Am J Physiol Lung Cell Mol Physiol 309: L1047–L1055, 2015.
First published October 2, 2015; doi:10.1152/ajplung.00279.2015.
Review
Homeostasis and its disruption in the lung microbiome
X Robert P. Dickson,1 John R. Erb-Downward,1 and Gary B. Huffnagle1,2
1
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical
School, Ann Arbor, Michigan; and 2Department of Microbiology and Immunology, University of Michigan Medical School,
Ann Arbor, Michigan
Submitted 11 August 2015; accepted in final form 29 September 2015
lung; ecology; physiology; 16S; culture-independent; microbial ecology; pulmonary; homeostasis; equilibrium
HOMEOSTASIS, the stability of a system via internal mechanisms
of self-regulation, resilient to external perturbation, is historically and philosophically central to the discipline of physiology. Claude Bernard’s (4) nineteenth century articulation of
the milieu intérieur (“the internal environment”) was inspired
by his observation that the blood glucose concentration of
healthy individuals remained remarkably stable whether in
fasting or feasting states:
The fixity of the milieu supposes a perfection of the organism such that the external variations are at each instant compensated for and equilibrated. . . all of the vital mechanisms,
however, varied they may be, have always one goal, to maintain the uniformity of the conditions of life in the internal
environment. . . the stability of the internal environment is the
condition for free and independent life.
Nearly a century and a half later, this “stability of the
internal environment”1 remains the defining concept of respiratory physiology. On the levels of the molecule, the cell, and
the organ system, our field studies how healthy lungs provide
unwavering stability in oxygen delivery, pH, pulmonary and
systemic arterial pressures, and countless other parameters
essential for “free and independent life.” Pathophysiology, in
turn, studies the disruption of homeostasis: the mechanisms by
1
Bernard’s selection of the word milieu (environment) is itself telling: had
the term “ecology” been in common use, he might have proposed an “ecology
of the internal environment.”
Address for reprint requests and other correspondence: R. P. Dickson,
Pulmonary & Critical Care Medicine, Univ. of Michigan Health System, 6301
MSRB III/SPC 5642, 1150 W. Medical Center Dr., Ann Arbor, MI 481095642 (e-mail: [email protected]).
http://www.ajplung.org
which our internal systems of self-regulation and compensation
are overwhelmed or become maladaptive.
If physiology studies homeostasis within organisms, ecology
studies homeostasis between organisms. Both seek to explain
how it is established, sustained, and eroded.2 Ecology is replete
with examples of stability of the external environment: order
emerging from the dynamic complexity of organism-organism
and organism-environment interactions. Across the world’s
oceans, the ratio of carbon to nitrogen to phosphorus in
seawater is a fixed 106:16:1 (the Redfield ratio), a ratio that
matches the atomic ratio of phytoplankton and remains constant despite the complex cycling of these elements between
the atmosphere, the oceans, and their organisms (46). The
Lotka-Volterra equations describe the periodic stability of
populations in predator-prey relationships (54) [such as, famously, the moose and wolves of Michigan’s Isle Royale
National Park (16)]. Ecology also offers its own examples of
disruption of homeostasis, either due to an external disturbance
[e.g., forest fire, volcanic eruption, or introduction of an invasive alien species (41)] or via destabilizing positive feedback
loops that propel explosive growth and collapse of communities (6) [e.g., the dramatic and sudden appearance of algal
blooms in water systems (51)].
In the past five years, these parallel concepts, physiological
and ecological homeostasis, have been bridged by the discovery of the lung microbiome. The long-held dogma that the
lungs are sterile in health has been debunked by the advent of
culture-independent techniques of microbial identification.
2
Another intellectual legacy of the nineteenth century that studies homeostasis is the field of economics, though economists prefer the terms static and
dynamic equilibrium. But that is a topic for another essay.
1040-0605/15 Copyright © 2015 the American Physiological Society
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Dickson RP, Erb-Downward JR, and Huffnagle GB. Homeostasis and its disruption
in the lung microbiome. Am J Physiol Lung Cell Mol Physiol 309: L1047–L1055, 2015.
First published October 2, 2015; doi:10.1152/ajplung.00279.2015.—The disciplines of
physiology and ecology are united by the shared centrality of the concept of homeostasis:
the stability of a complex system via internal mechanisms of self-regulation, resilient to
external perturbation. In the past decade, these fields of study have been bridged by the
discovery of the lung microbiome. The respiratory tract, long considered sterile, is in fact a
dynamic ecosystem of microbiota, intimately associated with the host inflammatory response, altered in disease states. If the microbiome is a “newly discovered organ,” ecology
is the language we use to explain how it establishes, maintains, and loses homeostasis. In this
essay, we review recent insights into the feedback mechanisms by which the lung microbiome and the host response are regulated in health and dysregulated in acute and chronic
lung disease. We propose three explanatory models supported by recent studies: the adapted
island model of lung biogeography, nutritional homeostasis at the host-microbiome interface, and interkingdom signaling and the community stress response.
Review
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HOMEOSTASIS AND THE LUNG MICROBIOME
Model 1: The Adapted Island Model of Lung Biogeography
Prior to the discovery of the lung microbiome, the microbial
composition of the lung was considered a binary phenomenon:
it contained either zero community members (in the “sterility”
of health) or one overwhelming community member (in pneumonia). [An exception to this, ahead of its time, was the
recognition of polymicrobial airway colonization in cystic
fibrosis (CF) (50)]. The implausible simplicity of this binary
model has been made apparent by more than 30 published
studies that have tested the lungs of healthy subjects with
modern molecular techniques of bacterial identification: none
has demonstrated the absence of bacterial signal.
Studies of the healthy lung microbiome have been quite
consistent in their key findings, across both study populations
and laboratories (3, 5, 8, 27, 39, 48). The lung microbiome, as
detected either by bronchoalveolar lavage (BAL) fluid or
protected specimen brushings (PSB), contains a bacterial burden that is greater than procedural control specimens and less
than that of the upper respiratory tract. The community members of the lung bacterial community more closely resemble
those of the mouth than other potential source communities
[e.g., the nose or a hematogenous source (3, 53)]; this observation holds regardless of whether the bronchoscope is inserted
nasally or orally (9, 15). The most abundant genera, mirroring
the mouth, are Prevotella, Veillonella, and Streptococcus.
Community richness (the number of species detected in each
specimen) of the lung is roughly half that of the mouth (3, 5).
