Metabolic and Gut Microbial Characterization of Obesity-Prone Mice

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Metabolic and gut microbial characterization of obesity-prone mice under high-fat diet Yu Gu, Can Liu, Ningning Zheng, Wei Jia, Weidong Zhang, and Houkai Li J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00945 • Publication Date (Web): 22 Feb 2019 Downloaded from http://pubs.acs.org on February 23, 2019

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Journal of Proteome Research

Metabolic and gut microbial characterization of obesity-prone mice under high-fat diet Yu Gu1, Can Liu2, Ningning Zheng1, Wei Jia3,4, Weidong Zhang1,5*, Houkai Li1* 1

Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of

Traditional Chinese Medicine, Shanghai, China 2

Department of Biochemistry and Molecular Biology, Bengbu Medical College, Anhui

Province, China 3

Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine,

Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China 4 University 5

of Hawaii Cancer Center, Honolulu, Hawaii, USA

Department of Phytochemistry, College of Pharmacy, Second Military Medical

University, Shanghai, China

*Corresponding

author:

Weidong Zhang: Address: No. 1200 Cai Lun Road, Pudong New District, Shanghai, China; No.

325

Guo

He

Road,

Yangpu

District,

Shanghai,

China.

E-mail:

[email protected].

Houkai Li: Address: No. 1200 Cai Lun Road, Pudong New District, Shanghai, China. Email: [email protected].

1

ACS Paragon Plus Environment

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ABSTRACT Obesity is characterized with high heterogeneity due to genetic abnormality, energy imbalance and/or gut dysbiosis. Obesity-prone (OP) and -resistant (OR) phenotypes are frequently observed in rodents even under high-fat diet (HFD). However, the underlying mechanisms are largely unknown. Male C57BL/6J mice were fed with chow or HFD for 8 weeks. OP and OR mice were defined based on body weight gain, and an integrated serum metabolic and gut microbial profiling was performed by gas chromatography-mass spectroscopy-based metabolomic and pyrosequencing of 16S rDNA of cecum contents. Sixty differential metabolites were identified in comparisons among Con, OP and OR groups, in which 27 were OP-related. These differential metabolites are mainly involved in glycolysis, lipids and amino acids metabolism, and TCA cycle. Meanwhile, OP mice had distinct profile in gut microbiota compared to OR or Con mice, which showed reduced ratio of Firmicutes to Bacteroidetes and increased Proteobacteria. Moreover, gut microbial alteration of OP mice was correlated with the changes of the key serum metabolites. OPenriched Parasutterella from Proteobacteria phylum correlated to most of metabolites, suggesting it was essential in obesity. OP mice are distinct in metabolic and gut microbial profiles, and OP-related metabolites and bacteria are of significance for understanding obesity development.

KEYWORDS Metabolic profiling; Gut microbiota; Obesity-prone; 16S rDNA; Obesity-resistant

2


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1 INTRODUCTION Obesity is the critical risk factor for metabolic diseases including type 2 diabetes, cardiovascular disease, metabolic syndrome (encompassing hypertension, dyslipidemia, and insulin resistance), and non-alcoholic fatty liver disease

1, 2.

The etiology of obesity

involves complicated interactions between genetic and environmental factors such as dietary nutrients and physical activity 3. In addition, numerous investigations have indicated that gut dysbiosis is intricately involved in the development of obesity

4, 5

and

obesity-related metabolic diseases 6, 7. Meanwhile, obesity is characterized with substantial heterogeneity either in humans or rodent models 8, 9. For instance, many maternal twins even with highly identical genetics exhibit significant differences in body weight and variable susceptibility to metabolic diseases

9-11.

Similarly, the extent of body weight gain of rodents with same genetic

background usually vary greatly when fed with identical high-fat diet (HFD). Animals that are sensitive to HFD-induced obesity are defined as obesity-prone (OP) phenotype, while those insensitive ones are defined as obesity-resistant (OR)

12.

