Linkages between Epithelial Microbiota and Host Transcriptome in the

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Omics Technologies Applied to Agriculture and Food

Linkages between epithelial microbiota and host transcriptome in the ileum during high grain challenges: implications for gut homeostasis in goats Jinzhen Jiao, Xiaoli Zhang, Min Wang, Chuanshe Zhou, Qiongxian Yan, and Zhi-Liang Tan J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b05591 • Publication Date (Web): 06 Dec 2018 Downloaded from http://pubs.acs.org on December 8, 2018

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Journal of Agricultural and Food Chemistry

Running title: Ileal adaptation to high-grain diets

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Linkages between epithelial microbiota and host transcriptome in the ileum during high grain

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challenges: implications for gut homeostasis in goats

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Jinzhen Jiao†,‡ , Xiaoli Zhang†,§, Min Wang†,‡, Chuanshe Zhou†,‡, Qiongxian Yan†,

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and Zhiliang Tan†,‡,*

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†

CAS Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute of

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Subtropical Agriculture, The Chinese Academy of Sciences; National Engineering Laboratory

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for Pollution Control and Waste Utilization in Livestock and Poultry Production; Hunan

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Provincial Engineering Research Center for Healthy Livestock and Poultry Production;

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Scientific Observing and Experimental Station of Animal Nutrition and Feed Science in

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South-Central, Ministry of Agriculture, Changsha, Hunan 410125, P. R. China.

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‡

Hunan Co-Innovation Center of Animal Production Safety, CICAPS, Changsha, Hunan

410128, P.R.China.

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§

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*

Graduate University of Chinese Academy of Sciences, Beijing 100049, P. R. China.

Corresponding author. Address: Institute of Subtropical Agriculture, the Chinese

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Academy of Sciences, Changsha, Hunan 410125, P.R. China; Email: [email protected]; Tel:

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+8673184619702; Fax: +8673184612685.

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1

ACS Paragon Plus Environment


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ABSTRACT

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A high-grain diet (HG) can result in ruminal subacute acidosis, which is detrimental to gut

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health and can lead to decreased productivity. This study investigated the ileal epithelial

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microbiota and its relationship with host epithelial function in goats fed an HG diet

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(concentrate: hay, 90: 10) and a Control diet (concentrate: hay, 55: 45) aiming to elucidate the

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mechanisms involved in ileal adaptation to subacute acidosis. The HG challenge increased

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ileal volatile fatty acid concentration (P = 0.030), altered the ileal epithelial microbiota by

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increasing (FDR < 0.05) relative abundances of active carbohydrate and protein degraders

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Synergistetes,

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Ruminococcaceae by 20.1-fold, 6.3-fold, 16.8-fold, 8.5-fold, 19.9-fold and 7.1-fold,

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respectively. However, the HG diet tended to reduce (FDR < 0.10) relative abundance of

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Candidatus_Arthromitus (38.8±36.1 vs. 2.1±3.1). Microbial functional potentials inferred

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using PICRUSt indicated that HG challenge elevated abundances of pathways associated

35

with metabolism of amino acid, glycan, cofactors and vitamins, whereras decreased pathways

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associated with signal transduction, xenobiotics biodegradation and metabolism. Additionally,

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in the ileal epithelium of HG goats, transcriptome analysis identified increment (FDR < 0.10)

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of candidate genes involved in metabolism of carbohydrates, lipids, proteins, vitamins and

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pro-inflammatory cytokine pathway, whist down-regulation of genes encoding antimicrobials

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and complements (FDR < 0.05). Collectively, high grain challenge shifted the structure and

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functional potentials of ileal microbial community, and affected the host responses in the

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ileum of goats toward increased metabolic activities of macronutrients and micronutrients,

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together with an increased risk of gut inflammation.

Prevotella,

Fibrobacter,

Clostridium,

2


Treponema

and

unclassified

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KEY WORDS: epithelial microbiome, bacterial community, gut function, high grain,

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transcriptome.

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INTRODUCTION

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The gut of humans and other mammals are home to microbial communities that includes

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bacteria, archaea, eukarya and viruses 1. The commensal bacteria reside in the gut is reported

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to be as high as 1014, whose microbial aggregate membership has been revised to be equal to

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the human cell number 2. This microbial consortium has been implicated in extracting energy

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from dietary carbohydrates 3, modulating immune system development 4, as well as

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production of vitamins and hormones 5. Dysbiosis in gut microbiota has been linked to

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metabolic and immune disorders such as inflammatory bowel diseases, obesity, major

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depressive disorder and even cancer 1.

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Different intestinal regions are characterized by a diverse biogeography with a distinct

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microbiota6. For instance, the small intestine is a relatively rigorous environment for

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microbial colonization due to the short transit time, high levels of oxygen and antimicrobial

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peptides

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carbohydrates in order to quickly adapt to the overall nutrient availability7. Furthermore,

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when compared to the microbiota residing in the lumen, their epithelial counterparts, which

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situated at the border of the mucosa, play vital roles in maintaining host metabolic and

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immune homeostasis 8. Despite its significance, up to now, the composition of ileal epithelial

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microbiota in ruminants and its interactions with host metabolism in still in its infancy.

6, 7

. Thereby, its resident microbiota preferably rapidly import and convert simple

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Numerous factors have been reported to continuously influence the taxonomic and

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functional composition of gut microbiome, including host genetics, diets, life style and

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antibiotics 1. In ruminants, to increase dietary energy density, inclusion of plentiful amounts

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of grains into the diets is commonly practiced in intensive feedlot management systems, such 4


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as dairy cows, beef cattle, sheep and goats

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animals to ruminal acidosis, leading to altered ruminal microbiota and microbial metabolites 9,

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reduced gut barrier function 11, translocation of endotoxin into systemic circulation 12, as well

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as reduced fiber digestion

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the hindgut and stimulate fermentation in the distal gut when high-grain diets are provided 14.

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The substrates flowed into the hindgut altered the abundance of bacterial populations in the

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lumen and mucosa 14. Increased microbial fermentation in the cecum and colon can result in

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reduced pH, increased lipopolysaccharide endotoxin (LPS) concentration, and disruption of

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epithelial tight junctions 12, 14, 15. Despite these, mechanisms underlying specific adaptation of

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ileal microbiota and molecular adaption of ileal mucosa to high-grain diets, however, are

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comparatively not well understood. Herein, we hypothesized that ileal epithelial microbiota

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of HG diets is different from that of Control diets, and that such difference could manipulate

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ileal molecular adaptation. To test the hypothesis, we applied a combination of 16S rRNA

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high-throughput sequencing and epithelial transcriptome to dissect bacterial diversity, as well

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as the overall host responses in the ileum. This work was undertaken to obtain a mechanistic

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insight of the intricate interplay among diet, host, and microbitoa in the epithelium of ileum

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during SARA (subacute acidosis) stress.

