Microbial interactions and community assembly at microscales · Microbial interactions and...

8
Microbial interactions and community assembly at microscales Otto X Cordero 1,2 and Manoshi S Datta 2 In most environments, microbial interactions take place within microscale cell aggregates. At the scale of these aggregates (100 mm), interactions are likely to be the dominant driver of population structure and dynamics. In particular, organisms that exploit interspecific interactions to increase ecological performance often co-aggregate. Conversely, organisms that antagonize each other will tend to spatially segregate, creating distinct micro-communities and increased diversity at larger length scales. We argue that, in order to understand the role that biological interactions play in microbial community function, it is necessary to study microscale spatial organization with enough throughput to measure statistical associations between taxa and possible alternative community states. We conclude by proposing strategies to tackle this challenge. Addresses 1 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 2 Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Corresponding author: Cordero, Otto X ([email protected]) Current Opinion in Microbiology 2016, 31:227234 This review comes from a themed issue on Environmental microbiology Edited by Steven J. Hallam and Mo ´ nica Va ´ squez For a complete overview see the Issue and the Editorial Available online 25th May 2016 http://dx.doi.org/10.1016/j.mib.2016.03.015 1369-5274/# 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creative- commons.org/licenses/by-nc-nd/4.0/). Introduction One of the most fundamental properties of complex biological systems is their multi-scale structure. Multicel- lular organisms are formed by the hierarchical organiza- tion of tissues, fibers, proteins, and amino acid motifs, all the way down to DNA [1]. Likewise, ecosystems have a hierarchical arrangement: from meta-communities to communities, populations, individuals, pathways, and genes (Figure 1). This hierarchical structure is more than just a convenient way to organize a textbook. It is, in fact, essential to the perceived macroscopic properties of the whole. For instance, the mechanical properties of bone tissue cannot be explained only from the properties of the collagen fibers that compose it. Instead they depend on the mesoscopic organization, for example, the packing of fibrils and the density of cross-links [1]. Similarly, in ecology, there is significant evidence that local, micro- scopic interactions between and within populations affect properties such as resistance to perturbations, efficiency of resource utilization, rates and yields of biomass pro- duction, etc. [24]. All these are macroscopic properties that depend on how the microscopic building blocks (genes, genotypes, and cells) are assembled. Yet, the classical mantra of microbial ecology (‘who is there and what are they doing’) suggests that functions of microbial ecosystems can be reduced to the functions of their building blocks genotypes and their genes without knowledge of how these building blocks interact. Over almost two decades, microbial ecology has experi- enced a revolution driven by -omic technologies, which has allowed researchers to enumerate the building blocks. Simultaneously, a number of efforts have been made to infer associations between these building blocks from - omics data [512]. However, these statistical associations are often inferred from coarse-grained samples, which are collected at the scales of ecosystems, and not at the scales of local communities or populations (Figure 1). In many natural environments (Table 1), cells of diverse taxonomic origins aggregate in patches of high local cell density, either attached to surfaces or to each other in multicellular flocs. Within those local patches, which are often on the order of 100 mm (Table 1), cellcell distances are short enough for diffusible metabolites to reach neighboring cells. At these scales, local ecological inter- actions, which can inhibit or promote growth of microbial populations, directly influence community structure and dynamics. But, these physical associations between microorganisms may be short-lived. Microscale commu- nities frequently assemble and disassemble by migration, attachment, and detachment from surfaces and cells. Thus, ecological interaction networks are highly dynamic and depend on the interplay between behavior (chemo- taxis, attachment, etc.) and cellcell interactions. How these discrete microscale communities assemble may dictate the structure-function mapping of microbial ecosystems. This is not only because ‘who sits next to whom’ can determine what type of metabolic conversions are realized and with what efficiency, but also because the combined effects of ecological interactions and spatial structure can influence community diversity and stability. Available online at www.sciencedirect.com ScienceDirect www.sciencedirect.com Current Opinion in Microbiology 2016, 31:227234

Transcript of Microbial interactions and community assembly at microscales · Microbial interactions and...

Page 1: Microbial interactions and community assembly at microscales · Microbial interactions and community assembly at microscales Otto X Cordero1,2 and Manoshi S Datta2 In most environments,

Microbial interactions and community assembly atmicroscalesOtto X Cordero1,2 and Manoshi S Datta2

Available online at www.sciencedirect.com

ScienceDirect

In most environments, microbial interactions take place within

microscale cell aggregates. At the scale of these aggregates

(�100 mm), interactions are likely to be the dominant driver of

population structure and dynamics. In particular, organisms

that exploit interspecific interactions to increase ecological

performance often co-aggregate. Conversely, organisms that

antagonize each other will tend to spatially segregate, creating

distinct micro-communities and increased diversity at larger

length scales. We argue that, in order to understand the role that

biological interactions play in microbial community function, it is

necessary to study microscale spatial organization with enough

throughput to measure statistical associations between taxa

and possible alternative community states. We conclude by

proposing strategies to tackle this challenge.