Substantial evidence has

revealed that the development of OP or OR phenotype is related with the difference in neural regulatory pathways 13-15. On the other hand, our previous investigation has observed significant differences in both hepatic gene expression and metabolic profiles of liver, serum, and urine samples between OP and OR rats by using combined transcriptomics and metabolomics. We identify some gut microbiota-derived metabolites such as phydroxyphenylacetic acid that are of significant difference between OP and OR phenotypes, suggesting that the development of OP phenotype might be associated with alteration of gut microbiota 14. Consistently, a recent study shows that bacteria of Bifidobacterium genus 3



from Actinobacteria phylum were significantly enriched in OP mice compared to that of OR ones

16.

Therefore, OP phenotype is associated with either the metabolic or gut

microbial alteration, however, little is known about the crosstalk between host metabolism and gut microbiota in regard to the differences between OP and OR phenotypes. In our current study, both OP and OR mice were obtained based on the extent of body weight gain among a group of mice under HFD feeding. Then, the serum metabolic and gut microbial profiling was performed by using GC/MS-based untargeted metabolomics and 16S rDNA sequencing on the bacterial genomic DNA from cecum contents, respectively. Our results showed distinct separation in either serum metabolic or gut microbial profile between OP and OR mice. Moreover, OP-related differential metabolites were identified which mainly included fatty acids, amino acids and their metabolites, formic acid, fructose, phosphoethanolamine, glycolic acid and etc, as well as the involved metabolic pathways. Meanwhile, gut microbial profiling revealed that the compositional alteration of gut bacteria was not only correlated with the body weight phenotypes, but also showed high correlation with some differential metabolites which highlights the crosstalk between host metabolism and gut microbiota in mediating the different phenotypes.

2 EXPERIMENTAL PROCEDURES 2.1 Animal treatment and sample collection Six-week-old male C57BL/6J mice (n=22) were purchased from Shanghai Laboratory Animal (Shanghai, China), and housed in a regulated barrier system facility at 23°C–24°C with 60%±10% relative humidity and a 12-h light/dark cycle. All of the mice were fed a standard chow diet during an initial 1-week acclimation period. Then, 6 mice were 4


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randomly selected for control group with continued standard chow diet (average initial body weight 21.5 ± 0.5 g), while the rest 16 mice were changed into HFD feeding (average initial body weight 21.0 ± 1.0 g). The detailed composition and percentage of both standard and HFD diet (Nantong Trophy Experimental Animal Feed Co., Ltd) was shown in Supplemental Table S1. After 8 weeks feeding, mice in the HFD group were ranked base on the extent of their body weight gain. Six mice with the highest or lowest body weight gain (about 1/3 in total) were defined as OP or OR mice respectively according to previous publication

12.

Finally, mice were sacrificed after anesthesia with 10% chloral hydrate

intraperitoneally for collecting liver and white adipose tissue, blood and cecum contents after an overnight fasting (16 hours). Serum samples were obtained by centrifuging blood with 4000 rpm at 4°C. All the animal experiments were admitted by Animal Experiment of Shanghai University of Traditional Chinese Medicine (Shanghai, China), and the protocol was approved by the institutional Animal Ethics Committee.

2.2 Lipids measurement Liver samples were subjected to isopropanol extraction before triglycerides (TG) and total cholesterol (TC) measurement according to the previous method 17. 50-mg liver tissue was homogenized in 1-mL chloroform: methanol (2: 1) before agitation at 200 rpm for 2 hours at room temperature. Then 20% volume of 0.9% NaCl (w/v) was added and subsequently centrifugated at 2000 rpm for 10 min. The lower solvent layer was mixed with 1% TritonX100 before being dried under N2. The sample was dissolved with 100-μL isopropanol. Finally, the liver TG and TC contents were measured with the TG or TC assay kits (jiancheng Bioengineer Institute, Nanjing, China). Consistently, the serum TG and TC 5



contents were measured with the same method.

