Specific microbiota dynamically regulate the bidirectional gut–brain

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Omics Technologies Applied to Agriculture and Food

Specific microbiota dynamically regulate the bidirectional gut– brain axis communications in mice fed meat protein diets Yunting Xie, Guanghong Zhou, Chao Wang, Xinglian Xu, and Chunbao Li J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b05654 • Publication Date (Web): 12 Dec 2018 Downloaded from http://pubs.acs.org on December 17, 2018

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Journal of Agricultural and Food Chemistry

Specific microbiota dynamically regulate the bidirectional gut-brain axis communications in mice fed meat protein diets Yunting Xie#, Guanghong Zhou#,+, Chao Wang#, Xinglian Xu#,+, Chunbao Li#, +, §*

#Key

Laboratory of Meat Processing and Quality Control, MOE; Jiangsu Collaborative

Innovation Center of Meat Production and Processing, Quality and Safety Control; Key Laboratory of Meat Products Processing, MOA; Nanjing Agricultural University; Nanjing 210095, P.R. China +Joint

International Research Laboratory of Animal Health and Food Safety, MOE, Nanjing

Agricultural University; Nanjing 210095, P.R. China §National

Center for International Research on Animal Gut Nutrition, Nanjing Agricultural

University; Nanjing 210095, P.R. China

*Corresponding author: Dr. Chunbao Li E-mail: [email protected]; Tel/fax: 86 25 84395679

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ACS Paragon Plus Environment


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Abstract

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The purpose of this study was to characterize the dynamic changes of different protein diets to

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gut microbiota and explore the influence on communications between the gut and the brain.

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C57BL/6J mice were fed casein, soy protein and four kinds of processed meat proteins at a

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normal dose of 20% for 8 months. Bacteroidales S24-7 abundance increased from 4 to 8 months,

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whereas the abundances of six genera including Akkermansia decreased remarkably.

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Lachnospiraceae Unclassified abundance in the emulsion-type sausage protein and stewed pork

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protein groups showed an opposite change from 4 to 8 months. Twenty-eight and 48 specific

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operational taxonomy units in cecum and colon respectively involved in regulating serotonin,

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peptide YY, leptin and insulin levels. Specific microbiota was involved, directly or indirectly

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through signaling molecules, in the regulation of body metabolism, which may affect the

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communications between the gut and brain and cause different growth performances.

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Keywords: meat proteins, 16S rRNA sequencing, fecal microbiota, gut-brain axis, signaling

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molecules

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Introduction

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In recent years, the gut-brain axis has attracted great interest, and previous studies have shown

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that the gut microbiota plays an important role in the bidirectional communications between the

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gut and the brain1. The brain ensures proper maintenance and coordination of gastrointestinal

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functions. In turn, the gut microbiota has a great influence on central nervous system activities

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and host behavior, with chemical signaling of the gut-brain axis being involved. The trillions of

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microbes in the gastrointestinal tract are considered a complex and dynamic ecosystem that has

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coevolved with the host2. Many factors have a certain impact on gut microbiota, e.g., genetics,

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geographic origin, age, medication and diet3, 4, among which diet is the dominant modulator of

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the composition and function of gut microbiota5. The majority of dietary proteins are digested

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into peptides and free amino acids in the small intestine, but some proteins cannot be digested

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and absorbed, and thus they enter the large intestine for microbial fermentation6, 7. High-protein

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diets have been shown to alter the gut microbial composition of mice or rats8, 9. The temporal

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microbial changes were also observed in the feces of rats after 6 weeks of a high-protein diet

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intake10. Moreover, some studies indicated that dietary protein sources affect the gut microbial

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composition11, 12.

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Meat is known to be an important source of high-quality protein that contains all essential amino

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acids. In processed meat, the processing methods may lead to different degrees of protein

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oxidation and denaturation, which cause protein aggregation and changes in secondary

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structures13, 14. Moderate denaturation will increase the degradation of meat proteins, but various

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amino acid modifications might lead to the formation of “limit peptides”, which are not further 3



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broken down and thus result in a reduction of protein bioavailability15, 16. Our in vitro studies

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showed that protein digestibility and digested products differed among cooked pork,

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emulsion-type sausage, dry-cured pork and stewed pork17. Most studies have focused on the

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short-term effects of dietary proteins, and few data are available on the temporal variations in gut

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microbial composition. This study aimed to investigate whether a relatively long-term intake of

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proteins from processed meat affects the gut microbiota and the bidirectional communications

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between the gut and the brain.

