Upload
drugmetabol
View
1.903
Download
0
Tags:
Embed Size (px)
Citation preview
Metagenomics: a gene-centric approach for the human gut microbiome research
Masahira HATTORI
Center for Omics and Bioinformatics / Dept. of Computational Biology
Graduate School of Frontier Sciences, University of Tokyo
http://www.cb.k.u-tokyo.ac.jp/hattorilab
・ Human-associated pathogens and commensals including intestinal microbiota・ Symbionts in insects・ Host-microbial interactions
The Kashiwa campus
Body Site bacteria/ml or gram
# species
? Nose 103-104 Oral 1010 total
>700
Saliva 108-1010 Gingival crevice 1012 Tooth surface 1011 Gastrointestinal Tract 1014 total
>1000
Stomach 100-104 Small intestines 104-107
Colon (feces) 1011-1012
Skin 1012 total
?
Surface 105
Urogenital 1012 total?
?
Vagina 109 Human cells 1013 total
Oral
Gastrointestinal
Skin
Urogenital
Nasal
The total number of these bacterial cells is estimated to be more than 1014, representing 10 times more than the total number of eukaryotic cells that compose a human individual .
An enormous number of microorganisms, of which the majority is bacterial species, are known to colonize and form complex communities (called the human microbiota) at various human body sites
The human microbiota
Among them, the largest and most complex is the gut microbiota, which is composed of more than 1,000 different intestinal microbes.
*多彩な代謝機能(ヒトとの共生関係 )
Many metabolic capabilities (mutualism between them and us)
*宿主の腸管上皮細胞の増殖と分化Proliferation and differentiation of host epithelial cells
*宿主の免疫系の成熟化(恒常性の維持)Development of the host immune system
*感染病原菌の防御Protection against pathogens
*細菌叢組成はさまざまな疾患の素因となる。Imbalance of the gut microbiota composition predisposes individuals to a variety of disease states ranging from inflammatory bowel diseases such as Crohn’s disease and ulcerative colitis to allergy, colon cancer, obesity and diabetes.
Human gut microbiota possess a strong impact on human physiology
The process of metagenomic analysis of the human gut microbiota
Microbial DNA(Metagenomic DNA)
Shotgun library
Fragmentation of DNA to ~ 3kb
…GGATCCATCGTACCGATTC……TTACAATTTACGGCCATCC…
…CCATGCGATCGATCGGAAT……CCATGGCCGAAATTTCGTA…
…AGCTAAAATTACCGGGGAT…
Shotgun reads (~ 800 bases)
Contig Contig Singleton
Assembly
Non-redundant sequence of microbial DNA
Intensive analysis of the sequences by bioinformatics
Gut microbiota
Lysis of microbiotaSequencer
Contigs Contigs Singletons
Non-redundatmicrobial sequences
Gene set
Classification of COGs to functional categories Replication Novel genesAmino acid
metabolismTranscriptionCarbohydrate
metabolismLipidmetabolism
Functional profile of microbiome
Metagenomics: a gene-centric analysis to explore the biological nature of microbiome, the collective genomes of microbiota
Clustering and similarity search (COG assignment)
COGs: Clusters of orthologous groups
Importantly, the functional profile becomes constant and unique to the community when the sequence amount is beyond the threshold which depends on the complexity of microbial composition.
Enriched COGs only in microbiome H
Comparative metagenomics between different microbiomes is powerful to identify enriched or depleted genes in an individual microbiome
Freq
uenc
y
H
High
G
B
C
D
E
F
A
COG
Commonly enriched COGs among all microbiomes
Var
ious
en
viro
nmen
tal
mic
robi
omes
Depleted COGs in microbiome ALow
Timeline of sequence-based metagenome projects since 2003Hugenholtz P and Tyson GW: Nature 455, 481-483 (2008)
3730 dye-terminator shotgun sequencing (black)Fosmid library sequencing (pink) 454 Pyrosequencing (green)
200 projects Sep. 09
Subjects
13 healthy Japanese individuals including 7 adults, 2 weaned children and 4 unweaned infants, from 3 months to 45 years old, and 2 unrelated families.
