dyhrman lecture partbv2 - home - evolution and...

• From genome to biome: Tracking themetabolism and microbiome of a keystoneN2 fixer

• Co-‐exis>ng in a sea of compe>>on:Leveraging transcriptome data to iden>fythe physiological ecology of phytoplanktonfrom key groups

Vigne&es

Genome -‐ enabled

Transcriptome -‐ enabled

Fe ZnB12

NO3-‐

PO4-‐

SiO42-‐

Light

Preda7on

Nutrients

Complex community dynamics driven in part by resources

Culture and field based approaches to physiological ecology

Genome and transcriptome-enabled advances allowing to querycells in their environment in a species specific way

Key themes

• Our focus is on groups that arguably serve a“keystone” role in the marine ecosystem

• Biogeochemical drivers of physiology:– What are the mechanistic underpinnings of resource

utilization and how does resource utilization vary overtime?

• Eco-evolutionary traits:– Are there functional group traits that drive the structure

and function of the NPSG?

• Metabolic plasticity and diversity:– How do diversity and metabolism influence the

physiogical ecology of a keystone group?

Hap

toph

ytes

Dia

tom

sD

inof

lage

llate

s

Harriet Alexander

Physiological ecology -‐ a tale of two diatoms in Narraganse&Bay

• Do closely related diatoms have the same expressed metaboliccapacity?

• Is this metabolic profile consistent over >me?• Are the resource responses the same between species, or is there

evidence of resource par>>oning of niche space?

Skeletonema T. rotula

Collect samplesfrom NB Bay Isolate mRNA AAAAAA

AAAAAAAAAAAA

AAAAAA

Submit samplesto sequencingcenter

Sequence usingRNAseq and mapreads to genomes

Sampling and pipeline

Stringent mapping

Target 60 million100 bp paired-‐end reads

Removed reads withambiguous mapping

Thalassiosira rotulaSkeletonema costatumand … rest of MMETSP~400 transcriptomes

48hr incuba>onat day S3

>5µm

MMETSP = Marine Microbial Eukaryo>c Transcriptome Project

Reference Database

Read Mapping andCoun7ng

Quality Control

Metatranscriptome Data Pipeline -‐ the gory details ….

Data Crea7on

Extract RNA

Sequence RNA:(Illumina 60M100bp PE)

Each read file = 15-‐25GbEach sample = 30-‐50Gb

Trimmoma7c:

Remove lowquality reads

Bow7e2:Map sequences to

referencetranscriptomes

Select referencetranscriptomes

CD-‐HIT-‐EST:Cluster at

98% iden>tyby species

Bow7e2-‐build:Create

database formapping

Re-‐annotate?

Renamecon>gs andconcatenate

Custom Script:Create ar>ficial

GFF file

Samtools:Sort and convertmapped file to

accessible format

HT-‐Seq/RSEM:Count reads by

gene

RawSequnceFile.fastq

TrimmedSequnces.fastq

RawCounts.tab

FastQC:Assess library

quality

MappedSequences.bam

MappedGenesSorted.sam

Ref.gff

Ref.fa

8

Collect samplesfrom NB Bay Isolate mRNA AAAAAA

AAAAAAAAAAAA

AAAAAA




Stringent mapping



Thalassiosira rotulaSkeletonema costatumand … rest of MMETSP~400 transcriptomes

48hr incuba>onat day S3

>5µm

Local isolates provide the most robust read mapping (15Xincrease for local isolate rela>ve to species in database) -‐ liclemapping (<<1%) to diatoms not present in this system