Addresses1 Department of Civil and Environmental Engineering, Massachusetts

Institute of Technology, Cambridge, MA 02139, USA2 Computational and Systems Biology Graduate Program,

Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Corresponding author: Cordero, Otto X ([email protected])

Current Opinion in Microbiology 2016, 31:227–234

This review comes from a themed issue on Environmental

microbiology

Edited by Steven J. Hallam and Monica Vasquez

For a complete overview see the Issue and the Editorial

Available online 25th May 2016

http://dx.doi.org/10.1016/j.mib.2016.03.015

1369-5274/# 2016 The Authors. Published by Elsevier Ltd. This is an

open access article under the CC BY-NC-ND license (http://creative-

commons.org/licenses/by-nc-nd/4.0/).

IntroductionOne of the most fundamental properties of complex

biological systems is their multi-scale structure. Multicel-

lular organisms are formed by the hierarchical organiza-

tion of tissues, fibers, proteins, and amino acid motifs, all

the way down to DNA [1]. Likewise, ecosystems have a

hierarchical arrangement: from meta-communities to

communities, populations, individuals, pathways, and

genes (Figure 1). This hierarchical structure is more than

just a convenient way to organize a textbook. It is, in fact,

essential to the perceived macroscopic properties of the

whole. For instance, the mechanical properties of bone

tissue cannot be explained only from the properties of the

www.sciencedirect.com

collagen fibers that compose it. Instead they depend on

the mesoscopic organization, for example, the packing of

fibrils and the density of cross-links [1]. Similarly, in

ecology, there is significant evidence that local, micro-

scopic interactions between and within populations affect

properties such as resistance to perturbations, efficiency

of resource utilization, rates and yields of biomass pro-

duction, etc. [2–4]. All these are macroscopic properties

that depend on how the microscopic building blocks

(genes, genotypes, and cells) are assembled.

Yet, the classical mantra of microbial ecology (‘who is

there and what are they doing’) suggests that functions of

microbial ecosystems can be reduced to the functions of

their building blocks — genotypes and their genes —

without knowledge of how these building blocks interact.

Over almost two decades, microbial ecology has experi-

enced a revolution driven by -omic technologies, which

has allowed researchers to enumerate the building blocks.

Simultaneously, a number of efforts have been made to

infer associations between these building blocks from -

omics data [5–12]. However, these statistical associations

are often inferred from coarse-grained samples, which are

collected at the scales of ecosystems, and not at the scales

of local communities or populations (Figure 1).

In many natural environments (Table 1), cells of diverse

taxonomic origins aggregate in patches of high local cell

density, either attached to surfaces or to each other in

multicellular flocs. Within those local patches, which are

often on the order of 100 mm (Table 1), cell–cell distances

are short enough for diffusible metabolites to reach

neighboring cells. At these scales, local ecological inter-

actions, which can inhibit or promote growth of microbial

populations, directly influence community structure and

dynamics. But, these physical associations between

microorganisms may be short-lived. Microscale commu-

nities frequently assemble and disassemble by migration,

attachment, and detachment from surfaces and cells.

Thus, ecological interaction networks are highly dynamic

and depend on the interplay between behavior (chemo-

taxis, attachment, etc.) and cell–cell interactions.

How these discrete microscale communities assemble

may dictate the structure-function mapping of microbial

ecosystems. This is not only because ‘who sits next to

whom’ can determine what type of metabolic conversions

are realized and with what efficiency, but also because the

combined effects of ecological interactions and spatial

structure can influence community diversity and stability.

Current Opinion in Microbiology 2016, 31:227–234

Page 2: Microbial interactions and community assembly at microscales · Microbial interactions and community assembly at microscales Otto X Cordero1,2 and Manoshi S Datta2 In most environments,

228 Environmental microbiology

Figure 1

Current Opinion in Microbiology

Ecological unit Possible driversLength scale

> m ecosystem biogeochemicalparameters

meta-communities& meta-populations

environmentalgradients(pH, O2, etc.)

local communities& populations

multispecies aggregates

ecologicalinteractions

single-cellphysiology

cm - m

10-103 µm

biofilmflocs

1-10 µm single cells

communityscaffold

Multi-scale nature of microbial ecosystems. At the scale of meters or kilometers, microbial communities are driven by coarse-grained

environmental parameters and may appear stable due to the averaging of multiple variable meso-environments and micro-environments. On the

other end of the spectrum, at scales of 1–10 mm, we can measure the behavior and function of single cells. However, in between, there are

multiple nested layers of ecological structure. At the scale of �cm-m, depending on the rate of mixing in the system, we are likely to sample

meta-communities, most likely in the form of ensembles of microscale aggregates connected by dispersal. Community properties at these scales

are likely driven by differences in dispersal and small-scale abiotic gradients. In environments like the ocean, dental plaques, sediments, and

others, the cell aggregates that comprise local communities are found at the scale of �10–1000 mm, but often at the lower end of this range. It is

at the scale of these cell aggregates (�100 mm) that biological interactions between organisms are most likely to have a measurable effect on

population dynamics and composition. However, current -omic techniques disrupt this structure and can only provide us with raw repertoires of

taxa and genes, while imaging techniques are limited in throughput.