2.3 Serum sample preparation for metabolic profiling Serum metabolic profiling was performed by using GC/MS-based metabolomic approach as previously described. Briefly, 20-μL aliquot of each serum sample was added with 10-μL internal standard (50 μg/mL phenylalanine-13C9-15N and dulcitol). Then, 80μL cold methanol-chloroform (3:1, v/v) was added into each sample, mixed for 60 s, and centrifuged at 14000 g for 15 min at 4 °C. 80-μL aliquot of supernatant was collected and transferred to the GC vial for dryness under N2. The dried samples were dissolved in 30 μL of methoxylamine hydrochloride in pyridine (20 mg/mL), and incubated at 70 °C for 60 min. Samples were derivatized by adding 30-μL BSTFA (1% TMCS) and incubating at 70 °C for 60 min. Quality control (QC) sample was prepared by mixing the same volume of serum from each sample, and prepared in parallel.

2.4 GC/MS analysis The derivatized samples were analyzed in an Agilent 7890A gas chromatography system coupled to an Agilent 5975C MSD system (Agilent Technologies, Santa Clara, CA, USA) and an Agilent J&W HP-5MS fused-silica capillary column (30 m × 0.25 mm × 0.25 μm). The analysis was performed using helium as the carrier gas (constant flow rate = 1 mL/min). An injection volume was 1 μL. Solvent delay time was 5.5 min, and collision energy was 70 eV. The initial oven was heated to 70 °C for 2 min, ramped to 160 °C at 6 °C/min, to 240 °C at 10 °C/min, and then increased at a rate of 20 °C/min to a final temperature of 300 °C for 6 min. The injector, transfer line, and ion source (electron impact) 6


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were heated to 250 °C, 260 °C, and 230 °C, respectively. Mass data were acquired in fullscan mode (m/z 50−600). All the samples were analyzed in a random sequence.

2.5 Data processing and statistical analysis The raw GC/MS data were preprocessed according to the described method including peak picking, alignment, deconvolution, and further processing 18. The detailed structure identification of metabolites was performed with the following method. AMDIS software was used to deconvolute mass spectra, and the purified mass spectra were automatically matched with an in-housed standard library including retention time and mass spectra, Golm Metabolome Database, and Agilent Fiehn GC/MS Metabolomics RTL Library. Then the normalized identified metabolite data were imported into the SIMCA software, version 14.1 (Umetrics, Umeå, Sweden), which was used to perform the principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). The metabolites were filtered by variable influence on projection (VIP) selection using values obtained from the PLS-DA model and the filtering conditions VIP > 1 and p < 0.01, with p-values determined using two-tailed Student’s t-tests.

2.6 Bacterial genomic DNA extraction of cecum contents To perform 16S rDNA sequencing, we first carefully pooled the cecum contents from the 6 mice of each group (Con, OP and OR). Then, the pooled samples were divided into 3 replicates for bacterial genomic DNA extraction using the QIAamp fast DNA Stool Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol 19. Briefly, 200 mg of cecum contents were added with 1 mL inhibit EX buffer and vibrated. After heating 7



the suspension at 70 ℃

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and vortex, sample was centrifuged for 1 min. Then the

supernatant was added into the 1.5 mL microcentrifuge tube with Proteinase K, and mixed thoroughly with 200 μL buffer AL before incubation at 70 ℃ for 10 min. 200 μL of ethanol was added to lysate and mix. 600 μL of lysate was applied to QIAmap spin column and then centrifuged. QIAmap spin column was placed in a new 2 mL collection rube. Then 500 μL butter AW1 and AW2 was added into spin column, respectively, with repeating above operation. Subsequently, 200 μL buffer ATE was pipetted onto QIAamp membrane to obtain purified DNA.

