# Application of Ecological Network Theory to  the  Human Microbiome

Authors: James A. Foster; Stephen M. Krone; Larry J. Forney
Journal: Interdisciplinary Perspectives on Infectious Diseases
 (2008)

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## Abstract

In healthy humans, many microbial consortia constitute rich ecosystems with dozens to hundreds of species, finely tuned to functions relevant to human health. Medical interventions, lifestyle changes, and the normal rhythms of life sometimes upset the balance in microbial ecosystems, facilitating pathogen invasions or causing other clinically relevant problems.  Some diseases, such as bacterial vaginosis, have exactly this sort of community etiology. Mathematical network theory is ideal for studying the ecological networks of interacting species that comprise the human microbiome. Theoretical networks require little consortia specific data to provide insight into both normal and disturbed microbial community functions, but it is easy to incorporate additional empirical data as it becomes available.  We argue that understanding some diseases, such as bacterial vaginosis, requires a shift of focus from individual bacteria to (mathematical) networks of interacting populations, and that known emergent properties of these networks will provide insights that would be otherwise elusive.


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## Body

## 1. Introduction

The
microbiota normally  associated  with the human body have an
important influence on human development, physiology, immunity, and nutrition
[1–6].  Also, communities of commensal and
mutualistic bacteria associated with the human body constitute the first line
of defense against infection by competitively excluding invasive nonindigenous
organisms that cause disease.  Yet despite their importance, surprisingly
little is known about the composition of resident communities, how they differ
between individual hosts or host environments, or such ecological relationships
of constituent members as trophic interdependencies.  Even so, human associated communities are
likely to resemble those found in other habitats in at least four fundamentally
important ways.  First, natural microbial
communities tend to be diverse in terms of species composition and
physiological potential.  Second, the
flow of energy and nutrients through the system follows basic principles of
microbial physiology, which results in the existence of trophic webs.  Third, nutritional interdependencies exist
wherein the “cross-feeding” of various vitamins, amino acids, and other
cofactors occurs.  And fourth, all
ecological niches are occupied resulting in a relatively stable community
composition.  Armed with this information
one can begin to postulate how external forces (e.g., invasive species such as
nonindigenous microorganisms and pathogens) or treatments (e.g., the administration
of antibiotics or changes in host diet) might affect the species composition
and function of microbial communities that constitute the human microbiome.Microbial
communities can be viewed as mathematical networks with structural features
that reflect how the networks developed and predict their responses to
perturbations.  In this paper, we will
introduce the basic mathematical foundations of networks and briefly summarize
some of their important structural properties.
This approach to understanding microbial communities of the human
microbiome is admittedly speculative, largely because of the lack of knowledge
about community composition and species interactions in the human microbiome.  Even so, it is based on a growing body of
research on evolving networks and may constitute a useful conceptual framework
for understanding how these communities help maintain human health and how
disturbances of the community structure and function could increase
susceptibility to infectious disease.  To
illustrate the importance of ecological networks in the human microbiome, we
will describe the biology of microbiota of the human vagina and how
disturbances to these communities may account for the clinical syndrome known
as bacterial vaginosis.


## 2. Mutualistic Relationships of the Vaginal Microbiome

The human vagina and the bacterial communities that reside
therein, form a finely balanced mutualistic association.  Previous studies indicate that indigenous
bacterial populations play a key role in preventing colonization by
“undesirable” organisms, including those responsible for bacterial vaginosis,
yeast infections, sexually transmitted diseases, and urinary tract infections
[7–12].  Historically, lactobacilli have been thought
to be the keystone species of vaginal communities in reproductive-age women, both in the sense of being the dominant species and in the
sense of being the species with the greatest impact on the vaginal ecosystem.  These microorganisms benefit the host by
producing lactic acid as a fermentation product that accumulates in the
environment and lowers the pH to ~4.5 [13]. While a wide range of other species
are known to be members of vaginal bacterial communities, their ecological
functions are largely unknown, as is the total number
of species present.  The host
provides benefit to the microbial communities by providing all the nutrients
needed to support bacterial growth.  This
is of obvious importance since bacteria are continually shed from the body in
vaginal secretions, and bacterial growth must occur to replenish their
numbers.  Some of the required nutrients
are derived from sloughed cells, while others are from glandular secretions.
Surprisingly, the precise composition and the concentrations of various
constituents are poorly understood, and this is an important knowledge gap.  Nonetheless, the data available indicate that
there are proteins and carbohydrates of various kinds in vaginal secretions, as
well as urea, K+, Na+, and, Cl− [14] and it seems likely that various amino
acids, peptides, and monosaccharides are also present.  The symbiotic relationships
between host and bacterial populations seem likely to be mutualisms, with each
species  benefiting from the presence of the other. (It should be noted that
bacterial populations of the human microbiome are often referred to as
commensal bacteria, which implies that only one member of the association
benefits while the other is unaffected.
In many cases, if not all, this is probably an incorrect
characterization of the ecological relationship between the two members.)