Here we argue that studying microbial communities in

high-replication at the local patch scale is necessary, both

to reconstruct ecological interaction networks and to

understand the functional impact of microbial interac-

tions (Box 1).

Local interactions and ecological functionOne implication of ‘who sits next to whom’ is that

populations in close physical proximity may have com-

plementary metabolic repertoires that improve their

functional productivity. Why combinations of taxa can

Current Opinion in Microbiology 2016, 31:227–234

perform certain functions better than taxa in isolation is

an exciting and relatively open question in microbial

evolution. In the most well-studied cases, populations

consume the metabolic waste products of others, often as

electron donor or acceptors in anaerobic environments

[13]. However, in many cases, these synergisms emerge

through direct complementation of eroded genetic reper-

toires [14]. For instance, many different lineages across

the microbial tree of life have lost the ability to synthesize

their own amino acids and rely on those produced by a

host or neighboring microbes [15,16].

www.sciencedirect.com

Page 3: Microbial interactions and community assembly at microscales · Microbial interactions and community assembly at microscales Otto X Cordero1,2 and Manoshi S Datta2 In most environments,

Microscale spatial structure and microbial interactions Cordero and Datta 229

Table 1

Examples of naturally occurring particulate microbial habitats. Besides the case of nutrient particles in aquatic environments, other types

of particle structures can be found in a variety of other environments as well. These include (but are not limited to): colonic crypts in the

human gut, trichomes and other surface structures on leaves, granules in activated sludge bioreactors, dental plaque, and many others

[29��,40–50]. Altogether, these particles represent discrete community units that can be individually sampled from the environment.

Further work should be aimed at studying the structure and function of communities on these naturally occurring patches.

Environment Structure Length scale Reference

Bacterial clusters

TEP

Algae

Bar-Zeev et al. (2012)

a Aquatic Organic detritus 1–1000 mm Kirchman (2010);

Bar-Zeev et al. (2012)

Aquatic Live copepods 1–2 mm Tang et al. (2010)

Aquatic Pink berries 500 mm–1 cm Wilbanks et al. (2014)

10 µm

Welch et al. (2016)

Terrestrial Soil aggregates 2–2000 mm O’Donnell et al. (2007);

Franklin and Mills (2007);

Raynaud and Nunan (2014)

Terrestrial Leaf surface

structures

(e.g., trichomes)

60–100 mm Esser et al. (2015)

Adapted from Gonzales-Gil andHolliger (2014)

Animal Colonic crypts 100–500 mm Donaldson et al. (2015)

Animal Food particles 50–500 mm Walker et al. (2008);

Van Wey et al. (2011)

Animal Dental plaques 10–100 mm Welch et al. (2016)

Valm et al. (2012)

Industrial Granular activated

sludge bioreactors

500 mm–1 cm Gonzales-Gil and

Holliger (2014)

Genomic studies suggest that metabolic complementa-

tion plays a crucial role in natural microbial communities.

For example, Zelezniak et al. [17��] showed computation-

ally that there is a general trend for locally co-occurring

populations to be enriched in metabolic complementa-

rities, suggesting that interactions among micro-organisms

www.sciencedirect.com

are common and likely to emerge from pairing of in-

complete or complementary metabolic pathways. In a

separate experimental study in methanogenic communi-

ties, Embree et al. [18��] showed that amino acid auxo-

trophies create interdependencies between populations

that control energy flux and contribute to community

Current Opinion in Microbiology 2016, 31:227–234

Page 4: Microbial interactions and community assembly at microscales · Microbial interactions and community assembly at microscales Otto X Cordero1,2 and Manoshi S Datta2 In most environments,

230 Environmental microbiology

Box 1 A case study: community ecology on particulate organic

matter.

In oceans, lakes, and other aquatic environments, organic parti-

cles — ranging from decaying crustaceans to fish fecal pellets to

polysaccharide gels — serve as nutrient-rich scaffolds for microbial

communities (Table 1) [51]. Microbes from the surrounding water

flock to these particles and assemble into dense multi-species

consortia that consume and recycle particle resources before they

sink out of zones of high productivity in the ocean. The assembly of

local communities on particles is shaped by the interplay between

cell behavior and ecological interactions. Traits such as swimming

speed, chemotaxis, and surface attachment [52] control the order of

arrival of organisms to a particle, as well as their residence time. At

the same time, ecological interactions such as quorum sensing

[53,54], chemical antagonism [55] and exploitation of public goods

[56] inhibit or facilitate growth. By modulating the abundance of

particle-degrading bacteria and their exposure to particle surfaces,

local interactions on POM can thus control the rates of particle

degradation and biomass production, and consequently, the rates of

carbon remineralization in the water column. Similar processes are

likely to control the assembly of microscale biofilms in many other

environments, including those in the oral microbiome or in granular

sludge in waste water treatment reactors [50].

robustness. Altogether, interactions through metabolic

complementarities are common in nature and can have

a large impact on community function.

