Archaea and Fungi of the Human Gut Microbiome

12 Pages·2013·4.53 MB·English

Archaea and Fungi of the Human Gut Microbiome:
Correlations with Diet and Bacterial Residents
Christian Hoffmann1,5, Serena Dollive1, Stephanie Grunberg1, Jun Chen6, Hongzhe Li3,4, Gary D. Wu2*,
James D. Lewis3,4*, Frederic D. Bushman1*
1Department ofMicrobiology,UniversityofPennsylvania School ofMedicine,Philadelphia,Pennsylvania, United StatesofAmerica, 2DivisionofGastroenterology,
UniversityofPennsylvaniaSchoolofMedicine,Philadelphia,Pennsylvania,UnitedStatesofAmerica,3CenterforClinicalEpidemiologyandBiostatistics,Universityof
PennsylvaniaSchoolofMedicine,Philadelphia,Pennsylvania,UnitedStatesofAmerica,4DepartmentofBiostatisticsandEpidemiology,UniversityofPennsylvaniaSchool
ofMedicine,Philadelphia,Pennsylvania,UnitedStatesofAmerica,5InstitutodeCieˆnciasBiolo´gicas,UniversidadeFederaldeGoia´s,Goiania,Goia´s,Brazil,6Departmentof
Biostatistics,HarvardSchoolofPublicHealth,Boston,Massachusetts,UnitedStatesofAmerica
Abstract
Diet influences health as a source of nutrients and toxins, and by shaping the composition of resident microbial
populations.Previousstudieshavebeguntomapoutassociationsbetweendietandthebacteriaandvirusesofthehuman
gut microbiome. Here we investigate associations of diet with fungal and archaeal populations, taking advantage of
samples from 98 well-characterized individuals. Diet was quantified using inventories scoring both long-term and recent
diet,andarchaeaandfungiwerecharacterizedbydeepsequencingofmarkergenesinDNApurifiedfromstool.Forfungi,
we found 66 genera, with generally mutually exclusive presence of either the phyla Ascomycota or Basiodiomycota. For
archaea,Methanobrevibacterwasthemostprevalentgenus,presentin30%ofsamples.Severalotherarchaealgenerawere
detectedinlowerabundanceandfrequency.Myriadassociationsweredetectedforfungiandarchaeawithdiet,witheach
other, and with bacterial lineages. Methanobrevibacter and Candida were positively associated with diets high in
carbohydrates, but negatively with diets high in amino acids, protein, and fatty acids. A previous study emphasized that
bacterialpopulationstructurewasassociatedprimarilywithlong-termdiet,buthighCandidaabundancewasmoststrongly
associatedwiththerecentconsumptionofcarbohydrates.Methobrevibacterabundancewasassociatedwithbothlongterm
and recent consumption of carbohydrates. These results confirm earlier targeted studies and provide a host of new
associations to consider inmodelingthe effects of dieton thegut microbiome and humanhealth.
Citation:HoffmannC,DolliveS,GrunbergS,ChenJ,LiH,etal.(2013)ArchaeaandFungioftheHumanGutMicrobiome:CorrelationswithDietandBacterial
Residents.PLoSONE8(6):e66019.doi:10.1371/journal.pone.0066019
Editor:ChonglePan,OakRidgeNationalLab,UnitedStatesofAmerica
ReceivedFebruary22,2013;AcceptedApril30,2013;PublishedJune17,2013
Copyright:(cid:1)2013Hoffmannetal.Thisisanopen-accessarticledistributedunderthetermsoftheCreativeCommonsAttributionLicense,whichpermits
unrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalauthorandsourcearecredited.
Funding:ThisworkwassupportedbyProjectUH2/3DK083981,thePennGenomeFrontiersInstitute;NationalInstitutesofHealth(NIH)DK089472(GDW);Penn
DigestiveDiseaseCenter(P30DK050306);TheJointPenn-CHOPCenterforDigestive,Liver,andPancreaticMedicine;S10RR024525;UL1RR024134,andK24-
DK078228.ThecontentissolelytheresponsibilityoftheauthorsanddoesnotnecessarilyrepresenttheofficialviewsoftheNationalCenterforResearch
Resources,NationalInstitutesofHealth,orPennsylvaniaDepartmentofHealth.Thefundershadnoroleinstudydesign,datacollectionandanalysis,decisionto
publish,orpreparationofthemanuscript.
CompetingInterests:Theauthorshavedeclaredthatnocompetinginterestsexist.