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MATERIALS AND METHODS

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Chemicals

13

. However, this feeding practice predisposes

. Concurrently, significant amount of undigested material reach

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The volatile fatty acid standards (acetate, propionate, butyrate) purchased from

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Sigma-Aldrich (Shanghai, China). Analytical grade reagents of chloroform, isopropanol and

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phosphate buffer saline, together with RNase-free water were obtained from Shanghai 5



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Macklin Biochemical Co., Ltd. (Shanghai, China). Agarose was purchased from Thermo

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Fisher Scientific Inc (Shanghai, China).

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Animals, diets and management

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All experimental procedures involving animals were approved by the Animal Care

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Committee, Institute of Subtropical Agriculture, the Chinese Academy of Sciences, Changsha,

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China, with protocol ISA-201603.

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Twelve Liuyang black goats (local breed, live weight 20.2 ± 1.5 kg) were used in this

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experiment. Goats were randomly allocated to two groups (Control vs. High grain, six

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animals per group). The experimental period lasted for 4 weeks, with the first 14 days used

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for adaptation to the diet and the last 14 days used for measurements. The control group

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(Control) was fed a concentrate: hay diet (55: 45), and the high grain group (HG) received a

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concentrate: hay diet (90:10). The concentrate ingredients (g/kg ) included 601.4 g rice, 173.9

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g soybean meal, 108.7 g wheat bran, 58.0 g fat powder, 9.1 g calcium carbonate, 19.9 g

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calcium bicarbonate, 10.9 g sodium chloride and 18.1 g mineral and vitamin premix. The

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concentrate chemical composition included DM (dry matter), 902 g/kg fresh matter; in g/kg

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of DM: CP (crude protein), 147; NDF (neutral detergent fiber), 182; ADF (acid detergent

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fiber), 131. The hay was rice straw, of which chemical composition contained 967 g/kg fresh

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matter; in g/ kg DM: CP, 34; NDF, 652; ADF, 480. The animals were housed in individual

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pens with free access to water and were fed twice daily at 08:00 h and 18: 00 h.

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Sample collection

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Immediately after the goats were slaughtered in the morning before feeding on d 28,

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ileal pH was recorded, and ieal contents were collected for subsequent VFA analysis. The 6


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ileal mucosa were scraped from the underlying tissue using a germ-free glass slide, divided

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into two proportions, immediately transferred into liquid nitrogen, and then stored at -80°C

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until microbial analysis and transcriptomic analysis.

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Volatile fatty acid analysis

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Volatile fatty acids were assayed from chromatograph peak areas using calibration with

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external standards using a gas chromatograph (7890A, Agilent, Wilmington, DE, USA)

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detailed in our previous work 16.

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Amplicon sequencing

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Total DNA was extracted from the mucosa using bead-beating method detailed in our

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previous work 17. The yield and purity of the extracted DNA were measured using NanoDrop

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ND-1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). The V3

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to V4 region of 16S rRNA gene was targeted using specific primers, 338F

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(5'-TGCTGCCTCCCGTAGGAGT-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3').

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Each individual DNA sample was amplified using a combination of the specific primers and

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an unique barcode. The PCR program was as follows: 94°C for 3 min, 30 cycles of 94°C for

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30 s, 55°C for 30 s, and 72°C for 30 s followed by 72°C for 7 min. Afterwards, amplification

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products were visualized by performing gel electrophoresis. The product quantities were

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calculated and PCR amplicons were mixed with equal molar ratios. The pooled amplicon

132

library was run in 1.5% agarose gel, and was purified with the Wizard SV Gel and PCR

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Clean-Up system (Promega, Madison, WI, USA) prior to submission for Illumina MiSeq

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sequencing.

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Microbial community analysis and function predication Raw data was filtered and analyzed using QIIME (Quantitative Insights Into Microbial 18

. The pair-end reads were overlapped into tags using FLASH

19

. Tags

138

Ecology) software

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were clustered as operational taxonomic units (OTUs) of 97% similarity using UPARSE

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Taxonomic assignment was carried out against the Greengenes database (May, 2013 release).

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Alpha diversity was performed with the alpha rarefaction workflow, and Principal coordinate

142

analysis (PCoA) was performed using bray curtis distance.

20

.

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Phylogenetic investigation of communities by reconstruction of unobserved states

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(PICRUSt) was used as a bioinformatics tool to predict the functional potentials of

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metagenomes using 16S rRNA gene data 21. The OTU table was imported into PICRUSt for

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functional gene predication by referencing to the KEGG database. Those pathways associated

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with organismal systems, human diseases and drug development were filtered out since they

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do not reflect microbial functions.

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Transcriptomic analysis

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Total RNA was extracted from mucosal samples using protocols detailed in our previous

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work 8. The RNA integrity was verified with an Agilent 2100 bioanalyzer, and RNA quantity

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was assessed with the use of Qubit® 2.0 Fluorometer. The transcriptomic library was

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constructed using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA)

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following manufacturer’s protocol. In brief, the poly A mRNA of host mucosa was purified

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using oligo(dT)-attached magnetic beads, followed by cleaved into small fragments. The first

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and second strands of cDNA were synthesized, purified and end repaired. Afterwards, the

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cDNA fragments of preferentially 150~200 bp in length were purified. PCR library 8


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enrichment was performed using NEBNext Q5 Hot Start HiFi PCR Master Mix, universal

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PCR primers and index primer. Finally, the library was purified using Agencourt AMPure XP

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Beads, and its quality was assessed. The library sequencing was carried out on an Illumina

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HiSeq X Ten system, and 150 bp paired-end reads were yielded.

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The sequences were quality filtered, and then aligned to the goat genome (Capra hircus 22

with Bowtie2

23

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ARS1) using TopHat2

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sample were obtained using RSEM

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transcript length per million fragments mapped (FPKM)25. The edgeR package 26 was used to

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identify differentially expressed (DE) genes between treatments. The DE genes were declared

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with thresholds of FDR (false discovery rate) < 0.05, and Fold change > 2 . The FDR was

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calculated on Benjamini and Hochberg multiple testing correction 27.

