The Emerging Global Community of Microbial Metagenomics Researchers

Opening Talk

Metagenomics 2007

Calit2@UCSD

July 11, 2007

Dr. Larry Smarr

Director, California Institute for Telecommunications and Information Technology

Harry E. Gruber Professor,

Dept. of Computer Science and Engineering

Jacobs School of Engineering, UCSD

Abstract

Calit2, the J. Craig Venter Institute, and UCSD's SDSC and Scripps Institution of Oceanography, is creating a metagenomic Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (CAMERA), funded by the Gordon and Betty Moore Foundation. The CAMERA computational and storage cluster, which contains multiple ocean microbial metagenomic datasets, as well as the full genomes of ~166 marine microbes, is actively in use. End users can access the metagenomic data either via the web or over novel dedicated 10 Gb/s light paths (termed "lambdas") through the National LambdaRail. The end user clusters are reconfigured as "OptIPortals," providing the end user with local scalable visualization, computing, and storage. Currently over 1000 users from over 40 countries are CAMERA registered users, with over a dozen remote OptIPortal sites becoming active. This CAMERA connected community sets the stage for creating a software system to support a social network of metagenomic researchers--a "MySpace" for scientists. We look forward to gathering ideas from Metagenomics 2007 participants for the functional requirements of such a system.

Calit2 Brings Computer Scientists and Engineers Together with Biomedical Researchers

• Some Areas of Concentration:– Algorithmic and System Biology

– Bioinformatics

– Metagenomics

– Cancer Genomics

– Human Genomic Variation and Disease

– Proteomics

– Mitochondrial Evolution

– Computational Biology

– Multi-Scale Cellular Imaging

– Information Theory and Biological Systems

– Telemedicine

UC Irvine

UC Irvine

Southern California Telemedicine Learning Center (TLC)

National Biomedical Computation Resource an NIH supported resource center

PI Larry Smarr

Paul Gilna Ex. Dir.

Announced January 17, 2006$24.5M Over Seven Years

Philip Papadopoulos,

SDSC/Calit22pm Friday

CAMERA 1.1 is Up and Running!

CAMERA Combines Genomic and Metagenomic Tools

Can We Create a “My Space” for Science Researchers? Microbial Metagenomics as a Cyber-Community

Over 1000 Registered Users From 45 Countries

USA 583United Kingdom 46Canada 35France 35Germany 32

70 CAMERA Users Feedback Session

Friday 2pm Paul Gilna

• Calit2 is Prototyping Social Networks for Reseachers

• Research Intelligence Project

– ri.calit2.net

• Add in:– MyProteins

– MyMicrobes

– MyEnvironments

– MyPapers

– MyGenomes

Emerging Capabilities That Tie Together Metagenomics Researchers

• Advanced Computing Techniques• Broad Coverage of Complete Microbe Genomes

– Moore Foundation– DOE JGI

• Proteomics of Microbes• Cellular Network Models

Metagenomic Challenge--Enormous Biodiversity:Very Little of GOS Metagenomic Data Assembles Well

• Use Reference Genomes to Recruit Fragments– Compared 334 Finished and 250 Draft Microbial Genomes

• Only 5 Microbial Genera Yielded Substantial and Uniform Recruitment – Prochlorococcus, Synechococcus, Pelagibacter, Shewanella, and Burkholderia

Source: Douglas Rusch, et al. (PLOS Biology March 2007)

Use of Self Organizing Maps to Identify SpeciesMassive Computation on the Japanese Earth Simulator

Human

Fugu

Arabidopsis

RiceC. Elegans

Drosophilia

www.es.jamstec.go.jp/publication/journal/jes_vol.6/pdf/JES6_22-Abe.pdf

T. Abe, H. Sugawara, S. Kanaya, T. IkemuraJournal of the Earth Simulator, Volume 6, October 2006, 17–23

SOM Created from an

Unsupervised Neural Network

Algorithm to Analyze

Tetranucleotide Frequencies in a Wide Range of

Genomes 10kb Moving Window

Using SOM, Sargasso Sea Metagenomic Data Yields 92 Microbial Genera !