## 3. Etiology of Bacterial Vaginosis: A Disease Linked to Community Disturbances

Bacterial vaginosis (BV) is
a syndrome that is often characterized as a disturbed microbial community [15] although
it is most often diagnosed based on the occurrence of three of the following
four criteria: (a) homogeneous, white adherent vaginal discharge; (b) a vaginal
pH > 4.5; (c) detection of “clue cells” by microscopy; and (d) the presence
of an amine odor upon addition of KOH to vaginal secretions [16].  Intensive efforts to identify etiological
agents have thus far been unsuccessful, and it has been suggested that the
disturbed communities themselves may account for the observed symptoms.BV has important
consequences for women’s health.  The
prevalence of BV among reproductive-age women ranges from 29% in U.S.
population-based surveys to over 50% in rural Ugandan villages [17]. It has
been associated with an increased risk of preterm delivery, first trimester
miscarriage in women undergoing in vitro fertilization, chorioamnionitis,
endometritis, and pelvic inflammatory disease (PID) [18–23].  Moreover, BV increases the risk of acquiring Neisseria gonorrhoeae and other sexually
transmitted diseases [11, 24] including HIV [8, 25].Historically,
BV has been associated with depleted numbers ofLactobacillus spp. and an elevated vaginal pH [26, 27]. However,
this simple view has been challenged [28] by recent findings that showed that
the vaginal communities of many normal and healthy Caucasian, black, and
Japanese women lack appreciable numbers of Lactobacillus spp., but instead include other taxa of lactic acid producing bacteria (LAB) [29, 30]. This has two important implications.
First, an important ecological benefit to the host—maintenance of a low vaginal pH—is conserved among individual women, although
the species composition of the microbial communities can vary.  This is consistent with the consensus
viewpoint that a low pH environment in the vagina is a key mechanism for
defending the host against potential pathogens.
And second, factors that alter the species composition, the
physiological activities of bacterial populations, or the overall community
function (reducing the pH of the local environment), could lead to the symptoms
associated with BV.Previous studies have
established that several distinct kinds of vaginal communities occur in
Caucasian and black women in North America [29, 30], and Japanese women in
Tokyo, Japan [Zhou, 2008; unpublished]. Since vaginal bacterial communities
differ in species composition [30–33], they are
likely to differ in how they respond to disturbances, and disruptions of
ecological equilibria may increase risk to invasion by infectious agents.  Conceptually this is important since vaginal
communities continually experience various kinds of chronic and acute
disturbances such as the use of antibiotics and hormonal contraceptives, sexual
intercourse, douching, menstruation, and many others.


## 4. Network Approaches to Understanding the Human Vaginal Microbiome

By analogy with microbial
communities in other ecosystems, we postulate that a complex food web exists
among member species of vaginal bacterial communities, and that various
populations occur in distinct trophic levels.
Given that the resource pool is diverse (as described above), it is
reasonable to project that the species composition, expressed physiological
traits, and kinds of nutritional interdependencies of vaginal bacterial
populations are strongly influenced by the kinds of nutrients available in the
vagina.  This implies that host
characteristics could be an important  “driver”
of microbial ecosystems, while the members of the microbial community are
stratified in such a way that one or more populations are primary consumers,
while others consume their metabolites, and so on.  The result is a “network” that reflects the
flow of energy and nutrients through the ecosystem in which the configuration
and strengths of ecological interactions determine the stability and resilience
of the community.  Such networks are
commonly referred to as microbial trophic webs (Figure1).Figure 1
A hypothetical trophic web with five species.
Species 1 and 2 are “grazers” at the bottom level, which acquire
nutrients directly from the environment and provide nutrients to species 3 and
5. Species 3, 4, and 5 form a dependent cycle, with 3 and 5 at the second level
of the web and 4 at the final level.In
dissimilatory microbial trophic webs a few species specialize in breaking down
larger, more complex organic molecules into smaller molecules [34,
page 102].  These specialists may require little
assistance from other species. There are likely to be more pathways (and
microbial species) able to metabolize these smaller molecules, and still other
species to consume the resulting metabolites.
If complete mineralization of carbon sources occurs, then carbon dioxide
is produced, but in the absence of suitable terminal electron acceptors,
fermentation products (such as lactic acid) accumulate in the environment.Some
populations in dissimilatory consortia may have secondary roles that regulate
the growth and function of other populations in the consortia.  For example, one population may produce
growth factors such as amino acids, peptides, or vitamins that are used, and
sometimes required, for other populations to grow.  Indeed, lactobacilli are notoriously
fastidious and have complex nutritional requirements [35–37]. This sort of
nutritional cross-feeding represents a “positive feedback loop.”  In contrast, various small molecules that
disrupt membrane function, antibiotics, and bacteriocidal proteins [38]
constitute “negative feedback loops.”
These positive and negative feedback loops play a role in governing the
size of different bacterial populations and their activities.  To understand such a complex network, one may
very well have to adopt a systems approach such as that described below [39].Since there
may be very few specialist species at the base of microbial trophic webs,
assembly rules may be strongly influenced by priority effects. A priority
effect [40, page 247] is the influence that one species exerts on whether another
can endure in an environment, simply by being there first.  Assembly rules describe the order in which
species tend to occupy habitats.  For
example, the first species to colonize a microbial ecosystem that specializes
in catabolizing the dominant nutrient or nutrients may determine which new
nutrients are then available, and thereby constrain which other species can
successfully colonize the habitat and persist.