Intrasubject variation in lung microbiota (sampled at different
sites within the lungs) is significantly less than that of intersubject variation (8): the microbiome of an individual’s right
middle lobe more closely resembles that of his/her left upper
lobe than it does his/her neighbor’s right middle lobe.
These facts, now thoroughly established, invite consideration of ecological homeostasis within the healthy respiratory
tract. The microbiome of the healthy lung exhibits the hallmark
of homeostasis: stability of the internal environment, order and
regularity within a complex and dynamic system of population
dynamics and host defenses. Some stabilizing process must
explain why the community richness and abundance of the
lung microbiome is consistently measured within a defined
range: greater than the background signal detected in laboratory reagents, less than that of the upper respiratory tract,
independent of the route of sampling (BAL or PSB, via the
mouth or via the nose). In articulating these mechanisms of
stability, we may draw inspiration from the principles and
seminal concepts of the field of ecology.
The lung microbiome, like any community (microbial or
otherwise), is determined by the balance of three factors:
immigration of community members, elimination/extinction of
community members, and the relative reproduction rates of
community members as determined by regional growth conditions (Fig. 1A). Any change in the community membership of
the lung microbiome, by first principles, must be due to some
combination of these three factors. Immigration to the microbiome is largely attributable to subclinical microaspiration,
which has been shown to be ubiquitous among healthy subjects
in multiple studies dating back to the 1920s (1, 24, 28, 45);
minor contributions to immigration may be attributable to
inhalation of air [which contains 104-106 bacterial cells per
cubic meter (32)] and direct contiguous spread of reproducing
microbial colonies. The mechanisms of microbial elimination
are familiar to respiratory physiologists: cough, the mucociliary escalator, and innate and adaptive immune defenses. Finally, the ecological factors that define regional growth conditions and relative reproduction rates of lung microbiota are the
physiological parameters of the lungs’ internal environment:
oxygen tension, pH, relative blood perfusion, relative alveolar
ventilation, temperature, and the concentration and activation
of host inflammatory cells. All of these physiological factors
have profound in vitro and in vivo effects on the relative
growth of bacteria; the growth of a given bacterial species is
selectively advantaged or disadvantaged by a given constellation of oxygen, temperature, pH, etc.
A key initial question in the field, now convincingly and
consistently answered by a variety of approaches, is to what
degree the lung microbiome is determined by the dynamic
turnover of immigration and elimination of microbiota, and
how much by the relative reproduction rates of resident microbiota due to differential selective pressures. The answer to this
question, now robustly supported by a variety of studies, study
populations, and analytic approaches, is that the ecological
determinants of the lung microbiome shift as patients progress
from health (in which immigration and elimination are the
dominant ecological factors) to severe lung disease (in which
regional growth conditions are determinant) (Fig. 1A).
One revealing approach has been the application of the
neutral model of biodiversity (39, 53). This model postulates
that the composition of a community (in this case the lung
microbiome) is determined by unbiased, “neutral” dispersal
from source communities, rather than by the selective pressures of local environmental conditions. Rejection of the neutral model (that is, demonstration that community composition
cannot be explained by unbiased dispersal) is evidence that a
community is shaped by differences in the relative growth rates
of its members. In a comprehensive analysis of the lung
microbiome compared with the microbiota of 15 other body
sites, Venkataraman et al. (53) found that the composition of
the lung microbiome in health was best explained by neutral
dispersal from the oropharynx. In a separate study, Morris et al.
(39) used the neutral model to directly compare upper and
lower respiratory tract microbiota in healthy volunteers. Al-
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Dozens of studies have confirmed that our “internal environment” is in fact the “external environment” for a diverse and
dynamic community of microbiota, present in health and altered in disease (10, 15). This revolution in respiratory microbiology demands a reassessment of the concept of homeostasis
within the human respiratory tract. Our new conceptual models
of respiratory homeostasis must span the host-microbe divide,
incorporating concepts of both physiology and ecology to
explain how we, and our respiratory microbiota, maintain and
lose the stability of this environment in health and illness. In
this essay, we propose three models of respiratory homeostasis,
all supported by recent experimental studies of the lung microbiome. We describe self-regulating systems at the hostmicrobiome interface that determine the number of species
present and the bacterial burden in the healthy human lung; we
also propose how this homeostasis is disrupted by self-propelling positive feedback loops resulting in the dysregulated
inflammation and injury of pneumonia and the acute respiratory distress syndrome (ARDS).
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HOMEOSTASIS AND THE LUNG MICROBIOME
B
REGIONAL
GROWTH
CONDITIONS
IMMIGRATION
ELIMINATION
AND
HEALTH
SEVERE LUNG
DISEASE
Immigration and Extinction Rates
A
Hi
gh
Im
m
ig
ra
tio
n
Ra
Hi
te
Low
Imm
igrati
on R
gh
Ex
tin
Low
ate
y
x
Numberof Bacterial Species Present
C
Supraglottic Space
Right Upper Lobe
Right Middle Lobe
Supraglottic Space: proximal
Right Upper Lobe: intermediate
Right Middle Lobe: distal
R
though the authors identified some minor community members
disproportionately represented in the lung compared with the
mouth [e.g., a Tropheryma species that comprises around 1%
of lung bacteria in health (33)], the vast majority of lung
community members were found in relative abundances consistent with neutral dispersal from the mouth.
A complementary approach to this key ecological question is
the study of spatial variation in the healthy human lung
microbiome (8). Significant spatial variation exists within
healthy lungs in physiological parameters of unambiguous
importance to bacterial growth [e.g., oxygen tension, pH,
temperature (11, 29, 55)]. If the healthy lung microbiome were
determined by selective pressures of local growth conditions
on resident bacteria, we should expect significant spatial variation in microbiota (e.g., enrichment of aerobic bacteria in the
oxygen-rich apices). Yet in a recently published study of
healthy volunteers, when we compared the microbiota detected
at four lung sites as well as the supraglottic space (representing
the source community of the upper respiratory tract), we found
little evidence of spatial variation: there is no “left upper lobe
microbiome” consistently distinct from that of the “right middle lobe microbiome.” Intrasubject variation in microbiota was
significantly less than intersubject variation, consistent with the
conclusion that the healthy human lung microbiome is relatively homogenous and largely determined by immigration
from the primary source community of the mouth rather than
local growth conditions.
n
Ra
te
ction
Extin
Rate
Fig. 1. The adapted island model of lung biogeography. The community of the lung microbiome is determined by 3 ecological factors (A):
immigration, elimination, and the effects of regional growth conditions on the reproduction
rates of community members. In health, the
community is primarily determined by the balance of immigration (from microaspiration) and
elimination (via cough, mucociliary clearance,
and immune defenses). The number of species
present at a given site in the respiratory tract in
health is a steady state of dynamic equilibrium,
determined by the balance of immigration and
elimination factors, influenced by anatomical,
functional and clinical factors (B). In an experimental validation of this model (C), proximity
to the source community of the upper respiratory tract was associated with increased microbial immigration and increased community richness. A adapted from Ref. 15. B adapted from
Ref. 36 with permission of John Wiley & Sons
Ltd. C adapted from Ref. 8 with permission of
the American Thoracic Society. Lung drawing
adapted from original by Patrick J. Lynch, medical illustrator, and C. Carl Jaffe, M.D., cardiologist, via Creative Commons Attribution 2.5
license 2006 (http://goo.gl/xuJRCO).