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Materials and Methods

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Animals and diets

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All experiments were carried out in compliance with the relevant guidelines and regulations of

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the Ethical Committee of Experimental Animal Center of Nanjing Agricultural University. A

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total of 60 4-week-old male C57BL/6J mice were housed in a specific pathogen-free animal

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center (SYXK 2011-0037, Nanjing, Jiangsu, China). The temperature (20.0 ± 0.5°C)

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and relative humidity (60 ± 10%) were kept constant during the experiment, with a 12-h light

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cycle. Mice were fed a standard chow diet during a 2-week acclimation period. Then, animals

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were assigned to one of six diet groups (ten mice in each group), that is, casein (C), emulsion

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sausage protein (ESP), dry-cured pork protein (DPP), stewed pork protein (SPP), cooked pork

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protein (CPP) and soy protein (SP) groups. Diet preparations were shown in supplementary file.

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Isolated soy protein and casein were commercially obtained. Isoflavones were removed from soy

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protein in 80% methanol. Meat proteins were isolated from processed meat products by 4


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removing fat and water in methylene chloride/methanol solvent (2:1, v:v). The amino acid

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profile in protein powders were shown in tables S1. The diets were prepared according to the

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AIN-93G diet formulation18 and the diet formulation was shown in tables S2. Mice were allowed

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to access water and diets ad libitum for 8 months. Body weight and feed intake of mice were

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routinely recorded for calculating the average daily gain (ADG) and average daily feed intake

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(ADFI).

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Sample collection

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At 4 and 8 months of feeding, the feces and blood of mice were collected without fasting. The

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fecal samples were stored at −80°C for the microbial composition analysis. Blood samples were

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collected in Eppendorf tubes. The tubes stood at room temperature for 45 min and then were

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centrifuged at 3,000 g for 30 min to pellet the blood cells. Serum samples were collected and

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stored at −80°C. At 8 months, all the mice were euthanized by cervical dislocation, and the

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epididymal adipose and liver tissues were taken and weighed. Relative weights of epididymal

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adipose and liver tissues were calculated according to body weight.

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Serum biochemical indicators, signaling molecules of the gut-brain axis and short chain fatty

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acids

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Serum biochemical indicators containing triglycerides (TG), total cholesterol (T-Cho), total

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protein (T-Pro), and urea nitrogen (UN) were detected by automated chemical analyzer

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(Spotchem EZ, Nakagyo-ku, Kyoto, Japan) with kits (Arkray, Nakagyo-ku, Kyoto, Japan)

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according to the manufacturer's instructions. The signaling molecules of the gut-brain axis,

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including peptide YY (PYY), leptin and insulin, were measured using the Milliplex magnetic 5



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bead mouse metabolic hormone multiplex panel (Mmhmag-44K; EMD-Millipore, Billerica MA),

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and serotonin (5-hydroxytryptamine, 5-HT) was quantified using a serotonin ELISA kit

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(KA2518, Abnova, Taipei, Taiwan, China) according to the manufacturer’s protocols. The short

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chain fatty acids (SCFAs) were characterized by gas chromatograph (GC-2010 Plus, Shimadzu,

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Japan) as previously described19. Briefly, fecal samples (50mg) were suspended in 250 μl ddH2O

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and were centrifuged at 12,000 g for 5 min in a micro-centrifuge (Microfuge 22R, Beckman

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coulter, California). The SCFAs analysis was carried out on the supernatants (200 μl), with

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crotonic acid as an internal standard. A flame ionization detector and a capillary column

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( Agilent Technologies, HP-INNOWax, 30 m×0.25 mm×0.25 μm, California ) were used, with

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an injector/detector temperature of 180°C/180°C, a column temperature of 130°C and a gas flow

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rate of 30 ml/min.

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16S rRNA gene sequencing

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Total genomic DNA in fecal samples was extracted using the QIAamp DNA Stool Mini Kit

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(Qiagen 51504, Dusseldorf, Nordrhein-westfalen, Germany) according to the manufacturer’s

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instructions. The DNA was quantified by a Nanodrop® spectrophotometer (Nanodrop2000,

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ThermoFisher Scientific, Waltham, MA). Purified DNA was used to amplify the V4 region of

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16S rRNA, which is associated with the lowest taxonomic assignment error rate20. Polymerase

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chain reaction (PCR) was performed in triplicate. Amplicons were extracted from 2% agarose

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gels and purified using the AxyPrep DNA Gel extraction kit (Axygen Biosciences, Union City,

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CA) according to the manufacturer’s instructions and quantified using QuantiFluor™ -ST

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(Promega, Madison, WI). The pooled DNA product was used to construct Illumina Pair-End 6


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library following Illumina’s genomic DNA library preparation procedure. Then the amplicon

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library was paired-end sequenced (2 × 250) on an Illumina MiSeq platform (San Diego, CA)

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according to the standard protocols.

