LFD+CR

(83 wk)

0.4

0.2

(62 wk)LFD+Ex

(83 wk) LFD+CR

(62 wk)

PC1 (35.1%)

0.2

0 0.2

0.2 0

PC2 (23.3%)

LFD

(62 wk)

LFD+Ex

LFD

(83 wk)

c

d

141 weeks LFD

83 weeks LFD

HFD

LFD+Ex

HFD+Ex

LFD+CR

HFD+CR

LFD+Ex

0.2

0.1

0.1

0.2

LFD+CR

HFD+Ex

0.2

0.1

0.1

0.2

0.3

0.4

0

HFD+CR

PC3 (13.1%)

PC3 (7.3%)

0

0.5

0.2

0.2 0.4 0

0

0 0

0.2

PC1 (39.1%)

0.5

PC2 (21.4%)

PC1 (49.4%)

0.2

0.2

0.4

1

0.2

0.6

PC2 (14.1%)

Figure 1 | Age-dependent and diet-responsive alteration trajectories of global gut microbiota structures. (a) Unweighted pair-group method using arithmetic average based on the weighted UniFrac distance of gut microbiota from the six groups of mice at 62, 83 and 141 weeks (wk) of age. The average relative abundance (% of total 16S rRNA gene V3 region sequences) of bacterial lineages of the gut microbiota within each group of mice is displayed as pie charts at the phylum level. Weighted UniFrac principal coordinate analysis of animals at (b) 62 (LFD, n 21; LFD CR, n 29; LFD Ex,

n 22; HFD, n 28; HFD CR, n 29; and HFD Ex, n 23), (c) 83 (LFD, n 16; LFD CR, n 22; LFD Ex, n 19; HFD, n 12; HFD CR, n 14; and

HFD Ex, n 15) and (d) 141 (LFD, n 6; LFD CR, n 15; LFD Ex, n 6; HFD, n 0; HFD CR, n 10; and HFD Ex, n 1) weeks of age.

In mid-life, 34 phylotypes at the OTU level were discovered as high-dimensional biomarkers for separating gut microbiota between LFD and LFD CR mice (Fig. 2a and Supplementary

Table S1). Sixteen of these OTUs were higher, and eighteen were lower in the CR than in the ad libitum group. For example, the abundances of these selected phylotypes in Streptococcaceae (OTU65 belonging to Lactococcus) and TM7 (OTU98) were lower in CR animals. Interestingly, OTU45 in the genus Lactobacillus was one of the most predominant phylotypes in bacterial communities of LFD CR mice but was notably low in

LFD mice (12.4% versus 0.05%, respectively; Po0.001, one-way ANOVA).

At the late-life age of 141 weeks, 27 OTUs were higher and 27 were lower in the LFD CR group than in the LFD group

(Fig. 2b and Supplementary Table S2). Ten of these OTUs were also signicantly different between the two treatment groups in mid-life. For example, although OTU45 in the genus Lactobacillus was not the predominant phylotype in bacterial

communities of LFD CR mice, the relative abundance of this

OTU was still higher in LFD CR mice than in LFD mice (1.7%

versus 0.024%, respectively; Po0.001, one-way ANOVA). Different from mid-life, the OTUs belonging to Bidobacterium were higher in LFD CR mice, but the OTU469 of Desulfovi

brionaceae was lower in LFD CR mice. Some members in the

genus Bidobacterium are well-known probiotic strains25, and some in the family Desulfovibrionaceae have previously been found to be positively associated with obesity and inammation13.

The mice had a signicantly different gut microbiota structure between the LFD and HFD groups (Supplementary Fig. S3), conrming results of previous studies13,18. CR also shifted the gut microbiota in mice fed with high-fat diet but not as dramatic compared with their low-fat diet companions (Fig. 1b,c and Supplementary Fig. S4). In mid-life, 30 phylotypes were selected as key variables for separating the gut microbiota under different food intake conditions (Fig. 2c and

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a LFD+CR 62 weeks LFD 62 weeks Helicobacter OTU68