24

. The abundance estimates for transcripts in each

, and were calculated as fragments per kilobase

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The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were

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performed to identify which DE genes were significantly enriched in metabolic pathways

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using KOBAS

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immune-related genes was gained from ImmPort Database 29. DE genes belonged to this list

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were selected as DE genes related to immune function.

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RT-qPCR validation of gene expression profiles

28

. Significant pathway were selected at FDR < 0.05. The list of

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The expression of target genes (Supplemental Table S1) in the ileal mucosa were

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measured with the use of validated primers in goats, using protocols detailed in our previous

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work

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were expressed relative to CON using 2−ΔΔCt method.

30

. Expression of the target genes was normalized by β-actin and GADPH. The HG

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Statistical analyses

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For comparison of abundance data concerning composition and functional potentials of

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epithelial micribiota, Wilcoxon rank-sum test was performed using stats package in R

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software, with P values adjusted with FDR. All the data were presented as means ± standard

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deviations unless otherwise indicated. Statistical significance was set at FDR < 0.05 and

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trends were considered as FDR < 0.10. The correlation between gene expression and

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microbial abundance data was performed using Spearman’s rank correlation, with a

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correlation coefficient value of 0.80 and a P value of 0.05 used as the cutoff values to select

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significantly correlated pairs.

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Data availability

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The 16S rRNA amplicon sequences have been deposited in the NCBI SRA

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(https://www.ncbi.nlm.nih.gov/sra/) under accession number PRJNA408019. Raw sequence

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data for RNA-Seq are available under NCBI SRA accession number PRJNA418055.

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RESULTS

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HG diet induced SARA stress in ileum of goats

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At the end of the experiment, no significant difference (P > 0.10) on DMI (dry matter

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intake) was observed between the HG (602 ± 63 g) and Control (572 ± 23 g) groups.

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However, ruminal pH value in the HG group (5.50 ± 0.29) was significantly lower (P < 0.05)

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than that in the Control group (6.34 ± 0.09), indicating HG goats have experienced a certain

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degree of SARA in the rumen.

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Moreover, ileal pH value in the HG group was tremendously lower (P = 0.004) than that

10


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in the Control group (Table 1). Although HG challenge increased ileal TVFA concentration (P

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= 0.030), it did not affect the molar percentages of individual VFAs (acetate, propionate and

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butyrate, P > 0.10).

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HG diet altered ileal epithelial bacterial community

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The samples were randomly normalized according to the lowest number of reads

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(28,254) to avoid potential variations caused by different sequencing depths. Good's coverage

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values were all above 99%, indicating sampling depth was sufficient to represent bacterial

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diversity. Alpha diversity indices (OTU number, ACE, Chao1 and Shannon) were greater

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(FDR < 0.05, paired Wilcoxon signed rank test), whist Simpson index (FDR = 0.013) was

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lower in the ileum of HG group when compared to those in the Control group (Table 2).

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Principal coordinate analysis (PCoA) revealed dissimilarities in bacterial profiles between the

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HG and Control groups (Fig. 1).

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The dominant bacterial phyla detected were Firmicutes, Proteobacteria and

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Bacteroidetes in both groups (Table 3). Relative abundances of Bacteroidetes, Fibrobacteres,

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Spirochaetes, Synergistetes and Verrucomicrobia were greater or tended to be greater (FDR
0.1%), and the top 10

237

most abundant pathways consisted of two pathways related to environmental information

238

processing, including transporters and ABC transporters; four pathways related to genetic

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information processes, including DNA repair and recombination proteins, ribosome,

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chromosome, and ribosome biogenesis; 4 pathways related to metabolism, including purine

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metabolism, pyrimidine metabolism, peptidases, and amino acid related enzymes (Fig. 2B).

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Comparison of different pathways (Table 5) revealed that HG diets altered the functional

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potentials of ileal epithelial microbiota. Specifically, when compared to the Control group,

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HG challenge elevated or tended to elevate (FDR < 0.10) abundances of five pathways 12


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associated with amino acid metabolism, two pathways associated with biosynthesis of other

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secondary metabolites, one pathway associated with lipid metabolism, four pathways

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associated with metabolism of cofactors and vitamins, as well as one pathway associated with

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glycan biosynthesis and metabolism. By contrast, HG challenge decreased or tended to

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decrease (FDR < 0.10) abundances of one pathway associated with cell motility, one pathway

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associated with signal transduction, together with two pathways associated with xenobiotics

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biodegradation and metabolism. For carbohydrate metabolism, abundances of two pathways

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(ko00040 and ko00500) were greater (FDR < 0.05), while abundances of two pathways

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(ko00053 and ko00562) were lower (FDR < 0.05) in the HG group than those in the Control

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group.

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HG diets induced local inflammation, whist increased metabolic activities of macronutrients

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and micronutrients in the ileal epithelium

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In total, 406 differentially expressed (DE) genes were identified between the HG and

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Control groups, with 157 genes up-regulated, and 249 genes down-regulated in the HG group.

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The integration of DE genes against KEGG pathway (Supplemental Table S2) indicated that

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genes involved in primary metabolism (ie., metabolism of amino acids, carbohydrates, lipids,

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energy, cofactors and vitamins), digestive system, membrane transport, immune diseases,

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immune system were significantly enriched in the ileum.

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For immune-related genes (Table 6), expression of most of genes encoding

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antimicrobials was down-regulated, expression of most of genes related to chemokines was

265

down-regulated, whilst expression of most of genes encoding cytokine signaling was

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up-regulated in the ileum of HG group when compared to those of the Control group. 13



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Moreover, expression of genes encoding C2, C4 and CFI was down-regulated, while

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expression of gene encoding MHC1 was up-regulated in the ileum of HG group in

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comparison with those in the Control group. For genes involved in metabolism of

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macronutrients and micronutrients (Table 7), expression of most genes related to metabolism,

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digestion and absorption of carbohydrates, proteins, lipids and vitamins was up-regulated in

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the ileum of goats when high grain diet was offered.

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Validation of DE gene expression using RT-qPCR revealed that expression of immune

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related genes DO1, NOS2, PI3, CCL20 and C2 was down-regulated (P < 0.05), while CCL20

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expression was up-regulated (P < 0.05) in the ileum of the HG group when compared to those

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in the Control group (Fig. 3A). Furthermore, expression of carbohydrate related genes

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MGAM and GLUT2, protein related genes SLC7A8 and SLC15A1, lipid related gene IFA38,

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vitamin related gene RDH16 was up-regulated (P < 0.05) in the ileum of HG goats (Fig. 3B).

