UC Davis EVE161 Lecture 18 by @phylogenomics

Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
Lecture 18:
EVE 161: 
Microbial Phylogenomics
!
Lecture #18:
Era IV: Metagenomics Case Study
!
UC Davis, Winter 2014
Instructor: Jonathan Eisen
!1

• Next week Student Presentations
• Each student gets 10 minutes total
• Eight minutes to present and 2 minutes for questions
• Possible presentation timing
! 2 minutes Overview and Methods
! 4 minutes R & D
! 2 minutes Conclusions and Future Ideas
! 2 minutes Questions
• Contact Holly Ganz hhganz@ucdavis.edu for non Eisen
guidance

ARTICLES
A human gut microbial gene catalogue
established by metagenomic sequencing
Junjie Qin1
*, Ruiqiang Li1
*, Jeroen Raes2,3
, Manimozhiyan Arumugam2
, Kristoffer Solvsten Burgdorf4
,
Chaysavanh Manichanh5
, Trine Nielsen4
, Nicolas Pons6
, Florence Levenez6
, Takuji Yamada2
, Daniel R. Mende2
,
Junhua Li1,7
, Junming Xu1
, Shaochuan Li1
, Dongfang Li1,8
, Jianjun Cao1
, Bo Wang1
, Huiqing Liang1
, Huisong Zheng1
,
Yinlong Xie1,7
, Julien Tap6
, Patricia Lepage6
, Marcelo Bertalan9
, Jean-Michel Batto6
, Torben Hansen4
, Denis Le
Paslier10
, Allan Linneberg11
, H. Bjørn Nielsen9
, Eric Pelletier10
, Pierre Renault6
, Thomas Sicheritz-Ponten9
,
Keith Turner12
, Hongmei Zhu1
, Chang Yu1
, Shengting Li1
, Min Jian1
, Yan Zhou1
, Yingrui Li1
, Xiuqing Zhang1
,
Songgang Li1
, Nan Qin1
, Huanming Yang1
, Jian Wang1
, Søren Brunak9
, Joel Dore´6
, Francisco Guarner5
,
Karsten Kristiansen13
, Oluf Pedersen4,14
, Julian Parkhill12
, Jean Weissenbach10
, MetaHIT Consortium{, Peer Bork2
,
S. Dusko Ehrlich6
& Jun Wang1,13
To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here
we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant
microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set,
,150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent)
microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The
genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire
cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are
also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of
functions present in all individuals and most bacteria, respectively.
Vol 464|4 March 2010|doi:10.1038/nature08821

ARTICLES
A human gut microbial gene catalogue
established by metagenomic sequencing
Junjie Qin1
*, Ruiqiang Li1
*, Jeroen Raes2,3
, Manimozhiyan Arumugam2
, Kristoffer Solvsten Burgdorf4
,
Chaysavanh Manichanh5
, Trine Nielsen4
, Nicolas Pons6
, Florence Levenez6
, Takuji Yamada2
, Daniel R. Mende2
,
Junhua Li1,7
, Junming Xu1
, Shaochuan Li1
, Dongfang Li1,8
, Jianjun Cao1
, Bo Wang1
, Huiqing Liang1
, Huisong Zheng1
,
Yinlong Xie1,7
, Julien Tap6
, Patricia Lepage6
, Marcelo Bertalan9
, Jean-Michel Batto6
, Torben Hansen4
, Denis Le
Paslier10
, Allan Linneberg11
, H. Bjørn Nielsen9
, Eric Pelletier10
, Pierre Renault6
, Thomas Sicheritz-Ponten9
,
Keith Turner12
, Hongmei Zhu1
, Chang Yu1
, Shengting Li1
, Min Jian1
, Yan Zhou1
, Yingrui Li1
, Xiuqing Zhang1
,
Songgang Li1
, Nan Qin1
, Huanming Yang1
, Jian Wang1
, Søren Brunak9
, Joel Dore´6
, Francisco Guarner5
,
Karsten Kristiansen13
, Oluf Pedersen4,14
, Julian Parkhill12
, Jean Weissenbach10
, MetaHIT Consortium{, Peer Bork2
,
S. Dusko Ehrlich6
& Jun Wang1,13
To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here
we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant
microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set,
,150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent)
microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The
genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire
cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are
also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of
functions present in all individuals and most bacteria, respectively.
Vol 464|4 March 2010|doi:10.1038/nature08821
THAT”S A LOT OF
AUTHORS

METHODS

Human faecal samples were collected, frozen immediately and DNA was
puriﬁed by standard methods22. For all 124 individuals, paired-end
libraries were constructed with different clone insert sizes and subjected
to Illumina GA sequencing. All reads were assembled using
SOAPdenovo19, with speciﬁc parameter ‘2M 3’ for metagenomics data.
MetaGene was used for gene prediction. A non-redundant gene set was
constructed by pair-wise comparison of all genes, using BLAT36 under
the criteria of identity .95% and overlap .90%. Gene taxonomic
assignments were made on the basis of BLASTP37 search (e-value ,1 3
1025) of the NCBI-NR database and 126 known gut bacteria genomes.
Gene functional annotations were made by BLASTP search (e-value ,1 3
1025) with eggNOG and KEGG (v48.2) databases. The total and shared
number of orthologous groups and/or gene families were computed using
a random combination of n individuals (with n 5 2 to 124, 100 replicates
per bin).

As part of the MetaHIT (Metagenomics of the Human Intestinal Tract)
project, we collected faecal specimens from 124 healthy, overweight
and obese individual human adults, as well as inﬂammatory bowel
disease (IBD) patients, from Denmark and Spain (Supplementary Table
1)

Supplementary Tables
Table 1 | DNA sample information.
All Danish individuals in the present subsample were originally recruited from a larger
population-based sample of middle-aged people living in the northern part of
Copenhagen region and sampled from the centralized personal number register. At the
original recruitment the individuals included in the present study had normal fasting
plasma glucose and normal 2 hour plasma glucose following an oral glucose tolerance
test. At the time of fecal sampling all were examined in the fasting state and had
non-diabetic fasting plasma glucose levels below 7,0 mmol/l. All of the IBD patients
were in clinical remission at the time of fecal sampling. N refers to no IBD, CD & UC
to Crohn’s disease and ulcerative colitis, respectively.
Sample Name Country Gender Age BMI IBD
MH0001 Denmark female 49 25.55 N
MH0003 Denmark male 69 33.19 N

METHODS!
& !
RESULTS!
(mixed)

Total DNA was extracted from the faecal specimens18 and an average of
4.5 Gb (ranging between 2 and 7.3 Gb) of sequence was generated for
each sample, allowing us to capture most of the novelty (see Methods
and Supplementary Table 2). In total, we obtained 576.7 Gb of sequence
(Supplementary Table 3).

!

Table 2 | Summary of Sanger reads. The reads were sequenced by 3730xl.
Low-quality sequences at both ends with phred score less than 20 were trimmed. Very
short reads with length less than 100 bp were filtered.
Sample ID # Sanger reads Average length (bp) Total length (bp)
MH0006 237,567 660.65 156,949,306
MH0012 230,768 670.26 154,675,458

Table 3 | Summary of Illumina GA reads. We constructed libraries with three
different insert sizes of about 135 bp, 200 bp, and 400 bp. The insert sizes of each
library were estimated by re-aligning the paired-end reads on the assembled contigs.
Sample
ID
Paired-end
insert size (bp)
Read
length
(bp) # of reads
Data
(Gb)
human
reads, %
# of high
quality reads
MH0047 136/378 75 35,355,400 2.65 0.18 26,932,064
MH0021 134/354 75 36,454,400 2.73 0.12 26,258,326
MH0079 135/360 75 38,011,600 2.85 0.40 27,418,899
MH0078 146/373 75 38,038,200 2.85 1.56 26,051,537
MH0052 141/367 75 39,538,000 2.97 0.08 28,575,036
MH0049 134/343 75 40,444,200 3.03 0.06 30,654,842
MH0076 134/409 75 40,697,000 3.05 0.42 30,650,106
MH0051 143/374 75 41,911,800 3.14 0.32 25,963,104
MH0048 143/349 75 42,923,600 3.22 0.26 26,972,970
O2.UC-14 141/355 75 43,343,000 3.25 0.06 26,942,750
MH0015 235 44 44,671,400 1.97 0.04 33,014,675
MH0018 233 44 45,081,400 1.98 2.14 36,609,695
MH0027 238 44 45,190,000 1.99 0.09 32,377,390
MH0017 223 44 45,557,200 2.00 0.04 36,154,362
MH0022 256 44 46,415,000 2.04 0.21 37,112,508
MH0023 237 44 48,598,400 2.14 0.04 37,782,998
MH0019 249 44 49,229,400 2.17 0.06 38,856,780
MH0026 156/398 75 49,812,000 3.74 0.05 37,484,066
MH0013 238 44 50,257,200 2.21 1.63 40,028,120
MH0005 237 44 50,704,800 2.23 0.23 39,407,333
MH0007 195 44 50,719,800 2.23 0.31 36,956,284
MH0008 219 44 51,411,000 2.26 0.10 38,156,496
V1.UC-7 141/356 75 51,911,400 3.89 14.67 36,788,540
MH0010 220 44 52,218,200 2.30 0.08 39,169,850
V1.CD-12 148/361 75 53,519,400 4.01 0.02 40,609,134
O2.UC-20 141/362 75 53,637,200 4.02 0.03 38,376,747
V1.CD-15 143/351 75 53,938,600 4.05 2.85 40,560,446
O2.UC-19 133/352 75 54,537,600 4.09 0.01 38,459,550
MH0004 218 44 55,829,800 2.46 0.95 40,288,492
MH0062 144/357 75 57,128,400 4.28 14.32 36,809,224
MH0066 147/429 75 57,234,200 4.29 0.05 36,114,997
O2.UC-21 142/362 75 57,856,000 4.34 0.03 34,832,308
V1.CD-13 139/352 75 58,145,800 4.36 0.04 42,560,831
MH0080 140/376 75 58,220,800 4.37 0.13 46,590,749
V1.UC-13 131/352 75 58,381,400 4.38 9.77 38,553,580
MH0032 142/370 75 58,822,400 3.93 0.39 50,110,067
O2.UC-12 153/384 75 58,927,800 4.42 0.12 36,908,526
MH0001 214 44 59,239,200 2.61 0.06 45,016,612
V1.UC-6 142/376 75 59,270,800 4.45 0.41 43,150,856
MH0060 142/367 75 60,156,000 4.51 0.07 41,112,227
MH0053 137/416 75 60,788,600 4.56 0.07 43,283,564
MH0002 139/370 75 61,077,000 4.58 0.15 46,570,095
O2.UC-11 142/377 75 61,253,800 4.59 0.14 38,507,042
MH0059 136/370 75 61,574,600 4.62 0.18 41,025,606