L
This balance between the influence of immigration and
elimination and that of regional growth conditions, however,
shifts dramatically in advancing lung disease (Fig. 1A). Venkataraman et al. demonstrated that the neutral model fails to
explain the composition of the lung microbiome in diseased
states (e.g., CF and interstitial lung disease) (53), indicating
that the lung microbiota of these conditions are instead determined by selective pressure within the lung environment. The
microbiota of diseased lungs [e.g., chronic obstructive pulmonary disease (18); CF (25, 56)] exhibit considerable spatial
variation in microbiota, confirming both the presence of resident microbiota as well as significant spatial variation in the
environmental growth conditions of diseased lungs.3 The clinically familiar phenomenon of “colonization” supports this
ecological shift: culture-identified colonizers are representative
community members that are well adapted to the specific
environmental conditions of diseased lungs and airways.
We can now articulate the central question of ecological
homeostasis in the healthy lung microbiome: how, despite its
constant exposure to the external environment, is the healthy
lung microbiome kept in its state of relative stability? What
3
This spatial variation in microbial growth conditions of advanced lung
disease will be of no surprise to clinicians familiar with the spatial heterogeneity of CT scan findings within the lungs of patients across disease states (e.g.,
apical emphysema, the bibasilar parenchymal changes of idiopathic pulmonary
fibrosis, the patchwork consolidation of ARDS).
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Number of Species Present
Bacterial Sequences
Analyzed per Specimen
io
ct
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HOMEOSTASIS AND THE LUNG MICROBIOME
point and influencing the lung microbiome. The slope of the
immigration curve will be made steeper (more negative) by
factors that accelerate microbial immigration to the lungs:
close proximity to the larynx, laryngeal dysfunction, the presence of an endotracheal tube. The slope of the elimination
curve will be made more shallow (less positive) by factors that
decelerate microbial elimination: impaired cough reflex (as in
lung transplantation or sedation), impaired mucociliary clearance (as in CF or ciliary dyskinesia), or impaired innate or
adaptive immunity (as in states of immunosuppression). In two
recent studies of the microbiota of healthy subjects, we have
validated predictions made by this adapted island model of
lung biogeography.
In the first experiment, a study of 28 healthy volunteers, we
applied the model to the aerodigestive tract by comparing the
microbiota of the mouth with two sites: the lungs and the
stomach (3). Although the mouth is the primary microbial
source community of both the lung and gastric compartments,
the two sites differ markedly in the relative balance of their
ecological determinants. Whereas microbial immigration via
microaspiration to the lungs is modest, microbial immigration
to the stomach via swallowing is high (two liters of oropharyngeal secretions are swallowed each day). Whereas microbial elimination from the lungs is active and continuous (via
cough, mucociliary clearance, and host immune defenses),
microbial elimination from the stomach is passive and slow
(via the 3-h mechanical passage of gastric contents to the
duodenum). Thus our model would predict the lungs to have
the low community richness and bacterial burden of a low
immigration, high extinction site (Fig. 1B, point x); in turn, the
model predicts that the stomach should have the high community richness and bacterial burden of a high immigration, low
extinction site (Fig. 1B, point y). These predictions were
validated by the experimental results: the community richness
of BAL specimens was, on average, half that of gastric specimens. The bacterial burden (as measured by 16S rRNA gene
copies per 5 ml) was 10-fold higher in the stomach than in the
lung; the bacterial burden of oral specimens was in turn 10-fold
higher than that of the stomach. Thus, considered on the level
of the organ system, the adapted island model accurately
predicts the ecological features of the aerodigestive tract in
health. As with all bacterial DNA-based experiments, this
analysis does not distinguish between the living and the dead;
the gastric pH of 2 is inhospitable to most oral bacteria.
In a second experiment, performed on 15 healthy volunteers,
we tested whether the adapted island model can predict the
ecological features of microbial communities sampled at various sites within the respiratory tract (8). The adapted island
model would predict that in health, proximity to the source
community should be the primary determinant of community
features within the respiratory tree. As shown in Fig. 1C, the
results were entirely consistent with the predictions of the
model. The community richness of the supraglottic space
(representing the source community of the upper respiratory
tract) was higher than that of intrapulmonary sites, just as
within MacArthur and Wilson’s model the community richness
of New Guinea is greater than that of its satellite islands.
Within the lungs, the right upper lobe had a community
richness greater than that of the right middle lobe, which is
more distal from the source community by the length of the
bronchus intermedius. This spatial relationship was echoed in
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stabilizing process ensures the consistency of bacterial community composition, diversity, and burden observed across
studies and populations? Finally, and critically, how does
perturbation of this stabilizing process (either by disease or
clinical intervention) influence the lung microbiome? We may
draw clarification and inspiration from one of ecology’s seminal models: MacArthur and Wilson’s equilibrium model of
island biogeography (11, 36), first proposed in 1963.
MacArthur and Wilson’s model arose from two key observations regarding the species richness (the number of plant and
animal species) of islands located in the tropical Pacific region
of Oceania. Firstly, they noted that the number of species
present on a given island was inversely correlated with its
distance from New Guinea, the nearest large land mass and
each island’s primary source community of immigrating species. They inferred that each island’s proximity to this large
land mass was the primary determinant of its immigration rate.
Secondly, they observed that the number of species present on
an island was directly correlated with its size; they inferred
from this that each island’s size was the primary determinant of
its extinction rate. The species richness for any given island (in
the absence of local selective pressures on reproduction rates)
is determined by the balance of these rates of immigration and
extinction (Fig. 1B).