Taxonomic distribu7on of reads

S 1- 5

Skeletonema

S 1- 5

Alexander et al. 2015 PNAS

C entral carbohydrate metabolismRibosom e

Carbon f ixationOther carbohydrate metabolism

Spliceosom eATP synthesis

Aminoacy l tR NAPurine metabolism

RNA processingBranched-chain amino ac id metabolism

Cysteine and m ethionine metabolismC ofactor and vitamin biosynthesis

Methane metabolismMetabolic capacity

Proteasom eUbiquitin system

Protein processingPhotosynthesis

Lysine metabolismRNA polymerase

Pyrimidine metabolismFatty ac id metabolism

Lipid metabolismAromatic amino ac id metabolismArginine and proline metabolism

Nitrogen metabolismSulfur metabolism

Terpenoid backbone biosynthesisSerine and threonine metabolism

Repair systemNucleotide sugar

Glycan metabolismPolyam ine biosynthesis

Glycosaminoglycan metabolismOther terpenoid biosynthesis

Replicat ion systemSterol biosynthesis

Histidine metabolismABC-2 type and other transport systems

Bacterial sec ret ion systemDNA polymerase

Sugar metabolismPhosphate and amino acid transport systemPheny lpropanoid and flavonoid biosynthesis

Saccharide and polyol t ransport systemAlkaloid and other secondary m etabolite biosynthesis

Other amino ac id metabolismTw o-component regulatory system

Mineral and organic ion transport systemMetallic cat ion and vitam in B12 transport system 1 0-5

1 0-4

1 0-3

1 0-2

1 0-1

S 1 S2 S3 S4 S5 S1 S2 S3 S4 S5S. costatum T. ro tula

Branched-chain amino ac id metabolism

Pe

rce

nt o

f KE

GG

An

no

tated

Re

ad

sKEGG Module

Expressed metabolic capacity

Quan>ta>ve Metabolic Fingerprint(QMF)

Plas>c responses over >me

Unique responses between diatom genera

Skeletonema


QMF highlights the metabolic traits and tradeoffs

Skeletonema

Variability in expressed metabolic capacity for N and P

Skeletonema


Transcripts are choreographed with proteins and ac7vi7es inthe model diatom Thalassiosira pseudonana

Dyhrman et al. 2012 PLoS ONE

+P -P

GDP

Transcripts are choreographed with proteins and ac7vi7es inthe model diatom Thalassiosira pseudonana

Dyhrman et al. 2012 PLoS ONE

+P -P

NPT

Inc uptake

Variability in expressed metabolic capacity for N and P

Skeletonema


Iden7fying the resource responsive gene set

• Resource responsive? Sta>s>callyiden>fy resource responsive set (-‐Nv. +N; -‐P v. +P)

• Stable? Sta>s>cally iden>fy stablereference genes (no change overincuba>ons)

• Normalize to reference genes(SGNC)

+P -PVS -N+N VSP-‐responsive

genesN-‐responsive

genes

• Incuba>ons performed with shiled N:P ra>o to provide context for insitu signals

Alexander et al. 2012 Front. Micro.

Resource responsive genes iden7fied in situ

• Resource responsive sets are dis>nct fromeach other and enriched in pathways of Nand P metabolism (e.g. urease,phosphatase, phosphate transport etc.)

• Can assign regula>on pacerns to novelgene families

• But how do we compare signals betweenspecies? Alexander et al. 2015 PNAS

Propor7onalize the resource responsive gene set

(SGNC-‐P) – (SGNC+P )

(SGNCfield) – (SGNC+P)STDP =

(SGNC-‐N) – (SGNC+N)

(SGNCfield) – (SGNC+N)STDN =

Standardized transcrip7onaldifferen7a7on scores

STDP ≈ 1 -‐PSTDP ≈ 0 +P

STDN ≈ 1 -‐NSTDN ≈ 0 +N


+P -PVS -N+N VSP-‐responsive

genesN-‐responsive

genes

STDP

High N or Low P

Low N or High P

-‐P

+P

-‐N+N

STDP

Visualizing resource responsive signals in the field

STDN


STDP

S2High N or Low P

Low N or High P

-‐P

+P

-‐N+N

STDP

T. rotulaSkeletonema

Up in -‐P

Up in -‐N

Visualizing resource responsive signals in the field

STDN


Species variability in response to the same environment

• Finely tuned species-‐specific varia>on -‐ each response (quadrant) issignificantly different from the others

• Responses are orthogonal between species in the same resourceenvironment


Summary - Biogeochemical drivers of physiology

• New approaches for the normaliza>on andcomparison of metatranscriptome data.