Interactions and (in)stability

Besides direct functional consequences like metabolic

complementation, ecological interactions and spatial as-

sembly can also affect the stability of the community (i.e.,

the ability to buffer environmental change) [19,20]. A

robust result of population dynamics models is that strong

mutual antagonism or interference competition — where

two species compete with each other more than with

themselves — makes coexistence between species glob-

ally unstable [21]. Therefore, in a well-mixed environ-

ment, one species wins over the other.

However, in the presence of spatial structure, the instabil-

ities created by mutually antagonistic interactions mani-

fest as spatial patterns and coexistence at large scales. For

instance, mutually antagonistic colonizers of nutrient

patches will exclude each other on any individual patch,

depending on order of arrival, but will coexist globally.

Thus, differences in early stages of colonization can drive

each individual micro-patch to alternative states charac-

terized by different patterns of species abundance

(Figure 2). Consistent with this hypothesis, strains of

Bacillus sampled from the same 4 cm2 area chemically

inhibit each other less frequently than bacteria from

distant (30 m) locations [22�]. Thus, community assembly

and interference competition may contribute to spatial

segregation of antagonistic genotypes in soil.

Alternative states on local patches can increase global

diversity and consequently, the functional stability of

ecosystems [23–25]. Ecosystems with more redundant

Current Opinion in Microbiology 2016, 31:227–234

groups of species can function under a larger number

of environmental conditions, an idea known as the spatialinsurance hypothesis [23]. This is because redundancy

‘insures’ ecosystems against fluctuations in the abun-

dance of any individual species, thereby allowing diver-

sity to improve the global health of ecosystems. However,

testing this hypothesis requires community structure

information in high-replication at the single patch level,

which is currently difficult to obtain.

Inverting the problem: from patterns tointeractions.To infer interaction networks from -omics data, various

network reconstruction algorithms have been proposed.

These include model-independent methods (e.g., species

correlations across samples), and model-dependent meth-

ods (e.g., fitting data to a Lotka–Volterra model)

[7,8,10,26–28]. Correlation-based methods assume that

positive interactions increase the likelihood that interact-

ing species mix or fluctuate in a correlated fashion,

whereas negative interactions increase the likelihood

that species segregate spatially or fluctuate in an anti-

correlated fashion. Thus, these methods infer the sign and

strength of ecological associations by exploiting the pat-

terns of spatial segregation and mixing described earlier.

However, these inference methods are limited by the fact

that interaction-derived spatial patterns typically have

characteristic length scales that are much smaller than

traditionally sampling scales. In particular, standard

coarse-grained taxonomic surveys collect community data

at ‘bucket’ scales, averaging over thousands of locally

assembled communities. No matter how sophisticated

the inference algorithm may be, the quality of the pre-

dictions can only be as good as the input data. For this

reason, taxon-taxon interactions inferred from coarse-

grained samples most likely capture similar responses

to abiotic parameters (e.g., temperature or pH), rather

than biotic interactions. This problem is likely pervasive

over a wide range of ecosystems. However, solving the

problem is not easy. It requires (i) sampling at the local

patch scale with high replication and (ii) differentiating

interaction-driven community structure patterns from

those created by abiotic physiochemical factors.

Characterizing spatial structure in naturalmicrobial communities in high throughputwith micron-scale resolutionSeveral methods exist that allow us to visualize ‘who sits

next to whom’ in complex natural communities. The

most well known among these is fluorescence in situhybridization (FISH), in which fluorescent probes bind

to specific microbial sequences and are visualized via

microscopy. Combining FISH-based techniques with

many modes of microscopy, researchers have character-

ized the microscale spatial structure of microbial commu-

nities in a diverse range of ecosystems, including the oral

www.sciencedirect.com

Page 5: Microbial interactions and community assembly at microscales · Microbial interactions and community assembly at microscales Otto X Cordero1,2 and Manoshi S Datta2 In most environments,