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Bioinformatics analysis

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Raw fastq files were trimmed and chimeric sequences were identified and removed from all

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samples to reduce noise, and operational taxonomic units (OTUs) were clustered with ≥97%

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similarity cutoff using UPARSE (version 7.1, http://drive5.com/uparse/). Then community

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richness estimator (Chao and ACE), diversity indices (Shannon and Simpson), and Good’s

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coverage were calculated21. Principal coordinate analysis (PCoA) and clustering analysis

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(http://sekhon.berkeley.edu/stats/html/hclust.html) were applied on the basis of the OTUs to

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offer an overview of the fecal microbial composition22. Multivariate analysis of variance

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(MANOVA) was conducted to further confirm the observed differences. Linear discriminant

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analysis effect size (LEfSe) analysis (http://huttenhower.sph.harvard.edu/galaxy/) was carried

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out to discover biomarkers for fecal bacteria and to distinguish between biological conditions

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among different groups23. Besides, the Spearman’s correlation coefficients were assessed to

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determine the relationships between microbiota and signaling molecules of the gut-brain axis.

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Correlation was considered significant when the absolute value of Spearman’s rank correlation

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coefficient (Spearman’s r) was >0.6 and statistically significant (P < 0.05). The R (pheatmap

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package) and Cytoscape (http://www.cytoscape.org/）were applied to visualize the relationships

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through correlation heatmap and network diagrams respectively.

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Functional prediction of the microbial genes 7



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Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt)

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(http://picrust.github.io/picrust/tutorials/genome_prediction.html#genome-prediction-tutorial)

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program based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to

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predict the functional alteration of fecal microbiota in different samples24. The OTU data

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obtained were used to generate BIOM files formatted as input for PICRUSt v1.1.09 with the

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make.biom script usable in the Mothur. OTU abundances were mapped to Greengenes OTU IDs

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as input to speculate about the functional alteration of microbiota.

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Statistical analysis

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The diet effect was evaluated by one-way ANOVA with SAS software (SAS Institute Inc., Cary,

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NC). Means were compared and the significance threshold was set at 0.05 for statistical analyses.

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Figures were constructed using the GraphPad Prism (version 5.0.3, San Diego, CA).

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More details about materials and methods can be found in the supplementary file.

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Results

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Composition and functions of gut microbiota

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Richness and diversity. Before diet change, fecal samples were selected for sequencing

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randomly from 10 mice to check the variations in gut microbiota composition among individuals.

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The PCoA showed a relatively small variation in fecal microbiota among individuals (Fig. S2).

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However, the gut microbiota in the C, SP and ESP groups was distinct from other meat protein

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diet groups at 4 months. The gut microbiota of diet groups were also well separated at 8 months,

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except the CPP and SPP groups. In addition, the gut microbiota showed a time-dependent change 8


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(Fig. 1A to C).

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The community richness estimators (Chao and ACE), and diversity indices (Shannon and

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Simpson) were calculated in order to evaluate the alpha diversity (Table 1). The protein diets

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significantly affected ACE and Chao values at both time points. The ACE and Chao values of

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the C and SPP groups were significantly lower than those of other groups at 4 months, while the

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values were the highest for the SP group at 8 months. The Shannon and Simpson values were not

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affected by diets at 4 months, but the Shannon value of the SP group was higher than those of

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other groups at 8 months. In addition, the Shannon values decreased with feeding time,

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indicating that the microbial diversity may be reduced during a long term feeding of the same

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diet.

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Composition of gut microbiota. At the phylum level, Bacteroidetes and Firmicutes were the

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predominant phyla (Fig. 2A and B). Hierarchical clustering analysis indicated that the microbial

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composition of the SP group was different from other groups at 4 months, but the DPP and CPP

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groups showed a significant difference from other groups at 8 months. Furthermore,

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Bacteroidetes abundance increased but Verrucomicrobia abundance remarkably decreased during

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feeding (Fig. 2C).

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At the genus level, Bacteroidales S24-7 was the most abundant genus at 4 months, accounting

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for 31.43% of the fecal microbial population, and followed by Rikenellaceae RC9 gut (9.75%).