*E-mail:[email protected](GDW);[email protected](JDL);[email protected](FDB)
Introduction consumption of carbohydrates, while abundant Bacteroides corre-
latedwithconsumptionofcholine,fats,andaminoacids.Ashort
Humans live in association with immense populations of
term controlled feeding study showed changes in the gut
bacteria, viruses, fungi and archaea [1–8]. Many groups have
microbiota associated with the dietary interventions, but not a
now contributed surveys using deep sequencing to characterize
change in the overall structure of the bacterial community
these populations, revealing that the human microbiome differs
analyzed, supporting a role for long-term diet in determining the
radically at different body sites and among individuals [9–12].
structure of the gut microbiome [10]. Another study recently
Differences in body sites are associated with availability of
reported that the diversity of the gut microbiota was linked with
nutrients, water, oxygen, and other site-specific features. The
long-term diet, where a more diverse diet was correlated with an
origin of differences between individuals is less clear, however,
increased gutbacterial diversity[19].
potentiallyreflectingdistinctcolonizationearlyinlifeanddifferent
Bacteriaareabundantmembersofthegutmicrobiome,butnot
environmental exposures such as antibiotic use [13–15]. Another
the only residents. Bacteriophage particles within the intestinal
environmentalexposure,ubiquitousbutincompletelyunderstood,
tractarepresentinpotentiallygreaternumbersthanbacterialcells
isdiet.
[20],[21].Recentlychangesinbacteriophagecommunitiesingut
Recently,wereportedcorrelationsoflong-termdietarypatterns
have beencorrelated withdietary interventions [5].
in 98 individuals and the bacterial lineages present in the gut
Archaea are also present in human gut, the most frequently
microbiota[10].Twogenera,PrevotellaandBacteroides,wereshown
occurring of whichisMethanobrevibacter smithii [22–25],a methane
to have reciprocal patterns of abundance, paralleling several
producerfrombyproductsofbacterialfermentation[23].Report-
reports from others [16–18]. Abundant Prevotella correlated with
edcolonizationratesbymethanogenicarchaearangefrom25%to
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CorrelationsofDietwiththeGutMicrobiome
95% of humans [26], [27]. In microbial ecosystems such as the top). Nitrosophaera was not detected after sequencing control
human gut, when H accumulates due to bacterial catabolism, amplifications with archaeal primers using products of blank
2
archaealgrowthisstimulatedassociatedwithincorporationofH DNApurificationsastemplate(nopositivesoutofeighttested).To
2
intomethane[23].Supportforsuchsyntrophyinthemammalian validatethedetectionofNitrososphaera,weusedanestedPCRassay
gut has been shown in a gnotobiotic mouse model, where co- to detect the AmoA gene, which encodes the ammonia mono-
colonization by M. smithii and Bacteroides thetaiotaomicron promoted oxyenaseenzyme,andisdistinctiveforNitrososphaera.Wefoundan
increased growthof bothspecies comparedtomono-colonization association between an AmoA positive PCR and Nitrososphaera
[28]. detection in the samples (p=0.014, Fisher’s exact test). The
Yeastshavebeendetectedinhumanstoolsamplesatleastsince association,whilesignificant,wasnotinvariant,probablybecause
1917 [29], and by the mid 20th century their presence in the ofdifficultiesindetectionduetothelowlevelofNitrosophaerainthe
human intestine had been proposed to have a saprotrophic role samples,andpossiblepresenceofAmoAinothermicrobesorfood
[30]. Gut fungi may also be involved in pathogenic processes. materials. Inspection of published work showed detection of
Anti-Saccharomyces antibodies are detected in inflammatory bowel Nitrosophaera in metagenomic sequences from one of two individ-
diseasecohortsandareusedasapredictorofdiseaseprogression ualsstudiedby[25],andinboth16Sandmetagenomicsequences
[31],[32].Recentworkusingamurinemodelhassuggestedthat in another cohort [41]. These data do not distinguish whether
normally mutualistic or commensal fungi species may exacerbate Nitrosophaeraisreplicatinginthehumangutoratransientpresent
intestinalinflammationinmicewithsensitizedgenotypes[33].In infood.
mice, over 14 fungal genera have been reported to be present
within the mucus layer lining the intestinal epithelium [34]. The Fungi of the Gut Microbiome
Available data is likely incomplete, because of reliance mostly on The fungal sequencing effort yielded 332,659 sequence reads,
culture-based methods. Recent reports using next generation resultingindetectionof66generaand13additionallineagesthat
sequencing also suggest diverse fungal communities in humans could not beclassified tothegenus level(Figure 1 andFigure S1
[35–37]. and S2). Fungal sequences were detected in every sample
Basedontheabove,wehypothesizedthatthegutarchaeaand analyzed. Only 12 fungal genera were detected in 9 or more
fungi are influenced by both diet and the other microorganisms samples (Figure 1B, Figure S2). The phyla Ascomycota and
present.Hereweinvestigatedtheseideasinacohortof96healthy Basidiomycota were mostly inversely correlated (Figure S1). The
individuals who were previously characterized for their bacteria/ most prevalent genus in this sample set was Saccharomyces (present
diet relationships [10]. Fungi were characterized by sequencing in89%ofthesamples),followedbyCandida(57%)andCladosporium
the Internal Transcribed Spacer region 1 (ITS1) of the rRNA (42%)(Figure S1).
