Pasture flock chicken cecal microbiome responses to ...

Pasture flock chicken cecal microbiome responsesto prebiotics and plum fiber feed amendments

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Citation Park, S. H. et al. "Pasture flock chicken cecal microbiome responsesto prebiotics and plum fiber feed amendments." Poultry Science 96,6 (June 2017): 1820-1830 © 2017 Poultry Science Association

As Published http://dx.doi.org/10.3382/ps/pew441

Publisher Elsevier BV

Version Final published version

Citable link https://hdl.handle.net/1721.1/125258

Terms of Use Creative Commons Attribution-NonCommercial-NoDerivs License

Detailed Terms http://creativecommons.org/licenses/by-nc-nd/4.0/

MICROBIOLOGY AND FOOD SAFETY

Pasture flock chicken cecal microbiome responses to prebiotics and plumfiber feed amendments

S. H. Park,∗,† A. Perrotta,‡ I. Hanning,§,# S. Diaz-Sanchez,¶,∗∗ S. Pendleton,¶ E. Alm,‡and S. C. Ricke∗,†,1

∗Cell and Molecular Biology Program, University of Arkansas, Fayetteville; †Center for Food Safety andDepartment of Food Science, University of Arkansas, Fayetteville; ‡Department of Civil and Environmental

Engineering and Biological Engineering, and the Center for Microbiome Informatics and Therapeutics, MIT,Cambridge, MA; §Department of Science, Lincoln International Academy, Managua, Nicaragua; #The GraduateSchool of Genome Sciences and Technology, University of Tennessee, Knoxville; ¶Department of Food Science

and Technology, University of Tennessee, Knoxville; and ∗∗SaBio IREC (CSIC-UCLM-JCCM),Ciudad Real, Spain

ABSTRACT When prebiotics and other fermentationsubstrates are delivered to animals as feed supplements,the typical goal is to improve weight gain and feed con-version. In this work, we examined pasture flock chickencecal contents using next generation sequencing (NGS)to identify and understand the composition of the mi-crobiome when prebiotics and fermentation substrateswere supplemented. We generated 16S rRNA sequenc-ing data for 120 separate cecal samples from groupsof chickens receiving one of 3 prebiotics or fiber feedadditives. The data indicated that respective feed addi-

tives enrich for specific bacterial community membersand modulate the diversity of the microbiome. We ap-plied synthetic learning in microbial ecology (SLiME)analysis to interpret 16S rRNA microbial communitydata and identify specific bacterial operational taxo-nomic units (OTU) that are predictive of the particu-lar feed additives used in these experiments. The resultssuggest that feed can influence microbiome compositionin a predictable way, and thus diet may have indirecteffects on weight gain and feed conversion through themicrobiome.

Key words: microbiome, prebiotics, pasture flock chicken2017 Poultry Science 96:1820–1830

http://dx.doi.org/10.3382/ps/pew441

INTRODUCTION

In agriculture, the addition of prebiotics to feed usu-ally has one or 2 goals: 1) replacing sub-therapeuticdosages of antibiotics to obtain similar growth pro-moting results and/or 2) increasing the populationsof beneficial bacteria by providing fermentation sub-strates that may subsequently reduce or eliminatezoonotic pathogens (Jones and Ricke, 2003; Pattersonand Burkholder 2003; Hofacre et al., 2005; Callawayet al., 2008; Gaggia et al., 2010; Ricke, 2015). Broilerchickens raised on pasture flock have different manage-ment requirements compared to conventional systemsin both rearing conditions as well as nutritional strate-gies. These chickens are grown in outdoor environmentswith feeds that do not include antimicrobial agents orgrowth promoters except natural feed supplements and

C© 2017 Poultry Science Association Inc.Received March 5, 2016.Accepted November 16, 2016.1Corresponding author: [email protected]

dietary amendments (AMS/USDA, 2008; Park et al.,2013).

