Who you are? or

What you do? Evolution of metabolic function shapes ocean

biogeochemistry in a gene based model

Victoria ColesUniversity of Maryland Center for Environmental

Science, Horn Point Laboratory

2018 OCB Workshop

Some key limitations of (many) functional group models for representing evolutionary processes

Diatoms CalcifiersN2-Fixers

Detritus Dissolved Nitrogen

Mixotrophs

2018 OCB Workshop

Will the taxa in each functional grouprespond similarly to changes in pH, temp, nutrient ratios and concentrations?

Doney et al. (2012)

2018 OCB Workshop

Follows, Dutkiewicz, Grant, Chisholm, Science 2007

Coles and Hood, 2015

Mismatch between observational “omics” and models

2018 OCB Workshop

Self-organizing communities of microbes - with randomly assembled genomes from a shared pool of metabolic functions - will establish realistic ocean biogeochemistry

Tish Yager, Adrian Burd, Byron Crump, Patricia Medeiros, Mary Ann Moran, John Paul, Jeff Richey, Maureen Brooks, Raleigh Hood, Mary Doherty, Caroline Fortunato, Brandon Satinsky, Shalabh Sharma, Christa Smith, Mike Stukel, Nick Ward, Brian Zielinski

2018 OCB Workshop

Adjacent Seawater

Leaf blades of Ulva Australis

MDS Plot of functional similarity

Burke et al, PNAS 2011

Adjacent surfaces are colonized by different microbial species with similar metabolic function

2018 OCB Workshop

Animator: Rainier Hood

2018 OCB Workshop

A library of “genes” coding for functions relevant to nitrogen cycling

Satinsky, Smith, Sharma, Landa, Medeiros, Coles,Yager, Crump, Moran, ISME J. 2016

Normalized expression ratio

Salinity

2018 OCB Workshop

Broad gene potential is maintained by ongoing insertion of new organisms to replace those that never become successful.

0

10

20

30

40

50

60

70

Organism

s

1980 1985 1990 1995

Year

2018 OCB Workshop

The emergent model community develops realistic size structure patterns – a key requirement for evaluating export

Satellite fields from Kostadinov et al, 2010

micro

pico+nano

pico+nano

micro

Satellite ModelEstimates

Satellite ModelEstimates

2018 OCB Workshop

The emergent community occupies diverse spatial niches

0.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v10

0.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v15

0.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v14

0.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v180.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v37

0.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v46

0.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v200.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v50

0.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v420.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v59

0.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v210.10.20.51.02.05.0

10.020.050.0

100.0200.0500.0

mmol N/m^2

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_euphsum_v63

12

2018 OCB Workshop

Emergent patterns in metagenomes and metatranscriptomes can be mapped in relation to substrates

0.00010.00020.00050.00100.00200.00500.01000.02000.05000.10000.20000.50001.00002.00005.000010.0000

vanA

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_layer_1_gene_vanA

0.0010.0020.0050.0100.0200.0500.1000.2000.5001.0002.0005.000

10.000mmol DON/m^3

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_1_v6

0.00010.00020.00050.00100.00200.00500.01000.02000.05000.10000.20000.50001.00002.00005.000010.0000

vanA

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_1_v86

Terrestrial DOM (e.g. lignins)

Metagenomes for aromatic compound metabolism pcaH(community potential)

Metatranscripts of pcaH (community activity)

pcaH genes

pcaH transcripts

13

2018 OCB Workshop

Emergent metagenomic patterns can be compared with observations and cost-benefit choices reviewed.

Terrestrial DOM (e.g. lignins)

Metagenomes for aromatic compound metabolism pcaH (community potential)

Metatranscripts of pcaH (community activity)

pcaH genes

pcaH transcripts

14

2018 OCB Workshop

Emergent metagenomic patterns can be compared with observations and cost-benefit choices reviewed.

