metagenomic as a tool to assess microbial diversity of ... · jimenez, francisco dini-andreote,...

Metagenomic as a tool to assess microbial diversity of:Sediment of São Paulo mangroves and Amazon soils

Vivian H. Pellizari ([email protected])Instituto Oceanográfico - USP

mailto:[email protected]

Metagenomic

Mangroves Amazon soil

Assessing microbial

diversity of sediments

and comparing with

other environments

Mangroves present

large potential for

methane production.

Link phylogeny to

function of active

methanotrophs

Land use change in the

Amazon can alter

microbial biodiversity

Distance-Decay

Patterns in

Biogeography

Biodiversity and functional activities of

microorganisms from mangrove of São Paulo

State

Itamar Soares de Melo

Embrapa Meio Ambiente (Coordenador),

Aline Aparecida Pizzirani-Kleiner Esalq/USP

Aline de H. Nunes Maia

Embrapa Meio Ambiente

Ana Olivia de Souza

Instituto Butantan

Célia Maria M. de Souza Silva


João Lúcio de Azevedo

Esalq/USP

Luiz Alberto B. de Morais

FFCLRP/USP

Marcos A. Vieira Ligo


Fernando Dini Andreote

Esalq/USP

Marli de Fátima Fiore

CENA/USP

Nilce Chaves Gattaz


Ricardo Harakava

Instituto Biolgóico

Rosana F. Vieira


Shirlei Scramin


Sui Mui Tsai

CENA/USP

Sonia C. N. de Queiroz


Vera L. Ferracini


Vivian H. Pellizari IO USP

METAGENOMIC ANALYSIS: Siu Mui Tsai, Lucas William Mendes, Fernando Dini Andreote, Diego Javier

Jimenez, Francisco Dini-Andreote, Armando Cavalcante Franco Dias, 1anice Mazzer Luvizotto, Rodrigo

Gouvêa Taketani, Diego Chaves, Sandra Baena, Itamar Soares de Melo, Rubens Duarte, Ana Carolina

Vieira.

Mangroves are tropical environments

Located in the intersection sea-land

Sediments are mostly anoxic and reductive

Very diverse and rich in animal species

Scarcely studied about the microbiology

Bertioga

Cananéia

N

Oil Mgv Ant Mgv Prs Mgv

Point 1

Point 2

Point 3

Sea

Land

SAO PAULO STATE

(Brazil)

COLOMBIA

BrMgv01 BrMgv02 BrMgv03 BrMgv04

Pyrosequencing 454 titanium

Clean sequences

BlastX

MG-RAST (vs SEED) – STAMP

WEBCARMA 1.0 (vs Pfam)

MEGAN 3.8 (vs NCBI-nr)

vs COGs

BlastN

MEGAN 3.8 (vs NCBI-nt)

16S rRNA data set extracted

MG-RAST – Hidden markov models

vs RDPII - Greengenes

1. Taxonomy and Functional Assignment

2. Comparison (Soil, Extreme environments, Ocean)

RAMMCAMP pipeline (Camera project)

F. D. Andreote – ESALQ USP

Previous Data: 16S rRNA clone libraries:

Dias et al. 2010 – Antonie van Leewvenhoek Dias et al. submitted – Microbial Ecology

Clean Sequences

Sequences were double cleaned, at 454 Software and additionally by

new pipeline created by GeBix team

RAW DATA SET Sequences Average Size %GCBRMgv01 255.529 379.5 bp 55,56BRMgv02 235.393 382,1 bp 54,44BRMgv03 218.525 389,9 bp 56,18BRMgv04 222.767 377,2 bp 54,37

CLEAN DATA SET Sequences Average size %GC BRMgv01 249.993 235,2 bp 55,75BRMgv02 231.233 238,2 bp 54,64BRMgv03 214.921 247,9 bp 56,36BRMgv04 217.605 222,9 bp 54,66

TOTAL 913.752 236,1 bp

215 Mbp - (~ 45 prokaryotes genomes)

Metagenomes comparisons -Tags affiliation at MG-RAST

Metagenome MG-RAST code Sequences Average MethodologyTaxonomy

classified sequencesEvalue cutoff (1e-10)