2.7 16S rDNA pyrosequencing Purified bacterial genomic DNA was used as a template for the amplification and DNA sequencing of the V3 region of 16S rRNA gene. The used primers were 338F (5’CCTACGGGAGGCAGCAG-3’) and 518R (5’- ATTACCGCGGCTGCTGG-3’). The PCR amplification with bar-coded primers, pyrosequencing of PCR amplicons, and quality control of raw data were performed as described previously 20.

2.8 Bioinformatics and multivariate statistical analysis The acquired valid and representative sequences of each sample were compared Greengenes database using the nearest alignment space termination algorithm

21,

and

constructed a neighbor-joining tree with ARB 22. Operational taxonomic units (OTUs) were delineated at 97% similarity level with Mothur software. The representative sequence of each OTU was selected with the most abundance and subjected to RDP classifier for taxonomical assignment with a bootstrap cutoff of 60% 8


23, 24.

Weighted Fast UniFrac

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principal coordinate analysis (PCoA), was performed with the phylogenetic tree constructed by each OTU generated with QIIME 25. The comparisons of bacteria relative abundance at phylum level between groups were performed with student’s t test after the normality analysis of all data. The top 30 genera were analyzed in R 3.2.4 with pheatmap packages.

2.9 Correlation analysis between bacterial taxonomy and differential metabolites Correlation analysis between bacterial taxonomy at genus level and differential metabolites was performed with Spearman’s correlation coefficient (SCC). The significant correlation between metabolites and microbial genera was performed with the criteria of both p < 0.05 and SCC > 0.7 and < -0.7, which was visualized in a cross-correlation maps including positive (red line) or negative (blue line) relationship.

3 RESULTS 3.1 Characterization of OP phenotype in HFD feeding mice After 8-week HFD or chow diet feeding, the phenotypic characteristics, body weight gain, organ weight and serum biochemistry were examined and shown in Figure 1. First, we observed that mice in HFD group showed different responses towards HFD feeding, and therefore OP and OR mice were determined on the basis of their body weight gain (Fig. 1A). Consistently, OP mice showed heavier WAT mass and WAT index compared to OR or Con mice (Fig. 1B-C), as well as the liver weight (Fig. 1D) and liver TG (Fig. 1F). The 5 (Fig. 1G). In addition, the levels of both serum TG and TC were significantly higher in OP mice compared to Con group, but of no difference between OP and OR mice (Fig. 1H-I), 9



as well as the comparable levels of liver TC among groups (Fig. 1G). Accordingly, the OP mice were of different characters compared to either OR or Con mice.

Fig. 1. Phenotypes of the control (Con, fed standard chow diet), obesity-prone (OP, fed high-fat diet), and obesity-resistant (OR, fed high-fat diet) mice (n = 6 per group). (A) Body weight gain. (B) WAT (white adipose tissue) weight. (C) WAT index (the weight of the WAT accounts for the body weight). (D) Liver weight. (E) Liver index (the weight of the liver accounts for the body weight). (F) Liver TG (G) Liver TC. (H) Serum TG. (I) Serum TC. The p value was calculated by two-tailed Student’s t-test. All data are presented as mean ± SEM.

3.2 Distinct metabolic changes in OP mice To elucidate the metabolic character of OP mice, the GC/MS-based untargeted metabolic profiling was performed on serum samples. First, 182 endogenous metabolites 10


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were determined in serum samples. Then, an unsupervised PCA was performed to visualize the general differences among samples. The PCA scatter plot showed that samples from the Con group were distinctly separated from the rest, whereas samples of OP mice also showed separation trend with OR mice (Fig. 2A). The supervised PLS-DA model showed clear separation among groups (Fig. 2B). Meanwhile, models of PCA and PLS-DA between either OP or OR and Con groups were conducted respectively (Supplemental Figure S1A-F). The evaluation data for the quality of PCA and PLS-DA models were summarized in Supplemental Table S2.