Eukaryotes

Prokaryotes

Viruses

Mitochondria

Chloroplasts

Input Genomes:

1500 Microbes 40 Eukaryotes 1065 Viruses 642 Mitochondria 42 Chloroplasts

5kb Window

T. Abe, H. Sugawara, S. Kanaya, T. IkemuraJournal of the Earth Simulator, Volume 6, October 2006, 17–23

Moore Microbial Genome Sequencing ProjectSelected Microbes Throughout the World’s Oceans

www.moore.org/microgenome/worldmap.asp

Microbes Nominated by Leading Ocean Microbial

Biologists

Moore Foundation Funded the Venter Institute to Provide the Full Genome Sequence of 155 Marine Microbes

Phylogenetic Trees Created by Uli Stingl, Oregon State

Blue Means Contains One of the Moore 155 Genomes

www.moore.org/microgenome/trees.aspx

Moore 155 Marine Microbial Genomes Gives Broad Coverage of Microbial “Tree of Life”

www.moore.org/microgenome/alpha-proteobacteria.aspx

Phylogenetic Trees Created by Uli Stingl, Oregon State

Joint Genome Institute is a Leading Microbial Genomic Source

2005

2006

termite hindgut (CalTech) planktonic archaea (MIT) EBPR sludge (UW/UQ) groundwater (ORNL)

AMD Alaskan soil (UW) Gutless worm (MPI) TA-degrading bioreactor (NUS)

Antarctic bacterioplankton (DRI) hypersaline mats (UCol) Korarchaeota enrichment Farm soil (Diversa)

2007 8 new metagenomic projects

JGI Metagenomics Projects (42 Projects)

Source: Eddie Rubin, DOE JGI

Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi

FirmicutesFusobacteria Actinobacteria

Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira

SynergistesDeferribacteres

Thermudesulfobacteria

Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter

Key Problem with Analysis of Microbial Metagenomic Data

At Least 40 Phyla of Bacteria,But Only a Few are Well Sampled

Source: Eddie Rubin, DOE JGI

Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi

FirmicutesFusobacteria Actinobacteria

Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira

SynergistesDeferribacteres

Thermudesulfobacteria

Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter

Well sampled phyla

No cultured taxa

DOE Genomic Encyclopedia of Bacteria and Archaea (GEBA) / Bergey Solution: Deep Sampling Across Phyla

Source: Eddie Rubin, DOE JGI

GEBA / Bergey Pilot Project at JGI

• Goal– To Finish ~100 Bacterial and Archaeal Genomes – Selected Based on:

– Phylogeny, – Availability of Phenotype Information – Community Interest

• Approach– Select 200 Organisms– Order DNA from Culture Collections (DSMZ and ATCC)– Sequence 100 for which DNA QC is Received

• Project Lead (Jonathan Eisen JGI/UC Davis)– Project Management (David Bruce JGI/LANL)– Methods for Sequencing in Changing Technology Landscape (Paul

Richardson JGI)– Linking to educational project (Cheryl Kerfeld JGI)

Input / Interactions with: Community Advisory Group ,

ASM, Academy of Microbiology,

Etc…

Source: Eddie Rubin, DOE JGI

• How many folds?• How many

sequences adopt the same fold?

• How does function vary as sequences diverge within a family?

• Are there still Kingdom-specific families?

• Can we determine function from structure?

• How diverse are metabolic pathways and networks?

Converting Genome Sequences to Protein Fold Space

JCSG: 2hxvJCSG: 2hxv

5-amino-6-(5-phosphoribosylamino)uracil reductase

Building Genome-Scale Models of Living Organisms

• E. Coli– Has 4300

Genes– Model Has

2000!