Microscale spatial structure and microbial interactions Cordero and Datta 231

Figure 2

(a) antagonism (b) mutualism (c) network of possible interactions (pre-assembly)

stable LK model unstable LK model

metastable segregation stable mixing

assembled community

network of realized interactions (post-assembly)

spec

ies

2

species 1 species 1sp

ecie

s 2

Current Opinion in Microbiology

y

interactions (post-assembly)

assembled commun

network of realized

nity

int

-

--

-

--

+

-

--

+

+

+

+

Ecological stability and community assembly. (a) A robust result of mathematical models of interacting populations is that interference competition

by antagonistic interactions creates bistability, where one population outcompetes the other depending on initial conditions. This is shown in the

upper panel, which depicts the phase space of a Lotka–Volterra (LK) model with interference competition. However, in stochastic cellular

automata simulations where initial populations are randomly initialized at 50:50 ratios (lower panel) the same type of interactions lead to the

emergence of large segregated patches dominated by one species or the other. (b) Mutualistic interactions lead to stable coexistence. Upper

panel shows the result of a Lotka–Volterra model with mutualistic interactions. In stochastic cellular automata (lower panel) mutualisms manifests

as strong mixing between species. (c) Extending these ideas to larger networks of potential interactions, antagonisms can create patterns of

exclusion that segregate locally assembled communities across patches, provided that the scale of the patch is comparable to the length scale

over which antagonistic effects manifest themselves. Thus, positive interactions like metabolic complementation should be more frequent within a

patch than expected from null models without spatial structure.

microbiome [29��], the mammalian intestine [30], in soil

[31], and on marine snow [32]. Furthermore, FISH has

been combined with mass spectrometry-based techni-

ques, including nanoscale secondary ion mass spectrom-

etry (NanoSIMS), to identify the metabolic roles of

individual cells within complex microbial consortia, rang-

ing from those living on symbiont-bearing coral polyps

[33] to mouse intestines [34]. Recently, FISH was used

with NanoSIMS to identify a syntrophic coupling based

upon direct electron transfers between methane-oxidiz-

ing archaea and sulfate-reducing bacteria in anoxic ma-

rine sediments [13]. Overall, using these techniques, we

can characterize the physical structure of a microbial

community at the microscale and with single-cell resolu-

tion.

Increasing replication with synthetic systems

Using these imaging techniques, a natural next step is

to characterize the statistical properties of individual

microscale communities in high-throughput, including

robust patterns of taxon co-occurrence and divergence

www.sciencedirect.com

to alternative states. Such an increase in throughput may

be technologically feasible, particularly with spectral

FISH methods [29��]. However, to interpret results in

light of underlying ecological interactions, we need to

control for variability in patch composition and historical

contingencies explicitly. In the ideal case, each patch

would be identical in physiochemical composition and

life history, but this type of controlled, highly replicated

patch structure does not exist in most natural ecosystems.

Therefore, synthetic or semi-synthetic laboratory sys-

tems, in which patch properties are tightly controlled,

can complement studies of naturally occurring microbial

communities.

Biologically inspired microfluidic systems, including soil-

on-a-chip [35], gut-on-a-chip [36,37], and coral-on-a-chip

[38], have been used successfully to study the microscale

structure and dynamics of microbial communities. These

systems provide precise control of the patch microhabitat

(e.g., nutrient concentration, temperature, pH, and fluid

flow), thereby producing defined patches for microbes to

Current Opinion in Microbiology 2016, 31:227–234

Page 6: Microbial interactions and community assembly at microscales · Microbial interactions and community assembly at microscales Otto X Cordero1,2 and Manoshi S Datta2 In most environments,

232 Environmental microbiology

Figure 3

(a) (b)

Community assemblyon synthetic particles

particles

taxa

taxa

taxaalginate bead magnetic cores

marine bacteria

20 µm

alternativestates

coordinate 1

coor

dina

te 2

Current Opinion in Microbiology

Sampling microscale communities with synthetic particles. (a) We have developed the use of synthetic particles as community scaffolds to study

microbial community structure and dynamics at the microscales where microbial interactions have the most significant impact on population

dynamics [39��]. Microbial communities self-assemble on particles and are then sampled and sorted to reconstruct the spatial distribution of taxa

and genes across micro-patches. This information can be used as input to network reconstruction algorithms, to get more accurate predictions of

interactions, and to measure the probability distribution over possible communities and identify alternative states. (b) Example of a colonized

alginate particle with magnetic cores. The particle was colonized by bacteria from the coastal ocean (Nahant Beach, MA), in incubation with

untreated seawater with overhead rotation over a period of 24 hours. Green patches correspond to biofilms stained with Syto9.

colonize. Furthermore, the colonization process can be

visualized in real-time by coupling microfluidic devices

with microscopy [35–38]. For example, a recent study

used a coral-on-a-chip system to visualize the dynamical

process by which a coral polyp is infected by a known

coral pathogen (Vibrio coralliilyticus) with a level of spa-

tiotemporal resolution that could not be achieved in a

natural marine ecosystem [38]. Altogether, microfluidic

devices are a powerful addition to the microbial ecology

toolbox, but to date, have not been extended to microbial

communities as diverse as those found in nature.