Fig. 2. Analyses of serum metabolites from Con, OR and OP group based on GC/MS (n = 6 per group). The SIMCA-P-derived (A) PCA and (B) PLS-DA plots among the Con, OR, and OP mice. Differential metabolites based on PLS-DA results using VIP>1 and P1 at multivariate statistical analysis or p1.2.

3.7 Correlation analysis between obesity-related phenotypes, bacterial genera and differential metabolites Given the fact that the development of obesity phenotype is associated with both the imbalance of metabolism and gut dysbiosis 36, we therefore are interested in correlating the 27 differential metabolites (clusters of I, III and V) with the 14 bacterial genera that were obviously altered in OP group compared to either Con or OR groups by spearman’s correlation analysis. The differential metabolites in cluster I (12 metabolites), and III (6 metabolites) were acquired by comparing OP with either Con or OR mice, while cluster V (9 metabolites) represented the commonly altered differential metabolites between OP and Con/OR groups. The correlation analysis showed that genera, Anaerotruncus, Butyricimonas, Parasutterella, Butyricicoccus, Rikenella, Xylanibacter, were positively correlated the differential metabolites from both cluster I and V, as well as the obesityrelated

phenotypes.

On

the

contrary,

genera,

19


Alistipes,

Oscillibacter,


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Lachnospiracea_incertae_sedis, Bacteroides and Wolinella, were negatively correlated with the metabolites from both cluster I and V, as well as the obesity-related phenotypes. The 14 selected genera showed minor to moderate correlation with the 6 differential metabolites in cluster III, except for the negative correlation between Alistipes with the 6 metabolites (Supplemental Figure S3). Then, a further cross-correlation map was conducted with the criteria of SCC > 0.7 or < -0.7 and p < 0.05, in which 19 differential metabolites and 11 bacterial genera were included (Fig. 5). Parasutterella genus (belonging to Proteobacteria phylum) were positively correlated with metabolites such as 2-hydroxyisovaleric acid, linoleic acid, glycerol-2-phosphate, glyceryl monooleate, uridine, glutamic acid, stearic acid, adenine, methylphosphate, cis-5,8,11-eicosatrienoic acid, whereas Wolinella genus (belonging to Proteobacteria phylum) were negatively correlated with metabolites such as 2hydroxyisovaleric acid, pantothenic acid, cis-5,8,11-eicosatrienoic acid, and DHA, suggesting that bacteria within these two types of genera might perform different roles in OP mice. Six genera within Bacteroidetes phylum were included in this correlation analysis such

as

Bacteroides,

Butyricimonas,

Alistipes,

Rikenella,

Xylanibacter

and

Lachnospiracea_incertae_sedis. Interestingly, only a small number of metabolites were correlated with these 6 genera in Bacteroidetes phylum. For example, Bacterroides were negatively 2-hydroxyisovaleric acid. Butyricimonas genus were positively correlated with alanine, but negatively correlated with hypotaurine. Lachnospiracea_incertae_sedis genus were negatively correlated with glutamic acid. In addition, Alistipes genus was negatively correlated with glycolic acid and 2-hydroxyadipic acid. Besides, 3 genera within Firmicutes were included such as Oscillibacter, Butyricicoccus and Anaerotruncus. 20


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Oscillibacter genus was negatively correlated with 4 metabolites including 4hydroxylbutyric acid, pantothenic acid, methylphosphate, and cis-5,8,11-eicosatrienoic acid. Butyricicoccus genus was positively correlated with 5 metabolites including palmitic acid, linoleic acid, oleic acid, stearic acid and glyceryl monooleate. Finally, only DHA was showed positive correlation with bacteria in Anaerotruncus genus.

Fig. 5. Spearman correlations between differential metabolites and genera in OP mice. Correlation linkage chart with positive correlations indicated by red lines and negative correlations indicated by blue lines. Spearman’s correlation coefficient less than -0.7 or more than 0.7 with p < 0.05 was selected. The selected genera were shown in cluster with its phylum and family information.