Regulatory Actions

Input Signals

Monomers &Energy

Proteins

Genomics

Transcriptomics

Proteomics

Metabolomics

EnvironmentInteractomics

Transcription &Translation

Metabolism

Regulation

E4PX5PGLC

G6P

F6P

FDP

DHAP

3PG

DPG

GA3P

2PG

PEP

PYR

AcCoA

SuccCoA

SUCC

AKG

ICIT

CIT

FUM

MAL

OAA

Ru5P

R5P

S7P

6PGA 6PG

ACTPETH

ATP

NADPHNADH FADH

SUCCxt

pts

pts

pgi

pfkA

fba

tpi

fbp

gapA

pgk

gpmA

eno

pykFppsAaceE

zwfpgl gnd

rpiA

rpe

talAtktA1 tktA2

gltA

acnA icdA

sucA

sucC

sdhA1

frdA

fumA

mdh

adhE

AC

ackA

pta

pckA

ppc

cyoA

pnt1A

sdhA2nuoA

atpA

ACxtETHxt

O2O2xt

CO2 CO2xt

Pi Pixt

O2 trx

CO2 trx

Pi trx

EXTRACELLULARMETABOLITE

reaction/gene name

Map Legend

INTRACELLULARMETABOLITE

GROWTH/BIOMASSPRECURSORS

ETH trxAC trx

SUCC trx

acs

FOR

pflA

FORxt

FOR trx

dld

LAC

LACxtLAC trx

PYRxt PYR trx

glpDgpsA

GL3P

GL glpK

GLxt

GL trx

GLCxtGLC trx

glk

RIB

rbsK

RIBxt

RIB trx

FORfdoH

pnt2A

H+ Qh2

GLX

aceA

aceB

maeB

sfcA

E4PX5PGLC

G6P

F6P

FDP

DHAP

3PG

DPG

GA3P

2PG

PEP

PYR

AcCoA

SuccCoA

SUCC

AKG

ICIT

CIT

FUM

MAL

OAA

Ru5P

R5P

S7P

6PGA 6PG

ACTPETH

ATP

NADPHNADH FADH

SUCCxt

pts

pts

pgi

pfkA

fba

tpi

fbp

gapA

pgk

gpmA

eno

pykFppsAaceE

zwfpgl gnd

rpiA

rpe

talAtktA1 tktA2

gltA

acnA icdA

sucA

sucC

sdhA1

frdA

fumA

mdh

adhE

AC

ackA

pta

pckA

ppc

cyoA

pnt1A

sdhA2nuoA

atpA

ACxtETHxt

O2O2xt

CO2 CO2xt

Pi Pixt

O2 trx

CO2 trx

Pi trx

EXTRACELLULARMETABOLITE

reaction/gene name

Map Legend

INTRACELLULARMETABOLITE

GROWTH/BIOMASSPRECURSORS

ETH trxAC trx

SUCC trx

acs

FOR

pflA

FORxt

FOR trx

dld

LAC

LACxtLAC trx

PYRxt PYR trx

glpDgpsA

GL3P

GL glpK

GLxt

GL trx

GLCxtGLC trx

glk

RIB

rbsK

RIBxt

RIB trx

FORfdoH

pnt2A

H+ Qh2

GLX

aceA

aceB

maeB

sfcA

E4PX5PGLC

G6P

F6P

FDP

DHAP

3PG

DPG

GA3P

2PG

PEP

PYR

AcCoA

SuccCoA

SUCC

AKG

ICIT

CIT

FUM

MAL

OAA

Ru5P

R5P

S7P

6PGA 6PG

ACTPETH

ATP

NADPHNADH FADH

SUCCxt

pts

pts

pgi

pfkA

fba

tpi

fbp

gapA

pgk

gpmA

eno

pykFppsAaceE

zwfpgl gnd

rpiA

rpe

talAtktA1 tktA2

gltA

acnA icdA

sucA

sucC

sdhA1

frdA

fumA

mdh

adhE

AC

ackA

pta

pckA

