2017-定量微生物组分析将肠道群落变异与微生物负荷联系起来.pdf

2 3 n o v e m b e r 2 0 1 7|v o L 5 5 1|n A T U r e|5 0 7LeTTerdoi:10.1038/nature24460Quantitative microbiome profiling links gut community variation to microbial loadDoris vandeputte1,2,3*,Gunter Kathagen1,2*,Kevin Dhoe1,2,3*,Sara vieira-Silva1,2*,mireia valles-Colomer1,2,Joo Sabino4,Jun Wang1,2,raul Y.Tito1,2,3,Lindsey De Commer1,Youssef Darzi1,2,Sverine vermeire4,Gwen Falony1,2&Jeroen raes1,2Current sequencing-based analyses of faecal microbiota quantify microbial taxa and metabolic pathways as fractions of the sample sequence library generated by each analysis1,2.Although these relative approaches permit detection of disease-associated microbiome variation,they are limited in their ability to reveal the interplay between microbiota and host health3,4.Comparative analyses of relative microbiome data cannot provide information about the extent or directionality of changes in taxa abundance or metabolic potential5.If microbial load varies substantially between samples,relative profiling will hamper attempts to link microbiome features to quantitative data such as physiological parameters or metabolite concentrations5,6.Saliently,relative approaches ignore the possibility that altered overall microbiota abundance itself could be a key identifier of a disease-associated ecosystem configuration7.To enable genuine characterization of hostmicrobiota interactions,microbiome research must exchange ratios for counts4,8,9.Here we build a workflow for the quantitative microbiome profiling of faecal material,through parallelization of amplicon sequencing and flow cytometric enumeration of microbial cells.We observe up to tenfold differences in the microbial loads of healthy individuals and relate this variation to enterotype differentiation.We show how microbial abundances underpin both microbiota variation between individuals and covariation with host phenotype.Quantitative profiling bypasses compositionality effects in the reconstruction of gut microbiota interaction networks and reveals that the taxonomic trade-off between Bacteroides and Prevotella is an artefact of relative microbiome analyses.Finally,we identify microbial load as a key driver of observed microbiota alterations in a cohort of patients with Crohns disease10,here associated with a low-cell-count Bacteroides enterotype(as defined through relative profiling)11,12.First,we collected a set of 40 fresh faecal samples(the study cohort),which were processed within one hour of egestion.We compiled an accompanying set of basic matching metadata,with an emphasis on anthropometrics and stool characteristics(Supplementary Table 1).Given expected dietary effect sizes2 and cohort limitations,participants were not requested to keep food records.Sample analysis was aligned with Flemish Gut Flora Project(FGFP)protocols2.Metadata explora-tion reaffirmed the previously reported association of stool consistency(Bristol Stool Scale(BSS)score13)with moisture14(Spearmans =0.45,P=5.2 103;Supplementary Table 2).Microbiome analysis of frozen faecal aliquots characterized the sample set as within the bounds of FGFP community space,distributed over four enterotypes that were identified on the basis of Dirichlet multinomial mixtures(DMM)(Fig.1a).Stool moisture and donor age were identified as the microbiome covariates that displayed the largest non-redundant effect size,jointly explaining 9.3%of inter-individual microbiota variation (stepwise distance-based redundancy analysis(dbRDA);Supplementary Table 3).Association analyses confirmed several previously reported FGFP genusmetadata associations,including the covariation of stool consistency with Akkermansia and Methanobrevibacter15,16(Supplementary Table 4).Next,we determined total microbial cell counts in faecal samples using flow cytometry.Because microbiome analyses often begin with frozen material and freezethaw cycles can affect cell integrity17,we compared counts obtained from both fresh and frozen faecal 1KU Leuven University of Leuven,Department of Microbiology and Immunology,Rega Institute,Herestraat 49,B-3000 Leuven,Belgium.2VIB,Center for Microbiology,Kasteelpark Arenberg 31,B-3000 Leuven,Belgium.3Research Group of Microbiology,Department of Bioengineering Sciences,Vrije Universiteit Brussel,Pleinlaan 2,B-1050 Brussels,Belgium.4Translational Research Center for Gastrointestinal Disorders(TARGID),KU Leuven,B-3000 Leuven,Belgium.