4 DISCUSSION In our current study, we first produced OP and OR phenotype in male C57BL/6J mice under 8-weeks HFD feeding, which is consistent with previous report 37. The gut microbial 21



and metabolic profiling revealed that OP mice were distinguished from either OR or Con mice. The 27 differential metabolites between OP and Con/OR mice were mainly involved in pathways of FA metabolism, TCA cycle, urea cycle and oxidative stress. Moreover, the correlation analysis showed that 11 genera were positively or negatively correlated with these OP-related differential metabolites, as well as obesity phenotypes including body weight gain, WAT weight and index, liver weight and TG levels. Obesity results from the interaction between genetic and environmental factors 38, and numerous studies have revealed the critical roles of energy imbalance 9, 39, as well as neural dysregulation in determining obesity formation

15, 40.

Moreover, recent advances in gut

microbiota have highlighted that gut dysbiosis is the common basis for various diseases such as obesity and obese-related metabolic diseases3, 16. Our previous investigation shows that OP and OR rats have significantly different metabolism under HFD feeding, in which some gut microbial-related metabolites are detected 14. In this study, we demonstrated that OP mice had very different metabolic profile compared to that of either Con or OR mice. Although the body weight gain was not statistically significant between OP and Con groups, OP mice showed significant increases in WAT weight (index), serum and liver TG, and serum TC, as well as higher extent of body weight gain than Con mice. Therefore, the OPrelated metabolic changes between OP and Con groups are mainly associated with these obese phenotypes, in addition to probable dietary impact. Subsequent analysis identified 21 differential metabolites between OP and OR mice, in which 6 of them were commonly altered between Con and OR mice and were excluded for further analysis. There were 6 differential metabolites in cluster III including arachidonic acid, 2-hydroxyadipic acid, hypotaurine, erithronic acid, fructose and glycolic acid, which 22


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are of higher concentration in OP mice than OR. Since OP and OR mice were fed with identical HFD, the acquired differential metabolites between OP and OR mice could be attributed to their metabolic differences, instead of dietary impact. Meanwhile, 9 differential metabolites stood out that were significantly up-regulated in OP mice compared to either Con or OR mice including DHA, cis-5,8,11-eicosatrienoic acid, methylphosphate, adenine, stearic acid, phosphoethanolamine, pantothenic acid, glutamic acid, and fumaric acid. Previous studies have shown that n-6 PUFA can stimulate adipogenesis by upregulating peroxisome proliferator-activated receptor γ (PPARγ) activity and sterol regulatory element-binding protein-1c (SREBP-1c) expression resulting to lipogenesis and TG storage in adipose tissue 41, 42, whereas opposite effects are observed by n-3 PUFA such as ARA 42, 43. Therefore, our current results suggested disordered fatty acid metabolism in OP mice. In addition to the OP-related metabolic characters, our results showed that OP mice had distinct feature in gut microbiota compared to either Con or OR mice. Previously, a large number of publications have confirmed that gut dysbiosis plays critical roles in obesity development either in humans or animal models

36, 44.

Although the composition of gut

microbiota is easily affected by diets, our current results revealed that the compositional alteration of gut microbiota in OP mice were diets-independent because of the observed significant differences in gut microbiota between OP and OR/Con mice. The decreased ratio of Firmicutes to Bacteroidetes is predominantly observed in obese subjects 35. In our current study, we found that OP mice showed decreased ratio of Firmicutes to Bacteroidetes compared to either OR or Con mice. Nevertheless, contradictory results are also reported in other studies

45, 46.

Proteobacteria is a major phylum of gram-negative 23



bacteria, which consists a large number of pathogens with an outer membrane composed of lipopolysaccharides (LPS). Obesity is also characterized as endoxemia because of the release of LPS into circulation

47.

