However, some of the observed dietary effects have not yet been associated with specific intestinal microbes or with specific metabolites. For instance, a maternal high-fat diet negatively impacts the social behavior of offspring, resulting in a deficiency in synaptic plasticity in the ventral tegmental area and in oxytocin production, but the administration of Lactobacillus reuteri restores oxytocin levels, synaptic plasticity, and healthy social behaviors in mice (Buffington et al., 2016). It has also been recognized that a Malawian diet may induce kwashiorkor, an enigmatic form of severe acute malnutrition. In a study involving 317 Malawian twin pairs, researchers found that an altered gut microbiota constitution in response to the Malawian diet significantly contributed to the development of kwashiorkor, although the mechanism involved remains unknown (Smith et al., 2013). Notably, oligosaccharides were less abundant in the milk from mothers of severely stunted infants, and the administration of sialylated milk oligosaccharides reversed infant undernutrition in a microbiota-dependent manner (Smith et al., 2013). Such results were also confirmed in piglets that received the same diet as the human infants (Charbonneau et al., 2016), suggesting that microbiota associated-metabolites of oligosaccharides may be a crucial factor in such processes. In young children, a negative association between dietary fiber and plasma insulin levels has been observed only in those whose gut microbiota showed a high abundance of Bacteroides and Prevotella and not in those whose gut microbiota exhibited a higher proportion of Bifidobacterium (Zhong et al., 2019). This suggests a potential function for Bacteroides and Prevotella in elevating insulin levels. Indeed, convincing epidemiological studies have indicated that specific dietary components may be crucial for the pathogenesis of some diseases such as asthma and allergies (Eder et al., 2006; Graham, 2006). For example, a carnitine-rich diet induces the symptoms of atherosclerosis in a gut microbiota-dependent manner in humans and mice (Koeth et al., 2013). Specifically, the gut microbiota in humans and mice mediates the metabolism of dietary choline and phosphatidylcholine to produce trimethylamine, which is further transformed into trimethylamine-N-oxide by hepatic flavin monooxygenases, thereby promoting the development of atherosclerosis. However, the specific microbial taxa contributing to this process require further investigation.

#### CONCLUSION AND FUTURE PERSPECTIVE

The beneficial effect of HMs and dietary therapy in several refractory diseases is generally appreciated, but the underlying mechanisms involved remain obscure. However, their interaction with the host microbiota seems to be a critical factor in such processes. Indeed, a growing number of studies indicate that the commensal microbiota plays a crucial role in maintaining host health and that the constitution of the intestinal microbiota exhibits large inter-individual differences. Moreover, most components in HMs and dietary interventions can modulate the constitution of the microbiota, which may disrupt or maintain homeostasis in the host. Collectively, it is not surprising that the gut microbiota, and especially microbiota-associated metabolites, may be a crucial mediator linking HMs or dietary therapy and the physiological status of the host. Therefore, it is important to consider the effects of biotransformation by commensal microbiota when designing herbal formula dietary therapy to achieve optimal success in treating diseases, particularly in the case of precision medicine. It is also essential to determine the optimal timing of administrating HMs and specific diets, in particular given that the composition of the gut microbiota exhibits diurnal variation. Indeed, microbiota-associated metabolites have several attractive properties, including known chemical structures and long shelf lives (Aguilar-Toalá et al., 2018). In particular, these metabolites are able to mimic the health effects mediated by probiotics while avoiding the administration of live bacteria, which can produce harmful reactions such as the local inflammatory response induced by the administration of Salmonella (Tsilingiri et al., 2012). However, the importance of postbiotics does not diminish the beneficial effect of probiotics when there is stable colonization of the gut, because live bacteria undoubtedly provide more metabolites than can be provided using postbiotics. The future of next-generation probiotics lies not only in supplementation using beneficial bacteria strains but also in providing and maintaining the ecological context necessary to sustain them. The direct administration of these probioticassociated metabolites should provide a great advantage over traditional probiotics for several types of patients, including those harboring intestinal pathogens. Furthermore, since metabolites from the intestinal microbiota can also partially mediate the toxicity of some medicines in vivo, it will also be valuable to further examine these associations in order to assist in developing novel approaches to reducing the toxicity of HMs and TCMs.

# AUTHOR CONTRIBUTIONS

YlW contributed to the conception, design, collection and assembly of references, discussion, interpretation, and writing of the manuscript. SQ contributed to the collection and assembly of references, interpretation of the article, and writing of the manuscript. JJ, LH, and FL contributed to the collection and assembly of references. FJ, ZR, and YfW contributed to the conception, design, interpretation of the article, and the final article approval.

# FUNDING

This work was supported by grants from the Key Laboratory of Virology of Guangzhou, China<PHONE_NUMBER>03), the National Natural Science Foundation of China (Nos. 81573471 and 81872908), Key Projects of Biological Industry Science and Technology of Guangzhou China (Grant No.<PHONE_NUMBER>48224), Guangzhou Industry, University and Research Collaborative Innovation Major Project (No.<PHONE_NUMBER>87), and the Public

#### REFERENCES

fphys-10-01343 October 25, 2019 Time: 17:16 # 8

Service Platform of the South China Sea for R&D Marine Biomedicine Resources, Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China.

a Chinese herbal formula. Syst. Appl. Microbiol. 36, 188–196. doi: 10.1016/j. syapm.2012.12.009

Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R., and Goodman, A. L. (2019b). Separating host and microbiome contributions to drug pharmacokinetics and toxicity. Science 363:eaat9931. doi: 10.1126/science. aat9931

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Wang, Qin, Jia, Huang, Li, Jin, Ren and Wang. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Effect of Fermented Corn-Soybean Meal on Serum Immunity, the Expression of Genes Related to Gut Immunity, Gut Microbiota, and Bacterial Metabolites in Grower-Finisher Pigs

Junfeng Lu, Xiaoyu Zhang, Yihao Liu, Haigang Cao, Qichun Han, Baocai Xie, Lujie Fan, Xiao Li, Jianhong Hu, Gongshe Yang and Xin'e Shi\*

Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, Laboratory of Animal Fat Deposition and Muscle Development, College of Animal Science and Technology, Northwest A&F University, Yangling, China

Fermented corn-soybean meal (fermented feed, FF) is commonly used in swine production, but the effects of FF on gut health remain unclear. In this study, serum immunity, mRNA abundances of antimicrobial peptides (AMPs) and Toll-like receptors (TLR1-9), bacterial abundance in the duodenum and colon, and colonic metabolic phenotypes were determined in crossbred barrows (Duroc × Landrace × Large White) fed FF or normal feed (unfermented feed, UF) (n = 6). When compared to the UF group, the results showed that serum levels of IgG and IgM were significantly increased in FF group pigs (P < 0.05). FF significantly decreased the abundances of Bacteroides and Verrucomicrobia in the duodenum and decreased the abundances of Bacteroides, Proteobacteria, and Verrucomicrobia in the colon while it significantly increased the abundances of Firmicutes and Actinobacteria (P < 0.05). Furthermore, a Spearman's correlation analysis showed that serum immunity and the expression of genes related to gut immunity were associated with bacterial strains at the family level. Moreover, differentially abundant colonic microbiota were associated with colonic metabolites. LC-MS data analyses identified a total of 1,351 metabolites that markedly differed between the UF and FF groups. C5-Branched dibasic acid metabolism was significantly upregulated whereas the purine metabolism was significantly downregulated (P < 0.05) in the colonic digesta of pigs in the FF meal group compared to the UF meal group. Collectively, these results indicated that FF meal could influence serum immunity and the expression of genes related to gut immunity, correlating with the gut microbiota and bacterial metabolites in grower-finisher pigs. This study may provide an alternative strategy for improving the intestinal health of grower-finisher pigs.

Keywords: fermented feed, corn-soybean meal, immunity, microbiota, metabolite, pig

#### Edited by:

Yuheng Luo, Sichuan Agricultural University, China

#### Reviewed by:

Jing Zhang, Shanghai Jiao Tong University, China Christine Ann Butts, The New Zealand Institute for Plant and Food Research Limited, New Zealand

\*Correspondence:

Xin'e Shi<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology

Received: 05 March 2019 Accepted: 28 October 2019 Published: 20 November 2019

#### Citation:

Lu J, Zhang X, Liu Y, Cao H, Han Q, Xie B, Fan L, Li X, Hu J, Yang G and Shi X (2019) Effect of Fermented Corn-Soybean Meal on Serum Immunity, the Expression of Genes Related to Gut Immunity, Gut Microbiota, and Bacterial Metabolites in Grower-Finisher Pigs. Front. Microbiol. 10:2620. doi: 10.3389/fmicb.2019.02620

#### INTRODUCTION

fmicb-10-02620 November 18, 2019 Time: 13:39 # 2

Antibiotics have long been used in swine production to maintain health and productivity. In recent years, the overuse of antibiotics in swine production has become an important concern (van der Fels-Klerx et al., 2011) and has led to the development of alternatives to antibiotics in feeds (Dibner and Richards, 2005), such as probiotics, prebiotics, and organic acids, for use in swine husbandry (Thacker, 2013; Markowiak and Slizewska, 2018). The microbial fermentation of feed has been proposed as a potential alternatives (Verstegen and Williams, 2002).

Extensive evidence has shown that microbial fermentation can improve the nutritional quality of pig feed by increasing the bioavailability of nutrients (Borling Welin et al., 2015; Jeong et al., 2016) and reducing the content of anti-nutritional factors (Missotten et al., 2015; Shi et al., 2017; Wang et al., 2018). Moreover, the use of fermented corn-soybean meal has been shown to improve the immune function of pigs (Zhu et al., 2017), as the consumption of FF has a positive impact on the gut microbiota and can improve the fecal microbial count and intestinal morphology in grower-finisher pigs (Dowarah et al., 2017). However, so far, information on the effects of FF on the health of pigs is limited.

Many studies have highlighted the correlation between gut microbiota and intestinal morphology, regulation of immunity, digestion of carbohydrates, and body health in livestock (Richards et al., 2005; Stanley et al., 2016; Gu et al., 2019; Guevarra et al., 2019). Recent evidence suggests that the gut microbiota plays a crucial role in livestock health and disease (Tremaroli and Backhed, 2012). A growing number of studies indicate that the homeostasis and composition of the gut microbiota is dynamically formed by many factors (Ananthakrishnan, 2015; Zhao et al., 2015), including age, time, feed, and probiotics (Isaacson and Kim, 2012). For instance, in grower-finisher pigs, fermented Mao-tai lees modulate the gut microbiota and increase the abundance of the potentially beneficial bacteria Lactobacillus and Akkermansia (Wang J. et al., 2019). Moreover, 45% feed of fish meal can be replaced by fermented soybean meal without negative effects on growth performance and intestinal integrity of juvenile large yellow croaker (Wang P. et al., 2019).

It remains uncertain whether FF boosts immunity and the composition of the gut microbiota of swine. Therefore, the aim of the present study was to assess the effects of corn-soybean FF on serum immunity factors and the expression of genes related to gut immunity. The results of an intestinal microbiota analysis indicated that corn-soybean FF significantly changed the composition of the gut microbiota and that metabolites significantly differed between pigs fed FF meal versus UF meal.

#### MATERIALS AND METHODS

#### Ethics Statement

This study was carried out in accordance with the recommendations of the Animal Welfare Committee of Northwest A&F University (Yangling, Shaanxi Province, China). The protocol was approved by the Animal Welfare Committee of Northwest A&F University.

#### Preparation and Composition of FF

Corn-soybean meal was purchased from Beijing Dabeinong Technology Group Co., Ltd. (Beijing, China). Effective MicroorganismsTM used in the present study are a mixture containing 60% Lactobacillus, 20% Clostridium, and 8% Bifidobacteria and were purchased from Nongfukang Biological Technology Co., Ltd. (Zhengzhou, China) and diluted 1:30 (w/v) with sterile water. In accordance with the manufacturer's instructions, the corn-soybean meal was mixed with probiotics and incubated at 27–32◦C for 36 h. We then determined the live bacteria in the fermented product, and the final number of microorganisms was guaranteed at a concentration of 2 × 109 CFU/g. The method for CFU determination was based on the previous study (Sieuwerts et al., 2008). The 16S rRNA sequences of the bacteria in FF are shown in **Supplementary Figure S1**. After fermentation, the fermented corn-soybean meal was dried at 30–40◦C to a moisture content of 10%. The ingredients and nutrient content (%) of the UF and FF are listed in **Table 1**. Both the UF and FF met all recommended nutrient levels (NRC, 2012), and neither contained antimicrobials or growth promoters.

#### Animals, Housing, and Treatment

A total of 48 growing barrow pigs (Duroc × Landrace × Large White) (53.90 ± 1.31 kg initial body weight) were randomly allocated into one of two feeding groups; one group was fed with commercial soybean meal (UF) and a second with fermented complete commercial soybean meal (FF). Each group consisted of 24 barrow pigs that were housed in six pens, with four pigs per pen, in an environmentally controlled facility under a constant temperature of 25–28◦C and with free access to feed and clean water throughout the experimental period.

1Premix supplied per kilogram of meal: VD3, 2800 IU; VE, 26 mg; VK, 2 mg; VB1, 50 mg; VB6, 3 mg; VA, 6480 IU; VB2, 4 mg; VB12, 0.03 mg; pantothenic acid, 9 mg; nicotinic acid, 20 mg; choline chloride, 300 mg; biotin, 0.2 mg; Fe, 200 mg; Cu, 95 mg; Mn, 30 mg; folic acid, 1.2 mg; Zn 100 mg; I, 0.35 mg; Se, 0.36 mg; P 0.1%; NaCl, 0.5%; lysine, 0.1%; Ca, 0.9%. 2Nutrient contents are calculated values.