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The alterations in ileal epithelial microbiota induced by HG challenge were correlated with

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mucosal immune homeostasis and epithelial metabolism

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As presented in Fig.4, for mucosal immune homeostasis, expression of two

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antimicrobials (IDO1 and PI3) was negatively correlated with (r < -0.8, P < 0.05) relative

283

abundance of Fibrobacter, Treponema, unclassified Lachnospiraceae, Mogibacteriaceae and

284

Ruminococcaceae,

285

Candidatus_Arthromitus (r = 0.83, P < 0.05). Expression of CCL20 was positively correlated

286

with relative abundance of Ruminobacter, whist negatively correlated with relative

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abundance of Staphylococcus and Streptococcus. Expression of C2 was positively correlated

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with relative abundance of Vibrio, whist negatively correlated with relative abundance of

whist

positively

correlated

with

14


relative

abundance

of

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Treponema. For epithelial metabolism, expression of carbohydrate-related genes MGAM and

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GLUT2 was positively associated with (r > 0.8, P < 0.05) relative abundances of CF231,

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Fibrobacter and Ruminobacter and unclassified Ruminococcaceae, whilst negatively

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associated with Acinetobacter relative abundance. Expression of protein-related gene

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SLC7A8 was positively correlated with relative abundances of CF231, Prevotella,

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Fibrobacter, Ruminobacter and unclassified Succinivibrionaceae, whist negatively correlated

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with relative abundance of Acinetobacter. Expression of lipid-related gene SLC15A1 was

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positively correlated with relative abundances of Ruminobacter and unclassified

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Succinivibrionaceae, whist negatively correlated with relative abundance of Acinetobacter.

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Expression of vitamin-related gene RDH16 was positively associated with relative

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abundances of Clostridium and Ruminobacter, whist negatively associated with relative

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abundance of Vibrio.

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DISCUSSION

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Grain challenge induced a reduction in ruminal pH to lower than 5.5 for the HG 31

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treatment, indicating that goats have experienced a certain degree of SARA

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steers, SARA led to increased ruminal LPS concentration, and further simulated ruminal LPS

305

translocation into the bloodstream, resulting in systemic inflammatory responses 32. Moreover,

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in dairy cows, a grain-based SARA challenge also resulted in elevated cecal LPS level, and

307

initiated local inflammatory responses 12. Similarly, in the current study, as reflected by both

308

transcriptomic data and RT-qPCR validation, local inflammation occurred in the ileum of

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goats when high grain diet was offered. Firstly, expression of potential antimicrobial factors

310

of the innate immune system, indoleamine-2,3-Dioxygenase, nitric oxide synthase (NOS), 15


. In Jersey


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interferon-induced GTP-binding protein Mx, were down-regulated in the ileum of the HG

312

group

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components of innate immune responses, indicates damaged host defense during HG

314

challenge

315

which could be secreted by epithelial intestinal cells, was noted 37.

33-35

. Secondly, down-regulation of complements C2 and C4, two important

36

. Finally, increased expression of one pro-inflammatory chemokine CCL20,

316

Accumulating evidence has revealed that intestinal epithelial microorganisms exhibit

317

indispensible roles in modulating host innate immune function during dietary manipulation 6,

318

38

319

bacterial diversity in favor of Clostridium as well as Turicibacter in the caecal mucosa, and

320

caused caecal mucosal pro-inflammatory injury 14. Our results further demonstrate that high

321

grain feeding also affects ileal epithelial bacterial community. It has been recognized that the

322

surface of the small intestine is colonized by several adherent microbiota, such as segmented

323

filamentous bacteria (SFB), Helicobacter and Lactobacillaceae spp. 6. During high grain

324

challenge, relative abundances of Clostridium and Treponema in the ileum increased by 8.5

325

and 19.9 folds, respectively, while relative abundance of Candidatus_Arthromitus decreased

326

by 95%. Many Clostridium species such as C. perfringens, C. difficile and C. tetani are

327

causative agents of intestinal enteric diseases in goats, being deleterious effects on the gut

328

health 39, and Treponema carriage is typically associated with intestinal pathology 40. To the

329

contrary, segmented filamentous bacteria (SFB), such as Candidatus_Arthromitus, adhere

330

intimately to the epithelial surface, and are considered to be beneficial by modulating host

331

immune homeostasis through coordination of T cell responses in the small intestine

332

observations that relative abundance of Candidatus_Arthromitus was positively correlated

. In goats, when compared to the hay diet (0% grain), high grain feeding (65% grain) altered

16


41

. The

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with, whereas the relative abundance of Treponema was negatively associated with

334

expression of antimicrobial PI3 further confirmed their potential functions in immune

335

modulation. Thus, the abundance shifts in these three genera might contribute to ileal local

336

inflammation in the ileum of HG goats.

337

Macronutrient metabolism constitutes of another typical way in which intestinal

338

microbiota communicates with its host 3. From the host's perspective, of particular interest,

339

under high grain stress, global expression profiling analysis indicated that the ileum

340

responded with several metabolic changes related to transport and metabolism of

341

macro-nutrients. Increased levels of concentrate in the HG diets has been demonstrated to

342

stimulate ruminal microbial protein synthesis, thereafter increase the amount of starch, simple

343

sugars and amino acids entering the ileum

344

Na+-independent monosaccharide transporter GLUT2

345

peptide transporters SLC7A8 and SLC15A1

346

high grain diet up-regulated expression of lipid related genes such as AKR1C3 (Aldo-Keto

347

Reductase 1 C3) and cytochrome P450s. These genes are involved in metabolism of

348

arachidonic acid, a typical ω6 long-chain fatty acid which exhibits pro-inflammatory

349

properties 47, and the activated arachidnic acid metabolism pathway might contribute to local

350

inflammation in HG group. From the prospective of commensal microbiota, it has been

351

suggested that ileal samples showed high expression of genes involved in pathways

352

responsible for the import of simple sugars by facultative anaerobes 7. Thus, an increment in

353

the proportion of oxygen-tolerant Treponema was observed during HG challenge. Oxygen

354

consumption by these microaerophiles is considered to be benefit other oxygen sensitive

42-45

46

. As anticipated, expression of facilitated 42

, large neutral amino acids and

were elevated. Another novel finding is that

17



17

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anaerobes

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Fibrobacter, unclassified Ruminococcaceae were observed during HG challenge. These