Helicobacter OTU1648 Lachnospiraceae OTU429

Lachnospiraceae OTU215

Lachnospiraceae OTU167

Lachnospiraceae OTU107

Lactobacillus OTU94

Lactobacillus OTU45

Lactobacillus OTU171

Lactobacillus OTU2219

Alistipes OTU401

Lannerella OTU267

Lannerella OTU155

Lannerella OTU119

Bifidobacterium OTU127

Lachnospiraceae OTU56

1.5

1

0.5

0.5

TM7 OTU98

Turicibacter OTU275

Ruminococcaceae OTU319

Peptostreptococcaceae OTU37

Lachnospiraceae OTU158

Lachnospiraceae OTU137

Lachnospiraceae OTU109

Lactococcus OTU65

Bacteroidales OTU162

Bacteroidales OTU162

Bacteroidales OTU162

Bacteroidales OTU162

Bacteroidales OTU162

Alistipes OTU365

Alistipes OTU197

Prevotellaceae OTU203

Porphyromonadaceae OTU88

Porphyromonadaceae OTU138

5 4 3 2 1 0 1 2 3 4 5 LDA score (log 10)

LFD+CR 62 weeks LFD 62 weeks

0

1

LFD 141 weeks

b LFD+CR 141 weeks LFD 141 weeks Firmicutes OTU340

Allobaculum OTU118

Allobaculum OTU80

Allobaculum OTU4

Clostridiales OTU130

Ruminococcaceae OTU104

Lactobacillus OTU171

Lactobacillus OTU45

Bacteroidales OTU36

Bacteroidales OTU36

Bacteroidales OTU36

Bacteroidales OTU36

Bacteroidales OTU36

Bacteroidales OTU36

Alistipes OTU401

Alistipes OTU266

Porphyromonadaceae OTU280

Porphyromonadaceae OTU202

Porphyromonadaceae OTU169

Porphyromonadaceae OTU133

Porphyromonadaceae OTU95

Porphyromonadaceae OTU77

Tannerella OTU267

Tannerella OTU155

Tannerella OTU119

Bifidobacterium OTU127

Bifidobacterium OTU27

Bilophila OTU469

Ruminococcaceae OTU271

Anaerotruncus OTU113

Lachnospiraceae OTU878 Lachnospiraceae OTU391

Lachnospiraceae OTU418 Lachnospiraceae OTU357

Lachnospiraceae OTU300

Lachnospiraceae OTU279 Lachnospiraceae OTU211

Lachnospiraceae OTU270 Lachnospiraceae OTU205

Lachnospiraceae OTU186

Lachnospiraceae OTU165 Lachnospiraceae OTU116

Lachnospiraceae OTU146

Lachnospiraceae OTU18

Lachnospiraceae OTU8

Lactococcus OTU65

Parabacteroides OTU161

Parabacteroides OTU28

Lachnospiraceae OTU109

Bacteroides OTU1814

Bacteroides OTU359

Bacteroides OTU64

Bacteroides OTU32

Bacteroides OTU2

5 4 3 2 1 0 1 2 3 4 5 LDA score (log 10)

LFD+CR 141 weeks

1

0.5

0

0.5

1

c HFD+CR 62 weeks HFD 62 weeks

Bilophila OTU30

Allobaculum OTU118

Ruminococcaceae OTU79

Lachnospiraceae OTU224 Bacteroidales OTU372

Bacteroidales OTU388 Bacteroidales OTU148

Prevotellaceae OTU203

Porphyromonadaceae OTU317

Porphyromonadaceae OTU302

Porphyromonadaceae OTU133

Porphyromonadaceae OTU105 Porphyromonadaceae OTU95

Porphyromonadaceae OTU97

Tannerella OTU119

Tannerella OTU155

1.0

0.5

0.5

1.0

Tannerella OTU267

Coriobacteriaceae OTU136

0

Allobaculum OTU274

Ruminococcaceae OTU383

Ruminococcaceae OTU111

Ruminococcaceae OTU67

Peptostreptococcaceae OTU272

Peptostreptococcaceae OTU37

Lachnospiraceae OTU56

Lactococcus OTU65

Bacteroidales OTU366

Rikenella OTU603

Alistipes OTU197

Bacteroides OTU201

5 4 3 2 1 0 1 2 3 4 5

LDA score (log 10)

HFD+CR 62 weeks HFD 62 weeks

Figure 2 | Key phylotypes of gut microbiota responding to life-long CR identied using LEfSe. (a) LFD (n 21) versus LFD CR (n 29) mice at

62 weeks. (b) LFD (n 6) versus LFD CR (n 15) mice at 141 weeks. (c) HFD (n 28) versus HFD CR (n 29) mice at 62 weeks. The left

histogram shows the LDA scores computed for features (on the OTU level) differentially abundant between the ab libitum and CR mice. The right heat map shows the relative abundance (log 10 transformation) of OTUs.