Wanting to generate an extensive catalogue of microbial genes from the
human gut, we ﬁrst assembled the short Illumina reads into longer
contigs, which could then be analysed and annotated by standard
methods. Using SOAPdenovo19, a de Bruijn graph-based tool specially
designed for assembling very short reads, we performed de novo
assembly for all of the Illumina GA sequence data. Because a high
diversity between individuals is expected8,16,17, we ﬁrst assembled each
sample independently (Supplementary Fig. 3). As much as 42.7% of the
Illumina GA reads was assembled into a total of 6.58 million contigs of a
length .500 bp, giving a total contig length of 10.3 Gb, with an N50
length of 2.2 kb (Supplementary Fig. 4) and the range of 12.3 to 237.6
Mb (Supplementary Table 4). Almost 35% of reads from any one sample
could be mapped to contigs from other samples, indicating the existence
of a common sequence core.

Wanting to generate an extensive catalogue of microbial genes from the
human gut, we ﬁrst assembled the short Illumina reads into longer
contigs, which could then be analysed and annotated by standard
methods. Using SOAPdenovo19, a de Bruijn graph-based tool specially
designed for assembling very short reads, we performed de novo
assembly for all of the Illumina GA sequence data. Because a high
diversity between individuals is expected8,16,17, we ﬁrst assembled
each sample independently (Supplementary Fig. 3). As much as 42.7%
of the Illumina GA reads was assembled into a total of 6.58 million
contigs of a length .500 bp, giving a total contig length of 10.3 Gb, with
an N50 length of 2.2 kb (Supplementary Fig. 4) and the range of 12.3 to
237.6 Mb (Supplementary Table 4). Almost 35% of reads from any one
sample could be mapped to contigs from other samples, indicating the
existence of a common sequence core.

Figure 3 | Flowchart of human gut microbiome data analysis process. We performed
de novo short reads assembly for each sample independently, then all the unassembled
reads were pooled for another round of assembly. ORFs were predicted in each of the
contig set, and were merged by removing redundancy. The non-redundant gene set was
used in all further analysis.

Figure 4 | Length distribution of assembled contigs. The number of contigs in
different length bins for each individual was computed, and the data from all 124
individuals were pooled. Boxes denote 25% and 75% percentiles, the red line
corresponds to the median, and the “whiskers” indicate interquartile range from either
or both ends of the box

Table 4 | Summary of de novo assembly results. Assembled sequences with length
below 500 bp were excluded from the contig set.
Sample ID # of contigs
Contig N50
(bp)
Total length
(Mb)
% reads
assembled
Unassembled
reads (Gb)
MH0001 14,301 1,618 19.69 46.34 1.06
MH0002 65,392 1,680 88.77 45.31 1.91
MH0003 68,658 2,640 119.59 54.40 1.72
MH0004 23,793 1,681 31.92 41.54 1.05
MH0005 14,339 1,684 19.62 40.22 1.04
MH0006 144,440 2,025 217.77 52.39 5.24
MH0007 28,108 1,270 32.00 29.15 1.16
MH0008 26,506 1,768 37.24 43.53 0.95
MH0009 70,014 2,440 112.96 44.14 2.45
MH0010 25,674 1,815 36.52 48.77 0.88
MH0011 86,201 2,158 134.25 46.09 2.37
MH0012 140,991 2,478 237.58 42.77 7.99
MH0013 20,495 2,332 32.20 41.22 1.05
MH0014 66,724 2,957 120.54 50.90 2.08
MH0015 25,933 1,645 34.46 35.53 0.94
MH0016 64,124 2,915 114.03 53.89 1.88
MH0017 24,948 1,679 34.06 39.57 0.96
MH0018 13,247 1,619 17.73 35.23 1.07
MH0019 28,786 1,977 41.95 46.76 0.91
MH0020 44,930 4,708 98.78 56.81 1.49
MH0021 54,101 1,608 70.67 46.49 1.06
MH0022 21,872 1,773 30.00 35.04 1.06
MH0023 16,214 2,100 25.57 35.80 1.07
MH0024 43,145 1,512 54.45 33.21 2.41
MH0025 76,287 1,968 111.48 42.24 2.11
MH0026 33,408 3,769 69.38 43.97 1.58
MH0027 20,369 985 19.72 19.96 1.14
MH0028 61,004 2,630 104.54 49.65 1.80
MH0030 39,267 2,828 66.60 36.73 2.15
MH0031 53,292 1,878 75.84 36.41 2.33
MH0032 37,287 1,921 54.46 20.72 2.60
MH0033 61,782 2,616 102.04 49.87 1.69
MH0034 24,508 2,107 37.63 14.53 2.40
MH0035 68,287 2,075 102.94 48.22 1.93
MH0036 58,690 2,330 94.35 49.44 1.81
MH0037 48,356 2,526 80.44 48.21 1.63
MH0038 50,381 2,921 90.35 47.75 1.79
MH0039 66,509 2,087 104.17 47.66 1.68
MH0040 73,068 2,225 115.15 49.53 1.68

To assess the quality of the Illumina GA-based assembly we mapped the
contigs of samples MH0006 and MH0012 to the Sanger reads from the
same samples (Supplementary Table 2). A total of 98.7% of the contigs
that map to at least one Sanger read were collinear over 99.6% of the
mapped regions. This is comparable to the contigs that were generated by
454 sequencing for one of the two samples (MH0006) as a control, of
which 97.9% were collinear over 99.5% of the mapped regions. We
estimate assembly errors to be 14.2 and 20.7 per megabase (Mb) of
Illumina- and 454-based contigs, respectively (see Methods and
Supplementary Fig. 5), indicating that the short- and long-read- based
assemblies have comparable accuracies.

Figure 5 | Validating Illumina contigs using Sanger reads. Illumina/454 contigs from
samples MH0006 and MH0012 were mapped to Sanger reads from the same samples.
Aligned regions were scanned for breakage of collinearity, and each unique break is
counted as an error. a. number of errors per Mb of Illumina/454 contigs mapped to
Sanger reads. b. percentage of collinear Illumina/454 contigs and collinear basepairs in
those contigs.
b

To complete the contig set we pooled the unassembled reads from all 124
samples, and repeated the de novo assembly process. About 0.4 million
additional contigs were thus generated, having a length of 370 Mb and an
N50 length of 939 bp. The total length of our ﬁnal contig set was thus
10.7 Gb. Some 80% of the 576.7 Gb of Illumina GA sequence could be
aligned to the contigs at a threshold of 90% identity, allowing for
accommodation of sequencing errors and strain variability in the gut
(Fig. 1), almost twice the 42.7% of sequence that was assembled into
contigs by SOAPdenovo, because assembly uses more stringent criteria.
This indicates that a vast majority of the Illumina sequence is represented
by our contigs.

To complete the contig set we pooled the unassembled reads from all
124 samples, and repeated the de novo assembly process. About 0.4
million additional contigs were thus generated, having a length of 370
Mb and an N50 length of 939 bp. The total length of our ﬁnal contig set
was thus 10.7 Gb. Some 80% of the 576.7 Gb of Illumina GA sequence
could be aligned to the contigs at a threshold of 90% identity, allowing
for accommodation of sequencing errors and strain variability in the gut
(Fig. 1), almost twice the 42.7% of sequence that was assembled into
contigs by SOAPdenovo, because assembly uses more stringent criteria.
This indicates that a vast majority of the Illumina sequence is
represented by our contigs.

the 90% identity threshold. A total of 70.1% and 85.9% of the reads
from the Japanese and US samples, respectively, could be aligned to
(the highest
indicates tha
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the existence
100
50
0
Assembled
contig set
Known human
gut bacteria
GenBank
bacteria
Coverageofsequencingreads(%)
Figure 1 | Coverage of human gut microbiome. The three human microbial
sequencing read sets—Illumina GA reads generated from 124 individuals in
this study (black; n 5 124), Roche/454 reads from 18 human twins and their
mothers (grey; n 5 18) and Sanger reads from 13 Japanese individuals
(white; n 5 13)—were aligned to each of the reference sequence sets. Mean
values 6 s.e.m. are plotted.
60

To compare the representation of the human gut microbiome in our
contigs with that from previous work, we aligned them to the reads from
the two largest published gut metagenome studies (1.83Gb of Roche/454
sequencing reads from 18 US adults8, and 0.79 Gb of Sanger reads from
13 Japanese adults and infants17), using the 90% identity threshold. A
total of 70.1% and 85.9% of the reads from the Japanese and US
samples, respectively, could be aligned to our contigs (Fig. 1), showing
that the contigs include a high fraction of sequences from previous
studies. In contrast, 85.7% and 69.5% of our contigs were not covered by
the reads from the Japanese and US samples, respectively, highlighting
the novelty we captured.

!

Only 31.0–48.8% of the reads from the two previous studies and the
present study could be aligned to 194 public human gut bacterial
genomes (Supplementary Table 5), and 7.6–21.2% to the bacterial
genomes deposited in GenBank (Fig. 1). This indicates that the reference
gene set obtained by sequencing genomes of isolated bacterial strains is
still of a limited scale.

Only 31.0–48.8% of the reads from the two previous studies and the
present study could be aligned to 194 public human gut bacterial
genomes (Supplementary Table 5), and 7.6–21.2% to the bacterial
genomes deposited in GenBank (Fig. 1). This indicates that the
reference gene set obtained by sequencing genomes of isolated
bacterial strains is still of a limited scale.