#### Sample Collection and Preparation

fmicb-10-02620 November 18, 2019 Time: 13:39 # 3

Upon reaching slaughter weight (approximately 110 kg), the control group was fed UF meal for 76 days and the treatment group was fed FF meal for 56 days. One pig from each pen was randomly selected and fasted for 12 h (with free access to water) before slaughter and sampling. Blood samples were collected from the external jugular vein into serum separation tubes and centrifuged at 2,500 rpm for 15 min at 4◦C, then stored at −80◦C until analysis. After blood sampling, all 12 pigs were anesthetized and slaughtered. After recovery of the duodenum and colon, duodenal and proximal colonic tissue samples were collected, washed with 0.9% saline, quickly frozen, and then stored at −80◦C until further analysis. Finally, the digesta in the duodenum and colon was immediately collected and frozen at −80◦C.

#### Serum Biochemical Indices and Immunoglobulin Analysis

In the present study, the serum concentrations of aspartate transaminase (AST), alanine transaminase (ALT), total protein, triglycerides, glucose, albumin, globulin, and low-density lipoprotein cholesterol were measured using a Hitachi-7180 Biochemical Analyzer (Hitachi Medical Corporation, Tokyo, Japan) provided by the Yangling Demonstration Zone Hospital. Serum concentrations of immunoglobulin (Ig) A, M, and G (IgA, IgM, and IgG, respectively) were determined with a commercial Enzyme-linked immunosorbent assay kit (BIM Biosciences, San Francisco, CA, United States). All procedures were performed in accordance with the manufacturers' instructions. Each sample was tested in triplicate.

#### Total RNA Extraction and Real-Time Polymerase Chain Reaction (PCR)

Total RNA was extracted from liquid nitrogen-frozen samples of the duodenum and colon using TRIzol Regent (Takara Bio Inc., Shiga, Japan). RNA integrity and quality were determined by agarose gel electrophoresis (1%) and spectrometry (A260/A280), respectively. A commercial reverse transcription (RT) kit (Takara Bio Inc.) was used for the synthesis of cDNA. The RT products (cDNA) were stored at −20◦C for relative quantification by PCR. For a real-time quantitative polymerase chain reaction (RTqPCR), every reaction was performed in triplicate using SYBR green kits on an Applied Biosystems ABI 7500 system (Thermo Fisher Scientific, Waltham, MA, United States). The expression levels of all genes were normalized to that of glyceraldehyde 3 phosphate dehydrogenase (GAPDH) using the 2−11CT method. The sequences of primers used for RT-qPCR are listed in **Table 2**.

#### Intestinal Digesta DNA Extraction and Pyrosequencing

Total genomic DNA was extracted from the duodenal and colonic digesta samples using the cetyltrimethylammonium bromide and sodium dodecyl sulfate method. DNA concentration and purity were monitored on 1% agarose gels. DNA extracted from each sample was used as a template to amplify the V4–V5 regions of 16S rRNA genes for later pyrosequencing by Novogene TABLE 2 | Primers sequences for RT-qPCR.

F, forward; R, reverse; PBD-1, porcine beta-defensin 1; PR39, proline-arginine rich 39-amino acid peptide; TLR, Toll-like receptor; GAPDH, glyceraldehyde-3 phosphate dehydrogenase.

Biological Information Technology Co. (Beijing, China), as described previously (Tian and Zhang, 2017). The forward and reverse primer sequences for the V4–V5 rRNA gene library preparation are presented in **Supplementary Table S1**. Raw reads were submitted to the Sequence Read Archive of the National Center for Biotechnology Information (SRA, No. PRJNA524989).

#### Bioinformatics Analyses

The paired-end reads were merged using FLASH (V1.2.7)1 (Magoc and Salzberg, 2011). Quality filtering on the raw tags was performed according to the QIIME 1.7.02 quality controlled process (Caporaso et al., 2010). Next, the tags were compared with the reference database (Gold database)3 (Edgar et al., 2011) using an UCHIME algorithm (UCHIME Algorithm)4 , to detect chimera sequences, and the chimera sequences were then removed (Haas et al., 2011). The Effective Tags were then finally obtained. High-quality clean tags were obtained and classified into the same operational taxonomic units (OTUs) at an identity threshold of 97% similarity by UPARSE software (Uparse

1http://ccb.jhu.edu/software/FLASH/

2http://qiime.org/index.html

3http://drive5.com/uchime/uchime\_download.html

4http://www.drive5.com/usearch/manual/uchime\_algo.html

v7.0.1001)5 (Edgar, 2013). For each representative sequence, the Greengene Database6 (DeSantis et al., 2006) was used based on a ribosomal database projects (RDPs) classifier (Version 2.2)7 (Wang et al., 2007) algorithm to annotate taxonomic information. In order to study the phylogenetic relationship of different OTUs, and the difference of the dominant species in different samples (groups), multiple sequence alignments were conducted using MUSCLE software (Version 3.8.31)8 (Edgar, 2004). OTUs abundance information was normalized using a standard of sequence number corresponding to the sample with the fewest number of sequences. Subsequent analyses of alpha diversity and beta diversity were all performed based on this normalized output data. Alpha diversity is applied in analyzing the complexity of species diversity for a sample through four indices, including Observed species, Chao 1, Shannon, Simpson, and ACE. All the indices in our samples were calculated with QIIME (Version 1.7.0) and displayed with R software (Version 2.15.3). The relative abundance at the phylum and genus levels was compared between the two groups, the top 30 most abundant families were defined as predominant genera and sorted for the comparison.

#### Untargeted Metabolomics Study Based on Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS)

Tissues (100 mg) were individually ground with liquid nitrogen and the homogenate was resuspended in pre-chilled 80% methanol (−20◦C) and then vortexed. The samples were incubated at −20◦C for 60 min and then centrifuged at 14,000 g and 4◦C for 20 min. The supernatants were subsequently transferred to a fresh Eppendorf tube and spun in a vacuum concentrator until dry. The dried metabolite pellets were reconstituted in 60% methanol and analyzed by LC-MS/MS.

LC-MS/MS analyses were performed using a Vanquish ultrahigh-performance liquid chromatography (UHPLC) system (Thermo Fisher Scientific) coupled with an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific) at Novogene Genetics, Beijing, China. Samples were injected into a Hypersil Gold column (100 × 2.1 mm, 1.9 µm; Thermo Fisher Scientific) using a 16 min linear gradient at a flow rate of 0.3 mL/min. The eluents for the positive polarity mode were eluent A (0.1% aqueous formic acid solution) and eluent B (methanol). The eluents for the negative polarity mode were eluent A (5 mmol/L ammonium acetate, pH 9.0) and eluent B (methanol). The solvent gradient was set as follows: 2% B, 1.5 min; 2–100% B, 12.0 min; 100% B, 14.0 min; 100–2% B, 14.1 min; and 2% B, 16.0 min. A Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific) was operated in positive/negative polarity mode with a spray voltage of 3.2 kV, capillary temperature of 320◦C, sheath gas flow rate of 35 arb, and auxiliary gas flow rate of 10 arb.

The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.0 (Thermo Fisher Scientific) to perform peak alignment, peak picking, and quantitation of each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 min; actual mass tolerance, 5 ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity, 100000. Afterward, peak intensities were normalized to the total spectral intensity. The normalized data were used to predict the molecular formula based on additive ions, molecular ion peaks, and fragment ions. Then, the peaks were matched with the mzCloud9 and ChemSpider10 databases to obtain accurate qualitative and relative quantitative results. The online KEGG database was used to identify metabolites by matching the molecular mass data. Finally, metabolites for separating the models were selected with the following requirements: variable importance in projection (VIP) > 1 and | P(corr)| ≥ 0.5 with 95% jack-knifed confidence intervals. The Student's t-test was applied to further analyze intergroup significance of the selected metabolites. Pathway analysis and enrichment analysis of differential metabolites were conducted on the MetaboAnalyst web server11 .

#### Statistical Analysis

Data are presented as the mean ± standard error of the mean (SEM). All statistical analyses were performed using IBM SPSS Statistics for Windows, version 19.0 (IBM Corporation, Armonk, NY, United States) using an one-way ANOVA with Turkey's multiple comparison test or Student's test. A probability (P) value of <0.05 was considered statistically significant. The correlations between the colonic microbial composition (relative abundance of family higher than 0.1%) and serum immunity and genes related to gut immunity that were significantly affected by FF treatment were assessed by a Spearman's correlation test using GraphPad Prism version 8.00 (GraphPad Software, San Diego, CA, United States). The correlations between the colonic microbial composition (relative abundance of family higher than 0.1%) and metabolites that were significantly affected by FF meal were assessed by a Spearman's correlation analysis using GraphPad Prism version 8.00 (GraphPad Software, San Diego, CA, United States).

# RESULTS

#### FF Increased Growth Performance

To investigate the effect of FF on body weight, after reaching the similar weight (53.19 ± 2.17 kg vs. 54.60 ± 1.62 kg), the pigs fed a corn-soybean meal were given free access to FF or UF. When compared with the UF group, the average daily weight gain was significantly increased in the FF group (P < 0.05) and there was no significant difference (P > 0.05) in the average daily intake among the two groups. Furthermore, the weight gain to food ratio was significantly increased in the FF group rather than in the UF group (P < 0.05) (**Table 3**).

5http://drive5.com/uparse/

6http://greengenes.lbl.gov/Download/

7http://sourceforge.net/projects/rdp-classifier/

8http://www.drive5.com/muscle/

9https://www.mzcloud.org/

10http://www.chemspider.com/

11https://www.metaboanalyst.ca/

Data are presented as the mean ± SEM (n = 6). UF, pigs fed with unfermented corn-soybean meal; FF, pigs fed with fermented corn-soybean meal.

# FF Changed Serum Immunity

fmicb-10-02620 November 18, 2019 Time: 13:39 # 5

Next, the effects of FF on immunity were assessed. As shown in **Figures 1A–C**, the ALT and AST concentrations tended to decrease in the FF-treated group as compared to the UF group (P > 0.05). There were no significant differences in the other serum parameters between the two groups (**Supplementary Table S2**). As shown in **Figures 1D–F**, FF significantly increased the serum concentrations of IgM and IgG (P < 0.05), while there was no significant difference in IgA levels between the two meals.

### Effect of FF on the Expression of Genes Related to Gut Immunity

The effects of FF on the expression of genes related to gut immunity was determined. The expression levels of the antimicrobial peptide-encoding genes PBD-1 and PR39 revealed the potential capacity for the eradication of invading pathogens. The results of the gene expression analysis of the intestinal tissue are presented in **Figure 2**. The FF meal significantly increased the mRNA abundance of AMPs and TLRs. In the duodenum, the mRNA abundances of TLR1, TLR2, TLR3, TLR4, PBD-1, and PR-39 in the FF-treated pigs were significantly increased (P < 0.05), while those of TLR5 and TLR9 were very significantly increased (P < 0.01; **Figure 2A**). In the proximal colon, the mRNA abundances of TLR4 and TLR5 in the FF-treated pigs were significantly increased (P < 0.05), while those of TLR1, TLR2, TLR7, and PBD-1 in the FFtreated pigs were very significantly increased (P < 0.01; **Figure 2B**).

# FF Shaped the Intestinal Microbiota

To explore the cause of improved immunity in FF-treated pigs, the intestinal microbiota were quantified by 16S rRNA sequence analysis, which identified a total of 65,249 ± 16,236 V4–V5 16S rRNA sequence reads from each sample. Binning sequences using a pairwise identity threshold of 97% obtained an average of 800 ± 231 operational taxonomic units per sample. MOTHUR plotting for the construction of a sparse curve between the number of reads and the number of operational taxonomic units revealed a tendency for plateau saturation (**Supplementary Figure S2**). Statistical estimates of species richness for 5,000 subsets of each sample at a genetic distance of 3% showed

that there was no difference in richness estimators (ACE and Chao 1) and the diversity indices (Shannon and Simpson diversity) (**Table 4**).

At the phylum level, Firmicutes and Bacteroidetes were the most predominant phyla in the duodenum and colon (**Tables 5**, **6**). FF meal significantly decreased the proportion of Bacteroidetes and Verrucomicrobia (P < 0.05) and tended to decrease the relative abundance of Spirochaetes in the duodenum (P = 0.069) (**Table 5**). FF meal significantly increased the proportion of Firmicutes and Actinobacteria (P < 0.01), whereas the proportion of Proteobacteria, Bacteroidetes, and Verrucomicrobia was decreased in the colon (P < 0.05) (**Table 6**).

A family-level analysis of the top 30 most abundant families revealed that Acidaminococcaceae and Clostridiales\_vadinBB60\_group were significantly decreased in relative abundance by the FF meal treatment in the duodenum (P < 0.05) (**Table 7**). A family-level analysis of the top 30 most abundant families revealed that Ruminococcaceae, Rikenellaceae, Christensenellaceae, Lactobacillaceae, and Family XIII were significantly increased in relative abundance by the FF meal treatment, whereas the abundance of Prevotellaceae, Lachnospiraceae, Clostridiaceae\_1, Bacteroidales\_RF16\_group, Streptococcaceae, Veillonellaceae, Erysipelotrichaceae, Peptostreptococcaceae, Acidaminococcaceae, Bacteroidaceae,

TABLE 4 | Diversity estimation of the 16S rRNA gene libraries from microbiota in the duodenum and colon of pigs fed the control UF and FF meals.

UF, pigs fed with unfermented corn-soybean meal; FF, pigs fed with fermented corn-soybean meal. Data are presented as the mean ± SEM (n = 6).

Succinivibrionaceae, Clostridiales\_vadinBB60\_group, Anaeroplasmataceae, Alcaligenaceae, Fibrobacteraceae, Rhodospirillaceae, and Unidentified\_Thermoplasmatales was decreased in the colon (P < 0.05) (**Table 8**).

#### Correlation Between Colonic Microbial Composition and Serum Immunity and Genes Related to Gut Immunity

To comprehensively analyze the relations between host serum immunity, genes related to gut immunity, and gut microbiota, a correlation matrix was generated by

TABLE 5 | Relative abundance of microbial phylum (percentage) in the duodenum of pigs in the FF and control groups.