0.00010.00020.00050.00100.00200.00500.01000.02000.05000.10000.20000.50001.00002.00005.000010.0000

vanA

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_layer_1_gene_vanA

0.0010.0020.0050.0100.0200.0500.1000.2000.5001.0002.0005.000

10.000mmol DON/m^3

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_1_v6

0.00010.00020.00050.00100.00200.00500.01000.02000.05000.10000.20000.50001.00002.00005.000010.0000

vanA

−80˚ −40˚ 0˚

0˚

20˚

1995_167_00_2db_1_v86

Terrestrial DOM (e.g. lignins)

Metagenomes for aromatic compound metabolism pcaH (community potential)

Metatranscripts of pcaH (community activity)

pcaH genes

pcaH transcripts

15

2018 OCB Workshop

Direct comparison of model with omics observations are reasonable

16

2018 OCB Workshop

All conversion of DON, PON and NH4 to NO3occurs through the emergent community and the genetic potential of each organism

Model Model

Model Ammonium

Model NitrateAMT Nitrate (uM)

AMT Ammonium (uM) Model Ammonium

−80˚

−80˚

−60˚

−60˚

−40˚

−40˚

−20˚

−20˚

0˚

0˚

0˚ 0˚

20˚ 20˚

2018 OCB Workshop

Gene counts from Agogué et al 2008, Nutrients from the Atlantic Meridional Transect Program

AMT Run 1 Run 2 Run 3 Run 4

Nitrate

Ammonium

Nitrite / amoAtranscripts

amoA Gene copy #

Model simulations with different organisms but common gene functions show similar biogeochemical gradients

−80˚

−80˚

−60˚

−60˚

−40˚

−40˚

−20˚

−20˚

0˚

0˚

0˚ 0˚

20˚ 20˚

2018 OCB Workshop

0100002000030000400005000060000

1980 1982 1984 1986 1988 1990 1992 1994 1996Nitrate

0

2000

4000

6000

Primary production

0

200

400

600

Autotroph growth rate

0

2

4

Nitrification rate

0

100

200

Total biomass

0

4

1980 1982 1984 1986 1988 1990 1992 1994 1996

Species introductions (evolution) can strongly reshape the model properties over the short term

New species introduction rate

2018 OCB Workshop

0.000.050.100.150.200.250.300.350.400.450.50

1980 1982 1984 1986 1988 1990 1992 1994 1996

expt_12.0_atl_sum_v98mot_transcripts/m^2day

0

1

2

3

4

5

1980 1982 1984 1986 1988 1990 1992 1994 1996

expt_12.0_atl_sum_v99

0

1

2

3

4

5

1980 1982 1984 1986 1988 1990 1992 1994 1996

expt_12.0_atl_sum_v99

0.000.050.100.150.200.250.300.350.400.450.50

1980 1982 1984 1986 1988 1990 1992 1994 1996

expt_12.0_atl_sum_v98mot_transcripts/m^2day

Changes in organism’s ability to nitrify at low ammonium concentrations appears to be a key model sensitivity Basin averaged amoAtranscript concentration

Basin averaged transcript concentration

2018 OCB Workshop

Species environmental niche is more flexible for temp. and light than for nitrate