A gene catalogue of the human gut microbiome

To establish a non-redundant human gut microbiome gene set we first
used the MetaGene20 program to predict ORFs in our contigs and found
14,048,045 ORFs longer than 100bp (Supplementary Table 6). They
occupied 86.7% of the contigs, comparable to the value found for fully
sequenced genomes (,86%). Two-thirds of the ORFs appeared
incomplete, possibly due to the size of our contigs (N50 of 2.2 kb). We
next removed the redundant ORFs, by pair-wise comparison, using a
very stringent criterion of 95% identity over 90% of the shorter ORF
length, which can fuse orthologues but avoids inflation of the data set due
to possible sequencing errors (see Methods). Yet, the final non-redundant
gene set contained as many as 3,299,822 ORFs with an average length of
704 bp (Supplementary Table 7).

Table 7 | Non-redundant genes. Genes were compared at 95 % identity cut-off. Those
that were overlapped over 90% length were considered redundant and removed.
Common and rare genes were present in >50% and < 20% of individuals, respectively.
# of genes Total length (bp) Mean length (bp)
Non-redundant gene set 3,299,822 2,323,171,095 704.03
Common 294,110 292,960,308 996.09
Rare 2,375,655 1,510,527,924 635.84

We term the genes of the non-redundant set ‘prevalent genes’, as they are
encoded on contigs assembled from the most abundant reads (see
Methods). The minimal relative abundance of the prevalent genes was ,
631027, as estimated from the minimum sequence coverage of the
unique genes (close to 3), and the total Illumina sequence length
generated for each individual (on average, 4.5 Gb), assuming the average
gene length of 0.85 kb (that is, 3 3 0.85 3 103/ 4.5 3 109).

We term the genes of the non-redundant set ‘prevalent genes’, as they
are encoded on contigs assembled from the most abundant reads (see
Methods). The minimal relative abundance of the prevalent genes was ,
63x-7, as estimated from the minimum sequence coverage of the unique
genes (close to 3), and the total Illumina sequence length generated for
each individual (on average, 4.5 Gb), assuming the average gene length
of 0.85 kb (that is, 3 3 0.85 3 103/ 4.5 3 109).

We mapped the 3.3 million gut ORFs to the 319,812 genes (target genes)
of the 89 frequent reference microbial genomes in the human gut. At a
90% identity threshold, 80% of the target genes had at least 80% of their
length covered by a single gut ORF (Fig. 2b). This indicates that the gene
set includes most of the known human gut bacterial genes.

We mapped the 3.3 million gut ORFs to the 319,812 genes (target genes)
of the 89 frequent reference microbial genomes in the human gut. At a
90% identity threshold, 80% of the target genes had at least 80% of their
length covered by a single gut ORF (Fig. 2b). This indicates that the
gene set includes most of the known human gut bacterial genes.

We examined the number of prevalent genes identiﬁed across all
individuals as a function of the extent of sequencing, demanding at least
two supporting reads for a gene call (Fig. 2a). The incidence- based
coverage richness estimator (ICE), determined at 100 individuals (the
highest number the EstimateS21 program could accommodate), indicates
that our catalogue captures 85.3% of the prevalent genes. Although this is
probably an underestimate, it nevertheless indicates that the catalogue
contains an overwhelming majority of the prevalent genes of the cohort.

We examined the number of prevalent genes identiﬁed across all
individuals as a function of the extent of sequencing, demanding at
least two supporting reads for a gene call (Fig. 2a). The incidence- based
coverage richness estimator (ICE), determined at 100 individuals (the
highest number the EstimateS21 program could accommodate), indicates
that our catalogue captures 85.3% of the prevalent genes. Although
this is probably an underestimate, it nevertheless indicates that the
catalogue contains an overwhelming majority of the prevalent genes of
the cohort.

Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014the cohort. For this purpose, we used a non-redundant set of 650 A complex pattern of species relatedness, character
Relative abundance
Blautia hansenii
Clostridium scindens
Enterococcus faecalis TX0104
Clostridium asparagiforme
Bacteroides fragilis 3_1_12
Bacteroides intestinalis
Ruminococcus gnavus
Anaerotruncus colihominis
Bacteroides pectinophilus
Clostridium nexile
Clostridium sp. L2−50
Parabacteroides johnsonii
Bacteroides ﬁnegoldii
Butyrivibrio crossotus
Bacteroides eggerthii
Clostridium sp. M62 1
Coprococcus eutactus
Bacteroides stercoris
Holdemania ﬁliformis
Clostridium leptum
Streptococcus thermophilus LMD−9
Bacteroides capillosus
Subdoligranulum variabile
Ruminococcus obeum A2−162
Bacteroides dorei
Eubacterium ventriosum
Bacteroides sp. D4
Bacteroides sp. D1
Coprococcus comes SL7 1
Bacteriodes xylanisolvens XB1A
Eubacterium rectale M104 1
Bacteroides sp. 2_2_4
Bacteroides sp. 4_3_47FAA
Bacteroides ovatus
Parabacteroides distasonis ATCC 8503
Eubacterium siraeum 70 3
Roseburia intestinalis M50 1
Bacteroides vulgatus ATCC 8482
Dorea formicigenerans
Collinsella aerofaciens
Ruminococcus lactaris
Faecalibacterium prausnitzii SL3 3
Ruminococcus sp. SR1 5
Unknown sp. SS3 4
Ruminococcus torques L2−14
Eubacterium hallii
Bacteroides thetaiotaomicron VPI−5482
Clostridium sp. SS2−1
Bacteroides caccae
Ruminococcus bromii L2−63
Dorea longicatena
Parabacteroides merdae
Alistipes putredinis
Bacteroides uniformis
–4 –3
Figure 3 | Relative abundance of 57 frequent microbial ge
individuals of the cohort. See Fig. 2c for definition of box
See Methods for computation.
1
Number of individuals sampled
Numberoforthologousgroups/genefamilies(×103
)
25 50 75 100 124
a b
c
320,000
280, 000
240,000
200,000
160,000
1.0 0.8 0.6 0.4 0.2 0
0.6
0.7
0.8
0.9
1.0
0.5
85%
90%
95%
0
5
10
15
20
0
1
2
3
4
1 20 40 60 80 100
OGs + novel gene families
Known + unknown OGs
Known OGs
Numberofnon-redundant
genes(×106
)
Numberoftargetgenescovered
Fractionoftargetgenes
covered
Number of samples Fraction of gene length covered
Figure 2 | Predicted ORFs in the human gut microbiome. a, Number of
uniquegenes as a function ofthe extent ofsequencing. The gene accumulation
curve corresponds to the Sobs (Mao Tau) values (number of observed genes),
calculated using EstimateS21
(version 8.2.0) on randomly chosen 100 samples
(due to memory limitation). b, Coverage of genes from 89 frequent gut
microbial species (Supplementary Table 12). c, Number of functions captured
by number of samples investigated, based on known (well characterized)
orthologous groups (OGs; bottom), known plus unknown orthologous
groups (including, for example, putative, predicted, conserved hypothetical
functions; middle) and orthologous groups plus novel gene families (.20
proteins) recovered from the metagenome (top). Boxes denote the
interquartile range (IQR) between the first and third quartiles (25th and 75th
percentiles, respectively) and the line inside denotes the median. Whiskers
denote the lowest and highest values within 1.5 times IQR from the first and
third quartiles, respectively. Circles denote outliers beyond the whiskers.
NATURE|Vol 464|4 March 2010

)
a b
c
0.
0.
0.
0.
1.
0.
20
0
1
2
3
4
1 20 40 60 80 100
Numberofnon-redundant
genes(×106
)
covered
Number of samples

Each individual carried 536,112 ±12,167 (mean 6 s.e.m.) prevalent genes
(Supplementary Fig. 6b), indicating that most of the 3.3 million gene
pool must be shared. However, most of the prevalent genes were found in
only a few individuals: 2,375,655 were present in less than 20%, whereas
294,110 were found in at least 50% of individuals (we term these
‘common’ genes). These values depend on the sampling depth;
sequencing of MH0006 and MH0012 revealed more of the catalogue
genes, present at a low abundance (Supplementary Fig. 7). Nevertheless,
even at our routine sampling depth, each individual harboured
204,05663,603 (mean ± s.e.m.) common genes, indicating that about
38% of an individual’s total gene pool is shared. Interestingly, the IBD
patients harboured, on average, 25% fewer genes than the individuals not
suffering from IBD (Supplementary Fig. 8), consistent with the
observation that the former have lower bacterial diversity than the
latter22.

Each individual carried 536,112 ±12,167 (mean 6 s.e.m.) prevalent genes
(Supplementary Fig. 6b), indicating that most of the 3.3 million gene
pool must be shared. However, most of the prevalent genes were
found in only a few individuals: 2,375,655 were present in less than
20%, whereas 294,110 were found in at least 50% of individuals (we
term these ‘common’ genes). These values depend on the sampling
depth; sequencing of MH0006 and MH0012 revealed more of the
catalogue genes, present at a low abundance (Supplementary Fig. 7).
Nevertheless, even at our routine sampling depth, each individual
harboured 204,056 ± 3,603 (mean ± s.e.m.) common genes, indicating
that about 38% of an individual’s total gene pool is shared. Interestingly,
the IBD patients harboured, on average, 25% fewer genes than the
individuals not suffering from IBD (Supplementary Fig. 8), consistent
with the observation that the former have lower bacterial diversity than
the latter22.

Figure 7 | Number of unique genes identified with increase of sequencing depth in
sample MH0006 and MH0012.

Figure 8 | Distribution of nonredundant bacterial genes in IBD patients and
healthy controls. The proportion of individuals having a given number of genes
(classes of 100 thousand genes were used) is shown.The average gene number for IBD
patients and individuals not suffering from IBD was425,397 + 126,685 (s.d.; n=25) and
564,070 + 121,962 (s.d.; n=99), respectively; p<10-6
(one-tailed Student t test).