locusandthearchaeabysequencingasegmentofthe16SrRNA
gene. Short-term diet was characterized using a Recall question- Co-occurrence Analysis using the Dice Index
naire, and long-term diet characterized using a Food Frequency Microbial communities in diverse settings have been shown to
questionnaire. Analysis showed notable correlations of the three formsyntrophiccommunities, inwhichmetabolicwaste products
Domains of life with each other, and with dietary components– from one microbe provide nutrients for another. Thus an initial
thus these data begin to specify potential multi-domain trophic analysis of these samples was carried out to determine which
interactions inthehuman gutmicrobiota. microbesco-occur,asscoredbytheDiceindex.Forthis,weonly
used data of relatively abundant genera (within Domain sample
Results proportion of0.01 orgreater).
Numerous associations were detected. Figure 2 shows these
SamplesforAnalysisoftheRelationshipbetweenHuman
interactions, incorporating data over all three Domains. Co-
Diet and Gut Microbial Populations occurrenceisindicatedbythecolorcode.Inspectionofthefigure
A total of 98 samples were collected from healthy volunteers, shows several examples of co-occurrence involving a high
andsequencesfrom96ofthesesampleswereusedinthisanalysis proportionofsamples(Figure2,lowerleftcorner,warmercolors).
afterqualityfiltering.Thearchaealandfungalcomponentsofthe Bacteroides occurred commonly with Parabacteroides, Lachnospiraceae,
microbiotawereassessedusingthe16SrRNAgeneandtheITS1 and Ruminococcaceae. Lachnospiraceae occurred commonly with
rRNAgenetags,respectively[38–40].Thebacterialpopulationof FaecalibacteriumandRuminococcaceaeaswellasBacteroides.Incontrast,
thesesampleswascharacterizedpreviouslyby454pyrosequencing co-occurrence of Methanobrevibacter and Nitrososphaera was low, as
of V1V2 segments of the 16S rRNA gene [10]. Volunteers were mentioned above.
screened to be free of chronic gastrointestinal disease, cardiac Candida and Saccharomyces were both associated with the group
disease,diabetesmellitusorimmunodeficiencydiseases,tohavea containingtheabovebacteria.Severalotherfungiachievedlevels
normalbowelfrequency(minimumonceevery2days,maximum sufficientforinclusionintheanalysis,butshowedlessclearcutco-
3timesperday),andbodymassindex(BMI)between18.5and35. occurrence with other community members. Methobrevibacter
showed modest levels of association with the above group of
The Archaea of the Gut Microbiome bacteria, but was similarly also associated with Prevotella and
A total of 99,131 archaeal sequence reads were obtained, several other bacterial groups. Nitrososphaera showed only modest
resultinginthedetectionof5genera(Figure1).Atotalof44ofthe associations with other lineages. The frequent co-occurrence of
96 samples analyzed were positive for at least one species. some of these microorganisms suggests candidate interactions
Methanobrevibacter sp was detected in 30 samples, and Nitrososphaera among gutmicrobes forfurther investigation.
sp was detected in 16 samples (Figure 1A). The two genera were
usually mutually exclusive,coexisting inonly6 samples. Covariation among Microbial Lineages
The detection of Nitrososphaera was surprising and so was We next examined covariation among the three Domains,
investigated further. Comparison of amplification efficiency taking into account the relative abundance of each lineage in
suggested that the Methanobrevibacter when present was relatively addition to presence-absence information. For the fungi and
abundant, while the Nitrososphaera was less abundant (Figure 1, bacteria, multiple lineages were seen, and multiple different
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CorrelationsofDietwiththeGutMicrobiome
Figure1.Thearchaealandfungalcomponentsofthehumangutmicrobiome.Theheatmapsshowtherelativeproportionsofmicrobial
lineagesdetectedbypyrosequencing.Thelineagesaremarkedontheright,withPhylum(abbreviated),Class,andGenus.Archaealgeneraareshown
in(A),representativebacterialgenerain(B),andfungalgenerain(C).ThetoptworowsshowtheDNAyieldfromPCRamplificationreactions,which
servesasaroughindicatorofabundance.Proportionswerecalculatedwithineachamplicon(archaeal16S,bacterial16S,orfungalITS)foreach
sequencing study separately. The abbreviations for phyla were as follows (Eur: Euryarchaeota; Tha: Thaumarchaeota; Act: Actinobacteria; Bac:
Bacteroidetes;Fir:Firmicutes;Asc:Ascomycota;Bas;Basidiomycota).OtherAscomycotaandOtherBasidiomycotaarecomposedofgenerawhich
weredetectedinonlyonesample(seeTableS7andFigureS2foracompletelistofdetectedgeneraandtheirprevalence).