Reflection on Fig. 1B reveals the utility of MacArthur and
Wilson’s elegant conceptual model. The slope of the curve of
immigration as a function of species richness is negative: with
each added species there is one less potential colonist from the
source community that has the potential to immigrate. The
slope of the curve of elimination as a function of species
richness is instead positive: with each additional species there
is one more potential species that can potentially be eliminated.
Changes in immigration and elimination rates alter the relative
slopes of these curves and their corresponding intersections. A
small island (with an accordingly high extinction rate), far
from the mainland (with an accordingly low immigration rate)
would be expected to have low species richness (Fig. 1B, point
x). Conversely, a large island close to the mainland would be
expected to have high species richness (Fig. 1B, point y). For
each island there exists an equilibrium point, a dynamic steady
state to which, if perturbed, the system returns. Although the
species richness of a given island is stable once it has reached
equilibrium, the constant turnover of newly immigrated and
eliminated species ensures that the population itself is dynamic.
This model serves as a rich and useful analogy for the
population dynamics of the healthy lung microbiome. Whereas
the islands of Oceania are populated by immigrating species
from New Guinea, the human respiratory tract is populated by
continuous immigration of microbiota from the primary source
community of the mouth. For any given anatomical site in the
airways or alveoli, the microbial population is a function of the
relative rates of immigration and elimination (Fig. 1, A and B).
Although the average bacterial burden and community richness
of healthy lungs is stable and consistent across studies, this
stability reflects the dynamic equilibrium created by these
balanced, opposing ecological forces of immigration and elimination.
This model readily permits predictions regarding how disease states and clinical interventions will alter the slopes of
immigration and elimination curves, shifting the equilibrium
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HOMEOSTASIS AND THE LUNG MICROBIOME
A
BAL Protein Content
(l/ml)
800
Nutrientpoor
B
Nutrient-rich
A
+
600
Bacterial
growth
-
-
Local
inflammation
400
B
200
0
Healthy
Subjects
ARDS
Alveolar
Pneumonia
Bronchial
Pneumonia
E
Nutrient
abundance
+
+
Endothelial and
epithelial injury
+
+
D
Intraalveolar
edema
C
other important ecological parameters: in community membership, relative abundance of the Firmicutes phylum, and degree
of intersubject variation, the right upper lobe more closely
resembled the upper respiratory tract than did sites more
remote from their common microbial source community.
This adapted island model of ecological homeostasis is now
thus supported empirically by multiple analyses of two distinct
studies of healthy volunteers. Looking forward, this model
provides a conceptual framework for coherent hypothesis testing. For a given perturbation, either pathological (e.g., laryngeal dysfunction) or therapeutic (e.g., inhaled corticosteroids),
we may predict the effect on the slopes of the immigration and
elimination curves. The intersection of these curves, in turn,
predicts the dynamic steady state for the microbiota present in
given patient’s respiratory tract.
Model 2: Nutritional Homeostasis at the Host-Microbiome
Interface
While the adapted island model provides a useful framework
for understanding the lung microbiome in health (when the
community is determined by the balance of immigration and
elimination), it does not incorporate any effect due to differential reproduction of community members on the composition
of the microbial community. It is thus of little utility in states
of advanced lung disease, when environmental pressures selectively influence the relative growth rates of community
members (Fig. 1A). We thus require a conceptual model of
ecological homeostasis in the lungs that can explain how the
specific communities of injured airways and alveoli are established and maintained in equilibrium and how this equilibrium
is abruptly disrupted in states of clinical decompensation (such
as pneumonia or ARDS).
Although the relative growth of bacteria within a community
may be influenced by any of a number of physiochemical
factors (e.g., temperature, oxygen tension, pH), a key determinant of all reproducing bacterial communities is nutrient supply. The bacteria that comprise the lung microbiome are
heterotrophic: they obtain their carbon via digestion of organic
compounds.4 Bacteria differ markedly in their ability to metabolize different carbon sources, a fact we exploit clinically to
identify cultured bacteria (e.g., a lactose-fermenting gramnegative rod) and manipulate the enteric microbiome (via
alterations in diet) (7). Thus if we seek to understand the
ecological determinants of the lung microbiome in disease, a
good place to start is asking what the bacteria are eating.
Although both are mucosa-lined luminal organs with a
common embryological origin, the lungs and gut differ markedly from the perspective of microbial nutrient supply (14).
Although the trachea and bronchi are, like the gut, lined with
the heavily glycosylated proteins of secreted mucus, the vast
majority of the lung’s surface area is covered instead with a
thin layer of lipid-rich surfactant. Whereas the gut lumen is
replete with a constant stream of carbohydrates, proteins, and
lipids, the lumen of healthy airways primarily contains inhaled
and exhaled air. Thus, in health, the respiratory tract is a
relatively nutrient-poor environment for most bacteria (Fig.
2A); this is one reason why local reproduction plays a minor
ecological role in community composition in health (Fig. 1A).
Yet in conditions of acute or chronic lung disease, bacterial
nutrient supply surges. The airways of patients with severe CF,
chronic bronchitis, bronchiectasis, and asthma are filled with
the dense, glycoprotein-rich growth medium of secreted mucus. Deeper down in the respiratory ecosystem, alveoli of
patients with ARDS and pneumonia are flooded with proteinrich edema from an injured alveolar-capillary barrier (26) (Fig.
2A). Thus the transition from health to respiratory illness is
also a transition from bacterial nutrient scarcity to abundance,
with a corresponding shift in the reproduction rates of the lung
microbiome.
In Fig. 2B, we propose a conceptual model of nutritional
homeostasis in the respiratory ecosystem. In health (or at least
4
The study of how bacteria maintain a stability of function (e.g., metabolism) despite perturbations to their external environment is its own discipline:
microbial physiology. Bacteria, in turn, harbor their own ecosystems of
phages, the most abundant biological entity on earth (52). There is thus a
host-microbiome interface within our host-microbiome interface.
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Fig. 2. Nutritional homeostasis at the host-microbiome interface. In health, the lungs are a nutrient-poor environment for bacteria, as evidenced by the low protein
content of bronchoalveolar lavage (BAL) fluid (A). This changes in acute respiratory distress syndrome (ARDS) and pneumonia, when protein-rich edema fills
the alveolar space. B: in homeostasis, the growth of a single bacterial species is inhibited by 2 negative feedback loops: increased bacterial growth provokes
increased local inflammation, which kills and clears bacteria, inhibiting further bacterial growth (arrow A); and as bacteria grow they consume their available
nutrient supply, inhibiting subsequent growth (arrow E). But if the provoked inflammation results in enough endothelial and epithelial injury (arrow B) to result
in leak of protein- and nutrient-rich fluid into the alveolar compartment (arrow C), the growth-limiting nutrient supply is restored (arrow D) and bacterial growth
is promoted (arrow E). The specific composition of available nutrients influences the relative growth rates of community members, shaping community
membership. A: figure generated with data from Ref. 26. B: adapted from Ref. 11.