Accordingly, bacteria in Proteobacteria phylum are

usually enriched in obesity and metabolic diseases 20. Our current results showed that the relative abundance of Proteobacteria phylum mildly increased in OP mice. Moreover, bacteria of Parasutterella genus were obviously enriched in OP mice compared to both OR and Con group, as well as some unclassified genera within Proteobacteria phylum. Consistently, relative higher abundance of Parasutterella has also been reported in OP rats 48.

Although the metabolic alteration and gut microbial contribution to obesity formation have been well-investigated, the correlation between the altered metabolism and gut dysbiosis in obese phenotype has been rarely addressed, let alone between OP and OR phenotypes. In our current study, the correlation analysis was evaluated between the obeserelated phenotypes, 14 genera of OP-related bacteria and 27 differential metabolites from cluster I, III and V. Interestingly, the relative abundance of Alistipes genus from Bacteroidetes phylum was negatively correlated with most of the metabolites and obeserelated phenotypes. Since Alistipes genus belongs to acetic acid-producing genera 49, and decreased levels of SCFAs are frequently observed in obese subjects 50, our current results suggested that the formation of OP phenotype might be associated with the abundance of SCFAs-producing bacteria. Butyricicoccus genus are SCFAs-producing bacteria, which are usually reduced in obese-related disorders and enriched by various interventions against obesity

51.

In our current study, we found that the relative abundance of Butyricicoccus

genus was moderately enriched in OP mice. Moreover, the positively correlated 24


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metabolites with Butyricicoccus genus were mainly middle chain fatty acids such as palmitic acid, linoleic acid, oleic acid, and stearic acid. These types of fatty acids usually play vicious roles such as aggravating insulin resistance and inflammation in metabolic disorders, excluding oleic acid

41, 52-54.

Therefore, our current results suggested that the

physiological functions of bacteria in Butyricicoccus genus were much more complicated than expected. Meanwhile, the relative abundance of Butyricimonas genus was reduced in OP mice compared to OR mice. Butyricimonas genus are also butyric acid-producing bacteria belong to Bacteroidetes phylum 55, which are usually reduced in obesity and obeserelated metabolic disorders

56,

and enriched by various anti-obesity treatments such as

pomegranate extract accompanied with body weight reduction and gut microbiome recovery

57.

Interestingly, the increased Butyricimonas genus were positively correlated

with alanine, but negatively with hypotaurine in OP mice. Hypotaurine is an intermediate in the biosynthesis of taurine from cysteine. Recent study reveals that infants born to obese mothers have enriched microbiome associated with a number of metabolic pathways such as taurine and hypotaurine pathway, suggesting that the susceptibility to obesity formation of these infants may be associated with the altered gut microbiome and taurine metabolism pathway 31. In addition, hypotaurine is one of the endogenous neurotransmitters of glycine receptor, and inhibition of glycine receptor-1 in the dorsal vagal complex can reduce food intake and body weight gain in rats

58.

Our current results, on one hand, consistently

revealed that increased hypotaurine might be involved in OP formation. On the other hand, the negative correlation between hypotaurine and butyric acid-producing bacteria in Butyricimonas genus suggested that bacteria in Butyricimonas genus might have profound ways in affecting host metabolism, in addition to butyric acid production. 25



Proteobacteria phylum are gram-negative and mainly pathogens. We observed reduced abundance of Wolinella and enriched Parasutterella genera in OP mice. The role of bacteria of Wolinella genus in obesity is poorly known yet. Nevertheless, it is noticeable that the relative abundance of Parasutterella genus was significantly enriched in OP mice, which was also positively correlated with a variety of metabolites and obesity-related phenotypes. The increased abundance of Parasutterella genus has been observed in obesity-related conditions. For example, Li R et al investigated the antibiotic exposureinduced body weight gain and gut microbiome alteration. Their results indicate that administration of florfenicol or azithromycin lead to obviously increased adipogenesis and body weight gain, which are accompanied with increases in abundance of Alistipes, Desulfovibrio, Parasutterella and Rikenella genera

59.