doi:10.1371/journal.pone.0066019.g001
lineages were present in all samples, allowing use of correlation- Several lineages co-varied with Methanobrevibacter, including the
basedmethods.However,forthearchaea,onlytwolineageswere commonly encountered bacteria Ruminococcus, and rarer lineages
detected with substantial frequency, Methanobrevibacter and Nitroso- such as Oxalobacter and Papillibacter. Of the fungi, Candida and
phaera, and these were mostly mutually exclusive. Thus for the SaccharomyceswerebothpositivelyassociatedwithMethanobrevibacter.
archaea, samples were divided into three categories (containing Bothfungal genera were negatively associated withNitrososphaera.
oneofthetwoarchaeaornoarchaea),andco-occurringbacteria The Prevotella/Bacteriodes ratio was implicated as important for
and fungi scored. For those few cases where both archaea were the gut microbiome structure in previous studies [10], [16], [41],
seen in a single sample, there was always a substantially greater so we assessed correlations with fungal and archaeal taxa. We
abundance of one, so the sample was assigned based on the performed a PermanovaG test using the Prevotella/Bacteroides ratio
predominant lineage. and the generalized Unifrac matrices obtained from the fungal
AsafirstglobaltestofassociationamongthethreeDomains,we composition data. A significant relationship was observed
conducted a Permanova test using the newly developed Gener- (p=0.0146) indicating a potential influence of the Prevotella/
alizedUnifracdistance[42],whichshowedsignificance(TablesS1 Bacteriodes ratio on fungi. A post-hoc test with the individual
and S2). To characterize the lineages involved, we used a non- weightedandunweightedunifracmatricesshowedthattherewas
parametric Kruskal-Wallis test to determine which bacterial and a significantly correlation between the Prevotella/Bacteriodes ratio
fungal genera co-varied with thearchaeal categories (Figure 3A). andtheweightedUnifracdistances(p=0.0133),butnotwiththe
unweighted Unifrac matrix. Thus the Prevotella/Bacteroides ratio
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CorrelationsofDietwiththeGutMicrobiome
Figure2.Analysisofco-occurrenceamongmicrobiallineagesscoredusingtheDiceindex.Diceindexesacrossallgenerapairspresentat
aproportion.=0.01areshownasaheatmap.ClusteringwascarriedoutusingWard’scriteria,basedontheEuclidiandistancebetweeneachgenus
pairusingtheirDiceindexacrossallothergenera.Domainmembershipiscolor-codedontheleft.DataaresummarizedinTableS9andS10.
doi:10.1371/journal.pone.0066019.g002
correlated with the amounts of fungi present, but not the types. Permanova test with the individual weighted and unweighted
For the archaea, Bacteriodes was significantly negatively correlated Unifrac matrices was significant only when using the weighted
with Methanobrevibacter, but positively correlated with both Nitroso- Unifracmatrix(p=0.0205andp=0.004,fortheAscomycotaand
sphaeraandnoarchaea.Prevotellashowedareciprocalpattern,butit Basidiomycota, respectively). A targeted analysis of the Fungi
did not achieve significance, probably because of the lower (Figure 3B) showed a negative association of Candida with
numbers of Prevotella-positive samples inthedataset. Bacteriodes. Togetherthese results indicate that thetypes offungal
We also used a PermanovaG test to determine whether the species in the gut were not correlated with the bacterial taxa
proportion of fungal phyla (Ascomycota or Basidiomycota) present, butrather theirrelative proportions.
correlated with the gut bacterial lineages. Both Ascomycota and
Basidiomycota were significantly correlated with the bacterial
lineages (p=0.0202 and p=0.0037, respectively). A post-hoc
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CorrelationsofDietwiththeGutMicrobiome
Figure3.Inter-genericrelationships.Theheatmapsquantifytheintergenericrelationships.(A)Normalizedz-scoreofthebacterialandfungal
proportions for samples grouped according to their archaeal status (Methanobrevibacter positive, Nitrososphaera positive, or archaea negative).