UF, pigs fed with unfermented corn-soybean meal; FF, pigs fed with fermented corn-soybean meal. Data are presented as the mean ± SEM (n = 6).

TABLE 6 | Relative abundance of microbial phylum (percentage) in the colon of pigs in the FF and control groups.

UF, pigs fed with normal commercial feed; FF, pigs fed with fermented meal. Data are presented as the mean ± SEM (n = 6).

calculating the Spearman's correlation coefficient. As shown in **Figure 3**, Spearman's correlation analysis showed that the serum IgM was positively associated with the abundance of Enterobacteriaceae but negatively related to the proportion of Lachnospiraceae in the colon. The gene expression of TLR1 was negatively correlated with the abundance of Ruminococcaceae and Erysipelotrichaceae. The gene expression of TLR2 was positively associated with the abundance of Veillonellaceae. The gene expression of TLR3 was negatively correlated with the abundance of Peptostreptococcaceae. The gene expression of TLR4 was positively correlated with the abundance of Enterobacteriaceae, Clostridiaceae\_1, and Bacteroidales\_S24-7\_group. The gene expression of TLR5 and PBD-1 were positively associated with the abundance Lachnospiraceae but negatively related to the proportion of Bacteroidales\_S24-7\_group. The gene expression of TLR7 was positively associated with the abundance Bacteroidales\_S24-7\_group but negatively related to the proportion of Lachnospiraceae. Additionally, the gene expression of TLR5 was also negatively related to the proportion of Veilonellaceae.

#### Effect of FF on Fermentation Metabolites in Colonic Digesta

To further study the effect of FF on the gut, the colonic digesta metabolic profiles of the two meals were acquired by LC-MS. As shown by the PCA score plots presented in **Figures 4A,B** which distinguished metabolic communities based on meal of colonic sampling, the metabolic communities were clustered. The partial least squares discriminant analysis (PLS-DA) score plots also reflected that FF led to significant biochemical changes (**Figures 4C,D**). Furthermore, LC-MS/MS (ESI−) and LC-MS/MS (ESI+) detected a total of 398 and 953 biomarker metabolites, respectively (**Figures 4E,F**).

In summary, there were significant differences in the 1,351 biomarker metabolites between the UF and FF groups (detailed information is presented in **Supplementary Data S1**). Next, the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to analyze the pathways of the metabolites that differed between the two meal groups (**Figure 5**). The effects of C5-branched dibasic acid metabolism were significantly upregulated while the purine metabolism was significantly downregulated in the FF group, as compared to the control group (P < 0.05). Meanwhile, the effects of dioxin degradation, phenylpropanoid biosynthesis, and aminobenzoate degradation were downregulated in the FF group (0.05 < P < 0.1).

TABLE 7 | Relative abundance (percentage) for the top 30 most abundant family in the duodenum of pigs in the fermented feed (FF) and control (UF) groups.

TABLE 8 | Relative abundance (percentage) for the top 30 most abundant family in the colon of pigs in the fermented feed (FF) and control (UF) groups.

UF, pigs fed with unfermented corn-soybean meal; FF, pigs fed with fermented corn-soybean meal. Data are presented as the mean ± SEM (n = 6).

#### Correlation Between Microbiota Communities and Related Metabolites

Metabolomics has been shown to be an important tool to reveal the potential crosstalk of host and gut microbiota. Therefore, correlations between metabolites and familylevel microbiota with significant differences between two meals were obtained via spearman's correlation analysis (**Figure 6**). As shown in **Figure 6**, ten bacterial strains (family Enterobacteriaceae, Lactobacillaceae, Prevotellaceae, Clostridiaceae\_1, Ruminococcaceae, Veillonellaceae, Peptostreptococcaceae, Erysipelotrichaceae, Lachnospiraceae, and Bacteroidales\_S24-7\_group) were most closely related to the metabolites in the FF group, as compared to the control group (P < 0.05).

 Data are presented as the mean ± SEM (n = 6).

microbiota has become the focus of much attention (Frese et al., 2015; Ji et al., 2018; Wang W. et al., 2019). FF, as an available feed alternative, has great potential to improve gut health and maintain gastrointestinal tract microbial homeostasis (Jin et al., 2017) and could modulate the host gut microbiota through dietary manipulation (Wang et al., 2018). In this study, the effects of FF versus normal feed on serum immunity, expression of genes related to gut immunity, gut microbiota composition, and bacterial metabolites were investigated in grower-finisher pigs. The results reflected that FF regulated the microbiota composition in the duodenum and colon of pigs, and it also selectively changed the metabolomics profiles.

Corn-soybean meal is the most frequently used meal for livestock production in China. Solid-state fermentation can improve the nutritional value of plant materials and has it been suggested to increase the use of FF in livestock feeds (Shi et al., 2017). In the animal production, pigs are often slaughtered at a constant body weight (market weight, around 110 kg) to maintain uniformity of pork products and maximize profits (Kim et al., 2005; Frederick et al., 2006; Vermeer et al., 2014). In the present

study, we performed the study at an average initial body weight (53.19 ± 2.17 vs. 54.60 ± 1.62 kg), and an interesting finding in this study was that FF meal greatly reduced the time to market (76 vs. 56 days). As compared with the UF group, the average daily weight gain and weight gain: food ratio in the FF group were significant increased (P < 0.05), which reflects improved growth performance and feed conversion efficiency, in accordance with the findings of previous studies (Canibe and Jensen, 2003). The fermentation process is believed to promote functional activities, such as antimicrobial and antioxidant activities, and increases the production of growth factors, hormones, and amino acids (Ng et al., 2011; Laskowska et al., 2017).

Proper function of the immune system is important for grower-finisher pigs. Immunoglobulins are immune-active molecules that play important roles in the humoral immune response (Kong et al., 2007). In the present study, the concentrations of IgG and IgM were significantly greater in the FF group (P < 0.05). The levels of IgG reflected immune status (Machado-Neto et al., 1987). IgM is associated with antiinflammation and a higher concentration reflects better immune status (Vaschetto et al., 2017). Our results were similar to those of Zhu et al. (2017), who reported that weaned piglets fed fermented soybean meal had higher serum concentrations of IgG and IgM, as compared with the controls. Some probiotic strains can be used as immunomodulators to enhance the concentrations of serum immunoglobulins (Vitini et al., 2000). Laskowska et al. (2017) found that dietary supplementation of Effective Microorganisms activated and enhanced the humoral and cell-mediated immune responses and protected against infection. Although high concentrations of immunoglobulins were observed, there was no intestinal inflammation, suggesting that the immunoglobulins were in the normal ranges. Moreover, FF meal decreased the concentrations of serum ALT and AST (P > 0.5). Reportedly, ALT is a liver-specific enzyme and ALT concentrations increased in response to acute liver injury (Robertson et al., 2016). Here, the lower levels of ALT and AST indicated that FF boosted overall health. In short, FF meal enhanced immune performance.

Toll-like receptors are the earliest discovered pattern recognition receptors and play critical roles in innate immunity (Newburg and Walker, 2007; Werling and Coffey, 2007), and TLR expression levels may indicate disease resistance in pigs (Uenishi and Shinkai, 2009; Cheng et al., 2015). Our data showed that the mRNA abundances of TLRs in the FF group were significantly greater than in the control group (P < 0.05). In this study, in the duodenum of FF-fed pigs, the mRNA abundances of PBD-1 and PR39 were significantly higher than that of the UF control pigs (P < 0.05). Meanwhile, the gene expression of PBD-1 in the colon was also higher than that of the UF meal pigs (P < 0.01). These results indicate that FF may benefits the pig gut immunity and in order to fully understand the regulating mechanism of FF on gut immunity, in vivo pathogenic challenge model is needed in further studies.

We then used a high-throughput sequencing method based on the 16S rRNA genes to demonstrate the effects of FF on the intestinal microbiota of grower-finisher pigs. In this study, FF had no effect on gut microbial community evenness (ace, Shannon H) and richness (Simpson and Chao 1). Previous studies have shown that probiotic-supplemented FF decreased microbial diversity, which may be linked with improved resistance to gastrointestinal disorders (Ott et al., 2004; He et al., 2017). Similar to previous studies (Kim et al., 2012; Looft et al., 2012;

FIGURE 4 | Multivariate statistical analysis of untargeted metabolomics data obtained using the LC-MS/MS approach. PCA score plot of colonic metabolomics data for treatment (blue) and control (red) pigs obtained by (A) LC-MS (ESI−) and (B) LC-MS (ESI+) (n = 6). (C) PLS-DA score plot of colonic metabolomics data obtained by LC-MS (ESI−); R2Y = 0.99; Q2 = 0.95. (D) PLS-DA score plot of colonic metabolomics data obtained by LC-MS (ESI+) data; R2Y = 1.00; Q2 = 0.97. (E) Score plot of LC-MS (ESI−) data with 2,395 metabolite signals detected. (F) Score plot of LC-MS (ESI+) data with 5,708 metabolite signals detected. Red circles in volcano plots are model-separated metabolites following the conditions of VIP > 1 and | P(corr)| ≥ 0.5 with 95% jack-knifed confidence intervals. Red or green rectangles indicate the numbers and tendency of metabolites to separate in the model when FF group pigs are compared with UF group.

Niu et al., 2015; Li et al., 2017), Firmicutes and Bacteroidetes were the most dominant phyla in the present study. Studies have shown that in obese animals, the ratio of Firmicutes to Bacteroides is increased (Ley et al., 2005, 2006b). In the present study, the ratio of the Firmicutes to Bacteroides was increased in both the duodenum and colon of the treatment group, which indicated that the use of FF changed the proportion of the microbiota and is beneficial to the weight gain of finisher pigs. In addition, a microbiome enriched in Firmicutes has been associated with an increased capacity for energy harvest and obesity (Ley et al., 2006a; Turnbaugh et al., 2006), and an increase of this phylum could therefore increase the amount of calories extracted from the diet. Compared with the control group, the relative abundance of Verrucomicrobia was significantly decreased in the FF group in the duodenum. Verrucomicrobia usually represents a minor population of intestinal microbiota in response to dietary shifts in mice (Pantoja-Feliciano et al., 2013). A relatively smaller proportion of Proteobacteria in the FF group was detected in our current study. It's reported that the members of the phylum Proteobacteria have a low abundance in the gut of healthy humans (Shin et al., 2015) and the increased levels of Proteobacteria may be indicative of a diseased state that commonly occurs during enteric infection or following perturbation of the microbiota (Singh et al., 2015). Moreover, FF significantly changed the gut microbiota composition, as indicated by decreased proportions, and significantly increased the proportions of Actinobacteria (P < 0.05). Meanwhile, we found there were five bacterial strains were significantly increased and 17 bacterial strains were significantly decreased in the colon in response to FF meal at the family level (P < 0.05); further studies are needed to investigate the roles of these gut bacteria in regulating the swine gut development.

The gut microbiota is important to host health and physiology status (Lalles, 2016). It is reported that microbes in the large intestine undertake more metabolism tasks (Zhao et al., 2015). Using a KEGG pathway analysis, we found that C5-Branched dibasic acid metabolism was significantly upregulated whereas the purine metabolism was significantly downregulated (P < 0.05). Similarly, Gomez et al. (2017) measured the fecal microbiota and calculated the functional potential of the microbial communities in healthy and diarrheic calves. They found that C5-branched dibasic acid metabolism was enriched in healthy calves, suggesting that C5-branched dibasic acid metabolism is related to energy generation (Turnbaugh et al., 2006). The purine content of animal feed is a concern because excess intake of purines may increase the risk of hyperuricemia and gout (Zheng et al., 2018). It is necessary to conduct further research beyond the scope of the present study.

Fermented feed meal altered the metabolic functions and phenotypes of gut microbiota in pigs. The relative abundances of bacteria at the family levels were closely associated with the concentration of specific microbial metabolites in the colonic digesta. For instance, Lachnospiraceae is a bacterial family known to be abundant in the intestinal ecosystem (Sagheddu et al., 2016), and it is reported that Lachnospiraceae was positively correlated with carbohydrate metabolism (Morgan et al., 2012). Moreover, we found that Lachnospiraceae was also positively correlated with Sorbitan monooleate, N-Acetyl-L-aspartic acid, N-Acetylcadaverine, and Mucronine B, whereas it negatively correlated with Caffeic acid phenethyl ester, Dodecanedioic acid, Torsemide, N-Acetyl-L-leucine, and Viloxazine. The mechanism underlying the dietary regulation of host phenotypes should be explored with a particular focus on metabolism and related receptors.

Further studies are needed to explore the benefits of FF benefits on the gut microbiota and health of pigs. To this end, fecal microbiota transplantation (FMT) may be an effective strategy. FMT refers to the process of transplantation of fecal bacteria from healthy individuals into a recipient (Drekonja et al., 2015). Growing evidence has shown that the host phenotypes,

such as obesity (Lee et al., 2019), Clostridium difficile infection (Khoruts and Sadowsky, 2016), and anti-seizure (Olson et al., 2018) and anti-tumor immunity (Sivan et al., 2015), can be altered by FMT in mammals, demonstrating the critical roles of gut microbiota in host health. Thus, FMT also helped evaluate the potential links between intestinal microbiota and host phenotypes. Recently, Hu et al. (2018) proposed the standardized preparation for FMT for use in pig production and to identify host microbiota-derived bacteriocin targets to determine diarrhea resistance in early-weaned piglets (Hu et al., 2018). In the current study, we determined the FF could benefits the pig's gut microbiota; it would be interesting to explore the mechanism and we would like to focus on this in our next study.

#### CONCLUSION

fmicb-10-02620 November 18, 2019 Time: 13:39 # 13

In conclusion, we found that long-term consumption of FF meal increased the serum concentrations of IgG and IgM and changed the expression of genes related to gut immunity, which may be associated with alterations in the microbiota community and microbial metabolites and benefits to the pig's health. In addition, the results suggest that FF altered the microbial composition and modulated the metabolic pathway of microbial metabolism in pig colons. These alterations provide an alternative strategy for improving the intestinal health of pigs.