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HOMEOSTASIS AND THE LUNG MICROBIOME
5
In the language of ecology, the population regulation of the lung microbiome is density dependent, constrained by the small carrying capacity afforded by the alveoli.
6
This observation itself is remarkable: the “sterile” exposure of LPS
instillation caused a fivefold surge in the burden of alveolar bacteria. How
many of LPS’s presumed direct effects on in vivo lung biology are in fact
secondary to dysregulation of the respiratory ecosystem?
possible centrality to the microbiome in states of lung injury:
both as its effect and as its cause.
Model 3: Interkingdom Signaling and the Community Stress
Response
A key feature of homeostatic systems in physiology is
signaling: molecular mechanisms by which an organism’s
tissues and cells communicate to other tissues and cells that the
internal environment has been perturbed. Broad classes of
signaling molecules correspond to their respective physiological disciplines: hormones (endocrinology, e.g., insulin’s role
in coordinating the response to altered blood glucose concentration), neurotransmitters (neurophysiology, e.g., the role of
catecholamines in mediating the sympathetic stress response),
and cytokines (immunology, e.g., TNF-␣’s role in the acute
phase response). In our conventional models of host physiology, these molecular mediators are the “code” used to coordinate the dynamic elements of our complex internal systems.
But an explosion of recent research has revealed that microbiota have cracked this code. In states of stress, microbes and
their human hosts speak the same language.
We evolved into the microbes’ world, not vice versa. The
very structure of our eukaryotic cells is a patchwork assembly
of our prokaryotic ancestors. Our cell membranes and core
metabolic pathways resemble those of Bacteria (49); our mechanisms of DNA replication, transcription, and translation resemble those of Archaea (17); our mitochondrial genome
specifically resembles that of Rickettsia prowazekii (2). It is
thus unsurprising that microbes can recognize and respond to
the molecules our cells use to communicate with each other.
And, indeed, a long and growing catalogue of human signaling
molecules have been shown in vitro to influence microbial
growth and behavior: hormones [e.g., glucocorticoids, estrogens, and androgens (42, 58)], neurotransmitters [e.g., catecholamines and endogenous opioids (34, 57)], and cytokines
[e.g., TNF-␣, IL-1, IL-6, IL-8 (30, 31, 43)]. Among the many
bacterial species that respond to host signaling molecules (23)
are many familiar respiratory pathogens: Streptococcus pneumoniae (37), Pseudomonas aeruginosa (22), Staphylococcus
aureus (40), Klebsiella pneumoniae (21), and Escherichia coli
(34). Study of this “interkingdom signaling” is now its own
discipline: microbial endocrinology (35, 47).
By focusing on one common respiratory pathogen (P.
aeruginosa, the most common cause of ventilator-associated
and hospital-associated pneumonia) and one class of signaling
molecules (catecholamines, the primary neuroendocrine mediators of the host stress response), we can start to develop a
conceptual model of pneumonia pathogenesis that spans the
host-microbiome divide. As shown in Fig. 3A, the in vitro
growth of P. aeruginosa is significantly enhanced in the
presence of catecholamines (norepinephrine and dopamine)
(22).7 Given that the effects of catecholamines on bacterial
growth are specific to each species rather than universal among
bacteria (20, 34), high catecholamine concentrations would be
expected to selectively favor the growth of select members of
7
Intriguingly, this growth enhancement is environment dependent: P.
aeruginosa responds to catecholamines when its culture medium is nutrient
poor, but not in conditions of nutrient abundance (20, 23). This is thus an
instance of bacteria altering their own behavior in response to environmental
perturbations: microbial physiology.
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clinical stability), bacterial growth is regulated by two negative
feedback loops (arrows A and E). A surge in bacterial growth
provokes a local increase in alveolar inflammation, which
inhibits further bacterial growth via the well-described mechanisms of microbial killing and clearance (arrow A). Additionally, the growth of a bacterial community depletes its own
finite nutrient supply, which in turn inhibits further growth
(arrow E).5 These tight, stabilizing negative feedback loops
can be overwhelmed, however, if the provoked inflammation
results in enough endothelial and epithelial injury (arrow B) to
cause leak of protein-rich edema into the alveolar compartment
(arrow C, Fig. 2A). This sudden abundance of organic substrate restores the community’s depleted nutrient supply (arrow D), promoting further bacterial growth (arrow E). This
growth, in turn, provokes further inflammation, and the positive feedback loop is perpetuated with a crescendoing intensity.
This model thus provides conceptual explanations both for the
homeostasis of a stable microbiome as well as the disruption of
homeostasis observed in the abrupt, progressive pathologies of
ARDS and pneumonia.
In an innovative and important recent study (44), Poroyko
and colleagues used a murine model of direct lung injury to
experimentally validate numerous elements of this conceptual
model of nutritional homeostasis in the lung microbiome.
Using intratracheal instillation of lipopolysaccharide (LPS),
they reliably provoked alveolar inflammation, injury, and an
influx of protein-rich edema. In the terms of our model (Fig.
2B), they bypassed the inciting event of bacterial growth
(arrow A) and directly provoked its downstream consequences (arrows B–D). Consistent with the prediction of this
model (arrow E), they observed a significant increase in the
bacterial burden of the lungs following injury.6 Bacterial community diversity did not drop following lung injury, evidence
that they did not accidentally provoke a pneumonia (12).