In our lab, we have developed a complementary approach,

which allows us to study community assembly on defined

nutrient patches, starting from a complex microbial mi-

lieu. We use nutrient-rich synthetic particles as spatially

isolated, chemically defined scaffolds for microbial com-

munities [39��]. We immerse these particles in diverse,

naturally occurring microbial assemblages (coastal seawa-

ter, sediments, soil samples, etc.), and over several days,

microbial communities self-assemble on these particle

scaffolds (Figure 3). By controlling the patch size and

composition, as well as the pool of potential colonizers,

this approach allows us to analyze many individual com-

munities as discrete entities, each of which is a self-

organized replicate from the same pool of colonizers. This

model system offers a new way to broach the question of

‘who tends to co-occur with whom’ at scales of 10–100 mm, a first step towards reconstructing interactions

between taxa in a complex community. By comparing the

microbial communities associated with many individual

particles, we hope to identify robust statistical associations

between taxa across many replicate communities, to

Current Opinion in Microbiology 2016, 31:227–234

probe the space of possible communities, and potentially

identify alternative stable states.

ConclusionsHere, we have highlighted the interplay between micro-

bial interactions and microscale spatial community assem-

bly. Studying the two phenomena in conjunction will

enable us to understand the potential for functional

complementation, as well as to improve inferences of

microbial interaction networks. Ultimately, however, the

challenge will be to understand how local interactions

influence ecosystem processes — for example, the rates

of biomass production and substrate turnover. This is

particularly challenging for non-trophic interactions such

as public good exploitation. Addressing this problem will

require a combination of biophysical modeling and con-

trolled experiments in the lab.

The study of locally assembled communities as discrete

entities with high replication has the potential to enable

the discovery of alternative community states driven by

ecological interactions and not by abiotic factors. Ulti-

mately, we hope to assess the robustness of microscale

community assembly, including if shifts in individual taxa

can move communities towards alternative states. In the

future, this knowledge could be used to control and even

engineer microbial communities.

Acknowledgments

MSD was supported by the Department of Defense through the NationalDefense Science and Engineering Graduate (NDSEG) FellowshipProgram. The authors also wish to thank Tim Enke and Nathan Cermak forthe particle image in Figure 3.

www.sciencedirect.com

Page 7: Microbial interactions and community assembly at microscales · Microbial interactions and community assembly at microscales Otto X Cordero1,2 and Manoshi S Datta2 In most environments,

Microscale spatial structure and microbial interactions Cordero and Datta 233

References and recommended readingPapers of particular interest, published within the period of review,

have been highlighted as:

� of special interest�� of outstanding interest

1. Cranford S, Buehler MJ: Materiomics: biological proteinmaterials, from nano to macro. Nanotechnol Sci Appl 2010,3:127-148.

2. Stachowicz JJ: Mutualism, facilitation, and the structure ofecological communities. Bioscience 2001, 51:235.

3. McCann KS: The diversity-stability debate. Nature 2000,405:228-233.

4. Cook-Patton SC, LaForgia M, Parker JD: Positive interactionsbetween herbivores and plant diversity shape forestregeneration. Proc Biol Sci 2014, 281:20140261.

5. Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D,Raes J, Huttenhower C: Microbial co-occurrence relationshipsin the human microbiome. PLoS Comput Biol 2012,8:e1002606.

6. Barberan A, Bates ST, Casamayor EO, Fierer N: Using networkanalysis to explore co-occurrence patterns in soil microbialcommunities. ISME J 2012, 6:343-351.

7. Faust K, Raes J: Microbial interactions: from networks tomodels. Nat Rev Microbiol 2012, 10:538-550.

8. Kurtz ZD, Muller CL, Miraldi ER, Littman DR, Blaser MJ,Bonneau RA: Sparse and compositionally robust inference ofmicrobial ecological networks. PLoS Comput Biol 2015,11:e1004226.

9. Berry D, Widder S: Deciphering microbial interactions anddetecting keystone species with co-occurrence networks.Front Microbiol 2014, 5:219.

10. Ma B, Wang H, Dsouza M, Lou J, He Y, Dai Z, Brookes PC, Xu J,Gilbert JA: Geographic patterns of co-occurrence networktopological features for soil microbiota at continental scale ineastern China. ISME J 2016.

11. Chaffron S, Rehrauer H, Pernthaler J, von Mering C: A globalnetwork of coexisting microbes from environmental andwhole-genome sequence data. Genome Res 2010, 20:947-959.

12. Guidi L, Chaffron S, Bittner L, Eveillard D, Larhlimi A, Roux S,Darzi Y, Audic S, Berline L, Brum J et al.: Plankton networksdriving carbon export in the oligotrophic ocean. Nature 2016.

13. McGlynn SE, Chadwick GL, Kempes CP, Orphan VJ: Single cellactivity reveals direct electron transfer in methanotrophicconsortia. Nature 2015, 526:531-535.

14. Morris JJ, Lenski RE, Zinser ER: The Black Queen Hypothesis:evolution of dependencies through adaptive gene loss. MBio2012:3.

15. Mee MT, Collins JJ, Church GM, Wang HH: Syntrophic exchangein synthetic microbial communities. Proc Natl Acad Sci U S A2014, 111:E2149-E5656.