Common bacterial core

Deep metagenomic sequencing provides the opportunity to explore the
existence of a common set of microbial species (common core) in the
cohort. For this purpose, we used a non-redundant set of 650 sequenced
bacterial and archaeal genomes (see Methods). We aligned the Illumina
GA reads of each human gut microbial sample onto the genome set,
using a 90% identity threshold, and determined the proportion of the
genomes covered by the reads that aligned onto only a single position in
the set. At a 1% coverage, which for a typical gut bacterial genome
corresponds to an average length of about 40 kb, some 25-fold more than
that of the 16S gene generally used for species identiﬁcation, we detected
18 species in all individuals, 57 in ≥90% and 75 in ≥50% of individuals
(Supplementary Table 8). At 10% coverage, requiring ,10-fold higher
abundance in a sample, we still found 13 of the above species in ≥90% of
individuals and 35 in ≥50%.

Common bacterial core

Deep metagenomic sequencing provides the opportunity to explore the
existence of a common set of microbial species (common core) in the
cohort. For this purpose, we used a non-redundant set of 650 sequenced
bacterial and archaeal genomes (see Methods). We aligned the Illumina
GA reads of each human gut microbial sample onto the genome set,
using a 90% identity threshold, and determined the proportion of the
genomes covered by the reads that aligned onto only a single position in
the set. At a 1% coverage, which for a typical gut bacterial genome
corresponds to an average length of about 40 kb, some 25-fold more than
that of the 16S gene generally used for species identiﬁcation, we detected
18 species in all individuals, 57 in ≥90% and 75 in ≥50% of
individuals (Supplementary Table 8). At 10% coverage, requiring ,10-
fold higher abundance in a sample, we still found 13 of the above
species in ≥90% of individuals and 35 in ≥50%.

When the cumulated sequence length increased from 3.96 Gb to 8.74 Gb
and from 4.41 Gb to 11.6 Gb, for samples MH0006 and MH0012,
respectively, the number of strains common to the two at the 1%
coverage threshold increased by 25%, from 135 to 169. This indicates the
existence of a signiﬁcantly larger common core than the one we could
observe at the sequence depth routinely used for each individual.

The variability of abundance of microbial species in individuals can
greatly affect identiﬁcation of the common core. To visualize this
variability, we compared the number of sequencing reads aligned to
different genomes across the individuals of our cohort. Even for the most
common 57 species present in ≥90% of individuals with genome
coverage .1% (Supplementary Table 8), the inter-individual variability
was between 12- and 2,187-fold (Fig. 3). As expected10,23,
Bacteroidetes and Firmicutes had the highest abundance.

Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014pose, we used a non-redundant set of 650 A complex pattern of species relatedness, characterized by clusters
Relative abundance (log10
)
Blautia hansenii
Clostridium scindens
Enterococcus faecalis TX0104
Clostridium asparagiforme
Bacteroides fragilis 3_1_12
Bacteroides intestinalis
Ruminococcus gnavus
Anaerotruncus colihominis
Bacteroides pectinophilus
Clostridium nexile
Clostridium sp. L2−50
Parabacteroides johnsonii
Bacteroides ﬁnegoldii
Butyrivibrio crossotus
Bacteroides eggerthii
Clostridium sp. M62 1
Coprococcus eutactus
Bacteroides stercoris
Holdemania ﬁliformis
Clostridium leptum
Streptococcus thermophilus LMD−9
Bacteroides capillosus
Subdoligranulum variabile
Ruminococcus obeum A2−162
Bacteroides dorei
Eubacterium ventriosum
Bacteroides sp. D4
Bacteroides sp. D1
Coprococcus comes SL7 1
Bacteriodes xylanisolvens XB1A
Eubacterium rectale M104 1
Bacteroides ovatus
Parabacteroides distasonis ATCC 8503
Eubacterium siraeum 70 3
Roseburia intestinalis M50 1
Bacteroides vulgatus ATCC 8482
Dorea formicigenerans
Collinsella aerofaciens
Ruminococcus lactaris
Faecalibacterium prausnitzii SL3 3
Ruminococcus sp. SR1 5
Unknown sp. SS3 4
Ruminococcus torques L2−14
Eubacterium hallii
Bacteroides thetaiotaomicron VPI−5482
Clostridium sp. SS2−1
Bacteroides caccae
Ruminococcus bromii L2−63
Dorea longicatena
Parabacteroides merdae
Alistipes putredinis
Bacteroides uniformis
–4 –3 –2 –1
Figure 3 | Relative abundance of 57 frequent microbial genomes among
individuals of the cohort. See Fig. 2c for definition of box and whisker plot.
See Methods for computation.
50 75 100 124
b 320,000
280, 000
240,000
200,000
160,000
1.0 0.8 0.6 0.4 0.2 0
0.6
0.7
0.8
0.9
1.0
0.5
85%
90%
95%
80 100
Known + unknown OGs
Known OGs
Numberoftargetgenescovered
covered
Fraction of gene length covered
n the human gut microbiome. a, Number of
fthe extent ofsequencing. The gene accumulation
bs (Mao Tau) values (number of observed genes),
(version 8.2.0) on randomly chosen 100 samples
. b, Coverage of genes from 89 frequent gut
ntary Table 12). c, Number of functions captured
tigated, based on known (well characterized)
ottom), known plus unknown orthologous
ple, putative, predicted, conserved hypothetical
ologous groups plus novel gene families (.20
e metagenome (top). Boxes denote the
tween the first and third quartiles (25th and 75th
d the line inside denotes the median. Whiskers
st values within 1.5 times IQR from the first and
Circles denote outliers beyond the whiskers.
2010 ARTICLES

A complex pattern of species relatedness, characterized by clusters at the
genus and family levels, emerges from the analysis of the network
based on the pair-wise Pearson correlation coefﬁcients of 155 species
present in at least one individual at$ ≥1% coverage (Supplementary Fig.
9). Prominent clusters include some of the most abundant gut species,
such as members of the Bacteroidetes and Dorea/Eubacterium/
Ruminococcus groups and also biﬁdobacteria, Proteobacteria and
streptococci/lactobacilli groups. These observa- tions indicate that similar
constellations of bacteria may be present in different individuals of our
cohort, for reasons that remain to be established.

A complex pattern of species relatedness, characterized by clusters at the
genus and family levels, emerges from the analysis of the network based
on the pair-wise Pearson correlation coefﬁcients of 155 species present in
at least one individual at ≥1% coverage (Supplementary Fig. 9).
Prominent clusters include some of the most abundant gut species, such
as members of the Bacteroidetes and Dorea/Eubacterium/Ruminococcus
groups and also biﬁdobacteria, Proteobacteria and streptococci/
lactobacilli groups. These observa- tions indicate that similar
constellations of bacteria may be present in different individuals of our
cohort, for reasons that remain to be established.

Figure 9 | Relations between the most abundant bacterial species. The network was
deduced from the analysis of 155 bacterial species present in at least 1 individual at a
genome coverage of 1%. Size of the nodes (circles) indicates species abundance over
the cohort, width of the edges (lines connecting the circles) indicates the value of the
Pearson correlation coefficient (only the 342 values above 0.4 or below -0.4 out of a
total of 11,935 were used for the network).

The above result indicates that the Illumina-based bacterial proﬁling
should reveal differences between the healthy individuals and patients.
To test this hypothesis we compared the IBD patients and healthy
controls (Supplementary Table 1), as it was previously reported that the
two have different microbiota22. The principal component analysis,
based on the same 155 species, clearly separates patients from healthy
individuals and the ulcerative colitis from the Crohn’s disease patients
(Fig. 4), conﬁrming our hypothesis.

The above result indicates that the Illumina-based bacterial proﬁling
should reveal differences between the healthy individuals and patients.
To test this hypothesis we compared the IBD patients and healthy
controls (Supplementary Table 1), as it was previously reported that the
two have different microbiota22. The principal component analysis,
based on the same 155 species, clearly separates patients from healthy
individuals and the ulcerative colitis from the Crohn’s disease
patients (Fig. 4), conﬁrming our hypothesis.

Almost all (99.96%) of the phylogenetically assigned genes belonged
were within t
This suggests t
(Supplementa
functions imp
We found t
required in all
40
30
20
10
0
Cluster(%)
1
Figure 5 | Clust
were ranked by
length and copy
clusters with th
groups of 100 c
that contains 86
•
•
•
•
•
• •
• •
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
• •
•
•
•
•
•
•
•
•
•
•
•
•
Healthy
Crohn’s disease
Ulcerative colitis
P value: 0.031
PC2
PC1
Figure 4 | Bacterial species abundance differentiates IBD patients and
healthy individuals. Principal component analysis with health status as
instrumental variables, based on the abundance of 155 species with $1%
genome coverage by the Illumina reads in at least 1 individual of the cohort,
was carried out with 14 healthy individuals and 25 IBD patients (21 ulcerative
colitis and 4 Crohn’s disease) from Spain (Supplementary Table 1). Two first
components (PC1 and PC2) were plotted and represented 7.3% of whole
inertia. Individuals (represented by points) were clustered and centre of
gravity computed for each class; P-value of the link between health status and
species abundance was assessed using a Monte-Carlo test (999 replicates).
ARTICLES

Functions encoded by the prevalent gene set

We classified the predicted genes by aligning them to the integrated NCBI-NR database of non-
redundant protein sequences, the genes in the KEGG (Kyoto Encyclopedia of Genes and
Genomes)24 pathways, and COG (Clusters of Orthologous Groups)25 and eggNOG26 data-
bases. There were 77.1% genes classified into phylotypes, 57.5% to eggNOG clusters, 47.0% to
KEGG orthology and 18.7% genes assigned to KEGG pathways, respectively (Supplementary
Table 9). Almost all (99.96%) of the phylogenetically assigned genes belonged to the Bacteria and
Archaea, reflecting their predominance in the gut. Genes that were not mapped to orthologous
groups were clustered into gene families (see Methods). To investigate the functional content of
the prevalent gene set we computed the total number of orthologous groups and/or gene families
present in any combination of n individuals (with n 5 2–124; see Fig. 2c). This rarefaction ana-
lysis shows that the ‘known’ functions (annotated in eggNOG or KEGG) quickly saturate (a value
of 5,569 groups was observed): when sampling any subset of 50 individuals, most have been
detected. However, three-quarters of the prevalent gut functionalities consists of uncharacterized
orthologous groups and/or completely novel gene families (Fig. 2c). When including these
groups, the rarefaction curve only starts to plateau at the very end, at a much higher level (19,338
groups were detected), confirming that the extensive sampling of a large number of individuals
was necessary to capture this considerable amount of novel/unknown functionality.