357

genera are known as a consortium of ubiquitous polysaccharide and simple sugar-degrading

358

bacteria in the intestine

359

active protein metabolism, and most members of Synergistetes degrade amino acids instead

360

of carbohydrates 48. Wetzels, et al. (2017) have reported a trend of increasing Synergistetes in

361

ruminal epithelial bacteria in the 4-wk high-concentrate diet compared to the baseline diet,

362

and suggest that this can be explained by increasing amounts of protein in the diet. In

363

agreement with the literature, high grain diet stimulated colonization of Synergistetes in the

364

ileal epithelium in this study. Moreover, small intestinal microbiota are demonstrated to be

365

critical transducers of dietary signals that allow the host to adapt to variations in lipid

366

digestion and absorption, and a reference strain Clostridium bifermentans can increases oleic

367

acid uptake and the expression of genes involved in triglyceride synthesis

368

observed increment in Clostridium genus might be associated with the elevated lipid

369

metabolism in the host epithelium. Collectively, the metabolism of host gut epithelium and its

370

commensal epithelial microbiota is driven by macronutrient availability, and they work in

371

potentially competing and synergistic ways in response to high grain challenge.

. Consequently, increases in the proportions of Clostridium, Prevotella,

16

. Additionally, epithelial microbes are believed to be involved in

50

. Thus, the

372

A striking finding of this study is the interplay between host and microbiota in terms of

373

metabolism of micronutrients, vitamins in particular. The RNA-Seq data showed that genes

374

involved in vitamin absorption and metabolism were up-regulated in the ileum of goats

375

undergoing HG diets, relative to their Control contemporaries. This was exemplified by the

376

increment in thiamine transporter SLC19A3

46

and retinol dehydrogenase RDH16

18


51

during

Page 19 of 40


377

HG challenge. These signaling processes involved in vitamin A and B families are

378

indispensible for the maintenance of numerous biological reactions by acting as cofactors and

379

transcription factors for the enzymes involved in the metabolism of carbohydrates, nucleic

380

acids, lipids and proteins

381

vitamin-related pathway is accompanied by augmentation of the metabolic activity of

382

macronutrients. Taking into account that vitamins cannot be synthesized by the animal body,

383

but can be synthesized by commensal bacteria

384

originated from the diet and commensal bacteria. The increment trend for potential vitamin

385

producers unclassified Bifidobacteriaceae in the HG group, and positive association between

386

RDH16 expression and Clostridium abundance, at least partly confirm this notion 5.

47

. Thereby, it is not surprising to notice the enhanced

5, 51

, the vitamins in the ileum are mainly

387

Functional potentials inferred from PICRUSt revealed that the top microbial functions

388

belong to those related to nutrient metabolism, such as membrane transport and metabolism

389

of carbohydrate, amino acid, lipid, cofactors and vitamins. Similar observation has been

390

reported in human gut microbiome

391

that HG challenge altered several pathways associated with macronutrient metabolism. This

392

sheds light on the concept that nutrient metabolism by ileal epithelial microbiota might alter

393

the microenvironment through production of short-chain fatty acids and biogenic amines,

394

eventually leading to above mentioned host's metabolic or immunological changes

395

Furthermore, decreased potentials for xenobiotics biodegradation and metabolism, including

396

polycyclic aromatic hydrocarbon degradation and dioxin degradation pathways, were

397

observed during HG challenge. Xenobiotics are compounds that are foreign to a biological

398

system, with dietary bioactive compounds, food additives, drugs and toxins included, most of

52

. Intriguingly, comparative pathway analysis revealed

19


1, 9

.


53, 54

Page 20 of 40

399

which are harmful to the host and may induce inflammation

400

these microbial functional potentials during HG challenge might contribute the local

401

inflammation in the ileum. Further metagenomics analysis is required to assess the real

402

functional composition of ileal epithelial microbiota.

. Hence, the decrease in

403

In summary, in the ileum of goats, epithelial molecular adaptation to a high grain diet

404

involved up-regulation of most genes involved in metabolism of macronutrients

405

(carbohydrates, lipids and proteins) and micronutrients (vitamins), reflecting an augmentation

406

of the metabolic activity. High grain diet also resulted in local inflammation in the ileum, as

407

characterized by down-regulation of genes encoding antimicrobials and complement pathway,

408

while up-regulation of genes encoding cytokine signaling. Additionally, high grain challenge

409

shifted the ileal epithelial microbiota in favor of amino acid degrader Synergistetes, as well as

410

carbohydrate degraders Prevotella, Fibrobacter, Clostridium, Treponema and unclassified

411

Ruminococcaceae.

412

Candidatus_Arthromitus was associated with local inflammation in HG diets. Microbial

413

functional potential predication identified several pathways affected by HG challenge. Our

414

results suggest that nutrient availability affects host metabolism, together with structure and

415

functional potentials of ileal epithelial microbiota. These data provide a more complete

416

understanding of the function of the gut microbiome in the epithelium, including novel

417

linkages between specific microorganisms (composition and functional potential) and host

418

metabolic responses (nutrient metabolism and immune function). Such understanding will be

419

essential to eliciting predictable changes in the gut microbiota to improve the productivity

420

and health of animals through nutritional strategies such as diet intervention.

Decreased

proportion

of

segmented

20


filamentous

bacteria

Page 21 of 40

421


ACKNOLEGEMENTS

422

This work was supported by grants from the National Natural Science Foundation of

423

China (grants 31601967, 31730092, 31561143009), and Youth Innovation Team Project of

424

ISA, CAS (2017QNCXTD_ZCS).

425

426

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gut microbiome. Science 2006, 312, 1355-9. 53. Spanogiannopoulos, P.; Bess, E. N.; Carmody, R. N.; Turnbaugh, P. J., The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat Rev Microbiol 2016, 14, 273-87. 54. Carmody, R. N.; Turnbaugh, P. J., Host-microbial interactions in the metabolism of therapeutic and diet-derived xenobiotics. J Clin Invest 2014, 124, 4173-81.

576 577 578

25



Page 26 of 40

579 580

Table 1. pH and volatile fatty acids of ileal contents in the Control and HG goats

581

Item

Control

HG

P value

pH

7.11±0.07

6.63±0.06

0.004

TVFA (mM)

3.67±1.18

7.44±2.93

0.030

Acetate

95.3±1.4

94.2±3.0

NS

Propionate

2.57±0.77

3.68±1.33

NS

Butyrate

2.15±0.62

2.16±1.89

NS

Acetate:Propionate(mol/mol)

39.2±9.2

28.0±8.5

NS

Individual VFA molar percentage (%)

582 583

Control, the control group; HG, the high grain group.