Tropical Forest Soil (DeAngelis et al. 2010)

4446153.3 780.588 412 bp 454 50.8%

Cultivated Soil(Tringe et al. 2005)

4441091.3 138.347 1116 bp shotgun 66.21%

High Andean Forest Soil(GeBiX 2010)

4445417.3 618.540 310 bp 454 43.7%

Hot Spring(GeBiX 2010)

4449206.3 270.789 190 bp 454 10.11%

Acid Mine Drainge (Tyson et al. 2004)

4441137.3 180.713 1004 bp shotgun 80.24%

Atlantic Ocean (Rusch et al. 2007)

4441572.3 317.180 1012 bp shotgun 82.43%

Pacific Ocean (Rusch et al. 2007)

4441587.3 257.581 1092 bp shotgun 82.52%

Mangrove Surface (Rusch et al. 2007)

4441598.3 148.018 1036 bp shotgun 75.73%

BRAZILIAN MANGROVE (BrMgv01) 4451033.3 248,979 235 bp 454 32.51%

BRAZILIAN MANGROVE (BrMgv02) 4451034.3 230.051 238 bp 454 30.41%



evalue cutoff (1e-10)


0% 20% 40% 60% 80% 100%

Gammaproteobacteria Deltaproteobacteria

Betaproteobacteria Alphaproteobacteria

Epsilonproteobacteria unclassified_"Proteobacteria"

16S rDNA Affiliation at Classifier vs RDP II – 396 sequences

BrMgv01

BrMgv02

BrMgv03

BrMgv04

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Crenarchaeota

WS3

Nitrospira

Fusobacteria

Firmicutes

Planctomycetes

Acidobacteria

Chloroflexi

Verrucomicrobia

OD1

Actinobacteria

Euryarchaeota

Bacteroidetes

Proteobacteria

unclassified

Rep

rese

nta

tio

n o

f p

hylo

ge

ne

tics g

roup

s (

%)

Representation of phylogenetics groups (%)


Metagenomes comparisons -Tags affiliation at MG-RAST


42.8

21.4

-0.8 0.8

-1.0

1.0

Eukaryota

Archaea

Alphaproteobacteria

BetaproteobacteriaGamaproteobacteria

delta/epsilon

Chlorobi

Cyanobacteria

Thermotogae

Actinobacteria

Chlamydiae/Verrucomicrobia

Planctomycetes

Spirochaetes

Chloroflexi

Synergistetes

Firmicutes

Fibrobacteres/

Acidobacteria

Deinococcus

Bacteroidetes

others

Hot Spring

AndeanForest

TropicalForest

BrazilianMangroves

EquatorianMangrove

Pacific

Sargasso Sea

BACTERIAL GROUPS

SAMPLES

Terrestrial

Marine

Aquatic

Mangrove

Metagenomes comparisons – Correspondence analysis

The interface sea-land is

also represented at

metagenomic level


| Pirosequencing - Cananéia Tsai, S. M. and L.W. Mendes – CENA USP

STABLE ISOTOPE PROBING / SIP - DNA

Who are the active members of methanotrophic bacteria

in mangrove?(unculture)

O Ciclo do Metano

Thauer et al., 2008

Activecontrol

Methanotrophic bacteria – rRNA 16S

SIP-DNA

Proteobacteria

Distinct mangroves seems to have similar functional microbial community structure

The mangroves harbor a vast set of yet unknown genes

Comparing to other environments, mangroves are an ecotone area from sea to land

SIP-DNA allowed identification of active methanotrophic bacteria in sediments

It must be compared the phylogeny of genes described in each of mangrove

assessed

The metatranscriptome or metaproteome could add information on differential

mangroves functioning

We know that…

Still to be explored…

19

III. Application of microbial biogeography and biodiversity

approaches to the Amazon Rainforest

Land use change in the Amazon

can alter microbial biodiversity

(from Borneman&Triplett 1997)

ForestPasture} }

(from da C Jesus et al. 2009)

The Amazon Rainforest Microbial

Observatory (ARMO)

ARMO

A mosaic of land use types

ForestSecondary

Forest

Establish

ed

Pasture

Old

Pasture

New

Pasture

Amazon Land Use

Chronosequence

Forest Sites

Primary Secondary

Pasture Sites

✚✚

★

Established★1911✚19721987

2004

✚

5 km

Pasture Sites

✚✚

Established

✚19721987

✚

Sampling Design

100 m

10 m

1 m

0.1 m0.01 m

* *

*

*

*

*

*

*

*

**

* *

Bacterial Biodiversity Analysis

DNA extraction from multiple 0.5g

samples of homogenized soil cores.