The ceca are present at the junction of the small andlarge intestines and some fermentation does occur inthe small intestine (Clench and Mathias, 1995), butis dependent on the type of substrate. Relative to thesmall intestines, the ceca are not a significant source ofnutrients and cecectomy does not impair growth (Sonand Karasawa, 2000). However, these pouches are heav-ily colonized with bacteria, which do provide the birdwith small amounts of short-chain fatty acids (SCFA)and vitamins (Baurhoo et al., 2007). In chickens, a ma-jority of the fermentation of prebiotics occurs in thececa. However, the ceca are also the primary habitatfor the zoonotic pathogens Salmonella and Campylobac-ter (Bailey et al., 1991; Patterson & Burkholder, 2003;Ricke, 2003, 2015; Hofacre et al., 2005; Hume, 2011).Typically, cecal samples are collected to culture targetorganisms of food safety concern or gastrointestinal in-digenous bacteria that are assumed to have an impacton bird intestinal health or growth rates as the pri-mary focus (Huyghebaert et al., 2011). However, thereis a prevalence of bacteria that are uncultivable with

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conventional culturing methods (Ricke and Pillai, 1999;Kim and Mundt, 2011) and strong evidence that the mi-crobial community is a factor dictating weight gain andfeed conversion (Hume, 2011; Huyghebaert et al., 2011;Stanley et al., 2012). Thus, understanding the structureof the entire chicken gut microbiome and its responseto different dietary amendments may be an importantstep in increasing their ability to harvest energy fromthe diet efficiently.

Prebiotic treatments can reduce zoonotic pathogensby supporting beneficial bacterial populations that po-tentially outcompete pathogens by lowering lumen pH,and increasing the concentrations of antimicrobial sub-stances (Hume, 2011; Ricke, 2015). Fructooligosaccha-rides (FOS) and galactooligosaccharides (GOS) haveboth been used as food ingredients and as prebioticsfor human consumption (Sako et al., 1999; Pattersonand Burkholder, 2003; MacFarlane et al., 2006; Tor-res et al., 2010; Ricke, 2015). These oligosaccharidesare composed of chains of fructose or galactose pos-sessing glycosidic bonds that resist hydrolysis by en-dogenous enzymes and are available for fermentation bybacteria in the intestinal tract (Hidaka and Mirayama1991; Alles et al., 1996; Donalson et al., 2007, 2008a, b;Park and Oh, 2010). Fermentation of prebiotics by in-testinal bacteria produces SCFA, lactate, CO2, andhydrogen and reduces the pH favoring the growth ofbeneficial bacteria and promoting calcium absorption(Gibson and Roberfroid, 1995; Ricke, 2003, 2015). Fruitpomaces, specifically plum, also can be considered as aprebiotic-like compound because they possess compo-nents that are indigestible by the host and may pro-mote the growth of beneficial bacteria (Milala et al.,2013; Jarvis et al., 2015; Roto et al., 2015; Hutkinset al., 2016). In addition to providing a fermentationsubstrate, fruit pomaces also can supply additionalhealth promoting factors such as polyphenols, antho-cyanins, and antioxidants (Milala et al., 2013). Giventhe differences in the composition of these three feedadditives and the potential differences in fermentationlocation and products, we hypothesized that the cecalmicrobial communities of the chickens would delineateamong treatments in response to each particular feedamendment. In this study, our objective was to eval-uate the effects of 2 prebiotics (FOS and GOS) anda plum fiber-based feed amendment on pasture raisedchicken cecal content microbiome.

MATERIALS AND METHODS

Bird housing

A total of 340 day-of-hatch Cornish Cross White Ply-mouth Rock commercial broiler chicks was acquiredfrom a local hatchery (Cobb 500; Cobb-Vantress, Fayet-teville, AR) and raised on a pasture flock operationas part of the study described previously by Hanninget al. (2012). The chickens were divided into 4 groupswith 85 chickens in each pen. The first 2 wk after hatch,the chickens were located in conventional housing pens

with approximately 50 ft2 (4.65 m2). After 2 wk ofage, the chickens were moved into outside pens on pas-ture flock measuring approximately 80 ft2 (7.23 m2)for an additional 4 weeks. Every 2 wk, 10 birds fromeach group were randomly selected up to 6 wk for cecaextraction. A total of 120 birds (30 birds [2, 4, and6 wk]) per group) was utilized for microbiome analysis.Further specific growth and environmental conditionsare discussed in detail by Hanning et al. (2012). TheUniversity of Arkansas Institute Animal Care and UseCommittee (IACUC) approval was exempted for thisresearch because all birds were raised in an off-campuscommercial facility and limited to microbiological eval-uation.