584

NS, not significant, P > 0.10

585 586

26


Page 27 of 40


587 588

Table 2. Alpha diversity indices of ileal epithelial bacterial community

589

in the Control and HG goats

590

Alpha indices

Control

HG

FDR

OTU

562±134

785±108

0.013

ACE

666±122

871±106

0.018

Chao

667±127

893±103

0.013

Shannon

2.75±1.17

4.80±0.25

0.013

Simpson

0.31±0.24

0.03±0.02

0.013

Coverage

0.995±0.001

0.996±0.001

NS

591 592

Control, the control group; HG, the high grain group; FDR, false discovery rate adjusted P value.

593

NS, not significant, P > 0.10.

594 595

27



Page 28 of 40

596 597

Table 3. Phylum level composition of ileal epithelial bacterial community

598


599

Phylum

Control

HG

FDR

Actinobacteria

1.62±0.78

2.08±1.35

NS

Bacteroidetes

4.2±4.0

19.5±11.2

0.057

Cyanobacteria

2.21±2.02

5.55±8.56

NS

Fibrobacteres

0.12±0.14

2.02±3.11

0.045

Firmicutes

57.7±28.8

42.0±18.9

NS

Proteobacteria

22.0±25.7

12.7±8.0

NS

Spirochaetes

0.15±0.13

3.02±2.85

0.045

Synergistetes

0.15±0.16

0.53±0.30

0.099

Tenericutes

1.22±1.75

1.41±0.98

NS

Verrucomicrobia

0.13±0.10

0.57±0.29

0.045

600 601


602


603 604

28


Page 29 of 40


605 606

Table 4. Genus level composition of ileal epithelial bacterial community

607 608


Phylum Actinobacteria Bacteroidetes

Fibrobacteres Firmicutes

Proteobacteria

Spirochaetes Tenericutes 609

Family Bifidobacteriaceae Coriobacteriaceae Bacteroidaceae S24-7 Paraprevotellaceae Prevotellaceae Fibrobacteraceae Christensenellaceae Clostridiaceae Clostridiaceae Lachnospiraceae Lachnospiraceae Lachnospiraceae Lachnospiraceae Mogibacteriaceae Peptostreptococcaceae Ruminococcaceae Ruminococcaceae Staphylococcaceae Streptococcaceae Turicibacteraceae Campylobacteraceae Enterobacteriaceae Moraxellaceae Oxalobacteraceae Succinivibrionaceae Succinivibrionaceae Vibrionaceae Spirochaetaceae Mycoplasmataceae

Genus Unclassified Unclassified 5-7N15 Unclassified CF231 Prevotella Fibrobacter Unclassified Candidatus_Arthromitus Clostridium Anaerostipes Butyrivibrio Coprococcus Unclassified Unclassified Unclassified Ruminococcus Unclassified Staphylococcus Streptococcus Turicibacter Campylobacter Salmonella Acinetobacter Ralstonia Ruminobacter Unclassified Vibrio Treponema Mycoplasma

Control 0.27±0.24 0.36±0.25 0.01±0.00 0.08±0.07 0.06±0.11 1.43±1.76 0.12±0.14 0.73±0.87 38.8±36.1 0.15±0.10 0.09±0.06 0.94±0.58 0.15±0.15 0.90±0.58 0.67±0.38 0.37±0.27 2.60±2.86 1.27±0.88 0.54±0.42 3.84±9.01 0.20±0.20 0.61±1.05 0.54±0.67 2.01±2.84 2.59±1.14 0.00±0.00 0.08±0.10 13.4±22.9 0.15±0.13 1.03±1.63

HG 0.63±0.34 0.66±0.68 0.87±1.28 2.01±0.96 0.70±0.50 8.94±8.20 2.02±3.11 0.37±0.19 2.1±3.1 1.27±1.57 0.56±0.88 2.43±1.99 0.66±0.46 2.84±1.09 1.87±1.44 0.92±1.30 5.50±4.76 8.97±4.17 0.09±0.09 0.03±0.03 0.55±0.81 0.49±0.52 0.03±0.04 0.11±0.08 3.48±4.53 0.71±0.77 5.04±3.29 0.01±0.02 2.99±2.84 0.03±0.03

610


611


29


FDR 0.099 NS 0.043 0.043 0.059 0.084 0.047 NS 0.099 0.043 NS NS 0.084 0.043 0.059 NS NS 0.043 0.084 0.084 NS NS NS 0.047 NS 0.043 0.043 0.047 0.047 NS


612

Table 5. KEGG pathways that showed different abundances between ileal epithelial microbiota of Control and HG goats Level 2 Amino acid metabolism

613 614

Page 30 of 40

Level 3 Alanine, aspartate and glutamate metabolism Valine, leucine and isoleucine biosynthesis Lysine biosynthesis Histidine metabolism Phenylalanine, tyrosine and tryptophan biosynthesis Biosynthesis of other secondary metabolites Novobiocin biosynthesis Streptomycin biosynthesis Carbohydrate metabolism Pentose and glucuronate interconversions Ascorbate and aldarate metabolism Starch and sucrose metabolism Inositol phosphate metabolism Cell motility Flagellar assembly Glycan biosynthesis and metabolism Other glycan degradation Lipid metabolism Sphingolipid metabolism Metabolism of cofactors and vitamins One carbon pool by folate Vitamin B6 metabolism Nicotinate and nicotinamide metabolism Pantothenate and CoA biosynthesis Metabolism of terpenoids and polyketides Polyketide sugar unit biosynthesis Metabolism of other amino acids Cyanoamino acid metabolism Glutathione metabolism Signal transduction Phosphatidylinositol signaling system Xenobiotics biodegradation and metabolism Polycyclic aromatic hydrocarbon degradation Dioxin degradation Control, the control group; HG, the high grain group; FDR, false discovery rate adjusted P value. NS, not significant, P > 0.10. 30


Pathway ID ko00250 ko00290 ko00300 ko00340 ko00400 ko00401 ko00521 ko00040 ko00053 ko00500 ko00562 ko02040 ko00511 ko00600 ko00670 ko00750 ko00760 ko00770 ko00523 ko00460 ko00480 ko04070 ko00624 ko00621