Metagenomics of 13 healthy Japanese gut microbiomesKurokawa K et al. DNA Res. 14, 169-181 (2007).
family family
Metagenomics of 13 Japanese gut microbiomes
Gut microbiota Bacterial DNA
DNA sequencingAbout 500 Mb assembled unique sequences from about 730 Mb data
660,000 genes found of which 160,000 were novel gene candidates
Further analyses
Kurokawa K et al. DNA Res. 14, 169-181 (2007).
Metagenomics of 13 healthy Japanese gut microbiomes
Total: 1,065,392 reads (727 Mb) / 13 samples80,000 sanger reads (55 Mb) / sample
20,063-67,740 genes ( 20 a.a.) / sample≧662,548 genes / 13 samples
1,617-2,921 COGs / sample3,268 COGs / 13 samples162,647 novel gene candidates (25%)
Sequencing
Gene identification in 479 Mb non-redundant sequence
Clustering and similarity search / COG assignment of genes
Kurokawa K et al. DNA Res. 14, 169-181 (2007).
Protein-coding gene prediction
A program, MetaGene, based on a hidden Markov Model (HMM) algorithm :
Noguchi H, Takagi T et al. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences.
NAR, 34, 5623-5630 (2006).
Genes were predicted from ORFs having ≥ 20 a.a. in non-redundant sequences.
The same COG
Gut microbiome Ref-DB
Orthologous genes
NR of the gut microbiome / NR of Ref-DB = Enrichment value ≧ 2
Normalized ratio (NR) = the number of genes / the total number of genes
Comparison of genes in the 13 human gut microbiomes with those in Ref-DB (constructed from genes in 243 microbes excluded gut microbes)
Survey of enriched COGs in human gut microbiomes
>>
Enriched COGs: COGs that contains orthologous genes with statistically higher frequency in the human gut microbiome than in Ref-DB.
0
2
4
6
8
10
12
14
16
18
20
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.5 >
Ave. 0.9Enriched COGs ≧ 2
Distribution of COG enrichment values for 126 eCOGs clustered by 150 essential genes of E. coli and B.subtilis
0.3 ≦ Enrichment values of 125 eCOGs ≦ 1.9
DepletedCOGs <0.3
179
7858
Adult-type (237)
Infant-type(136)
Total: 315 COGs
Identification of 315 gut-enriched COGs in 13 human intestinal microbiomes
315 all COGs
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
1
Functional categories of the 315 gut-enriched COGs identified in 13 human microbiomes
CarbohydrateCarbohydrateConserved but Conserved but
function unknownfunction unknown
Repair/modificationRepair/modification
Cell wall/membrane Cell wall/membrane
Energy production Energy production Inorganic ionInorganic ion
Amino-acidAmino-acid
Adult-type (237)
Infant-type (136)Total: 315 COGsOverlapped (58)
20% 33%
Carbohydrate metabolism
Functional adaptability of the gut microbiome
Transporter : infants > adults/children (e.g. phosphotransferase systems (PTSs))
Polysaccharide degradation : adults/children > infants (e.g. glycosyl hydrolases)
The functionality of gut microbiome is largely affected by diet.
Depleted function
Cell motility (genes for flagella and chemotaxis)
•Loss of possible antigens recognized by host immunity. •Peristaltic motion of the intestine.
Adaptive evolution of gut microbiome towards maintenance of host homeostasis
Intestinal microbes may have evolved to acquire and accumulate functions advantageous for colonization of gut habitat, while eliminating undesired appendages that could result in sensing for pro-inflammatory responses.
Content of gut-enriched genes (adult) in sequenced genomes of 371 representative microbes isolated from various environments
Ave. 9.2%
Ave. 4.0%Ave. 2.7%
Human gut microbes may have evolved by acquiring and accumulating adaptive genes to gut habitat.