Feed additives and feed formulation

The feed additives were added to the starter, grower,and finisher feeds in each group and feed and waterwere made available ad libitum. Each group consistedof 85 chickens and treatments were as follows: 1) con-trol (no feed additive); 2) plum fibers (California DriedPlum Board, Sacramento, CA) added at one kg per tonof feed; 3) FOS (GTC Nutrition, Golden, CO) addedat one kg per ton of feed; and 4) GOS (GTC Nutri-tion) added at 2 kg per ton of feed, respectively. Thosedosage levels were recommended by the manufacturers.Feed was supplemented with the prebiotics through-out the experimental period. Every 2 wk, a total of 10chickens from each group was selected randomly, euth-anized, and transported to the laboratory for necropsy.Both ceca were extracted and stored at −80◦C untilDNA extraction could be done.

Campylobacter isolation

A sub-sample of cecal contents was collected for cul-ture and quantification of Campylobacter. From the ce-cal contents, a sample weighing approximately one gwas weighed and suspended in phosphate bufferedsaline (PBS) at a ratio of 1:9. Dilution series were con-ducted from this suspension and plated onto Campy-lobacter Line Agar (CLA). All plates were incubatedin a microaerophilic atmosphere at 42◦C for 48 hours.With this method, the limit of detection was 100 CFUper gram of cecal contents.

DNA isolation

Total DNA from cecal contents was isolated usinga Qiagen Stool Mini Kit (Qiagen, Valencia, CA) withmodifications to increase DNA yields. Briefly, garnetbeads (0.7 mm, Mo Bio Laboratories Inc., Carlsbad,CA) were utilized to lyse cells. Samples were centrifugedto pellet unhomogenized materials and the suspensionwas transferred into a microcentrifuge tube contain-ing glass beads (0.1 mm, Mo Bio Laboratories Inc.).Vortexing was vigorously performed for 10 min andthe samples were incubated at 95◦C for 6 minutes.The remainder of the extraction protocol followed the

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manufacturer’s instructions. DNA concentration andpurity were measured using a NanoDrop ND-1000(Thermo Scientific, Wilmington, DE) and subsequentlystored at −20◦C until used.

16s rRNA amplicon library preparationand sequencing

Ten nanograms of DNA from each cecal content wereused in a paired-end Illumina HiSeq sequencer (Illu-mina, San Diego, CA) using a 2-step 16S rRNA PCRamplicon approach targeting the V4 region of the 16SrRNA. Primers U515F and E786R were used as de-scribed in Preheim et al. (2013). Samples were pro-cessed in sets no greater than 93 in a 96-well plate for-mat with at least 3 negatives per plate. Each time pointand treatment group was represented equally on eachplate. All libraries were sequenced on a single lane forpaired end 108 nt HiSeq sequencing runs at the Biomi-cro Center (MIT, Cambridge, MA) along with 284 othersamples from independent studies.

sequencing data analysis and taxonomicassignment

A total of 72,649,144 Illumina HiSeq reads was gen-erated, 20,048,322 of which represented the preparedsequencing libraries for this study. Quality filteringand de-multiplexing resulted in an average of 169,901reads per sample. The FASTQ files were filtered us-ing the split library fastq.py protocol as part of theQIIME default pathway (Caporaso et al., 2010) withthe following variables: The minimum length requiredwas 100 bp (-min per read length 100); the last badcharacter used was Q (phred = 17, calculated errorrate of 0.02) (–last bad character Q), no bad charac-ters were allowed (– max bad run length 0), no bar-code errors were allowed (– max barcode errors 0),no new cluster formation was allowed (-C), and a7 base barcode (–barcode type 7). All other parame-ters were default parameters. The diversity and primerregion were removed using the trim.seqs program inMothur (Schloss et al., 2009). The resulting 77 bpreads were then aligned to the greengenes database(gg otus 4feb2011) and clustered into operational taxo-nomic units (OTU) using the QIIME pick otus.py com-mand (-m uclust ref; all other parameters were defaultparameters). OTU tables subsequently were generatedusing the QIIME make otu table.py protocol. Sampleswere filtered for read counts, excluding samples withless than 1,000 reads, using the QIIME command fil-ter samples from otu table.py –n 1000. Five samples(A2.4wk, A3.2wk, A6.6wk, C5.6wk, and D2.2wk) wereexcluded from all further analysis due to a low numberof sequence reads associated with those samples. Af-ter initial quality processing, a total of 930 OTU wasidentified.