Control 1.37±0.28 1.03±0.24 1.25±0.04 0.85±0.21 1.23±0.13 0.18±0.04 0.45±0.04 0.65±0.16 0.51±0.25 1.16±0.23 0.27±0.05 1.59±0.80 0.24±0.02 0.17±0.03 0.86±0.09 0.30±0.03 0.69±0.05 0.85±0.15 0.23±0.06 0.32±0.05 0.51±0.05 0.19±0.03 0.22±0.01 0.18±0.04

HG 1.75±0.07 1.31±0.07 1.36±0.07 1.07±0.04 1.49±0.07 0.23±0.01 0.52±0.03 0.88±0.10 0.21±0.02 1.61±0.21 0.19±0.03 0.84±0.07 0.34±0.07 0.27±0.04 1.05±0.08 0.36±0.03 0.77±0.03 1.07±0.04 0.31±0.02 0.43±0.04 0.35±0.09 0.14±0.00 0.18±0.03 0.13±0.03

FDR 0.034 0.043 0.036 0.050 0.034 0.045 0.036 0.050 0.034 0.034 0.034 0.056 0.045 0.034 0.034 0.036 0.045 0.034 0.034 0.034 0.041 0.034 0.043 0.070

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615 616 617

Gene Symbol ACTA1 IDO1 ISG15 ISG20 KNG1 MX1 MX2 NOS2 PI3 PTX3 RSAD2


Table 6. Expression profile of differentially expressed (DE) immune-related genes

Antimicrobials Antimicrobials Antimicrobials Antimicrobials Antimicrobials Antimicrobials Antimicrobials Antimicrobials Antimicrobials Antimicrobials

logFC (HG vs. Control) 1.28 -1.88 -1.37 -1.36 -1.53 -1.13 -1.74 -1.55 -1.89 -1.78

4.82E-04 4.60E-08 2.91E-04 3.63E-04 1.11E-03 5.16E-03 7.31E-07 1.51E-05 8.06E-08 8.83E-04

Antimicrobials

-1.24

1.56E-03

Gene Name

Category

Actin, alpha skeletal muscle Indoleamine 2,3-dioxygenase 1 Ubiquitin-like protein ISG15 Interferon-stimulated gene 20 kDa protein Kininogen-1 Interferon-induced GTP-binding protein Mx1 Interferon-induced GTP-binding protein Mx2 Nitric oxide synthase, inducible Elafin, peptidase inhibitor 3 Pentraxin-related protein PTX3 Radical S-adenosyl methionine domain-containing protein 2 Protein S100-A8 Protein S100-A9 C-C motif chemokine 19 C-C motif chemokine 20 C-X-C motif chemokine 6 C-X-C motif chemokine 9 C-X-C motif chemokine 10 precursor C-X-C motif chemokine 11 precursor C-X-C motif chemokine 13 L-serine dehydratase/L-threonine deaminase Fibroblast growth factor 19 Neuromedin-B Guanylin Amphiregulin Interleukin-11 Complement C2 Complement C4 Complement factor I Class I histocompatibility antigen

S100A8 Antimicrobials -2.77 S100A9 Antimicrobials -2.66 CCL19 Chemokines -1.19 CCL20 Chemokines 1.30 CXCL6 Chemokines -1.30 CXCL9 Chemokines -1.42 CXCL10 Chemokines -1.95 CXCL11 Chemokines -2.18 CXCL13 Chemokines 1.12 SBDS Chemokines -1.27 FGF19 Cytokines 2.12 NMB Cytokines 2.10 GUCA2A Cytokines 1.38 AREG Cytokines -1.76 IL11 Cytokines 1.17 C2 Complement -1.08 C4 Complement -1.20 CFI Complement -1.04 MHC1 MHC 1.34 618 619 Control, the control group; HG, the high grain group; FDR, false discovery rate adjusted P value. 620 621 622

31


FDR

1.92E-05 6.30E-05 8.43E-03 1.02E-03 1.16E-03 1.10E-04 1.18E-08 2.41E-10 7.44E-03 1.96E-03 2.16E-07 4.87E-10 8.80E-05 4.63E-06 1.63E-02 8.48E-03 2.08E-03 1.37E-02 6.94E-04


623 624 625

Gene symbol

Table 7. Expression profile of differentially expressed (DE) genes related to metabolism, digestion and absorption of carbohydrates, lipids, amino acids and vitamins

Gene name

DPP4

logFC (HG vs. Control)

FDR

Butanoate metabolism

1.23

3.36E-03

Starch and sucrose metabolism

1.92

3.66E-09


1.74

2.18E-07


1.18

1.58E-03

Carbohydrate transporter

4.56

3.12E-09

Amino acid metabolism Amino acid metabolism Arginine and proline metabolism Amino acid metabolism

1.08 1.37 -1.55 1.09

2.91E-02 1.26E-04 1.51E-05 6.56E-03

Amino acid metabolism

-1.27

1.96E-03

-1.18

4.05E-03

1.21

1.17E-03

Amino acid transporter

2.18

1.99E-11

Protein digestion and absorption Protein digestion and absorption Amino acid transporter Amino acid transporter Amino acid transporter

2.78 1.19 1.36 1.23 1.16

6.10E-18 1.48E-02 1.32E-04 9.54E-04 2.37E-03

Arachidonic acid metabolism

1.08

6.25E-03

Arachidonic acid metabolism

1.37

1.44E-04

Lipid metabolism Lipid metabolism Steroid hormone biosynthesis Lipid metabolism Steroid hormone biosynthesis

2.03 1.22 2.69 2.77 1.63

1.79E-09 1.09E-03 2.61E-05 3.78E-16 1.44E-06

Steroid hormone biosynthesis

1.94

6.64E-09

Retinol metabolism

2.03

1.79E-09

Function

Carbohydrate-related Acyl-coenzyme A synthetase ACSM5, ACSM mitochondrial MGAM Maltase-glucoamylase Ectonucleotide pyrophosphatase/phosphodiesterase ENPP3 family member 3 SI Sucrase-isomaltase, intestinal Solute carrier family 2, facilitated SLC2A2,GLUT2 glucose transporter member 2 Protein-related CNDP1 Beta-Ala-His dipeptidase AOC1,ABP1 Amiloride-sensitive amine oxidase NOS2 Nitric oxide synthase, inducible DAO, aao D-amino-acid oxidase L-serine dehydratase/L-threonine SDS,SDH,CHA1 deaminase SerA, PHGDH