Functions encoded by the prevalent gene set

We classified the predicted genes by aligning them to the integrated NCBI-NR database of non-
redundant protein sequences, the genes in the KEGG (Kyoto Encyclopedia of Genes and
Genomes)24 pathways, and COG (Clusters of Orthologous Groups)25 and eggNOG26 data-
bases. There were 77.1% genes classified into phylotypes, 57.5% to eggNOG clusters, 47.0% to
KEGG orthology and 18.7% genes assigned to KEGG pathways, respectively (Supplementary
Table 9). Almost all (99.96%) of the phylogenetically assigned genes belonged to the Bacteria and
Archaea, reflecting their predominance in the gut. Genes that were not mapped to orthologous
groups were clustered into gene families (see Methods). To investigate the functional content of
the prevalent gene set we computed the total number of orthologous groups and/or gene families
present in any combination of n individuals (with n 5 2–124; see Fig. 2c). This rarefaction ana-
lysis shows that the ‘known’ functions (annotated in eggNOG or KEGG) quickly saturate (a
value of 5,569 groups was observed): when sampling any subset of 50 individuals, most have
been detected. However, three-quarters of the prevalent gut functionalities consists of
uncharacterized orthologous groups and/or completely novel gene families (Fig. 2c). When
including these groups, the rarefaction curve only starts to plateau at the very end, at a much
higher level (19,338 groups were detected), confirming that the extensive sampling of a large
number of individuals was necessary to capture this considerable amount of novel/unknown
functionality.

1
Numberoforthologousgroups/genefamilies(×103
)
25 50 75 100 124
c
160,000
1.0 0.8 0.6 0.4 0.2 0
0.5
95%
0
5
10
15
20
0
1 20 40 60 80 100
Known + unknown OGs
Known OGs
Nu
covered
Fra
Number of samples Fraction of gene length covered
Figure 2 | Predicted ORFs in the human gut microbiome. a, Number of

Bacterial functions important for life in the gut

The extensive non-redundant catalogue of the bacterial genes from the
human intestinal tract provides an opportunity to identify bacterial
functions important for life in this environment. There are functions
necessary for a bacterium to thrive in a gut context (that is, the ‘minimal
gut genome’) and those involved in the homeostasis of the whole
ecosystem, encoded across many species (the ‘minimal gut
metagenome’). The ﬁrst set of functions is expected to be present in most
or all gut bacterial species; the second set in most or all individuals’ gut
samples.

To identify the functions encoded by the minimal gut genome we use the fact that they
should be present in most or all gut bacterial species and therefore appear in the gene
catalogue at a frequency above that of the functions present in only some of the gut
bacterial species. The relative frequency of different functions can be deduced from the
number of genes recruited to different eggNOG clusters, after normalization for gene
length and copy number (Supplementary Fig. 10a, b). We ranked all the clusters by gene
frequencies and determined the range that included the clusters specifying well- known
essential bacterial functions, such as those determined experi-mentally for a well-
studied ﬁrmicute, Bacillus subtilis27, hypothesizing that additional clusters in this range
are equally important. As expected, the range that included most of B. subtilis essential
clusters (86%) was at the very top of the ranking order (Fig. 5). Some 76% of the
clusters with essential genes of Escherichia coli28 were within this range, conﬁrming
the validity of our approach. This suggests that 1,244 metagenomic clusters found
within the range (Supplementary Table 10; termed ‘range clusters’ hereafter) specify
functions important for life in the gut.

40
30
20
10
0
Cluster(%)
1 2,001 4,001 6,001 8,001 10,001
Cluster rank
Range
Figure 5 | Clusters that contain the B. subtilis essential genes. The clusters
were ranked by the number of genes they contain, normalized by average
length and copy number (see Supplementary Fig. 10), and the proportion of
clusters with the essential B. subtilis genes was determined for successive
groups of 100 clusters. Range indicates the part of the cluster distribution
that contains 86% of the B. subtilis essential genes.
and
us as
$1%
cohort,
cerative
Two first
NATURE|Vol 464|4 March 2010

We found two types of functions among the range clusters: those required
in all bacteria (housekeeping) and those potentially specific for the gut.
Among many examples of the first category are the functions that are part
of main metabolic pathways (for example, central carbon metabolism,
amino acid synthesis), and important protein complexes (RNA and DNA
polymerase, ATP synthase, general secretory apparatus). Not surprisingly,
projection of the range clusters on the KEGG metabolic pathways gives a
highly integrated picture of the global gut cell metabolism (Fig. 6a).

The putative gut-specific functions include those involved in adhe- sion
to the host proteins (collagen, fibrinogen, fibronectin) or in harvesting
sugars of the globoseries glycolipids, which are carried on blood and
epithelial cells. Furthermore, 15% of range clusters encode functions that
are present in ,10% of the eggNOG genomes (see Supplementary Fig. 11)
and are largely (74.3%) not defined (Fig. 6b). Detailed studies of these
should lead to a deeper comprehension of bacterial life in the gut.

We found two types of functions among the range clusters: those
required in all bacteria (housekeeping) and those potentially specific for
the gut. Among many examples of the first category are the functions
that are part of main metabolic pathways (for example, central carbon
metabolism, amino acid synthesis), and important protein complexes
(RNA and DNA polymerase, ATP synthase, general secretory apparatus).
Not surprisingly, projection of the range clusters on the KEGG metabolic
pathways gives a highly integrated picture of the global gut cell
metabolism (Fig. 6a).

The putative gut-specific functions include those involved in adhe-sion
to the host proteins (collagen, fibrinogen, fibronectin) or in harvesting
sugars of the globoseries glycolipids, which are carried on blood and
epithelial cells. Furthermore, 15% of range clusters encode functions that
are present in ,10% of the eggNOG genomes (see Supplementary Fig. 11)
and are largely (74.3%) not defined (Fig. 6b). Detailed studies of these
should lead to a deeper comprehension of bacterial life in the gut.

c
a b
map00565
map00350
map00629
resistance
β-Lactam
map00643
map00620
map00780
map00260
map00940
map00512
map00670
map00513
map00220
map00628
Limonene and pinene
degradation
map00040
map00632
map00563
map00920
biosynthesis II
Alkaloid
map00600
map00625
map00400
map00941
map00520
map00790
map00330
map00621
map00271
map00591
map00072
map00480
map00031
map00460
map00910
map00604
map00631
map00900
map00010
map00331
map00290
map00240
map00300
map00561
map00196
map00053
map00071
map00660
map00860
map00440
map00601
Tetracycline
biosynthesis
map00641
map00642
map00750
map00710
map00195
map00251
map00052
map00531
map00051
map00410
map00540
map00140
map00120
map00252
map00380
map00627
biosynthesis
Penicillins and cephalosporins
map00830
map00623
Monoterpenoid
biosynthesis
map00360
map00472
map00562
map00530
map00650
map00770
map00062
map00640
map00730
map00473
map00130
map00760
map00950
map00510
map00272
map00622
map00363
map00680
Diterpenoid
biosynthesis
Streptomycin
biosynthesis
map00340
map00791
map00564
map00020
map00500
map00720
map00362
map00310
map00230
map00550
map00630
map00603
map00471
map00901
map00602
map00590
map00351
map00626
map00030
map00534
map00532
map00190
map00740
map00430
map00624
map00061
map00150
biosynthesis
Novobiocin
map00280
map00906
map00100
map00361
map00930
map00450
Carbohydrate
metabolism
and metabolism
Glycan biosynthesis
metabolism
Amino acid
metabolism
Energy
Lipid
metabolism
xenobiotics
Biodegradation of
Metabolism of
other amino acids
metabolism
Nucleotide
Metabolism of
cofactors and vitamins
Biosynthesis of
secondary metabolites
0.0%
10.0%
20.0%
30.0%
General function - R
Translation - J
Amino acids - E
DNA - L
Unknown - S
Envelope - M
Carbohydrates - G
Energy - C
Transcription - K
Coenzymes - H
Nucleotides - F
Inorganic - P
Protein turnover - O
Lipids - I
Signal transduction - T
Secretion - U
Cell cycle - D
Defence- V
Second metabolites - Q
Cell motility - N
RNA - A
Chromatin - B
Extracellular - W
Nuclear structure - Y
Cytoskeleton - Z
Rare minimal
Rare minimal
Frequent min
Frequent min
Projection of the minimal gut genome on the
KEGG pathways using the iPath tool38. b,
Functional composition of the minimal gut
genome and metagenome. Rare and frequent
refer to the presence in sequenced eggNOG
genomes. c, Estimation of the minimal gut
metagenome size. Known orthologous groups
(red), known plus unknown orthologous groups
(blue) and orthologous groups plus novel gene
families (>20 proteins; grey) are shown (see
Fig. 2c for definition of box and whisker plot).
The inset shows composition of the gut minimal
microbiome. Large circle: classification in the
minimal metagenome according to orthologous
group occurrence in STRING739 bacterial
genomes. Common (25%), uncommon (35%)
and rare (45%) refer to functions that are
present in >50%, <50% but >10%, and <10%
of STRING bacteria genomes, respectively.
Small circle: composition of the rare
orthologous groups. Unknown (80%) have no
annotation or are poorly characterized, whereas
known bacterial (19%) and phage-related (1%)
orthologous groups have functional description.

b
map00350
resistance
β-Lactam
map00780
map00260
map00670
map00220
map00628
map00632
biosynthesis II
Alkaloid
map00400
map00790
map00330
map00271
map00910
map00290
map00240
map00300
map00860
Tetracycline
biosynthesis
map00750
map00251
map00380
biosynthesis
Penicillins and cephalosporins
map00830
map00623
map00360
map00472
map00770
map00730
map00130
map00760
map00950
map00791
map00362
map00310
map00230
map00351
map00626
map00740
0
map00624
biosynthesis
Novobiocin
map00930
metabolism
Amino acid
sm
Metabolism of
other amino acids
bolism
eotide
Metabolism of
cofactors and vitamins
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
General function - R
Translation - J
Amino acids - E
DNA - L
Unknown - S
Envelope - M
Carbohydrates - G
Energy - C
Transcription - K
Coenzymes - H
Nucleotides - F
Inorganic - P
Protein turnover - O
Lipids - I
Signal transduction - T
Secretion - U
Cell cycle - D
Defence- V
Second metabolites - Q
Cell motility - N
RNA - A
Chromatin - B
Extracellular - W
Nuclear structure - Y
Cytoskeleton - Z
Rare minimal genome
Rare minimal metagenome
Frequent minimal genome
Frequent minimal metagenome