>2-fold higher than DB>4-fold higher than DB
>10-fold higher than DB
Genes remarkably varied in frequency among individual microbiomes
Lower than DB
Deconjugation
Vitamin B12 biosynthesis
COG3250, 3119, 4225
COG1270, 1010
Deconjugation and Vitamin B12 biosynthesis
A D R S T V W
4.148 10.275 6.273 5.259 5.715 2.064 1.995 6.138
5.377 4.551 3.365 2.689 2.773 0.846 2.024 3.522
5.701 15.712 16.806 11.329 15.948 2.948 7.550 10.676
4.166 1.616 1.731 0.923 1.562 0.563 1.785 2.347
4.668 2.055 1.813 4.477 1.971 1.379 2.565 0.959
Glucuronated conjugatesSulfonated conjugates
Relative frequencies
Max/Min
Comparison of the KEGG pathways between the human gut and the sea surface
Sea-specific pathways Gut-specific pathways
Sphingolipid metabolismArachidonic/linoleic acid metabolism
The adult- and infant-types
Weaning may be the time to change from the infant type to the adult-type.
No strong association was found within family samples
Adult-type: stable, robust to environmentInfant-type: unstable, sensitive to environment
Overall sequence similarity of genes between individual microbiomes by reciprocal pairwise blastp analyses
Adults/children
Americans
Unweaned infants
Soil
Sea
Whale fall
・ Relatively high similarity among adults and weaned children
・ Relatively high variation among unweaned infants
The gut microbiota may be unique to individual
647 novel gene families composed only of 5 - 48 orthologous genes of human gut microbiomes
No-hit genes (162,647 genes) in the 13 human intestinal microbiomes
Clustering
Clusters
Novel gene families specific to the human gut microbiome
Possible functions : •Advantageous for competitive survival in human gut habitat. •Tolerant to transient but harsh conditions encountered during travel through the mouth and stomach to the gut.
+ No-hit genes in metagenomic data of sea and soil communities
About 100 conserved but function unknown gut-enriched genes
Good research targets to find novel functions of gut microbes
Sample: a human gut microbiome
ABI 3730xl (Sanger)
79,163
54.9 Mb
700
Production 30 days
Relative cost 1
Total bases
Read length
Read#
Metagenomic sequencing of gut microbomes by 454FLXTi based on pyrosequencing
Roche 454FLX Ti(1 run)
1,166,204
433 Mb
371.3
5 days
0.1
Gene# 40,300 186,000
No cloning process, no bacterial culture
Metagenomic sequencing of human gut microbiomes by 454 GSFLX Titanium
Total num of reads Human sequences Artifact reads Unique reads
APr01S00 1,423,122 0.40% 18.24% 81.36%
APr09S00 1,133,611 0.45% 14.57% 84.98%
APr16S00 818,894 0.55% 22.25% 77.19%
APr20S00 1,044,786 0.47% 16.94% 82.58%
APr29S00 1,117,685 0.39% 27.18% 72.42%
Problem in 454 data: artifact reads = reads having the same starting base
454 data (1 run) APr01S00 APr06S00 APr16S00 APr20S00 APr29S00
Total num of reads 1,423,122 1,133,611 818,894 1,044,786 1,117,685
Num of unique reads 1,157,883 963,351 632,118 862,794 809,466
(*Reason: multiple beads for one DNA molecule in one emulsion)
Sequencing of human microbesBy 3730xl only or 3730xl + 454FLX
Human microbes (in-house): 56 strains
HMP :247 strains (draft) released.
HMPInternational Human Microbiome Project
Shotgun reads or genes identified in individual samples
Genome 1 Genome 2 Genome 3 Genome 4 Genome 5
Reference genomes
Accurate assignment of shotgun reads or metagenomic genes to bacterial genomes
Mapping to reference genomes
Mapped reads: 47% (average)
454 data (1 run) APr01S00 APr06S00 APr16S00 APr20S00 APr29S00
Num of unique reads 1,157,883 963,351 632,118 862,794 809,466
Mapping of metagenomic reads on 1,236 reference bacterial genomes (including 247 HMP and 56 in-house strains)
Mapped Unmapped ≥ 90% identity, ≥ 100 bases
Taxonomic analysis of the Japanese gut microbiota based on mapping of metagenomic reads (Phylum level)
Actinobacteria Bacteroidetes Firmicutes
The genomes of 27 Bacteroides species have been sequenced.