ppc

cyoA

pnt1A

sdhA2nuoA

atpA

ACxtETHxt

O2O2xt

CO2 CO2xt

Pi Pixt

O2 trx

CO2 trx

Pi trx

EXTRACELLULARMETABOLITE

reaction/gene name

Map Legend

INTRACELLULARMETABOLITE

GROWTH/BIOMASSPRECURSORS

ETH trxAC trx

SUCC trx

acs

FOR

pflA

FORxt

FOR trx

dld

LAC

LACxtLAC trx

PYRxt PYR trx

glpDgpsA

GL3P

GL glpK

GLxt

GL trx

GLCxtGLC trx

glk

RIB

rbsK

RIBxt

RIB trx

FORfdoH

pnt2A

H+ Qh2

GLX

aceA

aceB

maeB

sfcA

E4PX5PGLC

G6P

F6P

FDP

DHAP

3PG

DPG

GA3P

2PG

PEP

PYR

AcCoA

SuccCoA

SUCC

AKG

ICIT

CIT

FUM

MAL

OAA

Ru5P

R5P

S7P

6PGA 6PG

ACTPETH

ATP

NADPHNADH FADH

SUCCxt

pts

pts

pgi

pfkA

fba

tpi

fbp

gapA

pgk

gpmA

eno

pykFppsAaceE

zwfpgl gnd

rpiA

rpe

talAtktA1 tktA2

gltA

acnA icdA

sucA

sucC

sdhA1

frdA

fumA

mdh

adhE

AC

ackA

pta

pckA

ppc

cyoA

pnt1A

sdhA2nuoA

atpA

ACxtETHxt

O2O2xt

CO2 CO2xt

Pi Pixt

O2 trx

CO2 trx

Pi trx

EXTRACELLULARMETABOLITE

reaction/gene name

Map Legend

INTRACELLULARMETABOLITE

GROWTH/BIOMASSPRECURSORS

ETH trxAC trx

SUCC trx

acs

FOR

pflA

FORxt

FOR trx

dld

LAC

LACxtLAC trx

PYRxt PYR trx

glpDgpsA

GL3P

GL glpK

GLxt

GL trx

GLCxtGLC trx

glk

RIB

rbsK

RIBxt

RIB trx

FORfdoH

pnt2A

H+ Qh2

GLX

aceA

aceB

maeB

sfcA

G1 + RNAP G1*

v1

nNTP

mRNA1 nNMPb4

b2

v2

v3=k1[mRNA1]

2aGTP

rib

rib1*

protein1b3

v4 (subject to global max.)

v5

aAA-tRNA

b7

2aGDP + 2aPib8

b5

b1 aAAatRNA

aATP

aAMP

+ 2aPi

b6

v6

2nPi

Pi

b9

G1 + RNAP G1*

v1

nNTP

mRNA1 nNMPb4

b2

v2

v3=k1[mRNA1]

2aGTP

rib

rib1*

protein1b3

v4 (subject to global max.)

v5

aAA-tRNA

b7

2aGDP + 2aPib8

b5

b1 aAAatRNA

aATP

aAMP

+ 2aPi

b6

v6

2nPi2nPi

Pi

b9

Pi

b9

G1 + RNAP G1*

v1

nNTP

mRNA1 nNMPb4

b2

v2

v3=k1[mRNA1]

2aGTP

rib

rib1*

protein1b3

v4 (subject to global max.)

v5

aAA-tRNA

b7

2aGDP + 2aPib8

b5

b1 aAAatRNA

aATP

aAMP

+ 2aPi

b6

v6

2nPi

Pi

b9

G1 + RNAP G1*

v1

nNTP

mRNA1 nNMPb4

b2

v2

v3=k1[mRNA1]

2aGTP

rib

rib1*

protein1b3

v4 (subject to global max.)

v5

aAA-tRNA

b7

2aGDP + 2aPib8

b5

b1 aAAatRNA

aATP

aAMP

+ 2aPi

b6

v6

2nPi2nPi

Pi

b9

Pi

b9

Gc2

tc2

Rc2

Pc2 Carbon2A

Oc2

Carbon1

(indirect)