Microbiome analyses

The diversity observed within a sample, alpha diver-sity, was measured using Shannon’s diversity index withsample diversity indices = [‘Shannon’] and dist mat(metric = ‘JS’) commands, respectfully, in pysurvey(https://bitbucket.org/yonatanf/pysurvey). The Shan-non diversity metric used was Shannon entropy and isdefined as H = −Sumi (pi

∗ log pi), with pi being thefraction of OTUi. The effective number of species oforder 1 represented in each sample was calculated bytaking the inverse of the natural log of the calculatedShannon entropy.

Diversity observed among samples, beta diversity,was measured using a weighted UniFrac (QIIME pack-age, evenly sampled with 5,716 sequences per samplefor all UniFrac calculations) and Spearman rank dis-tances. Discriminating differences in presence, absence,and relative abundance at the OTU, genera, family, andphyla level were calculated using a Mann Whitney U-test. All P-values calculated were corrected by estimat-ing the False Discovery Rate. All boxplots shown weregenerated using iPython (verion 2.7.3) and R (version3.2.3). All assigned OTU were included for diversitycalculations; all other analyses, unless noted otherwise,included only OTU with a relative abundance greaterthan 1e-03 considered for each treatment group withina time point.

Correlations with metadata such as age, treatment,and pathogen count were conducted using syntheticlearning in microbial ecology (SLiME) analysis (Papaet al., 2012). All OTU abundance data were analyzedwith a log transformation (–x log). All meta data cate-gories were analyzed without transformation except forpathogen count and weight data, which were run witha box cox transformation (–y boxcox). Analyses wererun using 3 forests (-n 3) and the data were shuffled100 times for P-value calculations (-s 100). All otherparameters were default parameters. Correlations be-tween OTU distributions and collected metadata werecalculated using Kendall tau correlation coefficients forcontinuous data and receiver operator characteristic(ROC) curves with area under the curve (AUC) calcu-lations for discrete data. Contributions of specific OTUto these correlations was calculated using percent in-crease mean squared error (IncMSE) and mean de-crease accuracy (MDA) for continuous and discretemetadata, respectively.

RESULTS AND DISCUSSION

Potential prebiotic feed additives can impact the gutmicroecology by changing the microbiota profile, mucinsecretion, and histology by selectively enriching thosetaxa that can ferment the feed additives (Kaplan andHutkins, 2003; Rebole et al., 2010; Roto et al., 2015;Hutkins et al., 2016). The end products of fermenta-tion are dictated by the fermentation substrates pro-vided (Baurhoo et al., 2007; Rebole et al., 2010; Roto

CECAL MICROBIOME OF PASTURE RAISED CHICKEN 1823

et al., 2015). Oligosaccharides such as FOS and GOScan be converted to lactate by lactic acid bacteria orother metabolites (Shoaf et al., 2006; Ricke, 2015). Xuet al. (2003) demonstrated that broilers raised withFOS supplemented feeds increased beneficial bacteriasuch as Lactobacillus and Bifidobacterium while reduc-ing Escherichia coli in the gut ecosystem. Shoaf et al.(2006) reported that GOS has an ability to inhibit theadherence of enteropathogenic E. coli on epithelial tis-sue cell. Aside from FOS and GOS, De Maesschalcket al. (2015) evaluated the beneficial effects of pre-biotic xylo-oligosaccharides (XOS) on broiler growthperformance and gut microbiota. They reported thatXOS stimulated butyrate-producing bacteria and in-creased lactobacilli strains as well as potentially hav-ing positive effects on gastrointestinal function suchas increase of villus length. For the birds used in thecurrent study, Hanning et al. (2012) reported chickengrowth performance results on villi and crypt mea-surement and body weights. At 2 wk of age, therewere no significant differences in body weight but theGOS supplemented group exhibited lower body weightsthan other groups at 6 wk of age. For villi and cryptmeasurements at the end of the study (8 wk), FOSsupplemented birds had the longest villi, while there

was no significant difference in crypt depth amonggroups.

Feed additives and bacterial taxonomiccomparison

From each treatment group described in Hanninget al. (2012), 9 to 10 chickens were collected and eachmicrobial community in the cecal contents was se-quenced. When viewing the samples individually withina treatment group, there was variability among the in-dividual samples and among time points (Fig. S1). Bac-teria belonging to the classes Bacteroidia and Clostridiawere highly abundant across all time points (Fig. S1).The average abundance of the phyla, families, and gen-era was calculated for each treatment (Table 1). At thelevel of phylum, the Firmicutes were dominant in allgroups at 2 wk of age (Table 1). The relative abun-dance of Firmicutes increased while the Bacteroidetesdecreased at the 4-week time point for all treatments,but not the control; this trend continued into the 6-weektime point for the FOS group only. Firmicutes appearedto be more prevalent than the Bacteroidetes in all ce-cal populations among the treatments, but this ratio

Table 1. Relative abundance of phyla, family, and genus for each treatment at each time point. Only those phylum, family, andgenus that were present at a minimum of 4% relative abundance at any time point in any treatment are shown.