Bacterial composition at the species level in the same genus by mapping of metagenomic reads
Bacterial composition at the species level in the BifidobacteriaThe genomes of 14 Bifidobacteriaum species have been sequenced.
The microbial composition is highly varied but the functionality is uniform between individuals.
Turnbaugh PJ et al. Nature 2009
Conclusion
1. The functionality of gut microbiome is largely affected by diet.
2. Intestinal microbes may have evolved to acquire and accumulate functions advantageous for colonization of gut habitat, while eliminating undesired appendages that could result in sensing for pro-inflammatory responses, towards maintenance of host homeostasis.
3. Many function-unknown genes are conserved and are present in intestinal microbes.
4. The microbial diversity is highly varied but the functionality is similar between individuals
5. The gut microbiota may be unique to individual and the origin of intestinal microbiota is unknown.(No strong association of the microbiota was found within the family)
Next and ongoing plans
1) More sampling of Japanese healthy individuals. ・ To standardize Japanese intestinal microbiome (including probiotics-
treated subjects and long-term chase of the same subjects)
2) Sampling of disease-afflicted subjects (IBD, colon cancer, allergy…..) and comparison with samples of healthy subjects
・ To identify bacteria, genes and gene products associated with the pathogenecity of the disease.
3) Comparison between samples of different nations who have different dietary style and genetic background each other.
・ To know how diet and genetic background affect the intestinal microbiota○ Sequencing of individual microbes isolated from human body sites
More than 100 strains isolated from the Japanese
○ Metagenomics and 16S analysis of human intestinal microbiomes.
○ Functional studies of intestinal microbes using germ-free mice
These works are being conducted as part of the International Human Microbiome Project that was launched in 2008.
Host genetic factors
Human genomeGenetic variation
Intestinal microbiomeGenetic diversity
Environmental factors
Interactions
To explore and identify both host and bacterial genes or their products as genetic and environmental factors involved in health promotion and maintenance as well as the etiology of diseases such as IBD (Crohn’s disease and ulcerative colitis) and allergy.
Whole genome sequencing Sequence-based metagenomics
Our goal is…
High-throughput sequencing technology + Bioinformatics
16S sequencing Metagenomic sequencing Genome sequencing
Sampling of microbiota from gastrointestinal and urogenital tracts, nasal, oral and skin of several hundreds of healthy and disease-afflicted subjects
Microbial diversity
Genetic and functional diversity
Sequencing of >1,000 species as reference genomes
Integrated database of human microbiomes and microbes
International Human Microbiome Project
International Human Microbiome Consortium (IHMC)
Australia, Canada, China, France (as EU), Ireland, Japan, Korea, Singapore, UK and US
Launched in 2008
+ Metadata of the subjects
Membersin Human MetaGenome Consortium Japan (HMGJ)
Kikuji Itoh
All in a day’s catch !
Ken Kurokawa, Hiroshi Mori, Takehiko Itoh, Hideki Noguchi
Graduate School of Information Science, Tokyo Institute of Technology
Institute of Health Biosciences, University of Tokushima Graduate School
Tomomi Kuwahara
Frontier Science Research Center, University of Miyazaki
Tetsuya Hayashi, Yoshitoshi Ogura
RIKEN Genomic Sciences Center
Hidehiro Toh, Atsushi Toyoda, Vineet K. Sharma, Tulika P. SrivastavaTodd D. Taylor, Yoshiyuki Sakaki
Japan Agency for Marine-Earth Science and Technology
Hideto Takami Graduate School of Frontier Sciences, University of Tokyo
Kenshiro Oshima, Kim Sok-Won, Chie Yoshino, Hiromi Inaba, Keiko Furuya, Yasue Hattori, Erika Iioka, Kanako Motomura, and Masahira Hattori
School of Veterinary Medicine, Azabu University
Hidetoshi Morita
Graduate School of Agricultural and Life Sciences, University of Tokyo
26 persons /10 Universities and Institutes
Hiroshi Ohno, Shinji FukudaRIKEN Center for Allergy & Immunology