#### DATA AVAILABILITY STATEMENT

The datasets generated for this study can be found in NCBI, No. PRJNA524989.

#### ETHICS STATEMENT

The use of animals and the performance of all experimental protocols were approved by the Northwest A&F University Animal Welfare Committee (Yangling, Shaanxi Province, China).

#### REFERENCES

The processing of animal experiments and sample collection strictly followed the relevant guidelines.

#### AUTHOR CONTRIBUTIONS

XS and JL designed the study, and wrote and revised the manuscript. JL, XZ, HC, QH, BX, and LF helped took samples and performed the experiments and analyses. JL, YL, XL, JH, GY, and XS edited the manuscript. All authors reviewed the final manuscript.

#### FUNDING

This study was supported by the Agricultural Special Fund Project in Shaanxi Province (NYKJ-2018-YL01) and the Technical Innovation Guidance Special (Foundation) Project of Shaanxi Province (2017ZKC07-114).

#### ACKNOWLEDGMENTS

We thank others in Li's team and Shi's team for their excellent support during this experiment.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2019.02620/full#supplementary-material

microbiota and gut health of grower-finisher crossbred pigs. Livestock Sci. 195, 74–79. doi: 10.1016/j.livsci.2016.11.006

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Lu, Zhang, Liu, Cao, Han, Xie, Fan, Li, Hu, Yang and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Dietary Supplementation With Citrus Extract Altered the Intestinal Microbiota and Microbial Metabolite Profiles and Enhanced the Mucosal Immune Homeostasis in Yellow-Feathered Broilers

Miao Yu1,2,3,4,5, Zhenming Li1,2,3,4,5, Weidong Chen1,2,3,4,5, Gang Wang1,2,3,4,5, Yiyan Cui1,2,3,4,5 and Xianyong Ma1,2,3,4,5 \*

1 Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China, 2 State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China, 3 Key Laboratory of Animal Nutrition and Feed Science in South China, Ministry of Agriculture, Guangzhou, China, 4 Guangdong Key Laboratory of Animal Breeding and Nutrition, Guangzhou, China, 5 Guangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and Evaluation, Guangzhou, China

#### Edited by:

Jie Yin, Institute of Subtropical Agriculture (CAS), China

#### Reviewed by:

Yong Su, Nanjing Agricultural University, China Jingrang Lu, United States Environmental Protection Agency, United States Wei Chen, Jiangnan University, China

> \*Correspondence: Xianyong Ma<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Systems Microbiology, a section of the journal Frontiers in Microbiology

Received: 13 August 2019 Accepted: 01 November 2019 Published: 26 November 2019

#### Citation:

Yu M, Li Z, Chen W, Wang G, Cui Y and Ma X (2019) Dietary Supplementation With Citrus Extract Altered the Intestinal Microbiota and Microbial Metabolite Profiles and Enhanced the Mucosal Immune Homeostasis in Yellow-Feathered Broilers. Front. Microbiol. 10:2662. doi: 10.3389/fmicb.2019.02662 The present study aimed to investigate the effects of citrus extract (CE) on intestinal microbiota, microbial metabolite profiles, and the mucosal immune status in broilers. A total of 540 one-day-old yellow-feathered broilers were randomly allotted into three groups and fed a basal diet (control group), or a basal diet containing 10 mg/kg of zinc bacitracin (antibiotic group), or 10 mg/kg of CE (CE group). Each treatment consisted of six replicates, with 30 broilers per replicate. After 63-day feeding, two broilers per replicate were randomly selected and slaughtered, and their ileal and cecal digesta and ileal tissue were collected for microbial composition, microbial metabolites, and gene expression analysis. The results showed that CE significantly increased the abundance of Barnesiella and Blautia than did the antibiotic group (adjusted P < 0.05), whereas it decreased the abundance of Alistipes and Bacteroides (adjusted P < 0.05). Meanwhile, the CE group also increased the numbers of Bifidobacterium and Lactobacillus than did the control and antibiotic groups (P < 0.05), whereas it decreased the number of Escherichia coli (P < 0.05). For microbial metabolites, dietary supplementation with CE increased the concentrations of lactate, total short-chain fatty acids, acetate, and butyrate in the cecum than did the control and antibiotic groups (P < 0.05), whereas it decreased the concentrations of amino acid fermentation products (ammonia, amines, p-cresol, and indole) (P < 0.05). Additionally, supplementation with CE up-regulated (P < 0.05) the mRNA expression of intestinal barrier genes (ZO-1 and Claudin) in the ileum than did both the control and antibiotic groups. However, antibiotic treatment induced gut microbiota dysbiosis, altered the microbial metabolism, and disturbed the innate immune homeostasis. In summary, these results provide evidence that dietary supplementation with CE can improve the intestinal barrier function by changing microbial composition and metabolites, likely toward a host-friendly gut environment. This suggests that CE may possibly act as an efficient antibiotic alternative for yellow-feathered broiler production.

Keywords: citrus extract, immune homeostasis, intestinal microbial community, microbial metabolites, yellowfeathered broilers

#### INTRODUCTION

fmicb-10-02662 November 22, 2019 Time: 16:28 # 2

In-feed antibiotics have been extensively used as growth promoters in livestock production to maintain health and to improve feed conversion efficiency, utilization, and growth performance (Castanon, 2007). However, the continuous and excessive use of in-feed antibiotics for animals' production has led to the development of antibiotic-resistant microbes and a number of residual antibiotics in animal products, both of which pose a potential threat to human health (Gadde et al., 2017). In China, the use of antibiotics in poultry feeds is still a common practice, although it has increasingly caused safety concerns and increased the consumer demand for antibiotic-free animal products; thus, the use of antibiotics for growth promotion in feed will be banned in the future. Consequently, it is necessary to develop novel feed alternatives that offer both security and efficiency with the potential to replace antibiotics while improving poultry health and product quality.

As a safe and efficient alternative to antibiotics, many plant extracts have been used as promising feed additives for livestock production. Among these potential alternatives, citrus extract (CE) is often used as one of promising candidates in poultry (Akbarian et al., 2013; Ebrahimi et al., 2015; Pourhossein et al., 2015). CE is a rich source of many important bioactive ingredients including vitamins, minerals, phenolic compounds, nobiletin, and flavonoids (Li et al., 2006; Parmar and Kar, 2010; Bermejo et al., 2011). Thus, CE may provide numerous health benefits, including as an antimicrobial agent against Escherichia coli and Salmonella typhimurium with the ability to selectively inhibit the growth of potentially pathogenic bacteria (Nannapaneni et al., 2008, 2009; O'Bryan et al., 2008) and enhance immune system activities (Chen et al., 2012). Indeed, the beneficial effects of CE were extensively investigated in poultry production. Several previous studies reported that the dietary supplementation with citrus products in broiler feed could enhance growth performance (Seidavi et al., 2015), stimulate IgG and IgM antibody production in serum (Pourhossein et al., 2015), and decrease the number of E. coli in the cecum digesta by using a culture-based approach (Ebrahimi et al., 2015; Alefzadeh et al., 2016). These results indicated that CE can modulate the intestinal microbiota and immune system activities. However, the effects of CE on the intestinal microbial community and epithelial immune status remain limited and require further investigation. Additionally, alterations in the microbiota by dietary treatment can also induce changes in the metabolic end-products of microbial degradation (Fouhse et al., 2015). However, whether dietary supplementation with CE affects the intestinal microbial metabolites in broilers remains unclear.

To test the hypothesis that CE as an antibiotic alternative may positively alter the microbial community and its metabolites, and that these alterations can also modulate the mucosa immune response in yellow-feathered broilers, the current study investigated the effects of dietary supplementation with CE on the microbial community, microbial metabolite profiles, and expression of immune-related genes in the intestine.

#### MATERIALS AND METHODS

#### Ethics Approval and Consent to Participate

The experimental proposals and procedures for the care and treatment of the broilers were approved by the Animal Care and Use Committee of Guangdong Academy of Agricultural Sciences (authorization number GAASIAS-2016-017).

#### Animals, Experimental Design, and Sampling

A total of 540 one-day-old yellow-feathered male broilers were randomly allotted into three groups. Each treatment consisted of six replicates, and each replicate had 30 broilers. There was no difference of statistics in initial average body weight of broilers among the three group (41.37 ± 0.35 g). The broilers of the control group were fed a basal diet without any antibiotics (control group); the antibiotic and CE groups were fed the same basal diet with 10 mg/kg of zinc bacitracin (antibiotic group) and 10 mg/kg of CE (CE group) during the whole trial period, respectively. The CE used in the current study was provided by the Guangdong Runsen Health and Environmental Technology Development Co., Ltd., Guangdong, China. The contents of total flavone, polysaccharide, citric acid, and chlorogenic acid in the CE were measured as previously described (Kong et al., 2009; Wan et al., 2016) and were 2.48, 1.20, 1.30, and 0.68%, respectively. The basal diets were formulated to either meet or exceed the nutrient requirements of Chinese yellow-feathered broilers (Ministry of Agriculture of P. R. China, 2004). The dietary composition and nutrient contents for the starting (1–21 days), growing (22–42 days), and finishing (43–63 days) phases are shown in **Table 1**. All broilers were housed in battery cages (3.0 m × 3.0 m × 0.9 m) in an environmentally controlled room with a continuous light regimen throughout the 63 day experimental period. The environment temperature was maintained at 33◦C for the first week and then decreased by 3 ◦C every week until a final temperature of 24◦C. All broilers

ME, metabolized energy; TP, total phosphorus; AP, available phosphate. 1Provided per kilogram of complete diet: pantothenic acid, 10.9 mg; nicotinic acid, 30 mg; folic acid, 0.95 mg; biotin, 0.16 mg; vitamin A, 8,000 IU; vitamin D, 2,800 IU; vitamin E, 19 mg; vitamin K3, 3.32 mg; vitamin B1, 1.7 mg; vitamin B2, 8.2 mg; vitamin B6, 2.78 mg; vitamin B12, 0.015 mg; Mn (MnSO4·H2O), 82 mg; Zn (ZnSO4·H2O e), 68 mg; Fe (FeSO4·H2O), 81 mg; Cu (CuSO4·5H2O), 9 mg; I (KI), 0.50 mg; Se (Na2SeO3), 0.27 mg. 2Values were calculated from data provided by Feed Database in China (2016) except that crude protein was analyzed.

were provided with diets and water ad libitum throughout the whole trial period.

At the end of the feeding period (day 63), two broilers per replicate were randomly selected and slaughtered after being fasted for approximately 12 h. The ileal and cecal digesta were collected, homogenized, and stored at –80◦C for later determination of the microbial communities and metabolites analyses. In addition, a segment of mid-ileum tissue was also rapidly removed and washed with phosphate-buffered saline (PBS, pH 7.0), then immediately frozen in liquid nitrogen, and stored at –80◦C for later gene expression.

#### Intestinal Microbial DNA Extraction, Illumina MiSeq Sequencing, and Bioinformatics Analysis

The microbial total genomic DNA extraction was conducted from 250 mg of ileal and cecal digesta samples, using the QIAamp PowerFecal DNA Kit (Qiagen, Hilden, Germany), according to the manufacturer's instructions. The concentrations of every DNA sample were quantified using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, United States). To balance the cost of the experiment and the number of replicates necessary, two samples from each replicate were pooled in equal proportion and were selected for 16S rRNA MiSeq sequencing. The V3–V4 region of the bacterial 16S rRNA gene was amplified using primer pairs 338F (50 -ACTCCTRCGGGAGGCAGCAG-30 ) and 806R (50 - GGACTACCVGGGTATCTA AT-30 ) (Mao et al., 2015). PCR products were purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, United States) to remove excess primer dimers and dNTPs. After purification, the amplicons were pooled in equimolar and performed on an Illumina MiSeq 2 × 250 platform (Illumina, San Diego, CA, United States) at Majorbio Bio-Pharm Technology (Shanghai, China) according to standard protocols (Caporaso et al., 2012). The raw reads in this study were submitted to the National Center of Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under accession number SRR9074899–SRR9074916.

QIIME (version 1.17) software package was used to demultiplex and quality filter the obtained sequences from 18 samples according to a previous study (Mu et al., 2016). Chimeric sequences were identified and removed using UCHIME (Edgar, 2010), and the high-quality sequences were clustered into operational taxonomic units (OTUs) with a cutoff of 97% similarity using UPARSE (version 7.1)1 . Each OTU was annotated using the Ribosomal Database Project (RDP) classifier against the Silva (SSU119) 16S rRNA database at a confidence threshold of 90%. The diversity of cecal microbiota (such as rarefaction analysis, ACE and Chao1 richness estimators, and Shannon and Simpson diversity indices) was performed using Mothur (version 1.36.1) according to a previous study (Schloss et al., 2009). Beta diversity was evaluated by principal coordinate analysis (PCoA) based on the Bray–Curtis distance. An unweighted distance-based analysis of molecular variance (AMOVA) was conducted to compare the significant differences between samples by using Mothur (Schloss et al., 2009). Linear discriminant analysis effect size (LEfSe) analysis was employed to explore the significantly different bacteria at the OTU level among the three groups (Segata et al., 2011).

#### Quantification of Microbes by Real-Time PCR

To identify the quantitative changes in bacterial groups, several key bacteria groups, such as total bacteria, Firmicutes, Bacteroidetes, Clostridium cluster IV, Clostridium cluster XIVa, E. coli, Bifidobacterium, Lactobacillus, Prevotella, and Ruminococcus, in the ileal and cecal digesta were quantified by real-time quantitative PCR using specific primers (**Supplementary Table S1**). qPCR was performed by using TB GreenTM Premix Ex TaqTM (Takara Biotechnology, Dalian, China) on the CFX96 Real-Time PCR Detection System (Bio-Rad, Hercules, CA, United States). The real-time PCR mixtures and conditions were set according to previously described methods (Yu et al., 2017b; Song et al., 2019a).