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Supplementary Table S3); 18 of them were higher and 12 were lower in the HFD CR group than in the HFD group. All the

phylotypes in Porphyromonadaceae were higher in the HFD CR

than in the HFD group. Most of the OTUs responding to CR in

HFD CR mice were not found in LFD CR mice. Only three

OTUs (in Lactococcus (OTU65), Bacteroidales (OTU366) and Peptostreptococcaceae (OTU37), respectively) were reduced, and three OTUs in Tannerella (OTU119, 155 and 267) were increased

a LFD 62 weeks LFD 141 weeks Allobaculum OTU261

Allobaculum OTU592

LFD62 weeks 141 weeks

1.5

1

0.5

0

0.5

1

Allobaculum OTU646

Allobaculum OTU251

Allobaculum OTU252 Allobaculum OTU134

Allobaculum OTU110

Allobaculum OTU83

Allobaculum OTU80

Allobaculum OTU50

Allobaculum OTU4

Lachnospiraceae OTU190

Bacteroidales OTU371

Bacteroidales OTU362

Bacteroidales OTU151

Bacteroidales OTU61

Actinobacteria OTU78

Coriobacteriaceae OTU136

Bifidobacterium OTU127

Bifidobacterium OTU27

Bilophila OTU30

Allobaculum OTU602

Bilophila OTU469 Ruminococcaceae OTU271

Lachnospiraceae OTU357

Lachnospiraceae OTU300

Lachnospiraceae OTU279

Lachnospiraceae OTU270

Lachnospiraceae OTU206

Lachnospiraceae OTU186

Lachnospiraceae OTU76

Lachnospiraceae OTU18

Lachnospiraceae OTU8

Parabacteroides OTU161

Parabacteroides OTU142

Parabacteroides OTU28

Bacteroides OTU359

Bacteroides OTU64 Bacteroides OTU32

Bacteroides OTU58 Bacteroides OTU2

5 4 3 2 1 0 1 2 3 4 5 LDA score (log 10)

b LFD+CR 62 weeks LFD+CR 141 weeks LFD+CR 62 weeks LFD+CR 141 weeks

Allobaculum OTU3793

Allobaculum OTU2563

Allobaculum OTU2231

Allobaculum OTU1821

Allobaculum OTU646

Allobaculum OTU602

Allobaculum OTU592

Allobaculum OTU315

Allobaculum OTU261

Allobaculum OTU252

Allobaculum OTU134

Allobaculum OTU118

Allobaculum OTU110

Allobaculum OTU83

Allobaculum OTU80

Allobaculum OTU4

Lachnospiraceae OTU205

Lachnospiraceae OTU165

Lachnospiraceae OTU129

Lachnospiraceae OTU109

Lactobacillus OTU2219

Lactobacillus OTU171

Lactobacillus OTU94

Lactobacillus OTU45

Actinobacteria OTU250

Actinobacteria OTU78

Bifidabacterium OTU127

TM7 OTU98

Bilophila OTU30

Firmicutes OTU340

Clostridiales OTU130

Ruminococcaceae OTU271

Papillibacter OTU101

Lachnospiraceae OTU76

Bacteroidales OTU411

Bacteroidales OTU393

Bacteroidales OTU162

Bacteroidales OTU149

Bacteroidales OTU148

Bacteroidales OTU36

Alistipes OTU365

Alistipes OTU238

Alistipes OTU197

Porphyromonadaceae OTU544

Porphyromonadaceae OTU395

Porphyromonadaceae OTU363

Porphyromonadaceae OTU302

Porphyromonadaceae OTU282

Porphyromonadaceae OTU280

Porphyromonadaceae OTU202

Porphyromonadaceae OTU169

Porphyromonadaceae OTU133

Porphyromonadaceae OTU97

Porphyromonadaceae OTU95

Porphyromonadaceae OTU88

Porphyromonadaceae OTU77

Tannerella OTU963

Tannerella OTU267

Tannerella OTU155

Tannerella OTU119

Parabacteroides OTU142

Bacteroides OTU1732

Bacteroides OTU623

Bacteroides OTU243

Bacteroides OTU173

Bacteroides OTU64

Bacteroides OTU58

Bacteroides OTU1

5 4 3 2 1 1

0 2 3 4 5 LDA score (log 10)

1.5

1.0

0.5

0

0.5

1.0

Figure 3 | Key phylotypes of gut microbiota responding to aging. (a) LFD CR mice (n 15). (b) LFD mice (n 6). The left histogram shows the LDA

scores computed for features (OTU level) differentially abundant between 62 and 141 weeks. The right heat map shows the relative abundance (log 10

transformed) of OTUs.