16. Mee MT, Wang HH: Engineering ecosystems and syntheticecologies. Mol Biosyst 2012, 8:2470-2483.

17.��

Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P,Patil KR: Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl AcadSci 2015, 112 201421834.

A systematic survey of potential for metabolic exchange in over 800 com-munities shows that metabolic complementarities are common in naturalcommunities and can provide an advantage in nutrient-poor conditions.

18.��

Embree M, Liu JK, Al-Bassam MM, Zengler K: Networks ofenergetic and metabolic interactions define dynamics inmicrobial communities. Proc Natl Acad Sci 2015, 112201506034.

Demonstration that amino acid auxotrophies arise in methanogenicconsortia, creating a layer of non-trophic interactions on top of the energyflow network.

www.sciencedirect.com

19. Bascompte J: Ecology. Structure and dynamics of ecologicalnetworks. Science 2010, 329:765-766.

20. Rohr RP, Saavedra S, Bascompte J: Ecological networks. Onthe structural stability of mutualistic systems. Science 2014,345 1253497.

21. Fukami T: Historical contingency in community assembly:integrating niches, species pools, and priority effects. AnnuRev Ecol 2015 http://dx.doi.org/10.1146/annurev-ecolsys-110411-160340.

22.�

Perez-Gutierrez R-A, Lopez-Ramırez V, Islas A, Alcaraz LD,Hernandez-Gonzalez I, Olivera BCL, Santillan M, Eguiarte LE,Souza V, Travisano M et al.: Antagonism influences assembly ofa Bacillus guild in a local community and is depicted as a food-chain network. ISME J 2013, 7:487-497.

Demonstrates that chemical antagonism is less common among Bacillusstrains sampled from the same cubic centimeter of soil, than among thosesampled from distant locations.

23. Loreau M, Mouquet N, Gonzalez A: Biodiversity as spatialinsurance in heterogeneous landscapes. Proc Natl Acad Sci U SA 2003, 100:12765-12770.

24. Figge F: Bio-folio: applying portfolio theory to biodiversity.Biodivers Conserv 2004, 13:827-849.

25. Schindler DE, Hilborn R, Chasco B, Boatright CP, Quinn TP,Rogers LA, Webster MS: Population diversity and the portfolioeffect in an exploited species. Nature 2010, 465:609-612.

26. Stein RR, Bucci V, Toussaint NC, Buffie CG, Ratsch G, Pamer EG,Sander C, Xavier JB: Ecological modeling from time-seriesinference: insight into dynamics and stability of intestinalmicrobiota. PLoS Comput Biol 2013, 9:e1003388.

27. Faust K, Lahti L, Gonze D, de Vos WM, Raes J: Metagenomicsmeets time series analysis: unraveling microbial communitydynamics. Curr Opin Microbiol 2015, 25:56-66.

28. Steele JA, Countway PD, Xia L, Vigil PD, Beman JM, Kim DY, ChowC-ET, Sachdeva R, Jones AC, Schwalbach MS et al.: Marinebacterial, archaeal and protistan association networks revealecological linkages. ISME J 2011, 5:1414-1425.

29.��

Mark Welch JL, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG:Biogeography of a human oral microbiome at the micronscale. Proc Natl Acad Sci 2016.

Detailed analaysis of the complex spatial structure in human dentalplaque using spectral fluoresence in situ hybridization. This techniqueoffers tremendous opportunities to systematically measure microscalespatial statistics in microbial communities.

30. Earle KA, Billings G, Sigal M, Lichtman JS, Hansson GC, Elias JE,Amieva MR, Huang KC, Sonnenburg JL: Quantitative imaging ofgut microbiota spatial organization. Cell Host Microbe 2015,18:478-488.

31. Berlemont R, Martiny AC: Phylogenetic distribution of potentialcellulases in bacteria. Appl Environ Microbiol 2013, 79:1545-1554.

32. Thiele S, Fuchs BM, Amann R, Iversen MH: Colonization in thephotic zone and subsequent changes during sinkingdetermine bacterial community composition in marine snow.Appl Environ Microbiol 2015, 81:1463-1471.

33. Wangpraseurt D, Pernice M, Guagliardo P, Kilburn MR, Clode PL,Polerecky L, Kuhl M: Light microenvironment and single-cellgradients of carbon fixation in tissues of symbiont-bearingcorals. ISME J 2016, 10:788-792.

34. Berry D, Stecher B, Schintlmeister A, Reichert J, Brugiroux S,Wild B, Wanek W, Richter A, Rauch I, Decker T et al.: Host-compound foraging by intestinal microbiota revealed bysingle-cell stable isotope probing. Proc Natl Acad Sci 2013,110:4720-4725.

35. Stanley CE, Grossmann G, Casadevall i Solvas X, deMello AJ:Soil-on-a-chip: microfluidic platforms for environmentalorganismal studies. Lab Chip 2016, 16:228-241.

