Current challenges in
Microbial Ecology:
1.) Innovative data mining
techniques
2.) Improving predictive models
Sarah Preheim
Assistant Professor
Department of Geography and
Environmental Engineering
Johns Hopkins University
Microorganisms are the dominant form
of biomass on Earth
1 µl of seawater
Natural water: ~100-1000 bacteria/ μl
http://www.sciencedaily.com/images/2006/08/060829081744.jpg
Microorganisms influence the ecosystems they
inhabit in many ways
Many benefits of human microbiome
Beach closures from algal blooms
????
Most bacterial phyla have
no cultured representatives
Culture dependent techniques
Culture independent techniques
No
Cultured
Rep.
70%
Cultured
Rep.
30%
~ 100 known bacterial phyla
http://www.allgeek.tv/2011/08/16/natural-preservativ
e-bisin-makes-food-last-for-years/
Achtman, M and Wagner, M. 2008. Nature Reviews Microbiology. 6:431-440
Catalogue which organisms are present
and correlate with environmental factors
Johnson et al 2006 Science. 311:1737
Current challenges in
microbial ecology
• Develop innovative techniques to
mine sequence data for important
information
• Determine how the environment
influence microbes and how microbes
influence their environment
• Improve predictive models of
microbial structure and function
Outline
Measure
Chemical concentrations
and bacterial diversity
Model
Bacterial
populations
Biogeochemical
model
Predict
Community response to and affecting
environmental change
Outline
Measure
Chemical concentrations
and bacterial diversity
Model
Bacterial
populations
Biogeochemical
model
Predict
Community response to and affecting
environmental change
Upper Mystic Lake is a model system
for studying microbial ecology
http://www.medfordboatclub.org/
Microbial actions in response to human
pollution create undesirable situations
Algal blooms
Arsenic release from sediment
Methane release to atmosphere
Beach closures
Chemical gradients form as a result of
microbial activity
chemical
concentration
summer
20 °C
thermocline
depth
thermocline
4 °C
oxygen
sulfate
nitrate
Iron(II)
Illustration of typical lake mixing
Measure chemical and biological
changes with depth at high resolution
surface
0m
1.5 m
3m
4m
5m
6m
7m
8m
9m
10 m
11 m
13 m
14 m
15 m
16 m
17 m
19 m
20m
21m
22m
sediment
Collect water at
1 meter intervals
Measure changes in bacterial community
through sequence-based survey
Extract community genomic DNA,
amplify species identifying gene
(16S ribosomal RNA gene)
Sequence ~300,000
counts per sample
Outline
Measure
Chemical concentrations
and bacterial diversity
-At high spatial and temporal resolution
Model
Bacterial
populations
Biogeochemical
model
Predict
Community response to and affecting
environmental change
Modeling ecologically cohesive
populations from sequence data
microbial community
Sequence data/
multiple sequences
per organism or population
Modeling ecologically cohesive
populations from sequence data
microbial community
Sequence data
Common bioinformatics method uses
arbitrary sequence identity cutoff to group
id = 0.97
Lower percent id cut-off:
Fewer groups/merged data
id = 0.99
Higher percent id cut-off:
Less merged data/more noise
abundance
high
Novel clustering method that uses distribution
information to identify population boundaries
Seq 1
low
Seq 2
surface
bottom
Preheim et al 2013 Appl. Environ. Microbiol. 79:6593
abundance
high
Sequences derived from the same distribution
should be merged together
Seq 1
low
Error 1
surface
bottom
Preheim et al 2013 Appl. Environ. Microbiol. 79:6593
Olesen: Spelling error or unique origin?