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Fat content

Body weight

Food intake

Lifespan

a

by CR both with mice fed with high-fat diet and low-fat diet. Because all the HFD mice had died before 141 weeks, we could not obtain any data regarding gut microbiota responding to restriction of high-fat diet intake at the late-life stage of mice.

We also employed partial least square discriminate analysis to conrm the results, and the identied specic phylotypes responding to life-long CR were similar to those from LEfSe (Supplementary Figs S5 and S6).

We also identied a few OTUs that were different between the mice in the exercise group and their ad libitum companions by LEfSe; however, the relative abundance of these OTUs was very low (Supplementary Fig. S7). Conversely, efforts to classify groups with or without voluntary exercise on the same diet with partial least square discriminate analysis did not establish validated models, indicating that the differences between the gut micro-biota of animals with or without voluntary exercise were not signicant in the present study.

Structural modulation of gut microbiota during aging. Further analysis suggested that CR signicantly affected the succession of gut microbiota during aging. Division-level analysis showed that the Firmicutes/Bacteroidetes ratio of gut microbiota in all the mouse groups decreased from 62 to 141 weeks (Fig. 1a). Using LEfSe, we compared the gut microbiota of mice in each group between mid-life and late life to identify the specic phylotypes with OTU levels associated with aging.

In LFD mice, the phylotypes mainly responsible for decrease of the phylum Firmicutes during aging were in the genus Allobaculum (12 OTUs; 28.1% at 62 weeks versus 0.20% at 141 weeks) (Fig. 3a and Supplementary Table S4). In LFD CR mice,

not only OTUs in the genus Allobaculum (29.7% at 62 weeks versus 7.2% at 141 weeks) but also OTUs in the genus Lactobacillus (21.0% at 62 weeks versus 2.0% at 141 weeks) made a signicant contribution to the decrease of Firmicutes during aging (Fig. 3b and Supplementary Table S5).

Conversely, the increase of the phylum Bacteroidetes during aging in LFD mice was due to the increase of OTUs in the genus Bacteroides (family Bacteroidaceae; 0.84% at 62 weeks versus26.7% at 141 weeks). However, in LFD CR mice, OTUs in the

family Porphyromonadeceae (9.4% at 62 weeks versus 28.2% at 141 weeks), instead of bacteria in Bacteroides, were largely responsible for the increase of Bacteroidetes with age. Thus, the apparent phylum level changes associated with aging were actually mediated by different phylogenetic groups in animals with or without CR treatment.

Correlation of mid-life gut microbiota with lifespan. We next used the Kendall tau rank correlation coefcient to directly measure the correlation between the phylotypes of gut microbiota in mid-life and the lifespan based on two types of diet. We identied that 45 OTUs signicantly correlated with lifespan in mice fed on low-fat diet. Except for one Bacteroidales OTU, the remaining 15 OTUs signicantly positively correlated with lifespan belonged to Firmicutes. Particularly, eight OTUs in Lacto-bacillus showed strong correlation with lifespan. The 30 phylotypes negatively correlated with lifespan were distributed in the ve Phyla of Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria and TM7 (Fig. 4a and Supplementary Table S6). In the mice on the high-fat diet, 20 OTUs were positively correlated, and18 OTUs were negatively correlated with lifespan, most of which were in Firmicutes and Bacteroidetes, except for two Actino-bacteria OTUs (Fig. 4b and Supplementary Table S7). Because of the strong impact of different diet backgrounds on the gut microbiota, only three OTUs in Lactococcus (OTU65), Bacteroi-dales (OTU366) and Peptostreptococcaceae (OTU37) showed the

same behaviour both in mice on low-fat diet and high-fat diet. These three OTUs were negatively correlated with lifespan. We also found that the OTUs belonging to the same family or genus could have opposite correlation with lifespan. For example, in the mice fed with low-fat diet, there were three OTUs in Lachnospiraceae positively correlated with lifespan, but the other four OTUs in the same family were negatively correlated with lifespan.

Mid-life metabolic phenotypes, such as food intake, body weight and fat content, were highly correlated with lifespan, a nding that has been reported by Zhou et al22. Most of the OTUs signicantly positively correlated with lifespan showed strong negative correlation with food intake, body weight and fat content, and vice versa (Fig. 4a,b).