AsterisksindicateKruskall-WallissignificantcomparisonsafterFDRadjustment(FDRof25,20,15,and10%aremarkedwith1,2,3or4asterisks,
respectively).Domainmembershipiscolor-codedontheleft.(B)SpearmancorrelationsbetweenFungiandBacteria.AsterisksinredindicateFDR
adjusted significant correlations (FDR 20%) and the remaining raw p-values are shown to illustrate general patterns within the data (p-values
,=0.05,0.01,0.005,0.001aremarkedwith1,2,3or4asterisks,respectively).
doi:10.1371/journal.pone.0066019.g003
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CorrelationsofDietwiththeGutMicrobiome
Associations with Diet We detected no fewer than 62 fungal genera and 184 species
Wenextinvestigatedcorrelationsbetweendietandthearchaeal level OTUs, paralleling and extending a study of one subject,
and fungal taxa. Dietary information was collected using diet whichalsoyieldedahighnumberoffungallineages[35].Whichof
inventories that scored usual (long-term) diet, and recent (short- these lineages are true gut residents, and which are transients in
term) diet. For each inventory, a clustering method was used to foodisunknown.Sixindividualshadsequencesbelongingtogenus
identify co-varying groups of dietary components, thereby Agaricus,thewhitebuttonmushroom,whichisconsumedasfood.
reducing the number of variables and reducing penalties for Thisgenuswasamongthosefilteredoutoftheanalysisduetolow
multiple comparisons in the statistical analysis. We found that prevalenceinthedataset,suggestingthatfungalDNAinfoodmay
many of the clusters formed natural categories, such as carbohy- bemostlydegradedduringdigestion.However,wecannotexclude
drates,animalprotein,andaminoacids.Correlationsbetweendiet the possibility that the high prevalence of Saccharomyces in the
and bacteria have been reported previously for this data set [10] fungaldataisduetotheingestionofyeast-containingfoodssuchas
and were recapitulated here after using the pre-clustered dietary bread andbeer.
data (below). As a first step, Permanova tests were carried out Nitrososphaera, which were encountered with unexpected fre-
using the nutrient cluster data and the weighted and unweighted quency in our data, are different enough from other archaeal
Unifrac distance matrixes to determine whether the fungal and groups to be placed in their own phylum, the Thaumarchaeota.
archaeal lineages were also associated with diet. Multiple Members of the Nitrososphaera genus are able to oxidize ammonia
significantassociationsweredetectedandarecatalogedinTables anddegradeurea,whichpresumablywouldalsofeednitrogeninto
S3 andS4. the gut microbial community. Nitrososphaera may have been
Relationships between archaea and nutrients were explored previously underappreciated in microbiome studies due to its
using a Kruskall-Wallis test on the Permanova-selected nutrient low abundance. Here, it was detected in 16% of the samples
cluster measurements. As above, samples were separated into analyzed, though in low abundance, in a mutually exclusive
archaea-negative, Methanobrevibacter-positive or Nitrososphaera-posi- pattern with Methanobrevibacter. The basis of possible antagonism
tivegroups.Ahigherintakeofcarbohydrateswascorrelatedwith between Methanobrevibacter and Nitrososphaera is unknown. Nitroso-
Methanobrevibacter-positive samples. This trend was observed in sphaera also showed a positive association with the ingestion of
comparisons to both the long-term and short-term dietary data. proteins and amino acids, both for usual and recent diet. The
For the long-term diet, samples with Nitrososphaera or no archaea correlation was not sufficient to survive correction for multiple
were enriched for clusters representing vegetable fat and poly- comparisons,butmayneverthelessindicateutilizationofammonia
unsaturated fats. Samples with no archaea were enriched for a and or urea to meet their energy and carbon requirements.
clusterrepresentingtotalfatandtotalmono-unsaturatedfatsinthe Alternatively, we cannot exclude the possibility that Nitrososphaera
recent diet data(Figure 4). was ingested withfoods,possibly associated with meats.
Fungal andbacterialproportionswere alsoscoredversususual Previously, interactions among microbial lineages of the gut
diet (Figure 5A) or recent diet (Figure 5B). Only diet categories wereproposedtoseparatehumanpopulationsinto‘‘enterotypes’’,
with at least one significant association are shown. As reported leading to considerable controversy. Arumugam etal. [16]
previously, bacterial proportions were most strongly correlated described three interacting networks of microbial lineages,
with components of the long term diet–Bacteriodes was more centeredonthepresenceofPrevotella,Bacteriodes,andRuminococcus,
abundant in individuals eating high levels of animal protein, together with other interacting taxa. However, subsequent work
amino acids, and fats, while Prevotella was higher among those indicated that the most prominent feature of the data was an
eating higher proportions of carbohydrates. For fungi significant inverse correlation between the Prevotella and Bacteroides genera
correlations were observed only with the recent diet inventory, [41],[10].HereweshowapositiveassociationofMethanobrevibacter
differing from the observations with bacteria. In the recent diet andCandidawiththePrevotellagroup,andeachofthesewasfurther
data, Candida was positively correlated with carbohydrates and correlated with a diet high in carbohydrates. A negative
negatively with total saturated fatty acids. A trend in the same correlation of Methanobrevibacter and Bacteroides was also observed,
directionwasseenforCandidaintheusuallong-termdiet,butitdid parallelingtheoriginalArumugametal.paper.However,wealso
not achieve significance. Aspergillus was negatively correlated with observed a strong positive relationship between Methanobrevibacter
shortchainfattyacidsintherecentdietdata.Notrendswereseen and Ruminococccus, which were initially described to belong to
withSaccharomyces.Thus,thesedataindicatethatfungalabundance distinctenterotypes.Thusourfindingsassociatearchaeaandfungi
isparticularlystronglyassociatedwiththecompositionofrecently with aspectsof theenterotype concept.