Common
Uncommon
Rare
Unknown
Known
Phage-associated
2,000
4,000
6,000
8,000
10,000
12,000
14,000
Minimalmetagenomesize
1 25 50 75 100
c
map00629
map00940
map00628
Limonene and pinene
degradation
map00941
map00621
map00331 map00627
map00623
Monoterpenoid
biosynthesis
map00622
map00363Diterpenoid
biosynthesis
map00791
map00362
map00901
map00351
map00626
map00190
map00361
map00930
xenobiotics
Biodegradation of
Biosynthesis of
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
Cytoskeleton - Z
acterization of the minimal gut genome and metagenome.
the minimal gut genome on the KEGG pathways using the
composition of the gut minimal microbiome. Large circle: c
the minimal metagenome according to orthologous group o

To identify the functions encoded by the minimal gut metagenome, we
computed the orthologous groups that are shared by individuals of our
cohort. This minimal set, of 6,313 functions, is much larger than the one
estimated in a previous study8. There are only 2,069 functionally
annotated orthologous groups, showing that they gravely underestimate
the true size of the common functional complement among individuals
(Fig. 6c). The minimal gut metagenome includes a considerable fraction
of functions (,45%) that are present in ,10% of the sequenced bacterial
genomes (Fig. 6c, inset). These otherwise rare functionalities that are
found in each of the 124 individuals may be necessary for the gut
ecosystem. Eighty per cent of these orthologous groups contain genes
with at best poorly characterized function, underscoring our limited
knowledge of gut functioning.

Of the known fraction, about 5% codes for (pro)phage-related proteins,
implying a universal presence and possible important ecological role of
bacteriophages in gut homeostasis. The most striking secondary
metabolism that seems crucial for the minimal metagenome relates, not
unexpectedly, to biodegradation of complex sugars and glycans harvested
from the host diet and/or intestinal lining. Examples include degradation
and uptake pathways for pectin (and its monomer, rhamnose) and
sorbitol, sugars which are omni- present in fruits and vegetables, but
which are not or poorly absorbed by humans. As some gut
microorganisms were found to degrade both of them29,30, this capacity
seems to be selected for by the gut ecosystem as a non-competitive
source of energy. Besides these, capacity to ferment, for example,
mannose, fructose, cellulose and sucrose is also part of the minimal
metagenome. Together, these emphasize the strong dependence of the gut
ecosystem on complex sugar degradation for its functioning.

!

Of the known fraction, about 5% codes for (pro)phage-related proteins,
implying a universal presence and possible important ecological role of
bacteriophages in gut homeostasis. The most striking secondary
metabolism that seems crucial for the minimal metagenome relates,
not unexpectedly, to biodegradation of complex sugars and glycans
harvested from the host diet and/or intestinal lining. Examples
include degradation and uptake pathways for pectin (and its monomer,
rhamnose) and sorbitol, sugars which are omni- present in fruits and
vegetables, but which are not or poorly absorbed by humans. As some
gut microorganisms were found to degrade both of them29,30, this
capacity seems to be selected for by the gut ecosystem as a non-
competitive source of energy. Besides these, capacity to ferment, for
example, mannose, fructose, cellulose and sucrose is also part of the
minimal metagenome. Together, these emphasize the strong dependence
of the gut ecosystem on complex sugar degradation for its functioning.

!

Functional complementarities of the genome and metagenome

Detailed analysis of the complementarities between the gut metage-
nome and the human genome is beyond the scope of the present work. To
provide an overview, we considered two factors: conservation of the
functions in the minimal metagenome and presence/absence of func-
tions in one or the other (Supplementary Table 11). Gut bacteria use
mostly fermentation to generate energy, converting sugars, in part, to
short-chain fatty acid, that are used by the host as energy source. Acetate
is important for muscle, heart and brain cells31, propionate is used in
host hepatic neoglucogenic processes, whereas, in addition, butyrate is
important for enterocytes32. Beyond short-chain fatty acid, a number of
amino acids are indispensable to humans33 and can be provided by
bacteria34. Similarly, bacteria can contribute certain vitamins3 (for
example, biotin, phylloquinone) to the host. All of the steps of biosyn-
thesis of these molecules are encoded by the minimal metagenome

Gut bacteria seem to be able to degrade numerous xenobiotics,
including non-modiﬁed and halogenated aromatic compounds (Sup-
plementary Table 11), even if the steps of most pathways are not part of
the minimal metagenome and are found in a fraction of individuals only.
A particularly interesting example is that of benzoate, which is a common
food supplement, known as E211. Its degradation by the coenzyme-A
ligation pathway, encoded in the minimal metagenome, leads to
pimeloyl-coenzyme-A, which is a precursor of biotin, indicating that
this food supplement can have a potentially beneﬁcial role for human
health.

DISCUSSION

Discussion

We have used extensive Illumina GA short-read-based sequencing of
total faecal DNA from a cohort of 124 individuals of European (Nordic
and Mediterranean) origin to establish a catalogue of non- redundant
human intestinal microbial genes. The catalogue contains 3.3 million
microbial genes, 150-fold more than the human gene complement, and
includes an overwhelming majority (.86%) of prevalent genes harboured
by our cohort. The catalogue probably contains a large majority of
prevalent intestinal microbial genes in the human population, for the
following reasons: (1) over 70% of the metagenomic reads from three
previous studies, including American and Japanese individuals8,16,17,
can be mapped on our contigs; (2) about 80% of the microbial genes
from 89 frequent gut reference genomes are present in our set. This result
represents a proof of principle that short-read sequencing can be used to
characterize complex microbiomes.

The full bacterial gene complement of each individual was not sampled
in our work. Nevertheless, we have detected some 536,000 prevalent
unique genes in each, out of the total of 3.3 million carried by our cohort.
Inevitably, the individuals largely share the genes of the common pool.
At the present depth of sequencing, we found that almost 40% of the
genes from each individual are shared with at least half of the individuals
of the cohort. Future studies of world-wide span, envisaged within the
International Human Microbiome Consortium, will complete, as
necessary, our gene catalogue and establish boundaries to the proportion
of shared genes.

Essentially all (99.1%) of the genes of our catalogue are of bacterial
origin, the remainder being mostly archaeal, with only 0.1% of eukar-
yotic and viral origins. The gene catalogue is therefore equivalent to that
of some 1,000 bacterial species with an average-sized genome, encoding
about 3,364 non-redundant genes. We estimate that no more than 15% of
prevalent genes of our cohort may be missing from the catalogue, and
suggest that the cohort harbours no more than ,1,150 bacterial species
abundant enough to be detected by our sampling. Given the large overlap
between microbial sequences in this and previous studies we suggest that
the number of abundant intestinal bacterial species may be not much
higher than that observed in our cohort. Each individual of our cohort
harbours at least 160 such bacterial species, as estimated by the average
prevalent gene number, and many must thus be shared.

`
We assigned about 12% of the reference set genes (404,000) to the 194
sequenced intestinal bacterial genomes, and can thus associate them with
bacterial species. Sequencing of at least 1,000 human- associated
bacterial genomes is foreseen within the International Human
Microbiome Consortium, via the Human Microbiome Project and
MetaHIT. This is commensurate with the number of dominant species in
our cohort and expected more broadly in human gut, and should enable a
much more extensive gene to species assign- ment. Nevertheless, we
used the presently available sequenced genomes to explore further the
concept of largely shared species among our cohort and identiﬁed 75
species common to .50% of individuals and 57 species common to .90%.
These numbers are likely to increase with the number of sequenced
reference strains and a deeper sampling. Indeed, a 2–3-fold increase in
sequencing depth raised by 25% the number of species that we could
detect as shared between two individuals. A large number of shared
species supports the view that the prevalent human microbiome is of a
ﬁnite and not overly large size.

How can this view be reconciled with that of a considerable inter-
personal diversity of innumerable bacterial species in the gut, arising
from most previous studies using the 16S RNA marker gene4,8,10,11?
Possibly the depth of sampling of these studies was insufﬁcient to reveal
common species when present at low abundance, and empha- sized the
difference in the composition of a relatively few dominant species. We
found a very high variability of abundance (12- to 2,200- fold) for the 57
most common species across the individuals of our cohort. Nevertheless,
a recent 16S rRNA-based study concluded that a common bacterial
species ‘core’, shared among at least 50% of individuals under study,
exists35

!

Detailed comparisons of bacterial genes across the individuals of our cohort will be
carried out in the future, within the context of the ongoing MetaHIT clinical studies of
which they are part. Nevertheless, clustering of the genes in families allowed us to
capture a virtually full functional potential of the prevalent gene set and revealed a
considerable novelty, extending the functional categories by some 30% in regard to
previous work8. Similarly, this analysis has revealed a functional core, conserved in
each individual of the cohort, which reflects the full minimal human gut metagenome,
encoded across many species and probably required for the proper functioning of the
gut ecosystem. The size of this minimal metagenome exceeds several-fold that of the
core metagenome reported previously8. It includes functions known to be important to
the host–bacterial interaction, such as degradation of complex polysaccharides,
synthesis of short-chain fatty acids, indispensable amino acids and vitamins. Finally, we
also identified functions that we attribute to a minimal gut bacterial genome, likely to be
required by any bacterium to thrive in this ecosystem. Besides general housekeeping
functions, the minimal genome encompasses many genes of unknown function, rare in
sequenced genomes and possibly specifically required in the gut.

Beyond providing the global view of the human gut microbiome, the
extensive gene catalogue we have established enables future studies of
association of the microbial genes with human phenotypes and, even
more broadly, human living habits, taking into account the environment,
including diet, from birth to old age. We anticipate that these studies will
lead to a much more complete understanding of human biology than the
one we presently have.