Rather, the complex and expanding bacterial community
shifted in its composition toward select members, present
before injury and selectively favored by the altered growth
conditions of injured lungs: Stenotrophomonas maltophilia and
Ochrobactrum anthropi, both confirmed via culture. Using
metabolic profiling of BAL fluid, the investigators confirmed
that the injured lungs contained numerous organic substrates
that are metabolized by the bacterial community members
enriched following lung injury. This key observation demonstrates the plausibility of selective nutrient abundance determining the composition of the postinjury lung microbiome
(Fig. 2B, arrow E). Finally, the authors “closed the loop” by
validating Fig. 2B’s arrow A: when a separate inflammatory
exposure (IL-6) was administered to healthy mice, the coadministration of bacterial pellets obtained from the BAL of
injured lungs provoked more inflammation and injury than did
the bacterial pellets taken from healthy lungs. The altered lung
microbiome itself potentiates further alveolar inflammation and
injury. Thus, taken together, this elegant study has revealed a
Review
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P. aeruginosa Growth
(Log CFU/ml)
A
B
9
*
8
*
7
6
Total Catecholamines
log[E + NE]
HOMEOSTASIS AND THE LUNG MICROBIOME
C
2.5
2.0
P = 0.0006
1.5
NE
Dopamine
In vitro Catecholamine Exposure
Intraalveolar
Catecholamines
Disordered
Bacterial
Community
Selective
Bacterial
Growth
1.0
0.5
0.0
20
Control
Alveolar
Inflammation
and Injury
40
60
80
100
Relative Abundance of Dominant Species
(% of total sequences)
Community Domination by
Single Species
the lung microbiome. In a recently published study of the
microbiota detected in human BAL specimens (13), we observed that increased intra-alveolar catecholamine concentrations were strongly associated with collapse in community
diversity and the emergence of a single dominant pathogen, the
most abundant of which was P. aeruginosa (Fig. 3B).
Taken together, these recent in vitro and in vivo observations demonstrate the plausibility of the positive feedback loop
outlined in Fig. 3C. Any source of alveolar inflammation and
injury (e.g., viral infection, toxic inhalation, or a large inoculum of aspirated bacteria) provokes an increase in alveolar
catecholamines, which are produced by alveolar macrophages
and neutrophils (19). This increase in alveolar catecholamine
concentration selectively favors the growth of certain catecholamine-responsive bacteria, which alters the composition of
the lung microbiome.8 In the case of complete community
collapse, a single dominant species overtakes the respiratory
ecosystem, both begetting more inflammation and benefiting
from the surge in catecholamines provoked.
Whereas negative feedback loops promote self-regulation
and stability, positive feedback loops promote dysregulation
and instability via amplification of signal. The feedback loop in
Fig. 3C may explain a fundamental feature of bacterial pneumonia: its uniformly acute onset. As clinicians know well,
bacterial pneumonia arises abruptly over hours rather than over
days or weeks. The chest X-ray of a ventilated patient may turn
from clear to consolidated overnight, even in the absence of
gross aspiration. This explosive suddenness bears the hallmarks of a self-amplifying positive feedback loop such as
outlined in Fig. 3C.
Catecholamines are only one class of host stress signals that
may participate in this form of interkingdom feedback loop.
Numerous other host inflammatory signals with in vitro potential to promote bacterial growth and virulence are also elevated
in the alveoli of patients with pneumonia [e.g., TNF-␣, IL-1,
8
It is suspicious that the Proteobacteria phylum (home to numerous familiar
gram-negative respiratory pathogens) is disproportionately represented both
among catecholamine-responsive bacterial species as well as among the
bacteria enriched in the microbiota of inflamed lungs (15).
and IL-6 (38)]. Furthermore, this conceptual model is likely not
specific to the pathogenesis of pneumonia: a similar but distinct
milieu of inflammatory molecules fill the alveoli of patients
with ARDS, likely influencing the microbiota of that disease.
Exogenous catecholamines and adrenergic agonists are
among the most commonly administered medications in patients with critical illness and respiratory disease: systemically
as vasopressors (norepinephrine, epinephrine, dopamine), as
antihypertensives (clonidine) and as sedatives (dexmedetomidine), and via the airways as bronchodilators. Catecholamine
antagonists are also ubiquitous in clinical practice (e.g., betablockers in cardiovascular disease). No study to date has
evaluated whether exogenous catecholamines independently
influence the lung microbiome. The mechanisms by which
catecholamines and cytokines affect the growth and virulence
of bacteria are largely unknown (35) and may represent an
enormous untapped reservoir of targets for prevention and
treatment of respiratory infection. The notion that “protective”
lung microbiota may participate in stabilizing feedback loops
with the host immune interface is attractive but to date has not
been demonstrated.
Conclusion
The disciplines of physiology and ecology converge conceptually with the concept of homeostasis; they converge biologically at the interface of the host and its microbiome. We here
propose three conceptual models by which the complex system
of the lung microbiome establishes and maintains homeostasis
in health, and loses it in states of clinical decompensation.
These models provide theoretical frameworks for the next
generation of physiological investigations into respiratory
health and illness.
GRANTS
Funding was provided by National Institutes of Health Grants
T32HL00774921 (R. P. Dickson), UL1TR000433 (R. P. Dickson),
U01HL098961 (G. B. Huffnagle), and R01HL114447 (G. B. Huffnagle).
Support was provided by the Host Microbiome Initiative of the University of
Michigan (R. P. Dickson), the Michigan Institute for Clinical & Health
Research (R. P. Dickson), and the University of Michigan Center for Integra-
AJP-Lung Cell Mol Physiol • doi:10.1152/ajplung.00279.2015 • www.ajplung.org
Downloaded from http://ajplung.physiology.org/ by 10.220.33.3 on June 18, 2017
Fig. 3. Interkingdom signaling and the lung microbiome. Host-derived catecholamines, including norepinephrine (NE) and dopamine, promote the in vitro growth
of Pseudomonas aeruginosa (A, adapted from Ref. 22 with permission of the American College of Chest Physicians). Intra-alveolar catecholamine concentrations
are strongly associated with collapse of the respiratory ecosystem around a single dominant pathogen (B, adapted from Ref. 13 with permission of the American
Thoracic Society). These observations suggest a positive feedback loop (C) that can propel the explosive disruption of homeostasis observed in respiratory
infections with select bacteria. Similar bacterial growth promotion has been reported with other host inflammatory response molecules, including TNF-␣, IL-1,
IL-6, IL-8, and glucocorticoids (30, 31, 42, 43).
Review
L1054
HOMEOSTASIS AND THE LUNG MICROBIOME
tive Research in Critical Care (R. P. Dickson). The content is solely the
responsibility of the authors and does not necessarily represent the official
views of the National Institutes of Health.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author (s).
AUTHOR CONTRIBUTIONS
R.P.D. conception and design of research; R.P.D. prepared figures; R.P.D.
drafted manuscript; R.P.D., J.R.E.-D., and G.B.H. edited and revised manuscript; R.P.D., J.R.E.-D., and G.B.H. approved final version of manuscript.