(-)

If [Carbon1] > 0, tc2 = 0

G2a

t2a

R2a

P2a BC + 2 ATP + 3 NADH

O2a

B(+)

G5

t5

R5

P5 C + 4 NADH

O5

(+)

3 E

If R1 = 0, we say [B] is not in surplus, t2a = t5 = 0

G6a

t6a

R6a

P6aH

O6a

(-)

Hext

If Rh> 0, [H] is in surplus, t6a = 0

Gres

tres

Rres

Pres O2 + NADH

ATP

Ores

O2

(+)

G3b

t3b

R3b

P3bG

O3b

(+)

0.8 C + 2 NADH

If Oxygen = 0, we say [O2] = 0, tres= t3b = 0

G + 1 ATP + 2 NADH

Gc2

tc2

Rc2

Pc2 Carbon2A

Oc2

Carbon1

(indirect)

(-)

If [Carbon1] > 0, tc2 = 0

G2a

t2a

R2a

P2a BC + 2 ATP + 3 NADH

O2a

B(+)

G5

t5

R5

P5 C + 4 NADH

O5

(+)

3 E

If R1 = 0, we say [B] is not in surplus, t2a = t5 = 0

G6a

t6a

R6a

P6aH

O6a

(-)

Hext

If Rh> 0, [H] is in surplus, t6a = 0

Gres

tres

Rres

Pres O2 + NADH

ATP

Ores

O2

(+)

G3b

t3b

R3b

P3bG

O3b

(+)

0.8 C + 2 NADH

If Oxygen = 0, we say [O2] = 0, tres= t3b = 0

G + 1 ATP + 2 NADH

E. coli i2K

Source: Bernhard PalssonUCSD Genetic Circuits Research Group

http://gcrg.ucsd.edu

JTB 2002

JBC 2002

in Silico Organisms Now Available

2007:

•Escherichia coli •Haemophilus influenzae •Helicobacter pylori •Homo sapiens Build 1•Human red blood cell •Human cardiac mitochondria •Methanosarcina barkeri •Mouse Cardiomyocyte •Mycobacterium tuberculosis •Saccharomyces cerevisiae •Staphylococcus aureus

Biochemically, Genetically and Genomically (BiGG) Genome-Scale Metabolic Reconstructions

H. influenzae

H. pylori

S. aureus

S. typhimurium

M. barkeri• 619 Reactions• 692 Genes

S. cerevisiae• 1402 Reactions• 910 Genes

E. coli• 2035 Reactions• 1260 Genes

S. aureus• 640 Reactions• 619 Genes Mitoc.

• 218 Rxns

RBC• 39 Rxns

H. sapiens• 3311 Reactions• 1496 Genes

S. typhimurium• 898 Reactions• 826 Genes

H. pylori• 558 Reactions• 341 Genes

H. influenzae• 472 Reactions• 376 Genes

M. tuberculosis• 939 Reactions• 661 Genes

Systems Biology Research Grouphttp://systemsbiology.ucsd.edu

Use of Tiled Display Wall OptIPortal to Interactively View Microbial Genome

Acidobacteria bacterium Ellin345 Soil Bacterium 5.6 Mb

Use of Tiled Display Wall OptIPortal to Interactively View Microbial Genome

Source: Raj Singh, UCSD

Use of Tiled Display Wall OptIPortal to Interactively View Microbial Genome

Source: Raj Singh, UCSD

OptIPortal–Termination Device for the Dedicated Gigabit/sec Lightpaths

Photo Source: David Lee, Mark Ellisman NCMIR, UCSD

Collaborative Analysis of Large Scale Images of

Cancer Cells

Integration of High

Definition Video

Streamswith Large

Scale Image Display Walls

NW!

CICESE

UW

JCVI

MIT

SIO UCSD

SDSU

UIC EVL

UCI

OptIPortals

OptIPortal

An Emerging High Performance Collaboratoryfor Microbial Metagenomics

UC Davis

UMich