Control FOS GOS Plum

Taxonomic level 2 wk 4 wk 6 wk 2 wk 4 wk 6 wk 2 wk 4 wk 6 wk 2 wk 4 wk 6 wk

Phylum Firmicutes 59 52 47 51 64 66 51 69 53 55 67 49Bacteroidetes 31 43 41 40 27 22 35 23 37 36 25 30Proteobacteria 9 3 6 5 5 4 11 4 3 6 4 13Tenericutes 2 1 3 5 2 3 2 2 2 2 2 4

Family Ruminococcaceae 30 20 16 26 31 27 19 36 18 20 27 21Lachnospiraceae 23 14 13 21 21 18 23 16 21 26 24 12Bacteroidaceae 30 7 5 40 6 7 23 10 15 35 5 6Enterobacteriaceae 6 0 0 – – – – – – 4 2 0Clostridiales 2 10 7 1 7 10 1 8 5 3 6 7Lactobacillaceae 2 2 5 3 2 5 6 4 3 5 8 3Catabacteriaceae 1 4 2 – – – – – – – – –Bacteroidales 1 18 4 0 7 2 10 3 2 1 4 2Rikenellaceae 0 15 29 0 12 11 1 9 17 – – –Porphyromonadaceae 0 4 2 – – – – – – – – –Rikenellaceae – – – 0 12 11 – – – 0 13 19Halomonadaceae – – – – – – 5 0 0 – – –Alcaligenaceae – – – – – – 4 2 1 – – –

Genus Bacteroides 30 7 5 40 6 7 23 10 15 35 5 6Ruminococaceae 20 15 12 21 21 19 14 23 13 14 21 16Lachnospiraceae 16 6 7 11 11 8 9 8 14 15 13 6Enterobacteriaceae 6 0 0 – – – – – – 4 2 0Clostridiales 2 10 7 1 7 10 1 8 5 3 6 7Lactobacillus 2 2 5 3 2 5 6 4 3 5 8 3Catabacteriaceae 1 4 2 – – – – – – – – –Bacteroidales 1 18 4 0 7 2 10 3 2 1 4 2Rikenellaceae 0 3 26 – – – – – – 0 1 7Alistipes 0 12 3 0 12 10 1 7 15 0 12 12Blautia – – – 3 2 4 – – – – – –Clostridium – – – 3 4 4 4 3 4 3 4 2Faecalibacterium – – – 1 5 2 2 6 2 2 4 1Eubacterium – – – – – – 5 1 0 – – –Alcaligenaceae – – – – – – 4 2 0 – – –Coprococcus – – – – – – – – – 5 2 1

The numbers stand for relative abundance (%)

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varied by age and treatment. Although a higher ratioof Firmicutes to Bacteriodetes is correlated with weightgain in humans and mice, the ratio of these phyla in thechicken ceca did not correlate with weight (Ley et al.,2006).

At the family taxonomic level, differences amongthe treatment groups were more obvious (Table 1).Ruminococcaceae, Lachnospiraceae, and Bacteroidaceaewere the most abundant families in all groups, but dif-ferences in relative abundances of those taxa among thegroups were observed over time. It appeared that eachtreatment had a unique impact on the microbial diver-sity. For example, the Halomonadaceae and Alcalige-naceae were present only in the GOS treatment group.Similarly, the Porphyromonadaceae and Catabacteri-aceae were present only in the control group. Interest-ingly, the beneficial bacteria of the family Lactobacil-laceae were underrepresented in all the samples. Similarto the family level, some genera were unique to specifictreatment groups or the control (Table 1). Blautia waspresent only in the FOS group, while Eubacterium andAlcaligenaceae were present only in the GOS group. Co-prococcus was unique to the plum group and Catabac-teriaceae was present only in the control group, respec-tively.