LETTERS
A core gut microbiome in obese and lean twins
Peter J. Turnbaugh1
, Micah Hamady3
, Tanya Yatsunenko1
, Brandi L. Cantarel5
, Alexis Duncan2
, Ruth E. Ley1
,
Mitchell L. Sogin6
, William J. Jones7
, Bruce A. Roe8
, Jason P. Affourtit9
, Michael Egholm9
, Bernard Henrissat5
,
Andrew C. Heath2
, Rob Knight4
& Jeffrey I. Gordon1
The human distal gut harbours a vast ensemble of microbes (the
microbiota) that provide important metabolic capabilities, includ-
ing the ability to extract energy from otherwise indigestible dietary
polysaccharides1–6
. Studies of a few unrelated, healthy adults have
revealed substantial diversity in their gut communities, as mea-
sured by sequencing 16S rRNA genes6–8
, yet how this diversity
relates to function and to the rest of the genes in the collective
genomes of the microbiota (the gut microbiome) remains obscure.
Studies of lean and obese mice suggest that the gut microbiota
affects energy balance by influencing the efficiency of calorie har-
vest from the diet, and how this harvested energy is used and
stored3–5
. Here we characterize the faecal microbial communities
of adult female monozygotic and dizygotic twin pairs concordant
for leanness or obesity, and their mothers, to address how host
genotype, environmental exposure and host adiposity influence
the gut microbiome. Analysis of 154 individuals yielded 9,920 near
full-length and 1,937,461 partial bacterial 16S rRNA sequences,
plus 2.14 gigabases from their microbiomes. The results reveal that
the human gut microbiome is shared among family members, but
that each person’s gut microbial community varies in the specific
bacterial lineages present, with a comparable degree of co-variation
between adult monozygotic and dizygotic twin pairs. However,
there was a wide array of shared microbial genes among sampled
individuals, comprising an extensive, identifiable ‘core micro-
biome’ at the gene, rather than at the organismal lineage, level.
Obesity is associated with phylum-level changes in the microbiota,
reduced bacterial diversity and altered representation of bacterial
genes and metabolic pathways. These results demonstrate that a
diversity of organismal assemblages can nonetheless yield a core
microbiome at a functional level, and that deviations from this core
are associated with different physiological states (obese compared
leanness (BMI 5 18.5–24.9 kg m22
) (one twin pair was lean/over-
weight (overweight defined as BMI $ 25 and , 30) and six pairs were
overweight/obese). They had not taken antibiotics for at least
5.49 6 0.09 months. Each participant completed a detailed medical,
lifestyle and dietary questionnaire: study enrolees were broadly
representative of the overall Missouri population for BMI, parity,
education and marital status (see Supplementary Results).
Although all were born in Missouri, they currently live throughout
the USA: 29% live in the same house, but some live more than 800 km
apart. Because faecal samples are readily attainable and representative
of interpersonal differences in gut microbial ecology7
, they were col-
lected from each individual and frozen immediately. The collection
procedure was repeated again with an average interval between
sampling of 57 6 4 days.
To characterize the bacterial lineages present in the faecal micro-
biotas of these 154 individuals, we performed 16S rRNA sequencing,
targeting the full-length gene with an ABI 3730xl capillary sequencer.
Additionally, we performed multiplex pyrosequencing with a 454
FLX instrument to survey the gene’s V2 variable region13
and its
V6 hypervariable region14
(Supplementary Tables 1–3).
Complementary phylogenetic and taxon-based methods were
used to compare 16S rRNA sequences among faecal communities
(see Methods). No matter which region of the gene was examined,
individuals from the same family (a twin and her co-twin, or twins
and their mother) had a more similar bacterial community structure
than unrelated individuals (Fig. 1a and Supplementary Fig. 1a, b),
and shared significantly more species-level phylotypes (16S rRNA
sequences with $97% identity comprise each phylotype)
(G 5 55.2, P , 10212
(V2); G 5 12.3, P , 0.001 (V6); G 5 11.3,
P , 0.001 (full-length)). No significant correlation was seen between
the degree of physical separation of family members’ current homes
Vol 457|22 January 2009|doi:10.1038/nature07540

in coverage, all analyses were performed on an equal number of
randomly selected sequences (200 full-length, 1,000 V2 and 10,000
V6). At this level of coverage, there was little overlap between the
sampled faecal communities. Moreover, the number of 16S rRNA
gene sequences belonging to each phylotype varied greatly between
Analysis of 16S rRNA data sets produced by the three P
methods, plus shotgun sequencing of community DNA (se
revealed a lower proportion of Bacteroidetes and a higher p
of Actinobacteria in obese compared with lean individua
ancestries (Supplementary Table 9). Combining the ind
values across these independent analyses using Fisher’s me
closed significantly fewer Bacteroidetes (P 5 0.003
Actinobacteria (P 5 0.002) but no significant diffe
Firmicutes (P 5 0.09). These findings agree with previ
showing comparable differences in both taxa in mice2
and a
ive increase in the representation of Bacteroidetes when 12 u
obese humans lost weight after being placed on one of two
calorie diets6
.
Across all methods, obesity was associated with a s
decrease in the level of diversity (Fig. 1b and Suppleme
1c–f). This reduced diversity suggests an analogy: the
microbiota is not like a rainforest or reef, which are adapte
energy flux and are highly diverse; rather, it may be m
fertilizer runoff where a reduced-diversity microbial co
blooms with abnormal energy input16
.
We subsequently characterized the microbial lineage
content of the faecal microbiomes of 18 individuals rep
six of the families (three lean and three obese European
monozygotic twin pairs and their mothers) through shotg
sequencing (Supplementary Tables 4 and 5) and BLASTX
isons against several databases (KEGG17
(version 44) and S
plus a custom database of 44 reference human gut microbia
(Supplementary Figs 7–10 and Supplementary Results). Ou
parameters were validated using control data sets compr
domly fragmented microbial genes with annotations in t
database17
(Supplementary Fig. 11 and Supplementary M
We also tested how technical advances that produce lon
might improve these assignments by sequencing faecal co
samples from one twin pair using Titanium pyrosequencing
(average read length of 341 6 134 nucleotides (s.d.) versus
nucleotides for the standard FLX method). Supplementa
shows that the frequency and quality of sequence assign
improved as read length increases from 200 to 350 nucleo
The 18 microbiomes were searched to identify sequences
domains from experimentally validated carbohydr
enzymes (CAZymes). Sequences matching 156 total CAZ
were found within at least one human gut microbiome, inc
glycoside hydrolase, 21 carbohydrate-binding module, 35
transferase, 12 polysaccharide lyase and 11 carbohydrat
families (Supplementary Table 10). On average, 2.62 6
b
a
*
0.66
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
Self
Twin–twin
Mono-
zygotic
Twin–mother
Unrelated
UniFracdistance
Dizygotic Mono-
zygotic
Dizygotic
2
22
42
62
82
102
122
0 2,000 4,000 6,000 8,000 1,0000
Number of sequences
Phylogeneticdiversity
Lean
Obese
*
***
MoresimilarMoredifferent
*
***
**
**
ns
Figure 1 | 16S rRNA gene surveys reveal familial similarity and reduced
diversity of the gut microbiota in obese individuals. a, Average unweighted
UniFrac distance (a measure of differences in bacterial community
structure) between individuals over time (self), twin pairs, twins and their
mother, and unrelated individuals (1,000 sequences per V2 data set;
Student’s t-test with Monte Carlo; *P , 1025
; **P , 10214
; ***P , 10241
;
mean 6 s.e.m.). b, Phylogenetic diversity curves for the microbiota of lean
and obese individuals (based on 1–10,000 sequences per V6 data set;
mean 6 95% confidence intervals shown).
NATURE|Vol 457|22 January 2009 L