REFERENCES
AJP-Lung Cell Mol Physiol • doi:10.1152/ajplung.00279.2015 • www.ajplung.org
Downloaded from http://ajplung.physiology.org/ by 10.220.33.3 on June 18, 2017
1. Amberson JB. A clinical consideration of abscesses and cavities of the
lung. Bull Johns Hopkins Hosp 94: 227–237, 1954.
2. Andersson SGE, Zomorodipour A, Andersson JO, Sicheritz-Ponten
T, Alsmark UCM, Podowski RM, Naslund AK, Eriksson AS, Winkler
HH, Kurland CG. The genome sequence of Rickettsia prowazekii and the
origin of mitochondria. Nature 396: 133–140, 1998.
3. Bassis CM, Erb-Downward JR, Dickson RP, Freeman CM, Schmidt
TM, Young VB, Beck JM, Curtis JL, Huffnagle GB. Analysis of the
upper respiratory tract microbiotas as the source of the lung and gastric
microbiotas in healthy individuals. mBio 6: 2015.
4. Bernard C. Leçons sur les pheínomeÌnes de la vie communs aux animaux
et veígeítaux. Paris: Baillere, 1878.
5. Charlson ES, Bittinger K, Haas AR, Fitzgerald AS, Frank I, Yadav A,
Bushman FD, Collman RG. Topographical continuity of bacterial populations in the healthy human respiratory tract. Am J Respir Crit Care Med
184: 957–963, 2011.
6. Crespi BJ. Vicious circles: positive feedback in major evolutionary and
ecological transitions. Trends Ecol Evol 19: 627–633, 2004.
7. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE,
Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA, Biddinger
SB, Dutton RJ, Turnbaugh PJ. Diet rapidly and reproducibly alters the
human gut microbiome. Nature 505: 559 –563, 2014.
8. Dickson RP, Erb-Downward JR, Freeman CM, McCloskey L, Beck
JM, Huffnagle GB, Curtis JL. Spatial variation in the healthy human
lung microbiome and the adapted island model of lung biogeography. Ann
Am Thorac Soc 12: 821–830, 2015.
9. Dickson RP, Erb-Downward JR, Freeman CM, Walker N, Scales BS,
Beck JM, Martinez FJ, Curtis JL, Lama VN, Huffnagle GB. Changes
in the lung microbiome following lung transplantation include the emergence of two distinct Pseudomonas species with distinct clinical associations. PLoS One 9: e97214, 2014.
10. Dickson RP, Erb-Downward JR, Huffnagle GB. The role of the bacterial microbiome in lung disease. Expert Rev Respir Med 7: 245–257,
2013.
11. Dickson RP, Erb-Downward JR, Huffnagle GB. Towards an ecology of
the lung: new conceptual models of pulmonary microbiology and pneumonia pathogenesis. Lancet Respir Med 2: 238 –246, 2014.
12. Dickson RP, Erb-Downward JR, Prescott HC, Martinez FJ, Curtis
JL, Lama VN, Huffnagle GB. Analysis of culture-dependent versus
culture-independent techniques for identification of bacteria in clinically
obtained bronchoalveolar lavage fluid. J Clin Microbiol 52: 3605–3613,
2014.
13. Dickson RP, Erb-Downward JR, Prescott HC, Martinez FJ, Curtis
JL, Lama VN, Huffnagle GB. Intraalveolar catecholamines and the
human lung microbiome. Am J Respir Crit Care Med 192: 257–259, 2015.
14. Dickson RP, Huffnagle GB. The lung microbiome: new principles for
respiratory bacteriology in health and disease. PLoS Pathog 11: e1004923,
2015.
15. Dickson RP, Martinez FJ, Huffnagle GB. The role of the microbiome in
exacerbations of chronic lung diseases. Lancet 384: 691–702, 2014.
16. Dixon KR, Cornwell GW. A mathematical model for predator and prey
populations. Res Popul Ecol 12: 127–136, 1970.
17. Edgell DR, Doolittle WF. Archaea and the origin (s) of DNA replication
proteins. Cell 89: 995–998, 1997.
18. Erb-Downward JR, Thompson DL, Han MK, Freeman CM, McCloskey
L, Schmidt LA, Young VB, Toews GB, Curtis JL, Sundaram B, Martinez
FJ, Huffnagle GB. Analysis of the lung microbiome in the “healthy”
smoker and in COPD. PLoS One 6: e16384, 2011.
19. Flierl MA, Rittirsch D, Nadeau BA, Chen AJ, Sarma JV, Zetoune FS,
McGuire SR, List RP, Day DE, Hoesel LM, Gao H, Van Rooijen N,
Huber-Lang MS, Neubig RR, Ward PA. Phagocyte-derived catecholamines enhance acute inflammatory injury. Nature 449: 721–725,
2007.
20. Freestone P. Communication between bacteria and their hosts. Scientifica
(Cairo) 2013: 15, 2013.
21. Freestone PP, Haigh RD, Williams PH, Lyte M. Stimulation of bacterial
growth by heat-stable, norepinephrine-induced autoinducers. FEMS Microbiol Lett 172: 53–60, 1999.
22. Freestone PP, Hirst RA, Sandrini SM, Sharaff F, Fry H, Hyman S,
O’Callaghan C. Pseudomonas aeruginosa-catecholamine inotrope interactions: a contributory factor in the development of ventilator-associated
pneumonia? Chest 142: 1200 –1210, 2012.
23. Freestone PP, Sandrini SM, Haigh RD, Lyte M. Microbial endocrinology: how stress influences susceptibility to infection. Trends Microbiol 16:
55–64, 2008.
24. Gleeson K, Eggli DF, Maxwell SL. Quantitative aspiration during sleep
in normal subjects. Chest 111: 1266 –1272, 1997.
25. Goddard AF, Staudinger BJ, Dowd SE, Joshi-Datar A, Wolcott RD,
Aitken ML, Fligner CL, Singh PK. Direct sampling of cystic fibrosis
lungs indicates that DNA-based analyses of upper-airway specimens can
misrepresent lung microbiota. Proc Natl Acad Sci USA 109: 13769 –
13774, 2012.
26. Günther A, Siebert C, Schmidt R, Ziegler S, Grimminger F, Yabut M,
Temmesfeld B, Walmrath D, Morr H, Seeger W. Surfactant alterations
in severe pneumonia, acute respiratory distress syndrome, and cardiogenic
lung edema. Am J Respir Crit Care Med 153: 176 –184, 1996.