While diversity increased in the FOS and plum fibersupplemented groups over time, the control group didnot gain a large number of additional OTU (Fig. 1).Interestingly, Fusobacterium was present at 6 wk innearly all the cecal samples from chickens receivingplum fibers. Fusobacterium is a known butyrate pro-ducer, and fermentation of pectin produces butyrate(Duncan et al., 2002). Butyrate has numerous bene-ficial impacts on health, but for the chicken specifi-cally, Salmonella growth is inhibited by butyrate (Don-alson et al., 2008a). Thus, plum fibers may be a favor-able choice as a feed additive for potential control ofSalmonella in the chicken cecum.

Variations in these microbial communities could pro-duce differences in metabolite profiles, vitamins, andimpacts on host-cellular expression. However, Qu et al.

(2008) found that even though diversity and evennessof bacterial taxa may differ among cecal samples, thefunctional gene content remained the same. Therefore,microbiome functionality may remain stable regardlessof variations in community OTU abundance. However,this question of functional stability could not be ad-dressed in this study, as metagenomic sequencing wasnot conducted.

We observed shifts in abundance, presence, and ab-sence of OTU in all groups over the experimentalperiod. Some OTU were present in specific groupsand absent from others. However, we observed thatweighted UniFrac clustering distances between sampleswere highly dependent on age, not feed additive (Fig. 2).It has been shown previously that diversity is affectedby factors other than age, including diet (Stanley et al.,2012). In this study, the diet formulation was changedat 2 wk of age (Hanning et al., 2012) and we haveshown previously in other studies that adjustmentsto the diet may create microbial shifts (Hanning andDiaz-Sanchez, 2015). In these trials, feed was formu-lated to contain greater protein content for the first 2wk to promote skeletal growth. This could partially ex-plain the differences in clustering between the 2 and4 wk age groups, but not between the 4 and 6 wkgroups as the diet was the same for wk 4 and 6. How-ever, 4 and 6 wk chickens digest nutrients differentlyas they age, which results in physiological variations,including enzymatic secretion, nutrient digestion, fat,and nutrient deposition. For example, while chickensdevelop the ability to digest fats with age, initially,fat digestion is very poor (Krogdahl, 1985). The abil-ity to digest fats increases gradually until 6 wk ofage (Katongole and March, 1980). Similarly, the di-gestion of carbohydrates improves with age becauseolder birds are more resistant to amylase inhibitorspresent in the maize (Cowieson, 2005). These physiolog-ical differences may have impacted the intestinal ecol-ogy and substrates resulting from digestives that wouldbe available for microbial succession downstream in thececa.

Figure 1. Shannon diversity for A) control, B) FOS, C) GOS, and D) plum fiber treated groups. ∗represents a q-value equal to or less than0.05 compared with control group at same time point.

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Figure 2. Principal Coordinate Analysis (PCoA) plots of beta diversity exhibit the clustering to understand microbial population distance ofeach bird based on age (panel A) and treatment (panel B).

Microbial diversity trends of the cecalmicrobiome associated with feed additivetype

In accordance with previous observations on thestrong temporal effects of host skeletal and weight gaingrowth on microbial gut communities (Lu et al., 2003),clustering samples by beta diversity showed that hostage, and therefore developmental stage, represented theprimary clustering distance (Fig. 2). This indicates thatalthough microbial populations can be shifted with pre-biotics, there remains a very strong host effect upon themicrobiome in the chicken ceca. Alpha-diversity (Shan-non index) increased over time for all treatment groups,but samples from chickens fed FOS (Fig. 1B) and plumfiber (Fig. 1D) exhibited significantly greater diver-sity than the control group at the 6-week time point(Fig. 1A). The increase in diversity over time was notsurprising as it is well known that diversity increaseswith age in many animals as well as humans (Rodriguezet al., 2015). Specific to the chicken, the intestinal tractis considered sterile at hatch but quickly becomes colo-nized with aerobic bacteria. The intestines are relativelyflat with minimal mucosa (Hanning and Diaz-Sanchez,2015). These aerobic bacteria stimulate the histologi-cal development and mucosa secretion, which leads toa suitable environment for anaerobes. The anaerobeseasily outcompete the aerobic bacteria and are usuallydominant by 2 wk of age (Hanning and Diaz-Sanchez,2015).

Correlation analysis of metadata and microbial com-munity distribution was conducted using SLiME (Papaet al., 2012), which identified 4 OTU that were signif-icantly predictive of important host factors includingage, weight, pathogen count, and treatment. Kendalltau correlation coefficients, AUC calculations for ROC

Table 2. Correlative analysis of meta data and microbial com-munity distribution.