distribution of Olsen
distribution of Olesen
Scott Olesen
high
low
Novel method (DBC) using ecology to inform
clustering forms more appropriate groups
sequence data
distribution data
desired clustering
Seq 1
+
Seq 2
Preheim et al 2013 Appl. Environ. Microbiol. 79:6593
DBC more accurately clusters
sequences from the same organism
1
0.9
accuracy
0.8
0.7
Interpretation
0.6
0.5
0.4
0.3
0.2
0.1
0
DBC
De novo
Ref-based Ref-based
(closed)
(open)
clustering method
Preheim et al 2013 Appl. Environ. Microbiol. 79:6593
Using ecology with DBC
provides more signal
4000
3500
interpretation
abundance
3000
DBC
other
2500
2000
1500
1000
500
0
0
5
10
15
20
25
depth (meters)
DBC pop. 1
DBC pop. 2
other method
Preheim et al 2013 Appl. Environ. Microbiol. 79:6593
Significance of novel
bioinformatics method
• Modeling
ecologically
cohesive populations
using distribution
profiles
• DBC is more
accurate with less
noise than other
methods
populations
more accurate results
Interpretation
Outline
Measure
Chemical concentrations
and bacterial diversity
-At high spatial and temporal resolution
Model
Bacterial
populations
Biogeochemical
model
-With a more accurate
bioinformatics method
Predict
Community response to and affecting
environmental change
Feedback between biology and
chemical environment
Bacterial Populations
Integrated metabolism
Environmental Change
Feedback
Preheim, Olesen et al 2015 Submitted
Multi-component transport model
integrating multiple chemical cycles
primary
redox
reactions
redox
zones
reaction
fronts
secondary
redox
reactions
surface
sediment
Hunter et al. 1998. Journal of Hydrology 209:53-80
Scott Olesen
Prediction of how the environment
and community change over time
nitrate
nitrate reduction
depth
Top
Bottom
Spring
Preheim, Olesen et al 2015 Submitted
Summer
Summer
Spring
time
low
high
Prediction of how the environment and
community change over time
Preheim, Olesen et al 2015 Submitted
Calibrate biogeochemical model
to
match chemical observations
Preheim, Olesen et al 2015 Submitted
Biogeochemical model provides mechanistic
understanding of community structure
Preheim, Olesen et al 2015 Submitted
Multiple, unrelated populations
correspond to modeled rate profile
depth (m)
Sulfate reduction
normalized relative abundance
Preheim, Olesen et al 2015 Submitted
Identify populations carrying genes for
specific biogeochemical functions
Cells with or without
gene of interest
Trap genome
in acrylamide
Species gene
Gene of interest
Species gene
Spencer, Tamminen et al, ISME doi:10.1038/ismej.2015.124
Single cell reactions
in emulsion
Not all populations corresponding to
sulfate reduction carry the dsrB gene
Preheim, Olesen et al
2015 Submitted
Significance of linking populations
with biogeochemical model
• Provides a hypothesis of the
functional role of many
populations at once
• Model describes how the
community structure
influences the environment
and vise versa
• Clustered populations may
link diversity to ecosystem
function and stability
Biogeochemical model
Clustered populations
Outline
Measure
Chemical concentrations
and bacterial diversity
-At high spatial and temporal resolution
Model
Bacterial
populations
-With a more accurate
bioinformatics method
Biogeochemical
model
-To gain a mechanistic understanding
of integrated community function
Predict
Community response to and affecting
environmental change
Towards a mechanistic understanding of
microbial processes in Chesapeake Bay
Murphy et al 2011 Estuaries and Coasts 34:1293–1309
ChesROMS: a tool to model
biogeochemical dynamics in the
Chesapeake Bay
• Chesapeake
Bay ROMS
Community
model
• Predictions for
sea nettles,
HAB and
water quality
• Missing sulfur
and iron
cycles
Banakar et al 2011 EcoHealth 8, 456–467, 2011
Towards a mechanistic understanding of
microbial processes in Chesapeake Bay
• Improve our
understanding of
dynamics of
biogeochemical
processes and
bacterial diversity
contributing to
the dead-zone in
Chesapeake Bay
Preliminary Data from
Chesapeake Bay dead-zone
June 18,2015
July 22,2015
Aug 20, 2015
Grace Kim, Anand Gnanadesikan
Outline
Measure
Chemical concentrations
and bacterial diversity
-At high spatial and temporal resolution
Model
Bacterial
populations
-With a more accurate
bioinformatics method
Biogeochemical
model
-To gain a mechanistic understanding
of integrated community function
Predict
Community response to and affecting
environmental change
-Apply techniques to improve modeling efforts in the
Chesapeake Bay
Acknowledgements
• Alm lab (MIT)
– Eric Alm, Scott Olesen, Caroline Antolik
• Parsons Laboratory (CEE, MIT)
– Harry Hemond, Ben Scandella
• Johns Hopkins University
– Grace Kim, Anand Gnanadesikan
• Funding
– ENIGMA: Department of Energy