Parasutterella genus has been observed in OP rats

Meanwhile, higher abundance of

48.

Accordingly, these observations

suggest that enriched bacteria in Parasutterella genus might play important role in obesity formation. In addition, the abundance of Parasutterella genus was positively correlated with 10 metabolites in our current study, and most of the metabolites are associated with obesity phenotypes. For example, glutamic acid takes part in obesity formation and even toxic to body 27, 28. Moreover, recent study reveals that higher levels of serum glutamic acid in obese subjects, which is due to reduced abundance of B. thetaiotaomicron, a glutamatefermenting commensal. Interestingly, B. thetaiotaomicron administration not only decreases plasma glutamate concentration, but also alleviates diet-induced obesity in mice 60.

Accordingly, the positive correlation between serum glutamate and Parasutterella

genus in our current report suggested that bacteria from Parasutterella genus might be also involved in elevating serum levels of glutamate, which is worthy further investigation. 26


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Altogether, our current report highlights that OP mice are characterized with distinct serum metabolic and gut microbial profiles. The altered metabolic pathways are mainly involved glycolysis, lipid metabolism, amino acid metabolism, and TCA cycle. Moreover, gut microbial alteration of OP mice is correlated with the changes of serum metabolites which provides novel information in regard to the gut microbial contribution to obesity phenotypes. Our current study provides novel evidence of some OP-related bacteria genera and potential biomarkers for OP phenotype. Future studies are warranted to reveal the underlying mechanisms that links between metabolic and gut microbial profiles.

ASSOCIATED CONTENT Supporting Information Figure S1. The SIMCA-P-derived PCA and PLS-DA plots for the GC/MS spectral analysis of serum samples (n = 6 per group). Figure S2. Alpha diversity of the gut microbiota in the among Con, OR and OP groups (n = 3 per group). Figure S3. Association map of the threetiered analysis integrating the phenotype, genera and differential metabolites. Table S1: The different substances and percentage between standard diet and HFD diet. Table S2: Summary of the GC/MS data sets used in PCA and PLS-DA modeling.

AUTHOR INFORMATION

Corresponding Author Weidong Zhang: Address: No. 1200 Cai Lun Road, Pudong New District, Shanghai, China; No. 325 Guo He Road, Yangpu District, Shanghai, China. E-mail: [email protected]. Houkai Li: Address: No. 1200 Cai Lun Road, Pudong New District, Shanghai, China. 27



E-mail: [email protected]. ORCID Houkai Li: 0000-0003-2846-7895 Conflict of Interest The authors declare no competing financial interest. Author Contributions Yu Gu was responsible for data analysis, figures and manuscript writing. Ningning Zheng and Can Liu conducted animal experiment. Wei Jia helped in project design. Weidong Zhang and Houkai Li designed the study and manuscript revision.

ACKNOWLEDGMENTS This work was funded by the National Natural Science Foundation of China (No. 81873059 & 81673662), National Key Research and Development Program of China (No. 2017YFC1700200), the Program for Professor of Special Appointment (Eastern Scholar) and Shuguang Scholar (16SG36) at Shanghai Institutions of Higher Learning from Shanghai Municipal Education Commission.

ABBREVIATIONS OP: obesity-prone; OR: obesity-resistant; HFD: high-fat diet; WAT: adipose tissue; TG: triglycerides; TC: total cholesterol; SCC: Spearman’s correlation coefficient; FAs: fatty acids; SFAs: long chain fatty acids; PUFAs: polyunsaturated fatty acids; DHA: cis4,7,10,13,16,19-docosahexaenoic acid; ARA: arachidonic acid; LA: linoleic acid; MUFA: monounsaturated fatty acid; GSH: glutathione; ROS: reactive oxygen species; LPS: 28


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lipopolysaccharide.

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Metabolic and Gut Microbial Characterization of Obesity-Prone Mice

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