*These authors contributed equally to this work.These authors jointly supervised this work.EnterotypeProjectFGFPStudy cohortCell counts(1011 cells per g)*Axis 1(19.2%)Axis 2(16.1%)Moisture contentAgeba3.53.02.52.01.51.00.50RPB2B1Bacteroides 1(B1)Bacteroides 2(B2)Prevotella(P)Ruminococcaceae(R)Figure 1|Faecal microbial loads vary across enterotypes.a,Genus-level faecal microbiome community variation,represented by principal coordinates analysis(BrayCurtis dissimilarity PCoA).Samples from the study cohort(full circles,n=40)and the FGFP cohort(crosses,n=1,106)were enterotyped and coloured accordingly.Stool moisture content and donor age were fitted onto the ordination(arrows scaled to contribution).The percentage of variance explained by the two first PCoA dimensions are reported on the axes.b,Microbial load differences between the four enterotypes in the study cohort(n=40).Box plot representation of microbial load(cells per gram of faeces)distribution across the four enterotypes.The body of the box plot represents the first and third quartiles of the distribution and the median line.The whiskers extend from the quartiles to the last data point within 1.5 interquartile range,with outliers beyond.Two-sided Dunns adjusted test,*P 0.01.Significant differences in microbial abundance between the Prevotella sample and other enterotypes were not assessed.The occurrence of a low-cell-count Bacteroides B2 enterotype was confirmed in a disease cohort,with the cell counts of Prevotella samples being similar to those of the Ruminococcaceae and Bacteriodes B1 enterotypes(Extended Data Fig.4).2017 Macmillan Publishers Limited,part of Springer Nature.All rights reserved.LetterreSeArCH5 0 8|n A T U r e|v o L 5 5 1|2 3 n o v e m b e r 2 0 1 7aliquots and found them to be strongly correlated(Pearsons r=0.91,P=4.9 1016;Extended Data Fig.1a).Although method-specific technical biases affect the outcomes of both cell-based and molecular microbial enumeration workflows18,a comparison between quantita-tive PCR(qPCR)and flow cytometric load assessment yielded compa-rable abundance profiles(Pearsons r=0.53,P=4.7 104;Extended Data Fig.1b).Focusing our analyses on frozen samples,we observed up to a tenfold variation in cell counts between individuals.Microbial loads were shown to vary between 4.3 1010 and 3.1 1011 cell counts per gram of faecal material(median 1.5 1011 cell counts per gram;Supplementary Table 5),in agreement with previous reports that addi-tionally characterized half of these cells as damaged or dead19.To assess longitudinal variation in abundance profiles,we quantified cell counts in stool samples collected from 20 healthy individuals(10 women and 10 men)over the course of a week(Supplementary Table 1).Individual microbial load profiles varied substantially(intraclass rank correlation coefficient(ICC)=0.46).Single daily fluctuations(load dayx+1 load dayx)ranged in magnitude between 1.1 108(participant LC02)and 1.6 1011 cell counts per gram(participant LC16;average daily fluc-tuation 3.8 1010 cell counts per gram;Extended Data Fig.2),empha-sizing the need to integrate longitudinal elements in microbiome study designs.Time-series cell counts decreased with stool moisture,although in the study cohort this association was not significant;this decrease was subsequently confirmed in an independent validation dataset(Supplementary Tables 1,6;Extended Data Fig.3a).To integrate cell counts in microbiota analyses,we assessed their associations with microbiome features.We found cell counts to correlate mildly with observed genus richness(Spearmans =0.36,P=2.3 102;Extended Data Fig.3b).In addition,we observed an association between microbial loads and the enterotypes identi-fied on the basis of DMM11,12(KruskalWallis test,P=1.23 103;Supplementary Table 5).Higher cell counts were observed in samples that contained large fractions of Ruminococcaceae,and cell densities differentiated two Bacteroides clusters(Fig.1b;Extended Data Fig.4).The observed genus richness and enterotype associations indicate that microbial load variation does not merely reflect cell concentration as a result of absorption processes along the intestinal tract,as might be suggested by correlations with stool moisture.Instead,cell count dynamics appear to be linked with the previously reported ecosystem differentiation associated with transit time20 and the accompanying reduction in water content2,15.Cell counts did not correlate with magnitudes of microbiome dissimilarity between individuals(dbRDA,R2=3.0%,P=0.29):although microbial loads differ between entero-types,relative microbiome composition cannot be used to infer overall microbiota abundance.Indeed,when screening for taxa that were significantly associated with cell counts,only a single positive corre-lation between Ruminococcus and microbial load was retained after correcting for multiple testing(Spearmans =0.51,false discovery rate(FDR)=4.4 102;Supplementary Table 7).Next,we used cell counts to transform sequencing data into an absolute microbiome abundance matrix that allowed quantitative microbiome profiling(QMP;in contrast to relative microbiome pro-filing,RMP),by modifying sequencing depth rarefying procedures.Despite criticism21,rarefying sequencing output to an equal number of reads per sample remains a common practice in microbiome research22.However,the observed variation in the number of reads produced in a sequencing process is a technical artefact;for example,it may be the result of equimolar pooling of sample DNA libraries to enhance sequencing effectiveness.In the