CR reduces antigen load to the hosts from the gut microbiota. Through long-term CR, the relative abundance of the OTUs negatively correlated with lifespan was signicantly reduced, and the relative abundance of OTUs positively correlated with lifespan was increased in mice both on low-fat diet and high-fat diet (Fig. 5a,b and Supplementary Figs S8 and S9). The serum levels of lipopolysaccharide (LPS)-binding protein (LBP) from all available animals at mid-life were measured to determine the antigen load from the gut microbiota. Compared with their ab libitum

Relative abundance of OTU

Allobaculum Papillibacter

Lachnospiraceae

Lactobacillus Bacteroidales

TM7

Alphaproteobacteria

Clostridiales

Ruminococcaceae

Papillibacter

Peptostreptococcaceae

Lachnospiraceae

Lactococcus Bacteroidales

Rikenella

Alistipes Prevotellaceae

Porphyromonadaceae

Tannerella

Parabacteroides

Bacteroides

Eggerthella

1

0

1.5

700

1,300

0.6 0.4Correlation cefficient Lifespan (days)

1,000

Fat content

Food intake

Body weight

Lifespan

b

Relative abundance of OTU

Allobaculum Ruminococcaceae

Porphyromonadaceae

Bacteroidales

Tannerella Antinobacteria

Peptostreptococcaceae

Ruminococcaceae

Lachnospiraceae Lactococcus

Bacteroidales

Porphyromonadaceae

Parabacteroides

Bacteroides

0.5 0.4Correlation cefficient Lifespan (days)

1

0

1

600 900 1,100

Figure 4 | Correlation of mid-life gut microbiota with lifespan. The phylotypes signicantly correlated with lifespan in mid-life (62 weeks) gut microbiota of animals on (a) low-fat diet (LFD, n 15; LFD CR, n 22;

and LFD Ex, n 17) and (b) high-fat diet (HFD, n 21; HFD CR, n 21;

and HFD Ex, n 18). The left heat map shows the correlation coefcient

between these OTUs and physiological parameters of mid-life. The right heat map shows the relative abundance (log 10 transformed) of OTUs. The bottom bar shows the lifespan of each mouse.

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a

c

d

22

20

18

16

36 32 28 24

20 16 12

Negative

Negative

Positive

Positive

Reltaive abundance (%)

Serum LBP (mg ml1)

Serum LBP (mg ml1)

**

++

*

**

0 LFD

0 HFD

LFD+Ex

LFD+CR

LFD

LFD+Ex

LFD+CR

14 LFD

LFD+Ex

LFD+CR

b

Reltaive abundance (%)

40

30

20

10

30

20

10

+

++

++

HFD HFD+Ex

HFD+Ex

HFD+CR

HFD+CR

HFD

HFD+Ex

HFD+CR

Figure 5 | Gut microbiota-associated antigen load changes. Relative abundance of the phylotypes negatively and positively correlated with lifespan of mice on (a) low-fat diet (LFD, n 15; LFD CR, n 22; and

LFD Ex, n 17) and (b) high-fat diet (n 21; HFD CR, n 21; and

HFD Ex, n 18) at 62 weeks. The boundary of the box closest to zero

indicates the 25th percentile, a line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles. The serum level of LBP of mice on (c) low-fat diet (LFD, n 7;

LFD CR, n 8; and LFD Ex, n 7) and (d) high-fat diet (HFD, n 8;

HFD CR, n 8; HFD Ex, n 7) at 62 weeks (shown as means.e.m.).

*Po0.05 and **Po0.01 versus LFD, Po0.05 and Po0.01 versus HFD by analyses of variance.

companions, LBP levels were lower in mice from the two CR intervention groups (Fig. 5c,d). Conversely, exercise showed no signicant impact on both the relative abundance of OTUs correlated with lifespan and the serum level of LBP. These results suggest that modulation of the gut microbiota by CR could signicantly reduce the antigen load to the host, contributing to CR-induced lifespan extension.

DiscussionAmong the efforts to understand the mechanisms by which reduced dietary intake improves health and lifespan, the current study is unique in that it focused on the changes of gut microbiota induced by life-long CR to determine whether these changes are associated with improved healthy phenotypes and lifespan.

Different dietary composition can mould the divergent structure of gut microbiota13,2629. For example, human populations with a modern western diet or a rural diet showed distinct gut microbiota structures29. The changes in overall structure of gut microbiota induced by high-fat diet versus normal chow may act as an important mediator in the aetiology of obesity and related metabolic diseases via disrupting host lipometabolism regulation15,18,3032 and inducing low-grade inammation13,14,19,20.