Irwin et al, PNAS 2015

2018 OCB Workshop

pcb−hl

pcb−ll

pbs−hl

pbs−ll

nrt−HA

nrt−LA

amtB−H

Aam

tB−LA

rhod

pcaH

AMA

AMA−

det

amoA

−Aam

oA−B nif

motG

mot−P sil

cheA

Chi−syn

Run=1

Run=2

Run=3

Run=4

10 20 30

Salinity

−4 −3 −2 −1 0 1

A

B

C

D

pcb−hl

pcb−ll

pbs−hl

pbs−ll

nrt−HA

nrt−LA

amtB−H

Aam

tB−LA

rhod

pcaH

AMA

AMA−

det

amoA

−Aam

oA−B nif

motG

mot−P sil

cheA

Chi−syn

Run=1

Run=2

Run=3

Run=4

10 20 30

Salinity

−1 0 1 2 3 4

Model simulations with different organisms have dissimilar metagenomes

2018 OCB Workshop

Type

Size

BioM

pcb.hl

nrt.H

Anrt.LA

amt.H

Aam

t.LA

amoA.A

amoA.B nif

pcaH

AMA

AMA.det

rhod mot

mot.P

cheA

Chi.syn sil

1340

1128

1371

325

860

1671

1166

1485

134

516

591

474

1084

1309

1503

629

1489

1791

1679

1507

1771

691

512

1629

1436

1328

346

78

0 0.4 1Value

Color Key

Type

Size

BioM

pcb.hl

nrt.H

Anrt.LA

amt.H

Aam

t.LA

amoA.A

amoA.B nif

pcaH

AMA

AMA.det

rhod mot

mot.P

cheA

Chi.syn sil

731

619

190

1074

599

871

997

214

752

122

483

36

265

406

207

638

144

459

722

1063

903

979

506

325

919

0 0.4 1Value

Color Key

Type

Size

BioM

pcb.hl

nrt.H

Anrt.LA

amt.H

Aam

t.LA

amoA.A

amoA.B nif

pcaH

AMA

AMA.det

rhod mot

mot.P

cheA

Chi.syn sil

1020

1501

303

602

416

661

980

1443

569

1244

638

550

995

79

669

176

210

757

195

192

1152

1455

118

1241

834

0 0.4 1Value

Color Key

Type

Size

BioM

pcb.hl

nrt.H

Anrt.LA

amt.H

Aam

t.LA

amoA.A

amoA.B nif

pcaH

AMA

AMA.det

rhod mot

mot.P

cheA

Chi.syn sil

1074

1280

89

1052

549

350

2009

106

467

1610

931

800

775

155

893

1623

927

970

1073

1062

440

1590

330

1891

1097

1505

750

1176

2005

1659

0 0.4 1Value

Color Key

Model communities differ, but share prochlorococchus and SAR analogs

Prochloroccocus analog

SAR

Run 1

Run 2

Run 3

Run 4

Organism #

Organism #

2018 OCB Workshop

The metatranscriptomes – reflecting realized function – are similar across runs in similar physical environments

1176 893

494

pcb−hl

pcb−ll

pbs−hl

pbs−ll

nrt−HA

nrt−LA

amtB−H

Aam

tB−LA

rhod

pcaH

AMA

AMA−

det

amoA

−Aam

oA−B nif

motG

mot−P sil

cheA

Chi−syn

Run=1

Run=2

Run=3

Run=4

10 20 30

Salinity

−4 −3 −2 −1 0 1

A

B

C

Dpcb−hl

pcb−ll

pbs−hl

pbs−ll

nrt−HA

nrt−LA

amtB−H

Aam

tB−LA

rhod

pcaH

AMA

AMA−

det

amoA

−Aam

oA−B nif

motG

mot−P sil

cheA

Chi−syn

Run=1

Run=2

Run=3

Run=4

10 20 30

Salinity

−1 0 1 2 3 4

Salinity

2018 OCB Workshop

Its not who you are underneath but what you do that defines you

1176 893

494

pcb−hl

pcb−ll

pbs−hl

pbs−ll

nrt−HA

nrt−LA

amtB−H

Aam

tB−LA

rhod

pcaH

AMA

AMA−

det

amoA

−Aam

oA−B nif

motG

mot−P sil

cheA

Chi−syn

Run=1

Run=2

Run=3

Run=4

10 20 30

Salinity

−4 −3 −2 −1 0 1

A

B

C

Dpcb−hl

pcb−ll

pbs−hl

pbs−ll

nrt−HA

nrt−LA

amtB−H

Aam

tB−LA

rhod

pcaH

AMA

AMA−

det

amoA

−Aam

oA−B nif

motG

mot−P sil

cheA

Chi−syn

Run=1

Run=2

Run=3

Run=4

10 20 30

Salinity

−1 0 1 2 3 4

Salinity

2018 OCB Workshop

Hellweger et al. Science 2014

Neutral evolution is fast enough to generate biogeographic provinces in marine microbes

2018 OCB Workshop

Dispersion timescales are too short to generate distinct biogeographic provinces in abundant marine microbes

Jonsson and Watson Nat. Comm 2015

2018 OCB Workshop

In the model:evolution of key metabolic functions sets basin biogeochemical gradients

Thus, the evolution of specific lineages could potentially be non-deterministic.

Many different trees of life could produce the present day ocean

2018 OCB Workshop

Discussion questions• If experimental evolution experiments are not terribly realistic, what are they

good for in the first place?

• How many traits are enough traits? How much effort should we put into phenotypic characterisations?

• Is it correct to assume that today's organisms are in their optimum environment / strongly locally adapted to their environment/ at a peak of the fitness landscape in their current environment?

• How often are interspecies interactions other than competition and exploitation important for ocean ecosystem functioning, and does evolution tend to preserve or disrupt them?

• When can we ignore contemporary evolution?• Do we need to pay attention to evolution if it isn't changing populations in ways

that we care or that we can measure?• What kinds of long term observations need to be put into place to measure

evolutionary change?• Can evolution reshape carbon export (over decadal timescales) or does physics

reign supreme?

2018 OCB Workshop