1http://drive5.com/uparse/

#### Analysis of Cecal Microbial Metabolites

fmicb-10-02662 November 22, 2019 Time: 16:28 # 4

Cecal digesta samples were analyzed for microbial metabolites. Short-chain fatty acids (SCFAs) concentrations were measured by gas chromatography (GC) according to a method described previously (Yu et al., 2017b). Lactate concentration was determined using a commercial assay kit (Nanjing Jiancheng Biological Engineering Institute, Nanjing, China) according to the manufacturer's instructions. Ammonia concentration was analyzed using a UV spectrophotometer (Shimadzu, Tokyo, Japan) according to the description of previous study (Chaney and Marbach, 1962). Biogenic amine concentrations were measured using high-performance liquid chromatography (HPLC) with precolumn dansylation method as previously described (Yang et al., 2014). The concentrations of phenolic and indolic compounds were determined by HPLC according to a method that has been previously described (Schüssler and Nitschke, 1999).

#### RNA Extraction and qPCR for Ileal Gene Expression

The total RNA of ileal tissue was extracted using TRIzol reagent (Takara Biotechnology, Dalian, China) according to the method described by manufacturer's instructions. The RNA concentration and purity were quantified using NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, United States), and the absorption ratio (OD260:OD280) of all samples ranged from 1.8 to 2.0. One microgram total RNA was reverse-transcribed using the PrimeScriptTM RT reagent Kit with gDNA Eraser (Takara Biotechnology, Dalian, China). The specific primer sequences used in the current study are presented in **Supplementary Table S2**. The target genes and GAPDH were determined by quantitative real-time PCR with TB GreenTM Premix Ex TaqTM (Takara Biotechnology, Dalian, China), and fluorescence was detected on CFX96 Real-Time PCR Detection System (Bio-Rad, Hercules, CA, United States). The reaction condition and real-time PCR condition were previously described (Yu et al., 2017a). GAPDH mRNA expression levels were used as a housekeeping gene. The results of target genes mRNA expression level calculated using the 2(−11Ct) method, where 11Ct = (Cttarget − CtGAPDH)treatment − (Cttarget − CtGAPDH)control.

#### Statistical Analysis

Experimental data were analyzed using the IBM SPSS statistics V20.0.0 software package (IBM Corp., Armonk, NY, United States). The Shapiro–Wilk test was used to assess whether all variables exhibited a normal distribution before assessing differences among the three groups. Then, the variables that showed a non-normal distribution were analyzed by Kruskal-Wallis one-way analysis of variance (ANOVA). To avoid type I errors, the resulting P values of bacterial abundance were adjusted via the Benjamini–Hochberg false discovery rate (FDR) multiple-testing correction (Benjamini and Hochberg, 1995). Bacterial abundance data are expressed as medians, and an FDR-adjusted P value < 0.05 was regarded as significant. The data of bacterial gene copy, microbial metabolites (SCFAs, lactate, and amino-acid derived metabolites), and ileal gene expression were analyzed via one-way ANOVA with a Tukey post hoc test. Differences were regarded significant at P < 0.05.

# RESULTS

### Growth Performance

During the whole trial, the outward appearance of broilers was healthy, and no mortality was observed. In the current study, dietary supplementation with CE significantly increased (P < 0.05) the average daily gain (ADG) of broilers (means ± SEM: 32.12 ± 0.35 and 34.59 ± 0.71 g/day in the control and CE groups, respectively) and decreased (P < 0.05) the feed conversion rate (F:G, means ± SEM: 2.57 ± 0.05 and 2.39 ± 0.04 in the control and CE groups, respectively) than did the control group. The ADG and F:G ratio in the antibiotic group showed no difference (P > 0.05) than those in the control and CE groups. Furthermore, no difference (P > 0.05) in the average daily feed intake (ADFI) was observed among the control group (means ± SEM: 82.61 ± 1.11 g/day), antibiotic group (means ± SEM: 83.30 ± 1.67 g/day), and CE group (means ± SEM: 82.43 ± 1.56 g/day).

#### Microbial Composition of Ileal and Cecal Digesta

To profile the microbial composition, the cecal microbiota of yellow-feathered broilers was analyzed by 16S rRNA MiSeq sequencing. In the current study, a total of 673,959 sequences from the 18 samples (with an average of 37,442 sequences per sample) after Illumina sequencing was revealed for the subsequent analyses. Mean-based rarefaction curves showed that the sampling of each group provided sufficient sequences to reflect the diversity and abundance of bacteria (**Supplementary Figure S1**). The ACE, Chao1 index, Shannon index, and Simpson index did not differ among the three different groups (**Supplementary Figure S2**). The PCoA with the Bray–Curtis distance indicated that the samples of antibiotic group gathered together and clearly separated from the samples of the control and CE groups (**Figure 1A**), suggesting that the microbial composition of yellow-feathered broilers in the antibiotic group differs from that of the control and CE groups. AMOVA also showed significant dissimilarities among the antibiotic, control, and CE groups (Fs = 2.84, Fs = 2.84, P < 0.001, among the control, antibiotic, and CE groups; Fs = 4.07, P < 0.01, antibiotic vs. CE; Fs = 3.07, P < 0.001, antibiotic vs. control; Fs = 1.27, P > 0.05, control vs. CE).

At the phylum level (**Figure 1B**), eight phyla were identified: Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, Tenericutes, Verrucomicrobia, TM7, and Elusimicrobia in the cecal digesta of yellow-feathered broilers. Among these phyla, Bacteroidetes, Firmicutes, and Proteobacteria formed the three dominant phyla, contributing 59.61, 27.11, and 10.84% in the control group; 56.46, 30.86, and 11.01% in the antibiotic group; and 53.78, 33.47, and 10.07% in the CE group, respectively. There were no significant changes in the abundances of any phyla among the three groups (P > 0.05).

At the genus level, the 30 most predominant genera of the cecal digesta are presented in a heat map (**Supplementary Figure S3**). The nine dominant genera (those with a relative abundance ≥ 2% in at least one group) included Barnesiella, Alistipes, Staphylococcus, Bacteroides, Salmonella, unclassified Ruminococcaceae, unclassified vadinBB60, unclassified Enterobacteriaceae, and unclassified Lachnospiraceae. Compared with that in the control group, the antibiotic treatment significantly decreased the abundance of Barnesiella, Blautia, and unclassified S24-7 in the cecum of broilers (adjusted P < 0.05) (**Figure 2**). Compared with that in the CE group, antibiotic treatment decreased the abundance of Barnesiella, Blautia, and unclassified S24-7 (adjusted P < 0.05), whereas it increased the abundance of Alistipes, Bacteroides, and unclassified ML635J-40 (adjusted P < 0.05).

At the species level, 2,477 effective OTUs were identified in the cecal samples. To confirm specific bacteria that are characteristic for dietary treatment, LEfSe analysis was also performed at the species level (**Figure 3**). A total of 33 OTUs were different among the three groups. Among these different OTUs, seven OTUs were higher in the control group, 13 OTUs were higher in the antibiotic treatment group, and 13 OTUs were higher in the CE treatment group (adjusted P < 0.05).

Real-time PCR was used to identify the number changes of several key bacteria groups in the ileal and cecal digesta

of broilers following treatment with antibiotic and CE. In the ileum (**Figure 4A**), antibiotic treatment significantly decreased the number of total bacteria, Firmicutes, Lactobacillus, Ruminococcus, and Prevotella compared with those in the control group (P < 0.05), whereas it increased the number of E. coli (P < 0.05). Antibiotic treatment also decreased the numbers of Lactobacillus, Ruminococcus, Prevotella, and Bifidobacterium (P < 0.05) compared with those in CE group, whereas it increased the number of E. coli (P < 0.05). Additionally, CE treatment increased the number of Bifidobacterium compared with those in control group (P < 0.05), whereas it decreased the number of E. coli (P < 0.05). The numbers of Bacteroidetes, Clostridium cluster IV, and Clostridium cluster XIV has no significant difference among the three groups (P > 0.05). Compared with the control and CE groups, antibiotic treatment significantly decreased the number of total bacteria and Lactobacillus in the cecum (**Figure 4B**) (P < 0.05), whereas it increased the number of E. coli (P < 0.05). Moreover, CE treatment increased the number of Lactobacillus compared with that in the control group (P < 0.05). However, there were no significant differences (P > 0.05) observed in the numbers of Firmicutes, Bacteroidetes, Ruminococcus, Prevotella, Clostridium cluster IV, Clostridium cluster XIV, and Bifidobacterium among the different dietary treatments. Overall, these results indicated that antibiotic and CE treatments significantly altered the intestinal bacterial community structure and the numbers of individual microbes.

#### Microbial Metabolites in the Ileal and Cecal Digesta

Lactate and SCFAs are the major carbohydrate fermentation products of gut microbes and serve as indicators of microbial activity. For lactate, dietary CE supplementation significantly increased the concentration of lactate compared with that in control and antibiotic groups in the ileum (**Figure 5A**) and cecum (**Figure 5C**, P < 0.05). Antibiotic treatment significantly decreased the lactate concentration in the cecum compared

with that in the control group (P < 0.05). For SCFAs, dietary supplementation with CE significantly increased total SCFA, acetate, and butyrate concentrations in the ileum (**Figure 5B**) and cecum (**Figure 5D**) compared with those in control and antibiotic groups (P < 0.05), whereas it decreased branchedchain fatty acids (BCFAs) and isovalerate concentrations in the cecum compared with those in the antibiotic group (P < 0.05). Furthermore, antibiotic treatment increased the concentrations of BCFA and isovalerate compared with those in the control group (P < 0.05). However, the concentrations of propionate, valerate, and isobutyrate were not affected by different dietary treatments (P > 0.05).

Ammonia and biogenic amines are amino acid deamination and decarboxylation products of gut microbes, respectively, and phenolic and indolic compounds are a product of aromatic amino acid degradation by gut microbes. For ammonia (**Figure 6A**), dietary CE supplementation decreased the concentration of ammonia compared with that in the control group (P < 0.05), whereas no significant differences were observed between the control and antibiotic groups (P > 0.05). For phenolic and indolic compounds (**Figure 6B**), CE supplementation decreased the concentration of p-cresol compared with that in the control and antibiotic groups (P < 0.05) and decreased the concentration of indole compared with that in the control group (P < 0.05). Dietary treatment showed no effect on the concentrations of phenol and skatole (P > 0.05). For amines (**Figure 6C**), cadaverine and spermidine were the major amines in the cecal digesta of broilers. Dietary supplementation with CE significantly decreased total amines, spermidine, methylamine, and tyramine concentrations compared with those in the control group (P < 0.05) and decreased total amines, spermidine, methylamine, putrescine, and tryptamine concentrations compared with those in the antibiotic group (P < 0.05). Furthermore, antibiotic treatment increased putrescine and tryptamine concentrations

compared with those in the control group (P < 0.05). However, the concentrations of cadaverine and spermine were not changed by different dietary treatments (P > 0.05). Overall, the results of the current study indicate that dietary supplementation with CE markedly enhanced the bacterial fermentation of carbohydrates and decreased the bacterial fermentation of protein in the cecum, whereas antibiotic treatment increased protein fermentation.

#### Gene Expression in the Ileal Tissue

The alteration of the gut microbial community and their metabolites could regulate intestinal epithelial gene expression. Thus, the mRNA expressions of genes involved in toll-like receptor (TLR), cytokines production, and mucosal defense were analyzed in the ileum. As shown in **Figure 7**, antibiotic treatment significantly up-regulated the relative mRNA expression of TLR4 and its downstream signal response genes (MyD88 and NF-κB) compared with those in the control and CE groups (P < 0.05). Dietary supplementation with CE down-regulated the relative mRNA expression of TNFα compared with that in the antibiotic group (P < 0.05), whereas it up-regulated IL-10, ZO-1, and Claudin expression (P < 0.05). Moreover, CE treatment also upregulated ZO-1 and Claudin expression compared with that in the control group (P < 0.05). There was no significant difference in the expression of IL-8, IL-1β, IFN-γ, occludin, and MUC2 among the three groups (P > 0.05).

#### Correlation Analysis Between Mucosal Gene Expression With Ileal Microbes or Their Metabolites

A Pearson correlation analysis was carried out to determine whether there was any relationship among mucosal gene

expression and main microbial numbers and the concentrations of metabolites (**Figure 8**). Correlation analysis revealed that the mRNA expression level of TLR-4 and NF-κB were positively correlated with the number of E. coli (P < 0.05), whereas it negatively correlated with the concentrations of acetate and total SCFAs, and the number of Lactobacillus, Bifidobacterium, and Prevotella (P < 0.05). The MyD88 mRNA expression level was positively correlated with the number of E. coli (P < 0.05), whereas it negatively correlated with the concentrations of acetate and total SCFAs, and the number of Lactobacillus (P < 0.05). The mRNA expression level of TNFα was negatively correlated with the concentrations of acetate, total SCFAs, and lactate, and the number of Bifidobacterium (P < 0.05). The mRNA expression level of IL-10 and ZO-1 was positively correlated with the concentrations of acetate, total SCFAs, and lactate, and the number of Bifidobacterium (P < 0.05), and the ZO-1 expression level also positively correlated with the butyrate concentration (P < 0.05), whereas the IL-10 expression level negatively correlated with the number of E. coli (P < 0.05). Meanwhile, the Claudin mRNA expression level was positively correlated with the acetate and butyrate concentrations (P < 0.05), whereas it negatively correlated with the number of E. coli (P < 0.05). Additionally, the correlation between microbes and

the concentrations of metabolites in the cecum was also analyzed and is shown in **Supplementary Figure S4**. Overall, these results indicated that the alteration in the ileal digesta microbiota and metabolites was correlated with the changes of epithelial gene expression in broilers.