consumed foods. Theintergenericrelationshipsdescribedhere,togetherwiththe
nutrient correlations, support models for specific interactions
Discussion among microbes in the human gut. An example of syntrophism
has been previously described for Ruminococcus and methanogens,
Hereweinvestigatedtherelationshipsofdietandthefungiand where the methanogens consume H , allowing Ruminococcus to
2
the archaea of the human intestinal microbiome. Previously we producetwiceasmanyATPmoleculesfromthesameamountof
reported,forthissamesetofsamples,thatpatternsinthebacterial substrate[43].Onepossiblesyntrophicguildspecifiedinourdata
part of the gut microbiome correlated with long-term diet. Here includes Candida, Prevotella, Ruminococcus and Methanobrevibacter
wecharacterizedthesamplesetbysequencingmarkergenesofthe (Figure 6). Candida is able to degrade starches, especially after
archaeal rDNA 16S and the fungal ITS1 region. Many pre-treatment with amylases [44] such as the human-encoded
interactionsamongmicroorganismsandnutrientswereidentified. amylases present in the mouth and small intestine. Thus, in one
Methanobrevibacter and Candida were positively correlated with the model,Candidamayassistinbreakingdownstarchincarbohydrate
ingestion of carbohydrates. This was most notable in short-term richfoods,whichinturnliberatessimplersugarstobefermented
dietdataforbothgroups,andinfactonlyachievedsignificanceat by bacteria such as Prevotella and Ruminococcus. Fermentation
all for Candida in the short-term diet data. These data support byproducts produced would then be consumed by Methanobrevi-
specific proposals for the interactions of members of the gut bacter with the subsequent production of CO and/or CH [43].
2 4
microbiome withdietary components andwitheach other. Alternatively,Prevotellamightdegradestarch(pre-treatedornotby
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CorrelationsofDietwiththeGutMicrobiome
Figure 4. Archaea-Diet relationships. Heatmap of normalized average means for nutrient cluster measurements of the samples classified
according to the dominant archaeal genus. Usual diet (A) and recent diet (B) relationships considered significant are marked with asterisks as
describedinFigure2A.
doi:10.1371/journal.pone.0066019.g004
human alpha amylases) and mannan containing polysaccharides [47]. All such hydrolysis is extra cellular, which would provide
from food to smaller poly- and monosaccharides [45]. Prevotella Candida with simpler sugars for fermentation (potentially down to
would then take up the smaller mono and polysaccharides, acetate).Ruminococcusmightthenconsumethesuccinateproduced
catabolizingthemtoproducesuccinateandotherbyproducts[46],
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CorrelationsofDietwiththeGutMicrobiome
Figure5.Fungi-Dietrelationships.HeatmapofSpearmancorrelationsbetweennutrientclustersandthebacterialandfungalgeneradetectedin
thedataset.CorrelationswhichwereconsideredsignificantusingtheUsual(A)andtheRecent(B)dietdataaremarkedwithasterisksasinFigure2A.
Domainmembershipiscolor-codedonthebottom.
doi:10.1371/journal.pone.0066019.g005
by Prevotella and produce H or acetate2/H+ for consumption by relationships between nutrition and microbial consortia within
2
Methanobrevibacter [44],[48],[46]. thehuman gut.
In summary, the findings presented here provide a broader
pictureofthehumangutmicrobiome,integratingitsfulldiversity
with human diet. The associations presented here, together with
other work [49], [41], [13], allow the proposal of specific
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CorrelationsofDietwiththeGutMicrobiome
mentaldata.[53]).ThenestedPCRwasperformedtoincreasethe
sensitivityoftheassayandimprovethedetectioninthesesamples.
ConventionalPCRwasnotsufficientlysensitivetodetectthe16S
gene efficiently. PCR reactions were carried out using Invitrogen
Accuprime. One mL of the total extracted DNA was used as
templatefortheinitialPCR.ThenestedPCRused1 mLofPCR1
astemplate(TableS6).Blankextractionswereusedtocontrolfor
environmental and reagent contamination. All PCR work was
carried out in a laminar flow hood and all consumables and
equipmentwereUVirradiatedforaminimumof30minutesprior
touse.