In the current study, we also observed shifting of gut microbiota induced by high-fat diet versus low-fat diet (data shown in Supplementary Materials). However, the most interesting result was that, compared with the ad libitum group, mice with 30% restriction of low-fat diet had a unique gut microbiota, demonstrating that the gut microbiota can be substantially modulated by only restricting the intake of diet. This is different

from previous reports that dietary restriction had little effect on the gut microbiota3335. This discrepancy may be due to the short duration of CR, different model systems used (rat and human) and limitation of technology for analysis of gut microbiota used in previous studies. The 454 pyrosequencing method used in the present study, although with its own bias due to the primer design (V3 region targeted) and chosen DNA extraction method, can still allow much deeper and more comprehensive analysis of changes in the gut microbiota responding to CR than that in previous reports.

Previous studies have suggested that because of the global impact on the physiology of the intestinal tract, including the decrease of intestinal motility, decline in the functionality of the immune system (immunosenescence), and changes in nutritional behaviour and life style of aged people, aging can seriously affect the composition of gut microbiota3639. In the current study, the gut microbiota in all mice changed during aging; however, the most interesting nding was that the shifts of gut microbiota with aging were different among different dietary intervention groups. This nding also suggests that life-long nutritional conditions have an impact on not only the structure and composition of gut microbiota but also the interaction between the host and gut microbiota.

Our approach of directly measuring lifespan as well as measuring dietary and other metabolic conditions, although extremely tedious and costly (more than 150 mice over a time span of 3 years), allowed us to identify the phylotypes of gut microbiota that may be directly associated with lifespan. Correlation analysis between the gut microbiota at mid-life and lifespan identied phylotypes that are positively correlated with lifespan and those that are negatively correlated with lifespan under each of the two dietary backgrounds. On both the high-fat diet and low-fat diet, CR increased those phylotypes that were positively correlated with lifespan, and decreased those that were negatively correlated with lifespan. Phylotypes positively correlated with lifespan may contain benecial bacteria, whereas those negatively correlated with lifespan may have harmful bacteria such as opportunistic pathogens. For example, the longest-lived and healthiest LFD CR group had a gut microbiota with

astonishingly high populations in Lactobacillus spp. Members in the genus Lactobacillus spp. have been known to inhibit pathogen adhesion to the intestinal wall, protect against pathogen-induced gut barrier disruption and reduce inammatory cytokines40,41. In addition, the LFD CR group had the lowest level of phylotypes

in Streptococcaceae and TM7. It has been shown in humans that some strains in Streptococcaceae can induce mild inammation, contributing to the disproportionate morbidity associated with chronic wounds among diabetics compared with nondiabetics42,43. Members in TM7 may have an important role in the early stages of inammatory mucosal processes in inammatory bowel diseases44.

The increase of benecial bacteria such as Lactobacillus and decrease of opportunistic pathogens may reduce antigen load to the host and help alleviate inammation and metabolic syndrome13,14,19,20. Our data showed that CR mice had reduced LBP levels in the serum. LBP is a soluble acute phase protein that binds to LPS, the most abundant gut antigen from the gut with the most potent inammation-provoking capacity, in eliciting immune responses by presenting LPS to surface pattern-recognition receptors, such as CD14 and TLR4, of immune cells45. LBP can also bind to antigens produced by Gram-positive bacteria and, thus, may represent one biomarker that links antigen load in the blood and the host inammatory response46. The signicant decrease of the ratio of the bacteria negatively and positively correlated with lifespan in animals under CR may minimize antigen entrance into the blood from the gut, a result

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that may constitute a crucial component in CR-mediated benets to the host13,14. Similar to the result of gut microbiota, the measurement of mid-life liver gene expression of mice from the same trial by whole-genome microarrays showed that LFD CR

mice demonstrated a distinctive expression pattern from other groups, which correlated with longevity and health status22. Most notably, the gene expression levels of the toll-like receptor signalling pathway and inammation-related pathways were negatively correlated with lifespan and positively correlated with body weight and metabolic deterioration. Because the toll-like receptor signalling pathway mainly responds to antigens such as endotoxins from the gut microbiota47, its downregulation supports the hypothesis that CR might display reduced antigen load from the gut microbiota to the hosts, possibly by modulating the structure of the gut microbiota to a more balanced state.