#### DISCUSSION

The present study employed a whole growth stage continuous feed strategy to evaluate the effects of dietary supplementation with CE on the microbial community, microbial metabolite profiles, and expression of immune-related genes in the intestine of yellow-feathered broilers. The results showed that dietary supplementation with CE dramatically increased the number of Bifidobacterium and up-regulated the mRNA expression of intestinal barrier genes (ZO-1 and Claudin) in the ileum, whereas it decreased the number of E. coli. Meanwhile, CE supplementation increased the number of Lactobacillus and the concentrations of lactate and SCFAs in the cecum, whereas it decreased the concentrations of protein fermentation products (ammonia, p-cresol, indole, total amines, spermidine, methylamine, and tyramine). These findings further highlight

the key role of CE in regulating intestinal microbiota, metabolic profiles, and mucosal immune system, suggesting that CE may act as an efficient alternative of antibiotics for yellow-feathered broiler production.

# Citrus Extract Altered the Intestinal Microbiota of Yellow-Feathered Broilers

The gastrointestinal microbes of mammals play an important role in prevention of infectious diseases, regulation of nutrient digestion and metabolism, maintenance of intestinal morphology, and immune homeostasis of the host (Nicholson et al., 2005; Hooper et al., 2012; Song et al., 2019b). Applying functional substances to the diet of animals is an advantageous strategy for the modulation gastrointestinal microbiota and maintenance of host health. Citrus products have antimicrobial activity against E coli and S. typhimurium in poultry production and have a positive effect on intestinal health (Nannapaneni et al., 2008; O'Bryan et al., 2008). In the current study, we found that dietary supplementation with CE selectively regulated intestinal

short-chain fatty acid.

fmicb-10-02662 November 22, 2019 Time: 16:28 # 10

microbiota, including stimulating bacterial species with beneficial function (Bifidobacterium and Lactobacillus) and inhibiting the number of potential pathogen (E. coli). Bifidobacterium is recognized as a beneficial bacterium and potential probiotic, and a number of species can produce acetate and lactate (Gibson et al., 2004) and have a positive effect on intestinal health of both humans and animals and offer the ability to normalize the ratio of pro-inflammatory and anti-inflammatory cytokines (O'Mahony et al., 2005). Lactobacillus is well known as a potentially beneficial species in the intestine, which can inhibit the colonization of potential pathogenic groups by competing with the epithelial binding sites and nutrients, and the productions of antimicrobial factors including lactate and bacteriocins, thus maintaining the homeostasis of the intestinal environment of host (Yu et al., 2018). E. coli is an opportunistic pathogenic bacteria that has been shown to be positively associated with numerous infections and the development of diseases, including bacillary dysentery or colitis disease (Barnich et al., 2007). Thus, these results indicate that dietary inclusion of CE may be beneficial for the intestinal health for broilers.

To date, the research for the effect of CE on intestinal microbiota is still limited. The beneficial functions of plant extract mainly depended on their specific bioactive components (such as organic acids, polysaccharide, and flavone), which can synthesize as antimicrobial agents against microbial infection and alter their composition (Lillehoj et al., 2018). In our study, CE contained plentiful contents of total flavone (about 2.48%), which can target and modulate microbiota composition. Dietary flavonoid intake significantly increased the abundance of Bifidobacterium in humans (Klinder et al., 2016) or mice (Espley et al., 2013). CE also has a high concentration of polysaccharide (about 1.2% in the current study), which has the capability to change the composition and diversity of intestinal microbiota, such as increase the abundance of Lactobacillus in mice (Li et al., 2019). Additionally, CE is also a potential source of organic acids (including 1.3% citric acid and 0.68% chlorogenic acid), which can decrease the pH in the gastrointestinal tract and then inhibit the growth of some pathogenic bacteria (such as E. coli) owing to their susceptibility to low pH (Feng et al., 2018; Zhang Y. et al., 2018). Thus, the intestinal microbiota alteration of broilers in response to administering CE may be attributed to the flavonoid, polysaccharide, and/or organic acid contents, whereas the underlying mechanisms should be further studied.

# Citrus Extract Increased Microbial Fermentation of Carbohydrate but Decreased Fermentation of Protein

The microbial metabolite profiles in the intestinal digesta can reflect the microbial activity and intestinal health. SCFAs and lactate are primarily fermentation products of the carbohydrate metabolism by bacteria in the gut. In the current study, CE significantly increased the concentrations of lactate, total SCFA, acetate, and butyrate in the cecum, which suggests that CE increased the carbohydrate fermentation by bacteria. Bifidobacterium and Lactobacillus are the main acetate, butyrate, and lactate producers in the gut (Gao et al., 2018). Increase in SCFAs and lactate concentrations was accompanied by an increase of the number of Bifidobacterium and Lactobacillus. Thus, one potential explanation for increased cecal digesta SCFAs and lactate concentrations may be due to increase the abundance or numbers of SCFA- and lactate-producing bacteria. SCFAs and lactate can exert many beneficial effects for host health. Acetate can be used as energy substrate of peripheral tissues, butyrate exerts an anti-inflammatory function and is the main energy source of colonic epithelial cells (Yu et al., 2018), and lactate can reduce the pH value in the gastrointestinal tract and inhibit the multiplication of pathogens that invade the gut (such as E. coli) (Gao et al., 2018; Yu et al., 2018). Thus, the increase of total SCFAs, acetate, butyrate, and lactate concentrations

in the current study suggests a host-friendly gut environment after CE treatment.

Nitrogenous compounds, such as undigested protein and amino acids, can also be fermented by intestinal bacteria and form putrefactive catabolites, such as ammonia, biogenic amines, phenol, and indole compounds. In the current study, the decrease in cecal ammonia, biogenic amines, phenol, and indole levels suggests that bacterial deamination and decarboxylation of amino acids were affected after CE administration. A high ammonia level has been shown to exert a negative effect on the health of the hosts, such as inhibiting the growth and differentiation of intestinal epithelial cells and increasing the incidence of diarrhea in the host (Dong et al., 1996). Additionally, high concentrations of biogenic amines (tyramine), indole, and p-cresol also exert adverse impacts on gut health and are regarded as co-carcinogens and colon cancer promoters (Nowak and Libudzisz, 2006; Davila et al., 2013). Thus, the decrease in the concentrations of ammonia, biogenic amines, indole, and phenolic compounds via CE supplementation may have a beneficial effect on gut health. Overall, together with the increase in lactate and SCFAs, these findings indicate that CE changed the microbial metabolic activity, increased microbial fermentation of carbohydrate, and decreased microbial protein catabolism.

#### Citrus Extract Affected the Ileal Mucosal Response Involved in the Intestinal Barrier Function

The intestinal barrier performs the essential function of defense against the passage of pathogenic agents and luminal antigens into the gastrointestinal epithelium while enabling the acquisition of dietary nutrients (Broom, 2018). In the present study, CE supplementation up-regulated the mRNA expression of intestinal barrier genes in the ileum compared with that in control and antibiotic groups, such as ZO-1 and Claudin. This suggests that CE may improve the integrity of the intestinal epithelium, consequently generating a hostfriendly gut environment, which could help defend against pathogen infection. The changes in intestinal microbes and associated metabolites can both positively and negatively affect the histological function of the gastrointestinal epithelium, such as intestinal barrier permeability. Previous studies have shown that a number of pathogens, such as the Enterotoxigenic E. coli K88, can impair intestinal barrier functions by downregulating ZO-1 and occludin gene and protein expression (Wang et al., 2017), whereas Lactobacillus reuteri LR1 up-regulated the expression of ZO-1 and occludin gene in piglets (Yi et al., 2018). Moreover, some bacterial fermentation product, such as butyrate, can maintain gut integrity. As described above, dietary inclusion of CE increased the numbers of Bifidobacterium and Lactobacillus and the concentrations of total SCFAs, acetate, and butyrate and decreased the number of E. coli. Our previous study also revealed that the Bifidobacterium and Lactobacillus numbers and butyrate concentration were positively correlated with mRNA levels of ZO-1 and occludin (Yu et al., 2019). Therefore, we might speculate that the high number of Bifidobacterium and Lactobacillus, SCFAs, and butyrate concentrations in the CE group may be the factors accounting to the up-regulation of intestinal barrier gene expression, although the underlying mechanism required further clarification.

#### Antibiotic Treatment Induced Intestinal Microbiota Dysbiosis, Altered Fermentation Profiles of Microbial Metabolism, and Disturbed the Innate Immune Homeostasis

Antibiotics can cause gut microbiota dysbiosis, inhibit the innate immune defenses, and lead to increased pathogen colonization and disease susceptibility (Zhang C.J. et al., 2018). In the current study, antibiotic treatment increased the abundance and the number of potential pathogens, while decreasing those with beneficial function, which was consistent with previous studies (Yu et al., 2017a, 2018). Among the affected bacterial groups, many bacterial species have previously been related to increased disease risk in humans or animals. For example, antibiotic treatment decreased the abundances of Barnesiella and Blautia, and the numbers of Lactobacillus, Ruminococcus, Prevotella, and Bifidobacterium, and increased the abundance of Alistipes and the number of E. coli. Barnesiella and Blautia are SCFA producers (Zhang et al., 2012) and have the ability to protect the integrity of the intestinal barrier function and alleviate dextran sulfate sodium-induced inflammation (Lu et al., 2018). Ruminococcus and Prevotella are key bacterial groups for the degradation of dietary fibers and polysaccharide to regulate the host metabolism (Wang et al., 2018). Alistipes can directly elevate inflammation levels and induce mucosal injuries in goats (Ye et al., 2016). The potentially beneficial function of Lactobacillus and Bifidobacterium and the potential adverse function of E. coli have been mentioned above. Taken together, these results point to a potentially detrimental impact of antibiotic treatment on the composition of intestinal microbiota in broilers.

Corresponding to the alteration of intestinal microbiota, antibiotics showed a marked impact on the microbial metabolism of carbohydrate and amino acids in the cecum, as indicated by the decrease in the concentrations of lactate and most SCFAs, and the increase in the concentrations of BCFA, most amines, and p-cresol. This is consistent with previous studies (Gao et al., 2018; Pi et al., 2018; Yu et al., 2018), which observed that antibiotic treatment led to a decrease in most SCFA concentrations, whereas it increased amine concentrations in pigs. Overall, the findings of this study provide clear evidence for a shift of the microbial metabolic activity, with lower microbial carbohydrate fermentation and higher microbial catabolism of amino acids after antibiotic treatment.

The alteration of intestinal microbes and their metabolites could regulate the immune homeostasis of intestinal mucosa. In the present study, we found that antibiotic treatment upregulated the mRNA expression of TLR4, NF-κB, MyD88, and pro-inflammatory cytokines TNFα. TLR4 can transfer signals to NF-κB via MyD88-dependent pathway and then induce the activation of pro-inflammatory cytokines (Zhang et al., 2017).

Thus, the increased gene expression levels of pro-inflammatory cytokines after antibiotic treatment may be attributed to an up-regulation of the TLR4-MyD88-NF-κB signaling pathway of broilers. Collectively, together with the alteration of intestinal microbiota, fermentation profiles of microbial metabolism, and the mucosal gene expression levels, our results indicate that antibiotic treatment induced the gut microbiota dysbiosis, altered the microbial metabolism, and inhibited the innate immune defenses, likely toward a host-adverse gut environment.

#### CONCLUSION

In conclusion, this study demonstrated that dietary supplementation with CE changed the intestinal microbial composition, microbial metabolite profiles, and immune status of broilers, likely toward a host-friendly gut environment. Intestinal microbes, such as the numbers of Bifidobacterium and Lactobacillus, increased after CE supplementation, whereas the number of E. coli decreased. Meanwhile, CE markedly increased the concentrations of lactate and SCFAs, whereas it decreased the concentrations of amino acid fermentation products. The expression of intestinal barrier genes (ZO-1 and Claudin) was increased. However, antibiotic treatment induced gut microbiota dysbiosis, altered the microbial metabolism, and disturbed the innate immune homeostasis, likely toward an unhealthy gut environment. These findings suggest that CE may act as an efficient antibiotic alternative for yellow-feathered broiler production.

#### DATA AVAILABILITY STATEMENT

The datasets generated for this study can be found in the following repository. The raw reads in this study were submitted to the National Center of Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under accession numbers SRR9074899–SRR9074916.

#### REFERENCES

# ETHICS STATEMENT

The experimental proposals and procedures for the care and treatment of the broilers were approved by the Animal Care and Use Committee of the Guangdong Academy of Agricultural Sciences (authorization number GAASIAS-2016-017).

#### AUTHOR CONTRIBUTIONS

MY, WC, and XM conceived and designed the whole trial. ZL and YC conducted the broiler trials. MY and ZL conducted laboratory analyses. MY, XM, and GW wrote the manuscript.

#### FUNDING

This work was supported by the National Key Research and Development Program of China (2016YFD0501210), the Presidential Foundation of the Guangdong Academy of Agricultural Sciences (201802B), Talent Project of the Guangdong Academy of Agricultural Sciences (201803), Guangdong Modern Agro-Industry Technology Research System (2019KJ115), and Science and Technology Planning Project of Guangdong Province (2019A050505007).