Category Kendall tau coefficient AUC P-value

Age 0.79 – <10e-12Weight 0.65 – <10e-12Pathogen count 0.41 – 8.53e-12Control vs. all – 0.89 2e-4FOS vs. control – 0.95 8.41e-11GOS vs. control – 0.93 1.03e-10Plum vs. control – 0.89 2.36e-1

curves, and corresponding P-values for all correlationsare listed in Table 2. An OTU identified as belonging tothe genus Alistipes increased with age in all treatmentgroups and was indicated as a predictive feature forage (>0.6% IncMSE), weight (>150% IncMSE), andCampylobacter populations (>10% IncMSE). As seenin Fig. 3, this OTU had significantly greater relativeabundance in treatment groups compared to the con-trol group at the 6-week time point.

A Lactobacillus intestinalis OTU was indicated in ouranalysis as being a predictive feature for Campylobacterpopulations (>25% IncMSE) and treatments (FOS vs.control > 0.0025 MDA; GOS vs. control 0.0025 MDA,plum fibers vs. control 0.003 MDA) (Fig. 4). This OTUwas particularly predictive for the FOS treatment com-pared to the control at 2 wk of age, which supportsother reports that FOS selectively enriches for Lacto-bacillus (Swanson et al., 2002). Lactobacillus species areknown to be capable of hydrolysing bile salts, whichhave been associated with host gut health (Feighnerand Dashkevicz, 1988; Tannock et al., 1989; Swennenet al., 2006) and the inhibition of pathogenic bacte-rial colonization (Dec et al., 2014). Although Lacto-bacillaceae were present in all groups at all time points(Table 1), they were relatively underrepresented in the

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Figure 3. Relative percent abundance of Alistipes (genus) OTU in A) control, B) FOS, C) GOS, and D) plum fibers treated groups. ThisOTU was highly predictive of age (0.65% IncMSE), weight (150% IncMSE), and pathogen count (>10% IncMSE). ∗ represents a q-value equalto or less than 0.05 and ∗∗ represents a q-value equal to or less than 0.005 compared with control group at same time point.

Figure 4. Relative percent abundance of Lactobacillus intestinalis OTU in A) control, B) FOS, C) GOS, and D) plum fibers treated groups.This OTU was predictive of treatment particularly for FOS compared to control at the 2-week time point, mean decrease accuracy of >0.015,and pathogen count, > 20% increase in mean squared error. ∗ represents a q-value equal to or less than 0.05 and ∗∗ represents a q-value equal orless than 0.005 compared with control group at same time point.

ceca ranging from 2 to 8% relative abundance of thepopulation. In contrast to the ceca, this genus domi-nates the small intestines, which is a region that favorsfacultative anaerobes and bile salt resistant species,while the cecal environment appears to be more suit-able for strict anaerobes (Knarreborg et al., 2002; Amit-Romach et al., 2004; Saengkerdsub et al., 2007; Saengk-erdsub and Ricke, 2014).

Prebiotics can selectively enrich bacterial taxa, andour analyses indicated that the FOS and GOS treat-ments selected for Faecalibacterium prausnitzii (Fig. 5Band C). This OTU had a relative abundance just be-low our analysis threshold but was included, as it iswell covered in the published literature. F. prausnitziiis highly abundant in the human colon where it isknown to produce butyrate (Lopez-Siles et al., 2012).Butyrate has been shown to reduce Salmonella infec-

tion, inhibit inflammation, and can promote cellularmucosal turnover rates (Cerisuelo et al., 2014; Parsonet al., 2014). F. prausnitzii has been identified in severalpublications as being involved in butyrate productionand bile acid hydrolysis and is enriched by prebiotictreatments in humans and mice (Feighner and Dashke-vicz, 1988; Ramirez-Farias et al., 2009; Lopez-Sileset al., 2012). The F. prausnitzii OTU was almost absentfrom all control samples but was significantly enrichedin the FOS and GOS treatment groups in the 6-weektime point (Fig. 5). Thus selective enrichment of thisspecies by FOS and GOS could be beneficial in termsof host health. It also should be noted that F. praus-nitzii is one of the few colonic community membersthat can ferment pectin (Lopez-Siles et al., 2012). How-ever, this OTU was not enriched in the plum pomacegroup.