PCR amplification of the V4 region of

bacterial 16S rDNA.

Pyrosequencing of amplicons

(~10,000 sequences per sample).

Taxonomic units defined as 95%

sequence similarity groups.

Predictions: Diversity

1. Loss of spatial structure

(biotic “randomization”).

Distance

Community

Similarity

Forest Pasture

Average

Community

Similarity

Diversity

2. Higher community similarity (biotic “homogenization”).

3. Lower diversity (both taxonomic and phylogenetic).

Primary P2004 P1987 P1972 P1911 Secondary

Soren

sen s

imila

rity

0.00

0.05

0.10

0.15

0.20

0.25

0.30

a

bc

cdde

e

F5,363 = 87.8

p < 0.001

Land use change does not

“homogenize” microbial

communitiesC

om

mu

nit

y S

imila

rity

Fores

t

Secondar

y

Forest

Establishe

d PastureOld

Pasture

New

Pasture

Amazon Land Use

Chronosequence

Primary P2004 P1987 P1972 P1911Secondary

Mean

OTU

richn

ess

0

100

200

300

400

500

600

F5,49

= 7.43

p < 0.001

a ab a

cbc

a

No

. of

un

iqu

e t

axa

pe

r co

re

Fores

t

Secondar

y

Forest

Establishe

d PastureOld

Pasture

New

Pasture

Amazon Land Use

Chronosequence


significantly reduce the number of

taxa

Primary P2004 P1987 P1972 P1911Secondary

Faith

's PD

0

10

20

30

40

50

60

70

F5,49

= 10.79

p < 0.001

a

ab

cbc

a

ab


significantly reduce phylogenetic

diversityFa

ith

’s P

D p

er

core

Fores

t

Secondar

y

Forest

Establishe

d PastureOld

Pasture

New

Pasture

Amazon Land Use

Chronosequence

Predictions: Community

Composition

1. Land use change alters

microbial community

composition.

Primary Forest

Pasture

++

++

+

xxx

x

x

x

Axis

1

Axis

2

Old Pasture

Secondary Forest++

++

+

xxx

x

x

x

++ +

+

Primary Forest

Axis

1

Axis 2

3. Pasture abandonment restores microbial community composition.

2. Community composition

varies with time since

conversion.

Primary Forest

Old Pasture

++

++

+

xxx

x

x

x

Axis

1

Axis

2

xxx

x

x

x New Pasture

Next steps: does land use change alter

microbial traits?

•Phylogenetic patterns

•Functional target genes

•Metagenome content

The ARMO TeamUniversity of Sao Paulo

Vivian Pellizari

Brigitte Feigl

Siu Mui Tsai

Wagner Piccinini

Fabiana Paula da Silva

Simone Tessaro

University of

Massachusetts

Klaus Nuesslein

. Kyung-Hwa Baek

George Hamaoui

University of Texas,

Arlington

Jorge Rodrigues

Babur Mirza

Michigan State University

JamesTiedje

Ederson da C. Jesus

University of Oregon

Brendan Bohannan

Rebecca Mueller

Fazenda Nova Vida

Ricardo Arantes

Sidney Rodrigues

Introduction Target AreaPrevious

KnowledgeMetagenomics

Data Conclusions

Clean Sequences

Sequences were double cleaned, at 454 Software and additionally by

new pipeline created by GeBix team

RAW DATA SET Sequences Average Size %GCBRMgv01 255.529 379.5 bp 55,56BRMgv02 235.393 382,1 bp 54,44BRMgv03 218.525 389,9 bp 56,18BRMgv04 222.767 377,2 bp 54,37