# ACKNOWLEDGMENTS

The authors would like to thank Dr. Dun Deng, Dr. Zhichang Liu, Mr. Ting Rong, Ms. Zhimei Tian, and Ms. Huijie Lu for their help in the successful completion of this study.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2019.02662/full#supplementary-material

the gut microbiota and consequences for the host". Pharmacol. Res. 68, 95–107. doi: 10.1016/j.phrs.2012.11.005

diet compared with a normal-protein diet. J. Nutr. 146, 474–483. doi: 10.3945/ jn.115.223990

fmicb-10-02662 November 22, 2019 Time: 16:28 # 13

different dietary protein levels. J. Anim. Sci. Biotechnol. 8:60. doi: 10.1186/ s40104-017-0192-2

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Yu, Li, Chen, Wang, Cui and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

fmicb-10-02662 November 22, 2019 Time: 16:28 # 14

# Effect of Diet on the Enteric Microbiome of the Wood-Eating Catfish *Panaque nigrolineatus*

*Ryan C. McDonald1 , Joy E. M. Watts2 and Harold J. Schreier1,3 \**

*1Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, United States, 2Department of Biological Sciences, University of Portsmouth, Portsmouth, United Kingdom, 3Department of Marine Biotechnology, University of Maryland, Baltimore County, Baltimore, MD, United States*

Wood is consistently found in high levels in the gastrointestinal tract of the Amazonian catfish *Panaque nigrolineatus*, which, depending on environmental conditions, can switch between xylivorous and detritivorous dietary strategies. This is highly unusual among primary wood consumers and provides a unique system to examine the effect of dietary change in a xylivorous system. In this study, microbiome and predictive metagenomic analyses were performed for *P. nigrolineatus* fed either wood alone or a less refractory mixed diet containing wood and plant nutrition. While diet had an impact on enteric bacterial community composition, there was a high degree of interindividual variability. Members of the Proteobacteria and Planctomycetes were ubiquitous and dominated most communities; Bacteroidetes, Fusobacteria, Actinobacteria, and Verrucomicrobia also contributed in a tissue and diet-specific manner. Although predictive metagenomics revealed functional differences between communities, the relative abundance of predicted lignocellulose-active enzymes remained similar across diets. The microbiomes from both diets appeared highly adapted for hemicellulose hydrolysis as the predicted metagenomes contained several classes of hemicellulases and lignin-modifying enzymes. Enteric communities from both diets appeared to lack the necessary cellobiohydrolases for efficient cellulose hydrolysis, suggesting that cellobiose is not the primary source of dietary carbon for the fish. Our findings suggest that the *P. nigrolineatus* gut environment selects for an enteric community based on function, rather than a vertically transferred symbiotic relationship. This functional selection strategy may provide an advantage to an organism that switches between dietary strategies to survive a highly variable environment.

Keywords: lignocellulose digestion, microbiome, 16S rRNA gene amplicon sequencing, predictive metagenomics, Amazonian catfish

#### INTRODUCTION

The Amazonian catfish *Panaque nigrolineatus* consumes large quantities of wood as part of its diet, a trait shared among a limited number of related fish species (Schaefer and Stewart, 1993). Depending on environmental conditions, *P. nigrolineatus* can adjust its feeding behavior, switching between xylivorous ("wood-eating"), detritivorous, and herbivorous feeding behaviors.

#### *Edited by:*

*Jie Yin, Institute of Subtropical Agriculture (CAS), China*

#### *Reviewed by:*

*Weida Gong, University of North Carolina at Chapel Hill, United States Woo Jun Sul, Chung-Ang University, South Korea*

> *\*Correspondence: Harold J. Schreier<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Microbial Symbioses, a section of the journal Frontiers in Microbiology*

*Received: 14 August 2019 Accepted: 05 November 2019 Published: 29 November 2019*

#### *Citation:*

*McDonald RC, Watts JEM and Schreier HJ (2019) Effect of Diet on the Enteric Microbiome of the Wood-Eating Catfish Panaque nigrolineatus. Front. Microbiol. 10:2687. doi: 10.3389/fmicb.2019.02687*

**566**

While jaw and tooth morphologies appear to be well adapted for wood consumption (Geerinckx et al., 2007; Adriaens et al., 2008), other features such as GI tract length, microvilli surface area, and gut retention times are inconsistent with a diet dependent primarily on wood consumption (German, 2009; German et al., 2010). It has been hypothesized that wood intake may serve a selective advantage during the Amazonian dry season (Araujo-Lima et al., 1986), or a consequence of epiphyte or fungal hyphae consumption. The nutritional benefits of wood feeding and subsequent digestion by *P. nigrolineatus* are, as yet, unknown.

Consuming wood as a primary food source poses many challenges. The physical and chemical properties of most woods make them highly recalcitrant and of poor nutritive value (Pu et al., 2011). Although rich in carbohydrates, these compounds are largely inaccessible due to their incorporation into structural polysaccharides, such as cellulose and hemicelluloses (Cosgrove, 2005). The crystalline nature of cellulose and presence of other structural plant cell wall polymers, such as lignin, limit microbial infiltration and exclude water, making the environment non-conducive to enzymatic hydrolysis (Cosgrove, 2005; Grabber, 2005). In addition to these challenges, wood is also nitrogen deficient; the nitrogen content of Amazonian woods is regularly below 0.5% (Martius, 1992). To overcome this deficiency, many wood-feeding organisms will also consume non-wood nutrient sources or rely on the activity of enteric microorganisms to supply reduced nitrogen compounds (Raychoudhury et al., 2013; Gruninger et al., 2016; Mikaelyan et al., 2017). With a few exceptions, nutrient acquisition by wood-eaters is mediated by their enteric microbial community, which liberates assimilatory sugars and generates nitrogenous compounds (Tokuda et al., 2014; Cragg et al., 2015).

The enteric bacterial community of *P. nigrolineatus* has been shown to possess a unique microbiome, with the potential of assisting lignocellulose degradation and conducting biological nitrogen fixation (McDonald et al., 2012). Included in these communities are several species of Rhizobiales, Flavobacteriales, Legionellales, Burkholderiales, and Clostridiales. Distinct communities have been identified in the fore, mid, and hindguts of the fish despite any well-defined anatomical features (e.g., sphincters or cecum) demarcating these regions. A diverse and distinct fungal community also resides and is associated with cellulose degradation in the GI tract (Marden et al., 2017). Culture-based analyses and biochemical tests confirmed the presence of a lignocellulolytic and diazotrophic community (Watts et al., 2013; McDonald et al., 2015). These analyses suggest that a considerable amount of microbial metabolic cross-feeding may be occurring within the fish GI tract, where carbohydrates are liberated by cellulolytic species and consumed by non-cellulose utilizers. In comparison to the cellulolytic community, the enteric diazotrophic community was less diverse and was comprised of known nitrogen-fixing Rhizobiales and *Clostridium* (McDonald et al., 2015).

Diet has been shown to have a major impact on enteric microbial communities of animals (David et al., 2014; Ringø et al., 2016). Many xylivores are highly adapted for wood consumption, but retain some capacity to survive on less refractory diets (Tanaka et al., 2006; Miyata et al., 2014). However, the enteric communities are specialized and necessary for the processing of digesta; their manipulation often has deleterious effects on the host (Rosengaus et al., 2011). For some wood-feeding organisms, the enteric communities appear highly stable, where dietary changes have minimal impact on community composition (Boucias et al., 2013; Wang et al., 2016). Dual feeding behavior in adult fish is not unique to *P. nigrolineatus*, but far less common to the ontogenetic diet shifts seen in other fish species (Bledsoe et al., 2016; Sánchez-Hernández et al., 2019). To examine the effects of diet on a primarily wood-feeding fish species, we characterized the enteric microbiomes of animals fed either a wood or mixed diet.

#### MATERIALS AND METHODS

#### Animals, Experimental Design, and Tissue Sampling

*Panaque nigrolineatus* (L190) were acquired through aquarium wholesalers. Fishes were randomly assigned to aerated, filtered tanks (29 ± 1°C), and acclimated for 3 weeks on an *ad libitum* mixed diet of red palm (*Cocos nucifera*), hearts of palm (*Euterpe precatoria*), and sinking algae wafers (Hikari, Hayward, CA). After acclimation, fishes were randomly assigned to either a mixed or wood diet for 6 weeks. Mixed diet fishes were provided algae wafers and hearts of palm daily with continuous access to wood. Wood-fed fishes were provided with wood exclusively. All wood was autoclaved twice prior to being placed in the tanks. To inhibit algae growth, all fishes were reared under low/no light conditions. Lights were turned on approximately 30 min each day for tank maintenance and feeding. Two independent feeding studies were carried out, designated feeding study 1 (two fishes, designated 1X and 1W, for mixed (X)-and wood (W)-fed diets, respectively) and feeding study 2 (six fishes, designated 2X, 2W, 3X, 3W, 4X, and 4W). Both feeding studies were conducted under identical experimental conditions.

At the conclusion of the feeding experiments, fishes were euthanized *via* an overdose of the anesthetic 3-aminobenzoic acid ethyl ester (MS-222, 50 mg/L) and were immediately transferred to a chilled dissecting tray where the ventral body plate was removed. The body cavity was filled with cold, sterile phosphate buffered saline (PBS) to facilitate removal of the GI tract. Once uncoiled, the intestines were disconnected by cutting at the anus and just posterior to the stomach, and measured for length. The intestine was divided into three equal lengths demarcating the fore (F), mid (M), and hindgut (H).

#### DNA Extraction, Amplification, and Sequencing

GI tract samples were processed for fish from each diet using the Qiagen (Germantown, MD, USA) DNeasy Blood and Tissue Kit with pre-treatments for Gram-positive and Gramnegative bacteria according to the manufacturer's instructions. DNA was extracted from three samples of each GI tract region for each fish and was pooled and processed for PCR amplification. To profile the bacterial community, 16S rRNA gene sequencing libraries were prepared according to the manufacturer's instructions (Illumina, San Diego, CA). Briefly, the V3-V4 region of the 16S rRNA gene was amplified using the primer pair evaluated previously (Klindworth et al., 2017) using the 2X KAPA HiFi HotStart ReadyMix (Sigma-Aldrich, St. Louis, MO) with the following polymerase chain reaction (PCR) program parameters: an initial denaturation step of 3 min at 95°C followed by 25 cycles of denaturation for 30 s at 95°C, annealing for 30 s at 55°C, and elongation for 30 s at 72°C, followed by a final elongation for 5 min at 72°C. Index PCR was performed using the Nextera XT Index Kit according to the manufacturer's instructions (Illumina, San Diego, CA). PCR products purified using AMPure XP beads (Beckman Coulter, Brea, CA), pooled in equimolar amounts, and sequenced using the Illumina MiSeq platform (250 bp paired-end reads). For the second feeding study, the standard Illumina primers for V3-V4 were modified to include a unique trinucleotide sequence (**Table 1**) between the overhang adapter and the 16S rRNA primer, which allowed for double-dual indexing of the samples (Fadrosh et al., 2014). A total of 10 samples were pooled in equimolar amounts prior to the standard index PCR reaction.

#### DNA Sequence Processing and Community Analysis

Raw reads were preprocessed using CLC Workbench (version 9) (Qiagen). Adapter sequences were removed and read pairs were quality trimmed (qual. limit = 0.05; ambiguous nucleotide maximum = 2; minimum sequence length = 100 bp) and merged (mismatch cost = 2; gap cost = 3; maximum unaligned = 0; minimum score = 8). Sequences were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) bioinformatics pipeline (Caporaso et al., 2010). Operational taxonomic units (OTUs) were picked using the open reference method against the Silva\_132 database (minimum OTU cluster size = 2; OTU similarity = 0.97) (Desantis et al., 2006). Taxonomies were summarized at multiple levels (L2-L6) using the *summarize\_ taxa.py* script. Rarefaction plots and alpha diversity measures were calculated using the *alpha\_rarefaction.py* and *alpha\_diversity. py* scripts, respectively. OTU matrices were normalized using DESeq2 variance stabilizing transformation prior to Bray-Curtis distance matrix generation. Principle coordinate analysis (PCoA) plots were generated using *principal\_cordinates.py*. OTU networks were generated for all tissue samples using the *make\_otu\_network. py* script. For this analysis, OTUs were re-picked according to the above method; however, the minimum cluster size was increased to 25 in order to reduce the number of nodes. The

TABLE 1 | List of modified Illumina V3-V4 primers and resulting read counts from the second feeding study 16S rRNA gene survey.

*The primers were modified to include a unique trinucleotide sequence between the 16S rRNA primer and overhang adapter. Successful amplification from the sample is indicated by (+).*

network was visualized using Cytoscape v3.2.1 using an edgeweighted spring-embedded layout.

#### Predictive Functional Profiling of Microbial Communities

Predictive functional profiling was performed using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) (Langille et al., 2013). For the PICRUSt analysis, all samples were normalized to 80,000 reads/sample prior to OTU picking. OTUs were picked using the closed reference method against gg\_13\_8 (minimum OTU cluster size = 2; OTU similarity = 0.94). The resulting BIOM table was normalized for 16S rRNA gene copy number prior to predicting functions for metagenomes. For this analysis, KEGG orthologs (KO) were recorded. KOs were collapsed into pathways (L1-L3) using the *categorize\_by\_function.py* script. The results were visualized in Rstudio (v 0.98.1083) using the heatmap.2 function of the gplots package. To determine which OTUs and samples were contributing particular functions, *metagenome\_contributions.py* was also run. To determine if functional profiles differed across tissue region or diet, principal component analysis (PCA) was performed on the predicted metagenome (not collapsed within the KEGG hierarchy) using the ggplot2 package in Rstudio.

#### RESULTS

#### Microbial Community Characterization

Microbial communities were analyzed from GI tract regions of wood-fed and mixed diet-fed fish by next generation 16S rRNA amplicon sequencing, and functional gene profiles were then extrapolated using *in silico* methods. Except for foregut samples from two wood-grown and one mixed-diet fish, which could not be amplified by PCR, more than 1.2 × 106 high quality reads were generated across all samples and redistributions are shown in **Table 1**. Rarefaction analysis (**Supplementary Figure S1**) and alpha diversity measures (**Table 2**) showed that the bacterial communities were sufficiently sampled, and further sequencing would be unlikely to significantly increase the observed microbial diversity detected. Species richness varied greatly between feeding study replicates, with a marked reduction in diversity observed in the second feeding experiment. Despite this, Good's coverage estimates remained high (>0.95 for all samples).