Pyrosequencing
Amplified 16S rDNA and ITS1 fragments were purified using
1:1 volume of Agencourt AmPure XP beads (Beckman-Colter,
Brea, CA). The purified PCR products from the stool samples
were pooled in equal amounts prior to pyrosequencing using
Roche/454GenomeSequencerJunior.DNApoolswereseparat-
ed byamplicon type.All samples were submitted forsequencing,
evencontrolsamplesforwhichnovisibleampliconwasobserved
in agarose gels. For such samples, 40 out 50mL of the bead-
purified PCR product was pooled with the other samples for
sequencing.
Figure 6. Possible syntrophic relationships in the human gut
consistentwithdatareportedinthisstudy.
doi:10.1371/journal.pone.0066019.g006 Sequence Analysis
Sequencesobtainedweredecodedandqualitycontrolledusing
Methods the QIIME pipeline [54]. OUT’s were formed at 97% and 95%
similarityforarchaealandfungalsequencesrespectively,andwere
Inadditiontothebelow,furtherinformationonmethodscanbe considered for further analysis if they had a minimum of 5
found in the Supplemental Information, Figures S1–S3, and sequencesdetectedacrossallsamples.Taxonomywasassignedto
Tables S1–S10. OTU representative sequences using the RDPclassifier [55] for
archaeal sequences and BROCC [36] for fungal sequences. All
Ethics Statement taxonomy assignments were manually curated to check for
TheInstitutionalReviewBoardoftheUniversityofPennsylva- accuracy and nomenclature using BLASTn against GenBank’s
nia approved all study protocols and all participants provided NR/NT database. ForArchaea, thetaxonomic assignmentgiven
written informed consent, or assent in the case of minors. Legal byRDPto2outofthe12archaealOUT’sdetectedwascorrected
guardiansprovidedwritteninformedconsentforminors(protocol duetolowcoverageofthosegroupsonRDP.ForITS,ofthe290
#810009). OUT’sdetected,10weremissingmid-level(betweenphylumand
genus) taxonomic information, which was filled in, and 5 yielded
Samples differingmidleveltaxonomies,ofwhichonlyoneofpergenuswas
Thesamplesdescribedin[10]wereusedinthisstudy.Bacterial used. Two ITS OUT’s were automatically classified down to
16Ssequencesdataanddietdatafrom[10]wasusedhere.Briefly, genususingBROCC,buthadtheirtaxonomyassignmentbrought
healthy volunteers were screened to be free from any chronic uptoOrderleveluponinspectionofBLASTresults(bothOUT’s
gastrointestinal disease, cardiac disease, diabetes mellitus or werepresentinonesampleeach).OTUsequencecountsforeach
immunodeficiency diseases, to have a normal bowel frequency sample were aggregated at genus level. All downstream analysis
(minimum once every 2 days, maximum 3 times per day), and was done at the Genus level using R unless otherwise noted.
body mass index(BMI) between18.5 and35. Demographicdata Generawereconsideredintheanalysisifpresentinatleast9out
wascollectandanalyzedasdescribedpreviouslyforbacterialtaxa of the 96 available samples, and its absolute sequence count was
([10], Table S8). One stool sample was provided per subject and equaltoorgreaterthan10.FortheITSamplicon,sampleswere
kept frozen at 280oC untilprocessed forDNA extraction[10]. included in downstream analysis if they yielded at least 200
sequences. All novel sequence data was deposited at NCBI’s
16S rDNA Gene, ITS1 Region and AmoA Gene PCR Sequence Read Archiveunderaccessionnumber SRP021021.
Pyrosequencing was carried out using barcoded composite
primers constructed as described in [50]. PCR reactions were Beta Diversity
carried out in triplicate using the Accuprime system (Invitrogen, Taxonomic information was used to obtain the Taxonomic
Carlsbad, CA, USA). Each reaction contained 50 nanograms of Distance between each genus using the R package Ade4, version
DNAand10picoMolofeachprimer.Archaealspecific16SrDNA 1.5[56],[57].Thetaxonomicdistancematrixwasusedasinputto
primersandITS1fungalprimerswereadaptedfromtheliterature calculateUnifracdistancesusingtheRpackageGUniFrac,version
andusedtoamplifyarDNA16Sfragment;thefinalPCRcycling 1.0 [42].
conditionswereoptimizedtomaximizespecificity[51],[52],[38],
[40],(Tables S5and S6). Inter-generic Relationships
A nested PCR using specific primers for the AmoA gene was Effectsofbacteriaonfungi,effectsofthefungionbacteria,and
used to confirm the detection of Nitrosophaera sequences (Supple- effectsofarchaeaonbacteriaandfungiwereinvestigatedusinga
PLOSONE | www.plosone.org 9 June2013 | Volume 8 | Issue 6 | e66019
CorrelationsofDietwiththeGutMicrobiome
Permanova test. Simulations of the effects of unequal variance in (TIF)
the different data sets compared indicated that differential
Figure S3 Number of Fungal and Bacterial genera per
variance was not a major confounder (Text S1). Within sample
sample. Samples were classified as Methanobrevibacter positive,
genus proportions were used to calculate Spearman correlations
Nitrososphaera positive, or Archaea negative. Difference between
between bacterial genera and fungal genera. As only one or very
groups was tested using a Kruskal-Wallis test, followed by a post
few archaeal genera were detected in any sample, sequence
hocDunn’smultiplecomparisontest.Asterisksindicatesignificant
proportions would be greatly skewed, invalidating any potential
comparisons(p,0.001).
