Describing polar marine microbial communities by their metabolic structure: Can we bridge the gap between community structure and ecosystem function? A poster presented at the 2015 GRC Polar Marine Science.

1. Describing polar marine microbial communities by their metabolic structure
Can we bridge the gap between community structure and ecosystem function?
Jeff S. Bowman* and Hugh W. Ducklow
Lamont-Doherty Earth Observatory, Columbia University
*bowmanjs@ldeo.columbia.edu
Introduction and Motivation
Ecologists typically describe microbial communities by the diversity of a taxonomic marker gene, such as the
16S rRNA gene. Although this data is well suited to evaluating differences between communities, and to cor-
relate community structure with other environmental parameters (e.g. chlorophyll concentration, tempera-
ture, salinity), it is less well suited to describing the metabolic capabilities (i.e. ecosystem function) of the
community. Although metagenomics and other techniques can bridge the gap between microbial commu-
nity structure and ecosystem function these techniques are costly, data intensive, and low throughput.
Our goal was to develop a high-throughput method for inferring community metabolism from community
taxonomy. By evaluating metabolic structure in place of community structure we capture key in-
ter-sample relationships and their impact on microbial ecosystem function. Our method produces
pathway genome databases (PGDBs) that describe the metabolic pathways likely to be present in the
sample. These PGDBs are amenable to flux-based metabolic modeling. Future work will focus on predict-
ing the flow of elements and energy through these pathways, providing a way to model the impact of
changing community structure on biogeochemical cycles.
Here we apply our method to a seasonally variable, depth stratified microbial community from the West Ant-
arctic Peninsula, a region undergoing unprecedented environmental change.
Key Points
• Microbial communities can be described by their metabolic structure.
• Metabolic structure provides information on potential microbial ecosystem functions.
• Representing a microbial community by metabolic structure may provide a way to model
the flow of elements and energy through the community.
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Height
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Distance by pathway abundance
Distancebyedgeabundance
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phenylalanine degradation II (anaerobic)
phenylacetate degradation II (anaerobic)
alginate degradation
maltose degradation
spheroidene and spheroidenone biosynthesis
thiamin salvage III
formate oxidation to CO2
salicylate degradation I
chlorosalicylate degradation
methylsalicylate degradation
guanylyl molybdenum cofactor biosynthesis
proline degradation
phenylacetate degradation I (aerobic)
lysine biosynthesis I
triclosan resistance
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Robiginitalea biformata HTCC2501
Lactobacillus sanfranciscensis TMW 1 1304
Actinosynnema mirum DSM 43827
Alteromonodales spp.
Arthrobacter aurescens TC1
Thermodesulfovibrio yellowstonii DSM 11347
Bartonella bacilliformis KC583
Colwellia psychrerythraea 34H
Nitrosopumilus maritimus SCM1
Bartonella quintana Toulouse
Thalassobaculum spp.
Ruegeria pomeroyi DSS 3
Saccharophagus degradans 2 40
Halothiobacillus spp
Parvibaculum_lavamentivorans DS 1
Capnocytophaga/Cellulophaga spp.
Hippea maritima DSM 10411
Tetragenococcus halophilus
Melissococcus plutonius spp.
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16S sequence
library, the bigger
the better!
Obtain all
completed
genomes
Build 16S rRNA
reference tree
Find consensus
genome for
each tree node
Place reads on
reference tree
Extract pathways
for each placement
Generate
confidence score
for sample
Predict
metabolic
pathways
Calculate
confidence for
each node
Evaluate
genomic
plasticity for
terminal nodes
Evaluate
relative core
genome size
Sample Analysis
Database Construction
Confidence Score
Fig. 1. Methods. Our metabolic inference pipe-
line uses a phylogenetic placement program (p-
placer) [1] to place query reads on a reference tree
of 16S rRNA genes from all completed genomes.
We determine a consensus genome for each point
of placement on the tree, and determine the met-
abolic pathways represented in these genomes.
Separately we determine a confidence score for
each point of placement on the reference tree
from a novel indicator of genomic stability.
Fig. 4. Sample locations within the Palmer LTER off the WAP (left) and inter-sample similarity (right). The location of Palmer Sta-
tion is given by the star. Summer surface and deep samples along with winter surface samples were analyzed [2]. A) Hierarchical cluster-
ing of samples by metabolic structure. B) Hierarchical clustering of samples by taxonomic structure. Note duplicate samples in both A
and B. C) Distances between samples are in good agreement between the two methods (R2 = 0.65).
Fig. 5. What taxa and metabolic pathways account for the most variance? Having determined that the relationship between sam-
ples can be accurately represented by metabolic structure we can begin to ask ecologically relevant questions. A frequent question
posed to community structure data is what taxa account for most variability? We can ask the same question of metabolic structure; what
metabolism account for the most variability? A) PCA of taxonomic structure. B) PCA of metabolic structure. C) Heatmap of high vari-
ance taxa. D) Heatmap of high variance metabolisms. These metabolisms represent ecosystem functions that may be differentially
provided by the microbial communities.
1. Matsen, F, R Kodner, E Armbrust. 2010. pplacer: Linear time maximum-likelihood and Bayesian phylogenetic placement of sequences
onto a fixed reference tree. BMC Bioinformatics, 11:538.
2. Luria, C, H Ducklow, L Amaral-Zettler. 2014. Marine bacterial, archaeal and eukaryotic diversity and community structure on the conti-
nental shelf of the western Antarctic Peninsula. Aquatic Microbial Ecology, 73:2 107-121.
www.polarmicrobes.org
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Terminal node
Relativeplasticity
I
II
III
IV
V
VI
VII
VIII
IX
Terminal Node
Terminal Node
Internal Node
Core genome
Accessory Genome
Fig. 2. Confidence score. Placements can be made to
terminal and internal nodes. To determine the confidence
(c) of a metabolic inference for a given placement we con-
sider the core genome size (Score
), the mean genome size
of the clade (Sclade
), and the mean index of plasticity for the
clade (rd
; Fig. 4).
2015 GRS and GRC, Polar Marine Science
Fig. 3-. Genomic plasticity of genomes in our database. A major impediment to
accurate metabolic inference is the genetic diversity that can exist within even a
narrow taxonomic clade. We developed a confidence metric for our inferred metab-
olisms that is based on the degree of genomic plasticity present inherent to each
genome. X-axis gives the position of each genome on our reference tree, Y-axis
gives the degree of plasticity. Unusually plastic genomes are indicated by Roman
numerals. I) Nanoarcheum equitans II) the Mycobacteria III) a butyrate producing
bacterium within the Clostridium IV) Candidatus Hodgkinia circadicola V) the Myco-
plasma VI) Sulcia muelleri VII) Portiera aleyrodidanum VIII) Buchnera aphidicola IX) the
Oxalobacteraceae.