The gender of the host is known to have an impact on lifespan, healthy phenotypes and the immune responses4850. In addition, gender may also be involved in the determination of the mammalian gut microbiota. The gender-related bacteria identied from different studies included species of Bacteroides Prevotella, Clostridia, Bacteroidetes and Proteobacteria and so on28,49,51. To avoid the inuence of gender as a confounding factor, we only used male mice. The response of gut microbiota to CR in female mice may be different from the results we observed in the current study, and it remains an interesting issue to be investigated.

The molecular cascades between dietary modulation, the gut microbiota and host health remain to be elucidated. The diet can be used by both the host and gut microbiota11,12. The competition between the host and gut bacteria for nutrients may determine the composition of the feeding medium for homoeostatic control of microbiota in the lower gut. It is conceivable that under conditions of restricted nutrient availability, as in the case of mice in the LFD CR group, the

host may extract nutrients (such as proteins and fats) more thoroughly, leaving primarily indigestible plant polysaccharides to the colon. In other words, CR without malnutrition might actually increase the relative content of bre in the animals diet. This oligotrophic condition with mainly bre available for gut microbes might promote the growth of benecial bacteria, such as gut barrier protectors and butyrate producers52, but suppress opportunistic pathogens53. This hypothesis warrants further studies.

Our results point to the health-promoting potential of a balanced gut microbiota architecture induced by CR, revealing a possible close connection between nutritional modulation of gut microbiota and healthy aging. More mechanistic studies are needed to validate and expand the interesting ndings provided here via this microbiome-wide association study54. Given the potential key role in mediating the health-promoting actions of CR, an architecturally improved gut microbiota may become a novel surrogate biomarker for the development of effective anti-aging dietary interventions.

Methods

Animal intervention and samples. The animal experimental procedures, approved by the Institutional Animal Care and Use Committee of the Institute for Nutritional Sciences, CAS, were described previously by Zhou et al22. Male C57BL/6 J mice at 5 weeks of age were randomly assigned to one of the six groups (n 30

for each group) and individually caged for a life-long trial: (1) low-fat diet with sedentary activity (LFD), (2) low-fat diet with 30% CR and sedentary activity (LFD CR), (3) low-fat diet with voluntary running exercise (LFD Ex), (4)

high-fat diet with sedentary activity (HFD), (5) high-fat die with 30% CR and sedentary activity (HFD CR) and (6) high-fat diet with voluntary running exercise

(HFD Ex). All faecal and serum samples for the current study were collected from

this animal trial.

Fresh faecal matter was collected from the above animal trial at 62 weeks (LFD, n 21; LFD CR, n 29; LFD Ex, n 22; HFD, n 28; HFD CR, n 29; and

HFD Ex, n 23), 83 weeks (LFD, n 16; LFD CR, n 22; LFD Ex, n 19;

HFD, n 12; HFD CR, n 14; and HFD Ex, n 15) and 141 weeks (LFD,

n 6; LFD CR, n 15; LFD Ex, n 6; HFD, n 0; HFD CR, n 10; and

HFD Ex, n 1) of age. At 62 weeks, eight randomly selected mice from each

group were humanely euthanized for serum samples collection for LBP analysis. All the faecal samples were stored at 80 C until analysis.

Pyrosequencing of the V3 region of 16S rRNA genes. DNA was extracted using the PSPsSpin Stool DNA Plus Kit (Invitek GmbH, Germany). The primers P1 and P2 (50-NNNNNNCCTACGGGAGGCAGCAG-30 and 50-NNNNNNATTAC

CGCGGCTGCT-30) correspond to positions 341 to 534 in the Escherichia coli 16S rRNA gene, with a sample-unique DNA barcode of six-mer sequences at the 50 end, were used to amplify the V3 region of each faecal sample by PCR. PCR reactions were run in a thermocycler PCR system (PCR Sprint; Thermo electron, Corp., UK) using the following programme: 3 min of denaturation at 94 C followed by 20 cycles of 1 min at 94 C (denaturation), 1 min for annealing (1 C reduced for every two cycles from 65 to 57 C, followed by one cycle at 56 C and one cycle at 55 C) and 1 min at 72 C (elongation), with a nal extension at 72 C for 6 min. The products from different samples were mixed at equal ratios for pyrosequencing using the GS FLX platform (Roch, Branford, CT, USA).