correlation results. Instead, samples were classified according to
(TIF)
thearchaeageneradetectedandbacterialandfungalproportions
were used on as input for Kruskall-Wallis tests. P-values were TableS1 PermanovaofArchaeaeffectsontheBacterial
considered significantusing a FDRof 25%. and Fungal parts ofthemicrobiome.
(XLSX)
Co-occurrence
TableS2 Permanova of Fungal PhylawithBacteria.
TheDiceindex[58]wasusedtodeterminetheco-occurrenceof
(XLSX)
genera across the entire dataset. Genera were considered present
in asample ifitssequence proportionwas at least0.01. Table S3 Permanova analysis of the association be-
tween usual dietary groups and bacterial, fungal, and
Diet Analysis archaeal lineages.
Dietary information from [10] was used in this analysis. Usual (XLSX)
diet was obtained using the Willett food frequency questionnaire
Table S4 Permanova analysis of the association be-
[59]. Recent diet was obtained from 3 interviews recalling all
tween recent dietary groups and bacterial, fungal, and
consumed food on 3 days within the week preceding the sample
archaeal lineages.
acquisition (NHANES method, [60]). All interviews were carried
(XLSX)
out by trained nutritionists. Nutrient measurements across
individuals obtained from the dietary questionnaires were used TableS5 Primersused inthisstudy.
inaclusteringproceduretoreducethenumberofcomparisonsto (XLSX)
bemade.First,Spearmancorrelationswerecalculatedpairwisefor
Table S6 PCR amplification conditions used in this
allnutrientvariablesavailableandthiscorrelationmatrixwasused
study.
as input in a clustering analysis, and 20 clusters (approximately
(XLSX)
10% of the total number of nutrients available) were selected.
Nutrients within each cluster were submitted to a Principal Table S7 Number of reads for each genus, in each
Component Analysis and the first principal component values sample analyzed.
were extracted and used as a surrogate dietary measurement (XLSX)
(Nutrient Cluster Measurement). These nutrient cluster measure-
Table S8 Permanova analysis of the association be-
ments were used in a Permanova analysis together with the
tween demographic factors and bacterial, fungal, and
taxonomy based, weighted and unweighted Unifrac distances
archaeal lineages.
calculated using the R packages Ade4 and GUniFrac. Clusters
(XLSX)
which were significant in the Permanova analysis were further
usedtocalculateSpearmanrankcorrelationsusingtheproportion Table S9 Co-occurring genera according to the calcu-
for each bacterial and fungal genus across all samples. The latedDiceindex.
Nutrient Cluster Measurements for each sample were also (XLSX)
classified according to their archaeal status and then submitted
to a Kruskall-Wallis test to determine archaea/diet relationships. Table S10 Positively co-varying genera. Archaeal covari-
P-values with a False Discovery Rate of 25% or less were ation represents the positive relationship detected in Figure 3A.
considered significant. Fungi/Bacterial relationships are the same as the positive
relationships represented inFigure 3B.
Supporting Information (XLSX)
TextS1 Supporting information text.
Figure S1 The Fungal phyla detected are shown as sequence
(DOCX)
proportionswithineachsample(A).ASpearmanrankcorrelation
fortheproportionsofAscomycotaversusBasidiomycotaacrossthe
sampleswas0.7456.Careshouldbetakenwheninterpreting this Acknowledgments
correlationastheproportionalnatureofsequencingdatanaturally
WethankmembersoftheBushmanLabforhelpfuldiscussionsandCaitlin
yields inverse correlations. The prevalence of each fungal genera
Greigforhelpinpreparingthemanuscriptsubmission.
detectedacrossallsamplesisdepictedin(B).Generaaregrouped
by their phylum affiliation and only genera sequences that could
Author Contributions
be assignedtothegenus levelare shown.
(TIF) Conceivedanddesignedtheexperiments:CHGDWJDLFDB.Performed
theexperiments:CHSDSG.Analyzedthedata:CHJCHL.Wrotethe
Figure S2 Heatmap with all Fungal genera detected in paper:CHFDB.
thestoolsamplesetused,andblankextractioncontrols.
Colors indicate relative proportion withineachsample.
PLOSONE | www.plosone.org 10 June2013 | Volume 8 | Issue 6 | e66019