36. Kim HJ, Li H, Collins JJ, Ingber DE: Contributions of microbiomeand mechanical deformation to intestinal bacterial

Current Opinion in Microbiology 2016, 31:227–234

Page 8: Microbial interactions and community assembly at microscales · Microbial interactions and community assembly at microscales Otto X Cordero1,2 and Manoshi S Datta2 In most environments,

234 Environmental microbiology

overgrowth and inflammation in a human gut-on-a-chip. ProcNatl Acad Sci 2015 http://dx.doi.org/10.1073/pnas.1522193112.

37. Bhatia SN, Ingber DE: Microfluidic organs-on-chips. NatBiotechnol 2014, 32:760-772.

38. Shapiro OH, Kramarsky-Winter E, Gavish AR, Stocker R, Vardi A: Acoral-on-a-chip microfluidic platform enabling live-imagingmicroscopy of reef-building corals. Nat Commun 2016,7:10860.

39.��

Datta MS, Sliwerska E, Gore J, Polz M, Cordero OX: MicrobialInteractions Lead to Rapid Microscale Successions on ModelMarine Particles. 2016:. [in revision].

Study with synthetic community scaffolds analogous to marine particlesshows that particle colonization dynamics in the ocean follows primarysuccession controlled by public good production and motility.

40. Kirchman DL: Microbial Ecology of the Oceans. John Wiley & Sons;2010.

41. Bar-Zeev E, Berman-Frank I, Girshevitz O, Berman T: Revisedparadigm of aquatic biofilm formation facilitated by microgeltransparent exopolymer particles. Proc Natl Acad Sci U S A2012, 109:9119-9124.

42. Wilbanks EG, Jaekel U, Salman V, Humphrey PT, Eisen JA,Facciotti MT, Buckley DH, Zinder SH, Druschel GK, Fike DA et al.:Microscale sulfur cycling in the phototrophic pink berryconsortia of the Sippewissett Salt Marsh. Environ Microbiol2014, 16:3398-3415.

43. O’Donnell AG, Young IM, Rushton SP, Shirley MD, Crawford JW:Visualization, modelling and prediction in soil microbiology.Nat Rev Microbiol 2007, 5:689-699.

44. Franklin R, Mills A: The Spatial Distribution of Microbes in theEnvironment. Springer Science & Business Media; 2007.

45. Raynaud X, Nunan N: Spatial ecology of bacteria at themicroscale in soil. PLOS ONE 2014, 9 e87217.

46. Esser DS, Leveau JHJ, Meyer KM, Wiegand K: Spatial scales ofinteractions among bacteria and between bacteria and theleaf surface. FEMS Microbiol Ecol 2015, 91 fiu034.

Current Opinion in Microbiology 2016, 31:227–234

47. Donaldson GP, Lee SM, Mazmanian SK: Gut biogeography of thebacterial microbiota. Nat Rev Microbiol 2015, 14:20-32.

48. Walker AW, Duncan SH, Harmsen HJM, Holtrop G,Welling GW, Flint HJ: The species composition of the humanintestinal microbiota differs between particle-associatedand liquid phase communities. Environ Microbiol 2008,10:3275-3283.

49. Van Wey AS, Cookson AL, Roy NC, McNabb WC, Soboleva TK,Shorten PR: Bacterial biofilms associated with food particlesin the human large bowel. Mol Nutr Food Res 2011, 55:969-978.

50. Gonzalez-Gil G, Holliger C: Aerobic granules: microbiallandscape and architecture, stages, and practicalimplications. Appl Environ Microbiol 2014, 80:3433-3441.

51. Azam F, Long RA: Sea snow microcosms. Nature 2001,414(495):497-498.

52. Yawata Y, Cordero OX, Menolascina F, Hehemann J-H, Polz MF,Stocker R: Competition-dispersal tradeoff ecologicallydifferentiates recently speciated marine bacterioplanktonpopulations. Proc Natl Acad Sci U S A 2014, 111:5622-5627.

53. Gram L, Grossart H-P, Schlingloff A, Kiørboe T: Possiblequorum sensing in marine snow bacteria: production ofacylated homoserine lactones by Roseobacter strainsisolated from marine snow. Appl Environ Microbiol 2002,68:4111-4116.

54. Hmelo LR, Mincer TJ, Van Mooy BAS: Possible influence ofbacterial quorum sensing on the hydrolysis of sinkingparticulate organic carbon in marine environments. EnvironMicrobiol Rep 2011, 3:682-688.

55. Long RA, Azam F: Antagonistic interactions among marinepelagic bacteria. Appl Environ Microbiol 2001, 67:4975-4983.

56. Cordero OX, Ventouras L-A, DeLong EF, Polz MF: Public gooddynamics drive evolution of iron acquisition strategies innatural bacterioplankton populations. Proc Natl Acad Sci U S A2012, 109:20059-20064.

www.sciencedirect.com