current dataset,this is con-firmed by the fact that sequencing depths did not reflect the microbial loads of the samples(Spearmans =0.17,P=0.28;Extended Data Fig.5).Although rarefying to equal sample size or sequencing depth is essential for comparing biological diversity between samples,it is inadequate if samples are drawn from ecosystems with markedly differ-ent population sizes and species abundance distributions23(Extended Data Fig.6).Given the observed tenfold variation in faecal microbial loads between individuals(Supplementary Table 5),we propose cor-recting for sampling intensity by rarefying to an even sampling depth(rather than to an even sequencing depth),calculated as 16S rRNA gene copy-number-corrected sequencing depth divided by sample cell count.For each sample,the resulting rarefied genus abundances are propor-tional to cell counts and can be extrapolated to the total microbial load of each sample.This extrapolation generates quantitative microbiome profiles expressed as the number of cells per gram(Fig.2;see Extended Data Fig.6).For interclass comparisons,fitting a negative binomial distribution that accounts for sampling depth rather than sequencing depth21 could provide an alternative to the quantitative rarefying approach described above.To assess the influence of QMP on the outcome of microbiome analyses,we investigated the effect of QMP on genus abundance aQMP genus abundance(1011 cells per g)RMP genus abundance(%)0123GeneraOtherFaecalibacteriumBlautiaDoreaRoseburiaBacteroidesRuminococcus 2FusicatenibacterAnaerostipesRuminococcusBifidobacteriumAlistipesCollinsellaCoprococcusBarnesiellaPrevotellaSC13SC25SC12SC18SC07SC38SC37SC23SC33SC20SC01SC28SC15SC08SC30SC21SC22SC31SC04SC26SC02SC14SC35SC11SC34SC03SC27SC40SC24SC10SC05SC06SC36SC16SC19SC39SC29SC32SC09SC170255075100SamplesbSC13SC25SC12SC18SC07SC38SC37SC23SC33SC20SC01SC28SC15SC08SC30SC21SC22SC31SC04SC26SC02SC14SC35SC11SC34SC03SC27SC40SC24SC10SC05SC06SC36SC16SC19SC39SC29SC32SC09SC17SamplesFigure 2|Relative versus quantitative microbiome profiling.Genus-level faecal microbiome composition of study cohort participants(n=40).a,Relative microbiota profiles deduced from standard microbiome sequencing protocols.b,Quantitative microbiome profiles deduced from complementing sequencing with microbial cell counts(cells per gram of faeces).Samples are ordered according to decreasing microbial load.The top 15 most abundant genera are depicted,with all others pooled into Other.2017 Macmillan Publishers Limited,part of Springer Nature.All rights reserved.Letter reSeArCH2 3 n o v e m b e r 2 0 1 7|v o L 5 5 1|n A T U r e|5 0 9profiles in healthy individuals.Because associations between micro-biomes and metadata are generally investigated using non-parametric methods(often on the basis of ranks),we analysed whether the sam-ple rank order for each genus was conserved between RMP and QMP analyses.We observed significant rank order position shifts for multiple genera and found that the extent of these shifts was dependent on genus.Rank order concordance varied widely even within the top ten most abundant genera,ranging from Faecalibacterium(lowest concord-ance)to Prevotella(highest concordance;Kendalls rank correlation test,range=0.600.88;Supplementary Table 8),confirming our hypothesis that absolute abundance profiles differ significantly from those generated by relative approaches.As expected,rank changes affected the outcomes of association analyses.Only three of eight FGFP genusmetadata correlations confirmed by RMP could be validated using the QMP approach,all of which were linked to variation in stool consistency(Supplementary Table 4).When assessing non-redundant effect sizes of the metadata in quantitative microbiome variation,we no longer detected an independent contribution of age.The effect of stool moisture on QMP remained significant and accounted for 4.3%of quantitative inter-individual microbiota variation(dbRDA,7.3%in RMP;Supplementary Table 3).In a comparative analysis,it is impossible to establish absolute growths or declines of particular genera on the basis of relative taxon abundances.Such genera abundance shifts are particularly relevant to deducing and interpreting species interaction networks that are based on co-occurrence,as these analyses have been shown to be susceptible to the compositionality effects that result from relative abundance measures5,24.To assess the impact of QMP on genus co-occurrence patterns,we reanalysed 66 samples that had been selected from the FGFP2 as healthy controls for a recent disease-focused microbiome study10.Genus co-occurrence networks were reconstructed using both RMP and QMP data matrices(Fig.3;Supplementary Table 9).A far larger number of significantly co-varying genus pairs were detected in the QMP network(76,versus 10 in the RMP network),most of which were part of a network module associated with total faecal micro-bial load.Most of the RMP network pairs were recovered in QMP analyses,with the noteworthy exc