and taxonomic similarity (see Supplementary Methods) disclosed a
significant association: individuals with similar taxonomic profiles
of b-diversity with respect to the relative abundance of bacte
(Fig. 3a), analysis of the relative abundance of broad functi
egories of genes and metabolic pathways (KEGG) revealed a
consistent pattern regardless of the sample surveyed (Fig
Supplementary Table 11): the pattern is also consistent wi
we obtained from a meta-analysis of previously published gu
biome data sets from nine adults20,21
(Supplementary Fig.
consistency is not simply due to the broad level of these ann
as a similar analysis of Bacteroidetes and Firmicutes refere
omes revealed substantial variation in the relative abundanc
category (see Supplementary Fig. 17). Furthermore, pairw
parisons of metabolic profiles obtained from the 18 microb
this study revealed an average value of R2
of 0.97 6 0.002
indicating a high level of functional similarity.
Overall functional diversity was compared using the
index22
, a measurement that combines diversity (the numb
ferent metabolic pathways) and evenness (the relative abun
each pathway). The human gut microbiomes surveyed had
and high Shannon index value (4.63 6 0.01), close to the m
possible level of functional diversity (5.54; see Suppl
Methods). Despite the presence of a small number of abunda
bolic pathways (listed in Supplementary Table 11), the ove
tional profile of each gut microbiome is quite even (Shannon
of 0.846 0.001 on a scale of 0–1), demonstrating that most m
pathways are found at a similar level of abundance. Interest
level of functional diversity in each microbiome was sig
linked to the relative abundance of the Bacteroidetes (R
P , 1026
); microbiomes enriched for Firmicutes/Actinobac
a lowerleveloffunctionaldiversity.Thisobservation isconsis
an analysis of simulated metagenomic reads generated from e
Bacteroidetes and Firmicutes genomes (Supplementary Fig
average, the Bacteroidetes genomes have a significantly high
both functional diversity and evenness (Mann–Whitne
P , 0.01).
At a finer level, 26–53% of ‘enzyme’-level functiona
(KEGG/CAZy/STRING) were shared across all 18 micr
whereas 8–22% of the groups were unique to a single mic
(Supplementary Fig. 19a–c). The ‘core’ functional groups p
all microbiomes were also highly abundant, representing 93
thetotal sequences. Given thehigherrelative abundance of th
groups, more than 95% were found after 26.11 6 2.02 meg
sequence were collected from a given microbiome, whereas
able’ groups continued to increase substantially with each a
megabase of sequence. Of course, any estimate of the total s
core microbiome will depend on sequencing effort, espe
0.990.980.97R2 value:
PC1 (20%)
Bacteroidetes(%)
a
b c
High Firmicutes/ActinobacteriaHigh Bacteroidetes
Twin versus
twin
Twin versus
mother
Unrelated
pairs
Functionalsimilarity(R2)
*
0.94
0.95
0.96
0.97
0.98
0.99
1
R2 = 0.96
0
20
40
60
80
100
–0.6 –0.4 –0.2 0 0.2 0.4 0.6
1.00 0.98 0.98 0.99 0.99 0.99 0.98 0.98 0.99 0.98
0.98 1.00 0.99 0.99 0.97 0.97 0.98 0.98 0.98 0.97 0.98 0.99
0.98 0.99 1.00 1.00 0.98 0.98 0.98 0.98 0.99 0.99 0.99 0.98 0.98 0.99
0.99 0.99 1.00 1.00 0.99 0.99 0.99 0.97 0.99 1.00 1.00 0.99 0.98 0.98 0.99
0.99 0.98 0.99 1.00 0.99 0.98 0.97 0.99 0.99 0.99 0.99
0.99 0.97 0.98 0.99 0.99 1.00 0.97 0.98 0.99 0.99 0.99
0.97 0.98 0.99 0.98 0.97 1.00 0.97 0.98 0.99 0.99 0.98 0.98 0.97 0.98
0.97 1.00 0.99 0.98
0.98 0.97 0.99 0.98 0.97 0.99 1.00 0.99 0.99 0.98
0.98 0.98 0.99 0.99 0.99 0.98 0.98 0.99 1.00 0.99 0.99
0.99 0.98 0.99 1.00 0.99 0.99 0.99 0.99 0.99 1.00 0.99 0.98 0.97 0.97 0.98
0.98 0.98 0.99 1.00 0.99 0.99 0.99 0.98 0.99 0.99 1.00 0.98 0.98 0.97 0.98
0.98 0.99 0.99 0.98 0.98 0.98 1.00 0.99 0.97 0.98 0.99 0.99
0.97 0.98 0.98 0.98 0.97 0.98 0.99 1.00 0.98 0.99 0.99 0.99
0.97 0.98 1.00 1.00 0.99 0.98
0.98 0.99 1.00 1.00 0.99 0.98
0.98 0.98 0.98 0.97 0.97 0.97 0.99 0.99 0.99 0.99 1.00 0.99
0.99 0.99 0.99 0.98 0.98 0.98 0.99 0.99 0.98 0.98 0.99 1.00
F1T1Le
F1T2Le
F1MOv
F2T1Le
F2T2Le
F2MOb
F3T1Le
F3T2Le
F3MOv
F4T1Ob
F4T2Ob
F4MOb
F5T1Ob
F5T2Ob
F5MOv
F6T1Ob
F6T2Ob
F6MOb
F1T1Le
F1T2Le
F1MOv
F2T1Le
F2T2Le
F2MOb
F3T1Le
F3T2Le
F3MOv
F4T1Ob
F4T2Ob
F4MOb
F5T1Ob
F5T2Ob
F5MOv
F6T1Ob
F6T2Ob
F6MOb
<0.97
Figure 2 | Metabolic-pathway-based clustering and analysis of the human
gut microbiome of monozygotic twins. a, Clustering of functional profiles
based on the relative abundance of KEGG metabolic pathways. All pairwise
comparisons were made of the profiles by calculating each R2
value. Sample
identifier nomenclature: family number, twin number or mother, and BMI
category (Le, lean; Ov, overweight; Ob, obese; for example, F1T1Le stands
for family 1, twin 1, lean). b, The relative abundance of Bacteroidetes as a
function of the first principal component derived from an analysis of KEGG
metabolic profiles. c, Comparisons of functional similarity between twin
pairs, between twins and their mother, and between unrelated individuals.
Asterisk indicates significant differences (Student’s t-test with Monte Carlo;
P , 0.01; mean 6 s.e.m.).
a COG categoriesBacterial phylum b
LETTERS NATURE|Vol 457|22 Janu

ds) disclosed a
nomic profiles
el test).
s representing
icutes and 14
clustering by
analyses of the
biome-derived
d 26 pathways
upplementary
eral carbohyd-
were enriched
our CAZyme
abundance of
glycosyltrans-
erases in the
n Microbiome
ntifiable ‘core
al capabilities
y of humans1
.
ed a high level
able’ groups continued to increase substantially with each additional
megabase of sequence. Of course, any estimate of the total size of the
core microbiome will depend on sequencing effort, especially for
h Monte Carlo;
[Q]
[P]
[I]
[H]
[F]
[E]
[G]
[C]
[S]
[R]
[O]
[U]
[W]
[Z]
[N]
[M]
[T]
[V]
[Y]
[D]
[B]
[L]
[K]
[A]
[J]
0
20
40
60
80
100
Relativeabundance(%)
OtherProteobacteria
F1T1Le
F1T2Le
F1MOv
F2T1Le
F2T2Le
F2MOb
F3T1Le
F3T2Le
F3MOv
F4T1Ob
F4T2Ob
F4MOb
F5T1Ob
F5T2Ob
F5MOv
F6T1Ob
F6T2Ob
F6MOb
F1T1Le
F1T2Le
F1MOv
F2T1Le
F2T2Le
F2MOb
F3T1Le
F3T2Le
F3MOv
F4T1Ob
F4T2Ob
F4MOb
F5T1Ob
F5T2Ob
F5MOv
F6T1Ob
F6T2Ob
F6MOb
a COG categoriesBacterial phylum b
ActinobacteriaBacteroidetesFirmicutes
Figure 3 | Comparison of taxonomic and functional variations in the human
gut microbiome. a, Relative abundance of major phyla across 18 faecal
microbiomes from monozygotic twins and their mothers, based on BLASTX
comparisons of microbiomes and the National Center for Biotechnology
Information non-redundant database. b, Relative abundance of categories of
genes across each sampled gut microbiome (letters correspond to categories
in the COG database).
millan Publishers Limited. All rights reserved

including those involved in transcription and translation (Fig. 4).
Metabolic profile-based clustering indicated that the representation
of ‘core’ functional groups was highly consistent across samples
(Supplementary Fig. 20), and included several pathways that are
To identify metabolic pathways associated with obesity, o
core associated (variable) functional groups were included i
parison of the gut microbiomes of lean versus obese twin
bootstrap analysis23
was used to identify metabolic pathw
were enriched or depleted in the variable obese gut micr
For example, similar to a mouse model of diet-induced
the obese human gut microbiome was enriched for phospho
ase systems involved in microbial processing of carbo
(Supplementary Table 12). All gut microbiome sequences w
pared with the custom database of 44 human gut genomes:
ratio analysis revealed 383 genes that were significantly
between the obese and lean gut microbiome (q value , 0
enriched and 110 depleted in the obese micr
Supplementary Tables 13 and 14). By contrast, only 49 ge
consistently enriched or depleted between all twin p
Supplementary Methods).
These obesity-associated genes were representative of t
nomic differences described above: 75% of the obesity-
genes were from Actinobacteria (compared with 0% of lean-
genes; the other 25% are from Firmicutes) whereas 42% of
enriched genes were from Bacteroidetes (compared with 0
obesity-enriched genes). Their functional annotation indic
many are involved in carbohydrate, lipid and amino-acid
ism (Supplementary Tables 13 and 14). Together, they com
initial set of microbial biomarkers of the obese gut microbi
Our finding that the gut microbial community structures
monozygotic twin pairs had a degree of similarity that was
able to that of dizygotic twin pairs, and only slightly mor
than that of their mothers, is consistent with an earlier finger
study of adult twins24
, and with a recent microarray-based
which revealed that gut community assembly during the fir
life followed a more similar pattern in a pair of dizygotic tw
12 unrelated infants25
. Intriguingly, another fingerprinting
monozygotic and dizygotic twins in childhood showed a
reduced similarity profile in dizygotic twins26
. Thus, compr
time-course studies, comparing monozygotic and dizygo
pairs from birth through adulthood, as well as intergen
analyses of their families’ microbiotas, will be key to dete
the relative contributions of host genotype and environmen
sures to (gut) microbial ecology.
The hypothesis that there is a core human gut microbiom
able by a set of abundant microbial organismal lineages th
share, may be incorrect: by adulthood, no single bacterial p
was detectable at an abundant frequency in the guts o
sampled humans. Instead, it appears that a core gut mic
exists at the level of shared genes, including an important com
involved in various metabolic functions. This conservation s
high degree of redundancy in the gut microbiome and sup
ecological view of each individual as an ‘island’ inhabited b
0 2 4 6 8 10 12 14
Transcription
Translation
Nucleotide metabolism
Amino-acid metabolism
Biosynthesis of
Replication and repair
Metabolism of
other amino acids
Glycan biosynthesis
and metabolism
Carbohydrate metabolism
Lipid metabolism
Biosynthesis of polyketides
Cell growth and death
Metabolism of cofactors
and vitamins
Energy metabolism
Xenobiotics biodegradation
and metabolism
Genetic information
processing protein families
Metabolism
protein families
Metabolism unclassified
Membrane transport
Folding, sorting,
and degradation
Cellular processes and
signalling protein families
Cellular processes and
signalling unclassified
Signal transduction
Poorly characterized
unclassified
Genetic information
processing unclassified
Cell motility
Signalling molecules
and interaction
Relative abundance (percentage of KEGG assignments)
KEGG category
Core
Variable
***
*
**
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
Figure 4 | KEGG categories enriched or depleted in the core versus variable
components of the gut microbiome. Sequences from each of the 18 faecal
microbiomes were binned into the ‘core’ or ‘variable’ microbiome based on
the co-occurrence of KEGG orthologous groups (core groups were found in
all 18 microbiomes whereas variable groups were present in fewer (,18)
microbiomes; see Supplementary Fig. 19a). Asterisks indicate significant
differences (Student’s t-test, *P , 0.05, **P , 0.001, ***P , 1025
;
mean 6 s.e.m.).
Macmillan Publishers Limited. All rights reserved©2009

UC Davis EVE161 Lecture 18 by @phylogenomics

UC Davis EVE161 Lecture 18 by @phylogenomics

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