Bioinformatics and statistical analysis of sequencing data. The standards for quality control of selecting valid reads for analysis were as follows: if a sequence (a) shows no mismatch to the barcode and 16S rRNA gene primer at sequencing end,(b) is more than 100 nucleotides in length, (c) has no more than two undermined bases in the sequence read and (d) nds 475% mach to a previously determined 16S rRNA gene sequence, as reported previously5557. The sequences were aligned using NAST, and delineation of OTUs was conducted with DOTUR at 97% cutoff58. The length of the sequence fragments used for the analysis was from 92 to 183 nucleotides (without primer and barcode). The alpha and beta diversities were performed using QIIME59. The GAST (Global Alignment for Sequence Taxonomy) process was used to select the top GAST match (es) of the representative sequence of each OTU to assign taxonomic classication60. The V3 reference databases (V3 RefDB) and software for GAST analysis were downloaded from http://vamps.mbl.edu/resources/software.php

Web End =http://vamps. http://vamps.mbl.edu/resources/software.php

Web End =mbl.edu/resources/software.php . The V3 RefDB is composed of publicly available, high-quality, full-length 16S rRNA sequences from Silva release 92 (http://www.arb-silva.de/

Web End =http://www. http://www.arb-silva.de/

Web End =arb-silva.de/ ) with taxonomic classications obtained from the RDP Classier (with a minimum 80% bootstrap score) and contains 381,203 V3 tags. The representative sequence of each OTU was assigned the taxonomic classication of the most similar reference sequence or sequences in the V3 RefDB as described previously60.

LEfSe24 is an algorithm for high-dimensional biomarker discovery and explanation that identies genomic features (genes, pathways or taxa) characterizing the differences between two or more biological conditions (or classes; see gure below). LEfSe emphasizes both statistical signicance and biological relevance, allowing researchers to identify differentially abundant features that are also consistent with biologically meaningful categories (subclasses). We performed LEfSe analysis on the website http://huttenhower.sph.harvard.edu/galaxy

Web End =http://huttenhower. http://huttenhower.sph.harvard.edu/galaxy

Web End =sph.harvard.edu/galaxy . The differential features were identied on the OTU level. The treatment groups or time points were used as the class of subjects(no subclass). LEfSe analysis was performed under the following conditions: (1) the alpha value for the factorial KruskalWallis test among classes is o0.05 and (2) the threshold on the logarithmic LDA score for discriminative features is 42.0.

Based on two diets, associations between each OTU (ltered for an OTU subject prevalence of at least 10%) at 62 weeks and lifespan were determined using the Kendall tau rank correlation coefcient under Matlab (ver. 7.1; The MathWorks, Inc.). The OTU was considered signicantly correlated with lifespan for Po0.05.

Thereafter, the Kendall tau rank correlation coefcient between these selected OTU and physiological parameters (food intake, body weight and fat content) of mid-life was also calculated.

Serum LBP measurements. Blood samples were collected from the tail vein after overnight fasting and centrifuged at 12,000 r.p.m. for 30 min to pellet blood cells, and the serum was stored at 80 C until further analyses. Serum LBP was

determined after a dilution of 1:800 using the Mouse Lipopolysaccharide Binding Protein ELISA Kit (Cell Sciences, Canton, MA, USA) according to the manufacturers instructions.

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Acknowledgements

We thank C.B. Newgard (Duke University) for the initial design of the animal intervention study. This work was supported by the following grants: China MOST (2012CB524900, 2011CB910900, 2007DFC30450, 2009ZX10004-601, 2006BAI11B08, 2088AA02Z315, and 2007CB513002); NSFC (No. 30730005, 3112106, No. 30988002, 31230036, 81021002 and 91213306); CAS (No. KSCX2-EW-R-09), Shanghai STC (No. 10XD1406400), and US ADA Research Award 7-06-RA-165.

Author contributions

L.Z., Y.L. and Y.Z. designed and supervised the research project and wrote the paper. S.L.,L.Y., P.H. and W.L. performed the animal studies and analysed the physiological data. C.Z. collected faecal samples, designed pyrosequencing barcodes, conducted pyrosequencing and bioinformatics analysis and wrote the paper. S.W. and G.Z. did the pyrosequencing. M.Z. and X.P. helped analyse the data.

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Additional information

Access code: Sequence information: all sequence data have been deposited in the NCBI Sequence Read Archive under accession code SRA012394.1.

Supplementary information accompanies this paper at http://www.nature.com/naturecommunications

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Competing nancial interests: The authors declare no competing nancial interests.

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How to cite this article: Zhang, C. et al. Structural modulation of gut microbiota in lifelong calorie-restricted mice. Nat. Commun. 4:2163 doi: 10.1038/ncomms3163 (2013).

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10 NATURE COMMUNICATIONS | 4:2163 | DOI: 10.1038/ncomms3163 | http://www.nature.com/naturecommunications

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