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Figure 5. Relative percent abundance of a Faecalibacterium prausnitzii OTU in A) control, B) FOS, C) GOS, and D) plum fibers treatedgroups. This OTU was predictive of treatment particularly FOS and GOS compared to control at the 6-week time point, mean decrease accuracyof > 0.005. ∗∗ represents a q-value equal to or less than 0.005 compared with control group at same time point.

Numerous additional OTU were indicated as predic-tive of treatment in this analysis but due to pen effectin treatment groups and low relative abundance, onlyOTU indicated in previously published literature and arelative abundance greater than 1e-03 were considered.It should be noted that many of the low abundanceOTU were identified as belonging to the family Ru-minococcacea whose members have been reported previ-ously to exhibit a butyrogenic effect and to be enrichedby prebiotic supplementation (Walker et al., 2011).

Microbiome characterization andCampylobacter populations in the ceca

Due to the relevance of Campylobacter in the poultryindustry (Horrocks et al., 2009), we conducted sepa-rate culturing counts for Campylobacter and correlatedthese counts with the cecal community members. Inall groups, Campylobacter counts were not significantlydifferent and concentrations ranged from 6 to 8 log cfuper gram of intestinal content (data not shown). Con-trol of Campylobacter has been difficult and controlmethods have had little success (Willis and Reid, 2008;Horrocks et al., 2009; Gaggia et al., 2010). One reasonmay be the symbiotic relationship Campylobacter haswithin the bacterial community (Indikova et al., 2015).Campylobacters do not code for phosphofructokinase,which is necessary for sugar fermentation (Velayudhanand Kelly, 2002). Thus, it relies on other bacteria forsecondary metabolites. Second, Campylobacter also canact as a hydrogen sink favoring the fermentation process(Laanbroek et al., 1978). Since excess hydrogen inhibitsacetate production, Campylobacter can promote a con-sistent concentration of acetate benefiting the rest ofthe microbial community. In fact, it is for this reasonthat some suggest Campylobacter is actually beneficialto the chicken intestinal health (Sergeant et al., 2014).

We did observe that Alistipes and Lactobacillus in-testinalis OTU appeared to be predictive of Campy-

lobacter population levels. Campylobacter species canutilize a wide range of organic acids including succinicacid, the major metabolic product of Alistipes species(Kaakoush et al., 2014). Similarly, lactate is the ma-jor fermented by-product of Lactobacillus, and Campy-lobacter possesses 2 enzyme pathways for the utiliza-tion of L-lactate. Although these 2 bacterial OTU werepredictive of Campylobacter, it is unclear if there wasa true mutualistic relationship. Since L. intestinaliswas present at a very low abundance numerically, anymetabolic by-products produced by L. intestinalis thatCampylobacter would also be low in abundance. Alis-tipes was present in greater abundance than L. intesti-nalis and this OTU was also predictive of age. Indepen-dently, Campylobacter is also predictive of age and it iswell known that this zoonotic pathogen colonizes olderchickens that have a mature gut histology and micro-biota (Dhillon et al., 2006). Given that Campylobactertypically becomes detectable when chickens are 2 to3 wk of age, the connection between Alistipes andCampylobacter may be a factor of age rather thanmetabolite sharing. In the future, in vitro studies fo-cused on co-culturing of Campylobacter and these or-ganisms’ cecal contents are needed to delineate any po-tential interdependent metabolic relationship.

CONCLUSIONS

In conclusion, the data collected here provide ge-nomic foundations for further refinement of prebioticadministration. The data gathered here can be usedto complement general assessments with more targetedapproaches of particular groups of cecal bacteria. Inaddition, targeted assays that quantify specific popu-lations could potentially be designed for use in otherstudies as well as establish interdependent microbialmetabolic groups occurring in the chicken ceca.

1828 PARK ET AL.

SUPPLEMENTARY DATA

Figure S1. Relative abundance of taxonomic classlevel in cecal contents of each bird by age and treat-ment. Each bar represents one bird.

Supplementary data are available at PSCIEN online.

ACKNOWLEDGMENTS

Research funding was awarded to author SCR by theUSDA/CSREES (Cooperative State Research, Educa-tion, and Extension Service, Washington DC) NationalIntegrated Food Safety Initiative (NIFSI, WashingtonDC) grant # 406 2008 51110, USDA CREES FoodSafety Consortium grant, the Plum Board (Sacramento,CA), and a donation of FOS and GOS prebiotics fromGTC Nutrition Co. (Golden, CO).

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