CLEAN DATA SET Sequences Average size %GC BRMgv01 249.993 235,2 bp 55,75BRMgv02 231.233 238,2 bp 54,64BRMgv03 214.921 247,9 bp 56,36BRMgv04 217.605 222,9 bp 54,66

TOTAL 913.752 236,1 bp

215 Mbp - (~ 45 prokaryotes genomes)



Data Conclusions

Functional Mangroves Comparisons by STAMP (BlastX vs SEED-nr )

BrMgv01 vs BrMgv02

BrMgv02 vs BrMgv03

BrMgv01 vs BrMgv03

BrMgv02 vs BrMgv04

BrMgv01 vs BrMgv04

BrMgv03 vs BrMgv04

A - RNA processing and modification

B - Chromatin structure and dynamics

C - Energy production and conversion

D - Cell cycle control, cell division, chromosome partitioning

E - Amino acid transport and metabolism

F - Nucleotide transport and metabolism

G - Carbohydrate transport and metabolism

H - Coenzyme transport and metabolism

I - Lipid transport and metabolism

J - Translation, ribosomal structure and biogenesis

K - Transcription

L - Replication, recombination and repair

M - Cell wall/membrane/envelope biogenesis

N - Cell motilityO - Posttranslational modification, protein turnover, chaperones

P - Inorganic ion transport and metabolismQ - Secondary metabolites biosynthesis, transport and catabolism

R - General function prediction only

S - Function unknown

T - Signal transduction mechanisms

U - Intracellular trafficking, secretion, and vesicular transportV - Defense mechanisms

W - Extracellular structures

Y - Nuclear structure

Z - Cytoskeleton



Data Conclusions

Tags affiliation at COGs (BlastX)



Data Conclusions

Mangroves Comparisons by STAMP – BrMgv01 vs BrMgv02

Sediment

Biblioteca de clones

pmoA (A189F/mb661R)

16S rRNA (27F/1401R)

DNA+CsTF

A

Ultracentrifugaç

ãoFracionamento e

quantificação do

DNA

PCR e biblioteca

RNAr 16S

Gradiente

de

Densidade

12C/ 13C

Extração de DNA (t1 e

t2)

Microcosmos

Sediment and 40mL

NMS

5g 5g5g 5g Metano 8%

Alimentações com CH4 marcado

ou não

Consumo CH4 (GC-FID)

t1 t2

Bactérias Metanotróficas - Bertioga

T1

active

control

Bactérias Metanotróficas – SIP

T2

-0.4 -0.2 0.0 0.2 0.4 0.6

-0.4

-0.2

0.0

0.2

0.4

Primary

Secondary

Pasture1911

Pasture1972

Pasture1987

Pasture2004

R= 0.494p < 0.001

Forest communities are different

from pasture

Pasture

Forest

ANOSIM

Forest v. pasture

R = 0.530

P < 0.001

-0.4 -0.2 0.0 0.2 0.4 0.6

-0.4

-0.2

0.0

0.2

0.4

Primary

Secondary

Pasture1911

Pasture1972

Pasture1987

Pasture2004

R= 0.494p < 0.001

Pasture composition varies with

time

Pasture

Forest

P2004P1987

P1972P1911

ANOSIM

Pasture over time

R = 0.513

P < 0.001

-0.4 -0.2 0.0 0.2 0.4 0.6

-0.4

-0.2

0.0

0.2

0.4

Primary

Secondary

Pasture1911

Pasture1972

Pasture1987

Pasture2004

R= 0.494p < 0.001

Abandonment “restores”

composition

Pasture

ForestPrimary

Secondary

P2004P1987

P1972P1911

Primary P2004 P1987 P1972 P1911 Secondary

Relat

ive ab

unda

nce

0

20

40

60

80

100

Who is causing these differences?P

roport

ion o

f Tota

l O

TU

s

Planctomycete

s

Firmicutes

Actinobacteria

Proteobacteria

Acidobacteria

metagenomic as a tool to assess microbial diversity of ... · jimenez, francisco dini-andreote,...

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