• Diatom metabolism is highly variable in thisestuarine environment

• Uniquely expressed metabolic capacityunderpins Skeletonema bloom … and trade offsin N and P metabolism that contribute to thedominance of one diatom over another

• Pacerns in resource par>>oning may reflectsegrega>on of the realized niche space andexplain why so many diatom species can co-‐occur

Phosphate transport

Nitrate reduc7on

What are the mechanistic underpinnings of resource utilization andhow does resource utilization vary over time?

Key themes







physiogical ecology of a keystone group?

Hap

toph

ytes

Dia

tom

sD

inof

lage

llate

s

The significance of the NPSG

Image credits: SeaWIFS Global Chlorophyll

North PacificSubtropicalGyre

Sta7on ALOHA

(Karl and Church, Nature Rev. Microbiol., 2014)

The keystone microbiome

Nutrient input,or other forcings

Hawaii Ocean Experiment: Dynamics of Light and Nutrients

(HOE-DYLAN)

Wilson et al. 2015 GBC

Harriet Alexander

Sampling a bloom

Deep water addi7on led to simulated “bloom”

Collect samplesfrom St. ALOHA Isolate mRNA AAAAAA

AAAAAAAAAAAA

AAAAAA




Stringent mapping



MMETSP ~400transcriptomes

6 day incuba>ons

>5µm


Marine Microbial Eukaryote Transcriptome Sequencing Project

Keeling et al. 2014 PLoS Biol.

Greatly expanded reference sequences in the tree of life

Within lineage diversity also greatly expanded

Keeling et al. 2014 PLoS Biol.

MMETSP improves read iden7fica7on

Sequence read iden>fica>on is substan>ally improved over usingphytoplankton genomes which do not capture the same diversity.

Read mapping against genomes v. MMETSP at St. ALHOA

Alexander et al. 2015b PNAS

Taxonomic distribu7on of reads

Distribu>ons is highly stable, much more so than what was observed incoastal system.

Increase in diatoms during simulated blooms

Read iden>fica>on is robust, most are dinoflagellates, but diatomsincrease in simulated blooms (+DSW)

DIA HAP DIN

ENERGYMETABOLISM

CARBOHYDRATEAND LIPID

METABOLISM

NUCLEOTIDE ANDAMINO ACIDMETABOLISM

GENETICINFORMATIONPROCESSING

METABOLISM


ENVIRONMENTALINFORMATIONPROCESSING

Quan7ta7ve metabolic fingerprint

Species are dis>nct from each other with 95% confidence, and all butdinoflagellates significantly shil in the simulated blooms

For each gene determineSignificance Post-‐p > 0.95 for 2-‐fold change

Gene shies in diatoms

Transcrip7onal response during simulated blooms


Functional groups have distinct and reproducible transcriptional responseduring blooms driven by nutrient input

42

Variable TranscriptAlloca>on Ra>o (VTAR)

Variable transcript alloca7on underpins traits

Haptophytes: “the survivalists”; VTAR<1Diatoms: “the scavengers”; VTAR>1

• Modeling transcript alloca>onfollowing nutrient addi>onhighlights differences in diatomand haptophyte traits

• Diatoms are more efficient thanhaptophytes


Transcrip7onal responses underscore func7onal group traits

Dinoflagellates

Alterna>ve trophicmodes?

FEW significant shils

BROAD transcript decreasesTARGETED transcript increase

“the scavengers” r-‐selected

“the survivalists” K-‐selected

MUTED increase and decreases

Diatoms

Haptophytes

Summary - Eco-evolutionary traits

• The becer the reference database(MMETSP) the becer resolu>on

• Phytoplankton groups appear to be limitedby resource availability in the NPSG.

• Dinoflagellates have licle transcrip>onalresponse to nutrient input.