#### Effect of Diet on Enteric Microbial Community Composition

All microbial communities analyzed were dominated by a few microbial phyla (**Figure 1**). These included Proteobacteria (19–99%), Planctomycetes (<1–66%), Fusobacteria (0–43%), Bacteroidetes (<1–26%), and Actinobacteria (<1–23%). While the relative abundance of these major phyla varied greatly across diet, tissue region, and feeding study replicate, the constituent members of these phyla largely remained unchanged (**Supplementary Table S1**). Planctomycetes were observed in high abundance in all the first experimental study samples, as well as the foreguts of mixed diet fish from the second feeding study; the most predominant classes were Pirellulales, Gemmatales, Planctomycetales, and Isosphaerales. Sequences with the highest sequence similarity with an unidentified *Cetobacterium* species in the class Fusobacteria were observed in the mid and hindgut of a single mixed (X) diet fish (2XM and 2XH) as well as the hindgut of a wood-fed (W) fish (1WH).

TABLE 2 | Observed OTUs, Chao1, and Good's coverage values were calculated to compare bacterial diversity among diets and tissue regions.

*All calculations were performed using QIIME with 0.97 OTU identity threshold.*

In the first experiment, Bacteroidetes were predominant in all tissue regions of the mixed diet fish, as well as the hindgut of the wood-fed fish. While the diversity of Bacteroidetes was high, most sequences had high sequence similarity to the endosymbiont *Candidatus* Cardinium. However, in both experiments, Actinobacteria was the most common phyla, found in nearly all tissue regions across feeding regimens and feeding study replicates. The highest proportions of Actinobacteria were identified in the mixed diet fish of the second feeding experiment and had high sequence similarity to the Corynebacteriales genera *Mycobacterium* and *Gordonia*.

Both the Proteobacteria and Verrucomicrobia populations showed marked composition differences between the two feeding study replicates (**Figure 1**). The Proteobacteria identified in the first experiment were predominantly Alphaproteobacteria, consisting of Rhizobiales and Rhodobacterales. The Proteobacteria from the remaining enteric samples of the second experiment were almost exclusively Gammaproteobacteria and consisted of Aeromonadales. Distinct differences in Proteobacteria were also identified in the tank water; sequences most closely aligned with Rhodospirillales were foremost in mixed diet tank water, while Sphingomonadales-like sequences dominated wood diet tank water. Verrucomicrobia was identified predominantly in the mixed diet-fed fish of both feeding studies. In the first experiment, the sequences had highest similarity to an uncultivated Verrucomicrobiales and *Luteolibacter* species, contrasting to the second experiment where the sequences were most similar to either a species of *Chthoniobacter* or *Prosthecobacter*.

Trends in community composition were detected; however, diet did not appear to select for specific microbial communities in any tissue region of the *P. nigrolineatus* GI tract. PCoA and UPGMA trees based on Bray-Curtis distances showed samples largely clustered based on experimental study and diet (**Figures 1, 2**). Feeding experiment 1 and 2 were distinguished along PC1 of the PCoA. UPGMA trees revealed that within experimental study groups, wood and mixed diet samples largely formed monophyletic clades. The exception to this is sample 3XH, which formed a paraphyletic group with the other mixed diet samples from the second experimental feeding group.

*P. nigrolineatus* possesses a taxonomically restricted core microbiome. Fifty-five OTUs were identified in >70% of samples across both feeding regimens (**Supplementary Table S2**). The majority of these OTUs had high sequence similarity to species of Pirellulales, Rhizobiales, Rhodobacterales, and Aeromonadales, and their relative abundance was highly variable. For this analysis, OTU relative abundance was not used to define the core microbiome (e.g., only including OTUs that represent >1% of all reads). Distinct core microbiomes were observed between wood and mixed diet-fed fish (**Supplementary Tables S3, S4**). Wood-fed fish had an expanded core microbiome (115 OTUs) relative to mixed diet-fed fish (72 OTUs) (**Supplementary Figure S2**), with minimal overlap between the two groups (~21%) (**Supplementary Figure S3**). Despite,

abundance of predominant bacterial phyla in the tank water (TW, diamond) as well as fore (F, triangle), mid (M, circle), and hindguts (H, square) of *P. nigrolineatus* fed either a wood (W, open symbols) or mixed diet (X, closed symbols). OTUs were picked using the SILVA\_132 16S database (OTU ID =0.97).

minimal OTU overlap, the core microbiomes were taxonomically compositionally similar (**Supplementary Tables S2, S3**).

Network analysis revealed that a small number of predominant core microbiome OTUs with high sequence similarity to members of the genera *Aeromonas* were primarily responsible for the observed shift in Proteobacteria abundance (**Figures 3B,C**). Most OTUs were shared among multiple gut regions, within and across feeding regimens. The reduced OTU network (**Figure 3A**) represents the most abundant OTUs (minimum OTU cluster size of 1 × 103 reads after rarefaction) and are identical with those identified in the core microbiome analysis. Consistent with the PCoA analysis, more OTUs were shared among samples within a feeding study. Sample node degree distributions also suggest increased biodiversity within the first feeding study (**Supplementary Figure S4**). This is consistent with the alpha diversity measures. Because the network analysis is constructed from a rarefied OTU table, the reduction in average node degree distributions likely represents a true reduction in biodiversity in these samples and is not a result of reduced sampling depth.

#### Predictive Metagenome Profiling

To gain insight into the metabolic capacity of the enteric microbiome, a PICRUSt analysis was performed to generate a predictive functional profile. To compare across feeding regimens, the predicted gene profiles from the mid and hindguts were averaged and collapsed at a higher level (L2) within the KEGG hierarchy (**Figure 4**). Based on this analysis, the relative abundance of several pathways was statistically different between feeding regimens. Wood-fed fish had a higher abundance of genes involved in transcription and enzyme families that include peptidases, cytochrome P450, and protein kinases, while mixed diet fishes were enriched for genes involved in the metabolism of amino acids, terpenoids, and polyketides, as well as the metabolism of other amino acids. These differences were reflected in the PCA analysis of the predicted microbiome, which shows clustering of wood and mixed diet samples along PC2 (**Supplementary Figure S5**). No significant differences were observed for the relative abundance of genes involved in carbohydrate metabolism or xenobiotic degradation. There were

no significant differences in pathway relative abundances between the mid and hindgut regions within feeding regimens suggesting the microbiome within these tissues may function similarly in regard to lignocellulose degradation. This finding was supported by PCA analysis (**Supplementary Figure S6**), which showed no distinction between any tissue regions.

Despite high similarity between wood and mixed diet-fed fish predicted metagenome at higher levels in the KEGG hierarchy, comparisons at lower levels (L3) revealed many differences (**Supplementary Table S5**). The majority of these KEGG pathways were associated with the metabolism of amino acids and carbohydrates. Mixed diet fish had an increase in pathways associated with amino acid transformation and included the metabolism of glycine, serine, threonine, histidine, lysine, tryptophan, and tyrosine, as well as the degradation of lysine valine, leucine, and isoleucine. However, pathways associated with carbohydrate utilization were found in high abundance in both feeding regimens. Mixed-diet fish had a higher relative abundance of pathways involved in the metabolism of butanoate, glyoxylate, dicarboxylate, propanoate, and pyruvate. In comparison, wood-fed fish had higher abundance of pathways involved with metabolism of amino and nucleotide sugars, fructose, mannose, galactose, starch, and sucrose, as well as the interconversion of pentose and glucuronate and a higher abundance of genes associated with degradation of other glycans. Additionally, pathways were found to be differentially abundant between feeding regimens and included processes involved with the metabolism of lipids, cofactors, and vitamins, as well as the degradation of xenobiotics.

Because wood degradation requires a diverse suite of enzymes for complete hydrolysis, the relative abundance of genes for several lignocellulose-active enzymes were also examined (**Figure 5**). Included in the analysis were enzymes active against cellulose, hemicellulose, lignin, and cello-oligosaccharides. Despite statistically significant differences in starch metabolism pathways at lower KEGG classifications, there were very few differences in the relative abundance of lignocellulose active enzymes. For most enzymes, neither diet nor tissue region appeared to influence relative abundances with the only significant differences between wood and mixed diet-fed fish seen in the relative abundances of lysophospholipase (EC <IP_ADDRESS>) and carboxylesterase (EC <IP_ADDRESS>), which both act upon hemicellulose. The vast majority of carbohydrate active enzyme genes were observed infrequently (~1 × 10−6 to 1 × 10−5 ); however, a limited number were predicted to be in higher abundance (>5.0 × 10−4 ). These were primarily limited to activities likely associated with lignin degradation (e.g., cytochrome C peroxidase, catalase-peroxidase, and glycolate oxidase), but also included glyceraldehyde 3-phosphate dehydrogenase.

Endocellulases (EC <IP_ADDRESS>), exocellulases (EC <IP_ADDRESS>), and ß-glucosidases (EC <IP_ADDRESS>) were detected in both feeding regimens (**Figure 5** and **Table 3**). The relative abundance of the individual cellulases varied (~1 × 10−7 to 4 × 10−4 ) but remained largely the same across tissue regions and diets. All tissue regions across both diets had a higher relative abundance of endoglucanases and ß-glucosidases than exocellulases (**Table 3**). Exocellulases were exceedingly rare in all samples and completely absent from several samples of the second feeding study. Distributions were calculated for each of the three cellulases in the predicted metagenome (**Figure 6**) and, like the relative abundance analysis, differences were observed

TABLE 3 | Relative abundance of the three classes of cellulose degrading enzymes based on predictive metagenomics.

*Abundances were calculated using PICRUSt (see methods) and compared across diet type and tissue region.*

between various classes of cellulases. Endocellulases were primarily attributed to Proteobacteria and Planctomycetes in both feeding regimens, while Bacteroidetes, Verrucomicrobia, Armatimonadetes, and Actinobacteria provided functions in specific samples. ß-Glucosidases were provided primarily by Proteobacteria and Actinobacteria. While the relative abundance of Actinobacteria and Proteobacteria varied between samples and diets, the taxonomic makeup was highly consistent and included the Proteobacteria genera *Enterobacter*, *Aeromonas*, *Citrobacter*, *Novospirillium*, *Cronobacter*, and *Rhodobacter*, as well as the Actinobacteria genus *Gordonia* and an unidentified Microbacteriacea. Exocellulases were provided by a single phylum of bacteria in nearly all enteric samples. In the first study, exocellulases were found to be exclusively represented by the Firmicutes and included members of the orders OPB54 and Clostridiales, while in the second study, they originated either Proteobacteria or Actinobacteria and included Rhizobiales and Actinomycetales.

#### DISCUSSION

Diet has been shown to have a major impact on enteric bacterial communities of a variety of animals. *P. nigrolineatus* provides a unique opportunity to examine the effects of diet change on a wood-consuming organism. Unlike other xylivorous animals, *P. nigrolineatus* is capable of shifting between diets with seemingly minimal deleterious health effects. In this study, enteric microbial communities were examined for *P. nigrolineatus* provided either a wood or mixed diet. Attention was given to abundance and distribution of putative lignocellulolytic microorganisms that are essential for deriving nutrition from a wood diet. Predictive metagenomics were also used to determine whether a wood diet enriched for pathways involved in lignocellulose degradation.

#### Enteric Bacterial Community Composition

Difficulties in obtaining large numbers of fish at one time necessitated two feeding experiments. Any differences between community composition for the two feeding studies might

the PICRUSt analysis.

be attributed to the environment where the animals were caught and their initial handling. While our study would have benefitted from examining all animals at the same time, we found that enteric bacterial community composition for both feeding studies were largely consistent with those previously established by previous 16S rRNA metagenomic library analyses (McDonald et al., 2012). Therefore, we believe that communities associated with either mixed diet or wood-fed fish are representative of their dietary regimen and differences observed for community structure are significant and diet influences the *P. nigrolineatus* enteric bacterial community. These results diverge from studies in termites where non-wood diets had minimal impact on bacterial community composition but did cause changes in relative bacterial abundance (Huang et al., 2013; Wang et al., 2016; Su et al., 2017). There was no obvious correlation between diet and OTU richness in any tissue region. Wood-fed fish in the first feeding experiment had a less diverse enteric community than mixed diet-fed fish, a trend that was reversed in the second feeding experiment. These results were inconsistent with studies showing that fishes that are dietary specialists have higher microbial diversity than dietary generalists (Bolnick et al., 2014).

Fish from the first feeding experiment had a higher relative abundance of Planctomycetes (~10–66%) than fish from the second feeding study (<1–35%). Most Planctomycetes were members of the class Pirellulales but also included Gemmatales, Isosphaerales, and Planctomycetales. Planctomycetes have been identified in variety of environments including the GI tracts of fish and termites, soils, and bioreactors (Van Kessel et al., 2011; Wang et al., 2011; Abdul Rahman et al., 2015). Although some Planctomycetes appear to be responsible for heteropolysaccharide degradation in diverse environments, this activity has not been described in Pirellulaceae (Wilhelm et al., 2019).

Proteobacteria were ubiquitous in all samples and were represented by several classes including alpha, beta, gamma, and delta. A higher proportion was observed in the second feeding experiment (~33–99%) than the first (~19–50%). Several proteobacterial families were distributed in a feeding study-, tissue-, and diet-specific manner. In the first feeding study, these families consisted primarily of Rhizobiales, Rhodobacterales, and Aeromonadales, while those from the second feeding study consisted of Rhodobacterales, Rhodospirillales, Aeromonadales, Betaproteobacteriales, Enterobacteriales, Legionellales, and Pseudomonadales. The diversity of Proteobacteria in mixed diet-fed fish of the second feeding study was higher than the wood-fed fish community, which was dominated by Aeromonas and Enterobacterales. Many of these families of Proteobacteria as well as Planctomycetes have been associated with fasting fish, but their role in the wood-fed fish is unclear (Kohl et al., 2014; Xia et al., 2014). Aeromonas has been identified as a major contributor of cellulases in the GI tract of herbivorous grass carp *Ctenopharyngodon idella*, but is also a known pathogen of fresh water fishes. Its role in *P. nigrolineatus* is unclear (Jiang et al., 2011; Ran et al., 2018).