## 5. Mathematical Representation of Networks

Microbial
trophic webs of the human microbiome are instances of a more general abstract
structure: mathematical networks.  In
ecology, trophic webs are typically visualized as nodes on a graph representing
individual species that are connected by directed edges that indicate who is
dependent on whom for nutrition. These webs are sometimes called “food webs,”
with a tacit assumption that the relationship is one of who eats whom.
Predatory-prey relationships exist at all scales of life. But both macro- and
microbial trophic relationships are much richer than predation alone. For
example, species interactions often involve cross-feeding, where each species
acquires nutrients, or   compounds that inhibit growth, that are produced by
other species. In microbial systems, these
indirect  products are molecular, while in macrobial systems they may be much
larger.Collections
of nodes and edges such as those used to visualize trophic webs are instances
of mathematical networks. One useful characteristic of this mathematical
abstraction is its general applicability.
Any collection of “individuals” and “relationships” can be expressed and
analyzed as a network, regardless of details about the individuals or the
relations.  In particular, networks are
not limited to trophic webs.The
simplest mathematical networks indicate only whether or not two nodes are
connected by an edge by setting the corresponding “adjacency” term to 1 or 0;
thus,ai,j is set to 1 if the ith individual is connected to the jth individual, and to 0 if they are not
connected.  These networks are often
summarized in an adjacency matrix A with the term ai,j appearing in the ith row and jth column.  These
connections are undirected when the matrix is symmetric, meaning that ai,j=aj,i  (visually, reflecting
the matrix across the main diagonal leaves it unchanged).  One can represent additional information
about the relation between two individuals by letting the matrix entries be
numbers other than 0 and 1 (Figure 2).
For example, an ecological network could correspond to a system of
Lotka-Volterra differential equations describing species interactions  dui/dt=ui(ri+∑jai,juj) where ri is the   intrinsic
growth rate of species i and ai,j is the “effect” of
species j on species i. Here, the interactions are described
by a matrix A = (ai,j) of real
numbers.  For example, if i is a prey species and j a predator, we would have ai,j<0  and aj,i>0
(Figure 3).  Food webs are special cases
of ecological networks in which the interactions are all of predator-prey type
with predators in one trophic level feeding on prey from a lower level (Figure
1).Mathematical network with undirected edges, representing the structure of the
trophic web in Figure1, pictorially and as an equivalent adjacency matrix. The
connectivity of the nodes is one for node 2, two for nodes 1 and 4, three for
node 3, and four for node 5 (which is a hub node).Directed
graph representing (hypothetical) strengths of species interactions and the
corresponding matrix of interaction strengths. Positive (negative) values
indicate increase (decrease) in receiving species’ fitness. Units of
interaction are unspecified in this example, but may be observed changes in
biomass. For example, species 1 may produce a metabolite beneficial to species3(a31=1.2), while 3
occasionally harms 1(a13=−0.3) while consuming the metabolite. Species 3
and 4 are competitors, 5 and 4 are mutualists, and other pairs resemble
predator/prey.It can
extremely difficult to obtain information about trophic interactions
(especially interaction strengths) in real ecological networks. However, it is
becoming easier to gather quantitative data for networks given advances in high
throughput sequencing technologies and sophisticated
computational biology algorithms. For example, as more annotated genomes become
available, it becomes easier to form hypotheses about potential metabolic
pathways. It is encouraging that genome annotation, comparative genomics, and
hypothetical pathway reconstruction are autocatalytic, each improving the
accuracy and efficiency of the others. With such positive feedback, we
anticipate that it will become increasingly easy to parameterize network models
accurately.Surprisingly,
however, one does not need accurate parameters in an abstract network, since
the network structure alone can tell one a great deal about the system that it
represents. A characteristic that matters in all networks is the number of
links or “connectedness” of each node, and this of course varies from one node
to another within a network [41]. For example, a property of many natural
networks is that they are “scale free,” roughly meaning that there is no single
degree of connectedness that is characteristic of the network.  In scale free networks, most nodes are
connected to a small number of other nodes, and a small number of nodes act as
“hubs” in that they are connected to many nodes.  A scale free network is usually robust to the
removal of randomly selected nodes but can be violently destabilized when hub
nodes are removed.  In a very real way,
these hubs are analogous to keystone species in biological ecosystems.  When the population size or activity of a