• Diatom are par>cularly responsive(VTAR>1) to nutrient input which likelyunderpins their dominance in blooms

Are there functional group traits that drive the structure andfunction of the NPSG?

Key themes







physiological ecology of a keystone group?

Hap

toph

ytes

Dia

tom

sD

inof

lage

llate

s

Diving beyond the func7onal group…

Tracking strain specific responses in the calcifyinghaptophyte, Emiliania huxleyi (Ehux)

Image: Jeremy Young

asexual division

2NDiploid

Calcifying

asexual division

1NHaploid

MEIOSIS(diploid divides to

haploid)

SYNGAMY(two haploids fuse

to diploid)

Naked

E. huxleyi life cycle

Emiliania huxleyi: a cosmopolitan, globally significant species

Image: NASA Earth Observatory

• Calcifica>on -‐ cri>cal role inglobal carbon cycle andstrongly linked to climatedriven ocean acidifica>on

• Source of paleoproxies forclimate reconstruc>ons

• Form dense blooms, driverslargely unknown

• First marine phytoplankton tohave mul>ple strainssequenced, iden>fying pangenome

10,000 cells per ml> 100,000 km2

Figure from Read et al., 2013

Emiliania huxleyi has many diverse isolates

Cultured strains are highly diverse -‐ isolated from a broadtemperature range and displaying considerable physiological diversity

E. huxleyi has a pan genome, with core and variable genecontent

Key func>onal genes are variably distributed across cultured isolates.

Hawaii Ocean Experiment: Dynamics of Light and Nutrients

(HOE-DYLAN)

Wilson et al. 2015 GBC

C

Control

+NDSW

Deep seawater amendment

-‐N +P-‐P

Treatments with added N

Responses to shies in resource ra7os

Collect samplesfrom St. ALOHA Isolate mRNA AAAAAA

AAAAAAAAAAAA

AAAAAA




Stringent mapping



MMETSP Ehux straintranscriptomes

6 day incuba>ons

>5µm


Summary -‐ Metabolic plas7city and diversity

• Metatranscriptome data can be parsed atthe strain level with appropriate referencedata.

• Apparent abundance of Ehux strains andtheir physiology is driven by nitrogenaddi>on

• Everything is perhaps everywhere and theenvironment selects for what is dominant

How do diversity and metabolism influence the physiogical ecologyof a keystone group?

Summary

• Biogeochemical drivers of physiology:– Plasticity in diatom physiology may allow for co-

occurrence of diverse species.

• Eco-evolutionary traits:– Diatoms appear to be more efficient in turning over

their transcriptome than haptophytes which likelyunderpins their success in response to nutrient pulsesin the NPSG.

• Metabolic plasticity and diversity:– E. huxleyi strains vary in the field in response to N, but

harbor conserved responses to nutrient pulses in theNPSG.

• New resources and ways of parsingmetatranscriptome data are leading to newinsights!

Hap

toph

ytes

Dia

tom

sD

inof

lage

llate

s

• Dyhrman Lab: Dr. Rachel Wisneiwski Jakuba, Dr. Elizabeth Orchard, Dr. LouieWurch, Abigail Heithoff, Harriet Alexander, Kyle Frischkorn, Dr. Alena Sevucu, Dr.Julia Diaz, Dr. Monica Rouco Molina, Hannah Joy-Warren, Sheean Haley andmany undergraduate interns.

• Collaborators: Ben Van Mooy, C-MORE partnership, Mike Lomas and the BATSProgram, Jim Ammerman and the ATP3 Program, Eric Webb, Claudia Benitez-Nelson, Ellery Ingall, Dan Repeta, Dennis McGillicuddy, John Waterbury, CabellDavis, Mak Saito, Bethany Jenkins, Tatiana Rynearson, C-MORE investigators

Acknowledgements

• C-‐MORE• Simons Founda>on• NSF• EPA• NOAA• DOE, JGI

dyhrman lecture partbv2 - home - evolution and...

Documents