Page 32 of 40

D-3-phosphoglycerate dehydrogenase

Dipeptidyl peptidase 4 Large neutral amino acids transporter SLC7A8,LAT2 small subunit 2 MEP1A Meprin A subunit alpha COL9A Collagen alpha-3(IX) chain SLC7A7 Y+L amino acid transporter 1 SLC1A1,EAAT3 Excitatory amino acid transporter 3 SLC15A1,PEPT1 Solute carrier family 15 member 1 Lipid-related CBR1 Carbonyl reductase (NADPH) 1 Docosahexaenoic acid CYP4F3 omega-hydroxylase CYP4F3-like CYP2C Cytochrome P450 2C9 AKR1C3 Dihydrodiol dehydrogenase 3 SULT1E1,STE Estrogen sulfotransferase CYP3A Cytochrome P450 3A28 CYP2D Cytochrome P450 2D14 HSD17B12, Very-long-chain 3-oxoacyl-CoA IFA38 reductase-B Vitamin-related CYP2C Cytochrome P450 2C9

Glycine, serine and threonine metabolism Protein digestion and absorption

32


Page 33 of 40


CYP3A RDH16 CYP2C CUBN LRAT SLC19A3,THTR APOA1 626

Cytochrome P450 3A28 Retinol dehydrogenase 16 Cytochrome P450 2C18 Cubilin Lecithin retinol acyltransferase Thiamine transporter 2 Apolipoprotein A-I

Retinol metabolism Retinol metabolism Retinol metabolism Vitamin digestion and absorption Vitamin digestion and absorption Vitamin transporter Vitamin digestion and absorption

2.77 1.45 1.08 1.89 1.16 1.50 1.23


627 628

33


3.78E-16 3.11E-05 9.10E-03 6.42E-08 4.01E-03 1.23E-03 8.28E-04


629

Figure legends

630

Figure 1. Principal coordinate analysis (PCoA) of ileal epithelial bacterial community in the Control

631

and HG groups.

632


633 634

Figure 2. The top 10 predicted metagenomic functions at level 2 (A) and level 3 (B) of the KEGG

635

pathways. The bars stand for the percentage of relative abundance of each predicted function.

636 637

Figure 3. Reverse transcription quantitative real-time PCR (RT-qPCR) validation of gene expression

638

profiles.

639

Different superscripts indicate statistically significant difference.

640


641

IDO1, indoleamine 2,3-dioxygenase precursor; MX2, interferon-induced GTP-binding protein Mx2;

642

NOS2, nitric oxide synthase 2; PI3, peptidase inhibitor 3; CCL20, C-C motif chemokine 20; CXCL10,

643

C-X-C motif chemokine 10 precursor; C2, complement C2; C4, complement C4; SI,

644

sucrase-isomaltase, intestinal; MGAM, maltase-glucoamylase ectonucleotide; GLUT2, facilitated

645

glucose transporter member 2; SLC7A8, large neutral amino acids transporter small subunit 2;

646

SLC7A7, Y+L amino acid transporter 1; SLC15A1, solute carrier family 15 member 1; AKR1C3,

647

dihydrodiol dehydrogenase 3; IFA38, very-long-chain 3-oxoacyl-CoA reductase-B; RDH16, retinol

648

dehydrogenase 16; SLC19A3, thiamine transporter 2.

649 650 34


Page 34 of 40

Page 35 of 40


651

Figure 4. Spearman's rank correlation coefficient between epithelial bacterial genera and expression

652

of genes encoding immune function and nutrient metabolism in the ileum (only Spearman's rank

653

correlation coefficient value > 0.80 and P < 0.05 were considered significant correlated pairs and

654

presented).

655

IDO1, indoleamine 2,3-dioxygenase precursor; PI3, peptidase inhibitor 3; CCL20, C-C motif

656

chemokine 20; C2, complement C2; MGAM, maltase-glucoamylase ectonucleotide; GLUT2,

657

facilitated glucose transporter member 2; SLC7A8, large neutral amino acids transporter small subunit

658

2; SLC15A1, solute carrier family 15 member 1; RDH16, retinol dehydrogenase 16.

659 660

35



661

662 663

Figure 1. Principal coordinate analysis (PCoA) of ileal epithelial bacterial community

664

in the Control and HG groups.

665

36


Page 36 of 40

Page 37 of 40


666

667 668

Figure 2. The top 10 predicted metagenomic functions at level 2 (A) and level 3 (B) of the KEGG

669

pathways. The bars stand for the perecentage of relative abundance of each predicted function.

670

37



671

672 673

Figure 3. Reverse transcription quantitative real-time PCR (RT-qPCR) validation of gene expression

674

profiles.

675

Different superscripts indicate statistically significant difference.

676


677

IDO1, indoleamine 2,3-dioxygenase precursor; MX2, interferon-induced GTP-binding protein Mx2;

678

NOS2, nitric oxide synthase 2; PI3, peptidase inhibitor 3; CCL20, C-C motif chemokine 20; CXCL10,

679

C-X-C motif chemokine 10 precursor; C2, complement C2; C4, complement C4; SI,

680

sucrase-isomaltase, intestinal; MGAM, maltase-glucoamylase ectonucleotide; GLUT2, facilitated

681

glucose transporter member 2; SLC7A8, large neutral amino acids transporter small subunit 2;

682

SLC7A7, Y+L amino acid transporter 1; SLC15A1, solute carrier family 15 member 1; AKR1C3,

683

dihydrodiol dehydrogenase 3; IFA38, very-long-chain 3-oxoacyl-CoA reductase-B; RDH16, retinol

684

dehydrogenase 16; SLC19A3, thiamine transporter 2.

685 38


Page 38 of 40

Page 39 of 40


686

687 688

Figure 4. Spearman's rank correlation coefficient between epithelial bacterial genera and expression

689

of genes encoding immune function and nutrient metabolism in the ileum (only Spearman's rank

690

correlation coefficient value > 0.80 and P < 0.05 were considered significant correlated pairs and

691

presented).

692

IDO1, indoleamine 2,3-dioxygenase precursor; PI3, peptidase inhibitor 3; CCL20, C-C motif

693

chemokine 20; C2, complement C2; MGAM, maltase-glucoamylase ectonucleotide; GLUT2,

694

facilitated glucose transporter member 2; SLC7A8, large neutral amino acids transporter small subunit

695

2; SLC15A1, solute carrier family 15 member 1; RDH16, retinol dehydrogenase 16.

696

39



697

698 699

Graphic abstract

40


Page 40 of 40

Linkages between Epithelial Microbiota and Host Transcriptome in the

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