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At 8 months, Bacteroidales S24-7 and Faecalibaculum were the most prevalent genera,

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accounting for 44.88% and 9.81% of the total microbial population in all diet groups,

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respectively (Fig. 2D and E). Moreover, seven species showed time-dependent changes. The 9



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abundance of Bacteroidales S24-7 increased from 4 to 8 months, whereas those of Rikenellaceae

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RC9 gut, Akkermansia, Alistipes, Clostridiales vadinBB60, Clostridium sensustricto 1 and

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Anaerotruncus were dramatically reduced (Fig. 2F). These data provide a general overview of

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gut microbiota composition at two time points.

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Linear discriminant analysis of fecal microbiota. LEfSe analysis revealed significant

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differences in 35 and 47 OTUs among six groups at 4 and 8 months, respectively. As shown in

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the biological cladograms, taxonomic distributions confirmed specific microbial taxa from

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phylum to genus associated with protein diets (Fig. 3A and B). At 4 months, Coriobacteriaceae,

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Erysipelotrichaceae, Comamonadaceae and Oxalobacteraceae were more dominant in the ESP

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group. Clostridiales vadinBB60 and Anaeroplasmataceae were more abundant in the DPP group,

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with Defluviitaleaceae in the SP and Hyphomicrobiaceae in the CPP group. However, at 8

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months, Rikenellaceae, Aerococcaceae, Peptostreptococcaceae, Desulfovibrionaceae and

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Moraxellaceae were more enriched in the SP group, with Clostridiales vadinBB60 and

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Deferribacteraceae in the ESP group and also Erysipelotrichaceae and Anaeroplasmataceae in

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the SPP group. These results strongly suggest time-specific alterations of gut microbiota that is

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mediated by protein diets.

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Few data are available on the differences of gut microbiota between the diets based on processed

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meat. Thus the top 20 genera in the four meat protein groups were further analyzed. The

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abundances of 3 and 6 dominant genera were observed to differ significantly with diets at 4 and

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8 months, respectively (Figs. 4A and B). At 4 months, Anaerotruncus, Lachnospiraceae

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NK4A136 group and Lachnospiraceae Unclassified were less abundant in the ESP and CPP 10


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groups than in the SPP group. However, at 8 months, Faecalibaculum in the SPP group, and

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Alistipes and Clostridiales vadinBB60 group_norank were more abundant in the DPP group than

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those in the CPP group. Rikenellaceae RC9 gut group, Lachnospiraceae Unclassified and

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Coriobacteriaceae UCG-002 were the most abundant in the DPP, ESP and CPP groups,

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respectively.

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Functional prediction of microbial genes. The diet has been known to affect the composition

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and structure of microbiota, and thus, the PICRUSt package was applied to predict microbial

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gene functions. Protein diets significantly changed the microbial functions related to metabolism

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(Fig. 5A and B). At 4 months, the ESP diet upregulated microbial carbohydrate metabolism

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compared with the C, SP and DPP groups. At 8 months, the SPP, CPP and DPP diets

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downregulated the microbial xenobiotics biodegradation and metabolism, while they upregulated

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the microbial carbohydrate metabolism, enzyme families, and nucleotide metabolism compared

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with the C group. Different microbes encode different enzymes. These results suggested that

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dietary proteins may mediate microbial metabolism by altering the microbial composition.

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SCFAs. SCFAs are important metabolites of the gut microbiota. Dietary proteins significantly

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affected the levels of SCFAs at both time points (Table 2). At 4 months, the levels of acetate,

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butyrate and total SCFAs in the DPP and ESP groups and the levels of isobutyrate and branch

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chain fatty acids (BCFAs) in the SPP and C groups were significantly lower than these in the

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CPP group. Propionate and isovalerate levels were the highest in the C and CPP groups,

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respectively. At 8 months, the levels of acetate, isobutyrate and BCFAs were dramatically higher

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in the CPP group than in the other groups except the SPP group. The butyrate and valerate levels 11



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were significantly higher in the CPP group than in the SPP and DPP groups. The level of total

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SCFAs was dramatically lower in the C group than in the CPP group, however, isovalerate levels

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showed the opposite pattern in the two groups. The propionate levels were significantly lower in

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the processed meat protein groups compared with the SP group. These results showed that

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dietary proteins significantly affected the metabolism of gut microbiota.

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Variations in signaling molecules of the gut-brain axis

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To further explore the effect of protein diets on bidirectional communications between the gut

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and the brain via the peripheral circulatory system, several signaling molecules, that is, leptin,

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insulin, PYY, and serotonin in serum were quantified. The protein diets significantly affected the

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concentrations of these signaling molecules. The leptin level did not differ with diets before

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changing diets but increased greatly afterwards (Fig. 6). At 4 months, the leptin level was lower

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in the SPP group than in the DPP and SP groups (P

Specific microbiota dynamically regulate the bidirectional gut–brain

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