keystone species is changed, or the species is entirely removed, dramatic
changes occur in the varieties and population densities of all other species in
the community.It is even
possible to learn a great deal with neither accurate graph topologies nor
extensive empirical parameterization. Theoreticians construct artificial
networks with different types of assembly rules, essentially reverse
engineering the abstractions of natural networks.  This discipline has been
aptly termed the statistical mechanics of complex networks [42].Remarkably,
two informative properties consistently emerge from such simulations. First, in
both real and simulated ecological networks one finds a “many weak, few strong”
pattern in which most, but not all, species interactions are weak.
Specifically, the average interaction strength (average of|ai,j|’s) times
the square root of the average number of edges per node, often converges to a
constant over time [43–45]. A second
“emergent” property is that networks tend to evolve to the point where they are
at the brink of instability, being in some sense most productive when living on
the edge. Extinction events in an ecological network, either by “natural” means
or by artificially removing nodes, typically lead to occasional avalanches of
secondary extinctions [43, 46]. In fact, this is where the “many weak, few
strong” pattern comes from: extinctions of most species have minor effects,
while removal of those species that are strongly connected can destabilize the
entire ecosystem, resulting in a cascade of extinctions. This instability
essentially arises from “successful” interactions that form in the evolving
network through, for example, collaborative consortia. Such interdependencies in
collaborations can ultimately lead to instability, since disturbing any one
species in the consortium can affect many others.These
features are among the self-organizing principles that reveal themselves in
many natural and simulated networks. This suggests that the study of evolving
networks can enable one to predict microbial ecosystem behavior, even without
quantifying all the details of the interactions between species in a complex
ecological network. When studying the complex communities of the human
microbiome, where very little is known, this is a great advantage.The application of
theoretical network modeling to real ecological networks has thus far been
focused primarily on attempts to capture observed features of the
networks.  One of the reasons for the
rapid growth of network theory is the stunning regularity with which certain
course-grained “topological” properties emerge in real ecological (and social
and technological) networks. These properties, depending on global
characteristics of the network such as the number of links, connectance, and so
on, appear in such a wide variety of settings that it was natural to try to
come up with simple models that would produce the same features.  Thus, there appear both static and dynamic
models that reproduce some of the topological properties of real networks
[44, 45, 47, 48]. As one moves
to more fine-grained properties (e.g., degree distribution) or seeks to develop
predictive models, however, one must rely increasingly on dynamic models that
carry more details about the system. Most studies of real ecological networks
are restricted to food webs wherein all links between species are of the
predator-prey type.An example of how the
models are applied to the real networks is in trying to understand the
stability of an ecosystem to extinctions or other perturbations. Some models
predict stability or instability based on the connectivity of the network. For
example, the scale free property observed in many real food webs carries with
it a prediction of stability under removal/extinction of weakly connected
species but become highly unstable with avalanches of secondary extinctions
when one of the few highly connected species is removed. There are limitations,
however, to our current understanding since the stability analyses have been
rather restricted and the models lack some details that could play essential
roles.


## 6. Summary

We have argued that mathematical
networks provide a system-level
approach to characterizing microbes and microbial interactions, which may
improve descriptions of how consortia in the human microbiome are related to
disease etiology, diagnosis, and treatment. Networks may capture specific
biological information, such as how nutrients flow through the species in a
microbial consortium. Ecological principles applied to such microbiome-specific
networks are likely to constrain how the microbiome will respond to invasive
species or to purportedly benign disturbances such as antibiotic treatment.
Moreover, network structure sometimes suffices to indicate how a consortium is
likely to have evolved or to identify keystone species, even when interaction
strengths have not been quantified. This is particularly useful when detailed
data on the constituents and species interactions in a consortium are
unavailable. In short, for some human diseases such as bacterial vaginosis, it
may be more useful to examine the forest, rather than the trees.



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