27. Hilty M, Burke C, Pedro H, Cardenas P, Bush A, Bossley C, Davies J,
Ervine A, Poulter L, Pachter L, Moffatt MF, Cookson WO. Disordered
microbial communities in asthmatic airways. PLoS One 5: e8578, 2010.
28. Huxley EJ, Viroslav J, Gray WR, Pierce AK. Pharyngeal aspiration in
normal adults and patients with depressed consciousness. Am J Med 64:
564 –568, 1978.
29. Ingenito EP, Solway J, McFadden ER Jr, Pichurko B, Bowman HF,
Michaels D, Drazen JM. Indirect assessment of mucosal surface temperatures in the airways: theory and tests. J Appl Physiol 63: 2075–2083,
1987.
30. Kanangat S, Meduri GU, Tolley EA, Patterson DR, Meduri CU, Pak
C, Griffin JP, Bronze MS, Schaberg DR. Effects of cytokines and
endotoxin on the intracellular growth of bacteria. Infect Immun 67:
2834 –2840, 1999.
31. Kaza SK, McClean S, Callaghan M. IL-8 released from human lung
epithelial cells induced by cystic fibrosis pathogens Burkholderia cepacia
complex affects the growth and intracellular survival of bacteria. Int J Med
Microbiol 301: 26 –33, 2011.
32. Lighthart B. Mini-review of the concentration variations found in the
alfresco atmospheric bacterial populations. Aerobiologia 16: 7–16, 2000.
33. Lozupone C, Cota-Gomez A, Palmer BE, Linderman DJ, Charlson
ES, Sodergren E, Mitreva M, Abubucker S, Martin J, Yao G, Campbell TB, Flores SC, Ackerman G, Stombaugh J, Ursell L, Beck JM,
Curtis JL, Young VB, Lynch SV, Huang L, Weinstock GM, Knox KS,
Twigg H, Morris A, Ghedin E, Bushman FD, Collman RG, Knight R,
Fontenot AP. Widespread colonization of the lung by Tropheryma whipplei in HIV infection. Am J Respir Crit Care Med 187: 1110 –1117, 2013.
34. Lyte M, Ernst S. Catecholamine induced growth of gram negative
bacteria. Life Sci 50: 203–212, 1992.
35. Lyte M, Freestone PP. Microbial Endocrinology: Interkingdom Signaling in Infectious Disease and Health. New York: Springer
Science⫹Business Media, 2010.
36. MacArthur RH, Wilson EO. An equilibrium theory of insular zoogeography. Evolution 373–387, 1963.
37. Marks LR, Davidson BA, Knight PR, Hakansson AP. Interkingdom
signaling induces Streptococcus pneumoniae biofilm dispersion and transition from asymptomatic colonization to disease. mBio 4: 2013.
38. Monton C, Torres A, El-Ebiary M, Filella X, Xaubet A, de la Bellacasa JP. Cytokine expression in severe pneumonia: a bronchoalveolar
lavage study. Crit Care Med 27: 1745–1753, 1999.
39. Morris A, Beck JM, Schloss PD, Campbell TB, Crothers K, Curtis JL,
Flores SC, Fontenot AP, Ghedin E, Huang L, Jablonski K, Kleerup E,
Lynch SV, Sodergren E, Twigg H, Young VB, Bassis CM, Venkataraman A, Schmidt TM, Weinstock GM. Comparison of the respiratory
microbiome in healthy nonsmokers and smokers. Am J Respir Crit Care
Med 187: 1067–1075, 2013.
Review
HOMEOSTASIS AND THE LUNG MICROBIOME
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
ment of lung microbiome with supraglottic taxa is associated with increased pulmonary inflammation. Microbiome 1: 19, 2013.
Shapiro JA. Evolution: A View From the 21st Century. Upper Saddle
River, NJ: FT Press Science, 2011.
Sibley CD, Rabin H, Surette MG. Cystic fibrosis: a polymicrobial
infectious disease. Future Microbiol 1: 53–61, 2006.
Sunda WG, Graneli E, Gobler CJ. Positive feedback and the development and persistence of ecosystem disruptive algal blooms. J Phycol 42:
963–974, 2006.
Thurber RV. Current insights into phage biodiversity and biogeography.
Curr Opin Microbiol 12: 582–587, 2009.
Venkataraman A, Bassis CM, Beck JM, Young VB, Curtis JL,
Huffnagle GB, Schmidt TM. Application of a neutral community model
to assess structuring of the human lung microbiome. mBio 6: 2015.
Volterra V. Fluctuations in the abundance of a species considered mathematically. Nature 118: 558 –560, 1926.
West JB. Regional differences in the lung. Chest 74: 426 –437, 1978.
Willner D, Haynes MR, Furlan M, Schmieder R, Lim YW, Rainey PB,
Rohwer F, Conrad D. Spatial distribution of microbial communities in
the cystic fibrosis lung. ISME J 6: 471–474, 2012.
Zaborina O, Lepine F, Xiao G, Valuckaite V, Chen Y, Li T, Ciancio
M, Zaborin A, Petroff E, Turner JR, Rahme LG, Chang E, Alverdy
JC. Dynorphin activates quorum sensing quinolone signaling in Pseudomonas aeruginosa. PLoS Pathog 3: e35, 2007.
Zhang X, Essmann M, Burt ET, Larsen B. Estrogen effects on Candida
albicans: a potential virulence-regulating mechanism. J Infect Dis 181:
1441–1446, 2000.
AJP-Lung Cell Mol Physiol • doi:10.1152/ajplung.00279.2015 • www.ajplung.org
Downloaded from http://ajplung.physiology.org/ by 10.220.33.3 on June 18, 2017
40. O’Donnell PM, Aviles H, Lyte M, Sonnenfeld G. Enhancement of in
vitro growth of pathogenic bacteria by norepinephrine: importance of
inoculum density and role of transferrin. Appl Environ Microbiol 72:
5097–5099, 2006.
41. Pickett STA, White PS. The Ecology of Natural Disturbance and Patch
Dynamics. San Diego: Academic, 1985.
42. Plotkin BJ, Roose RJ, Erikson Q, Viselli SM. Effect of androgens and
glucocorticoids on microbial growth and antimicrobial susceptibility. Curr
Microbiol 47: 514 –520, 2003.
43. Porat R, Clark BD, Wolff SM, Dinarello CA. Enhancement of growth
of virulent strains of Escherichia coli by interleukin-1. Science 254:
430 –432, 1991.
44. Poroyko V, Meng F, Meliton A, Afonyushkin T, Ulanov A, Semenyuk
E, Latif O, Tesic V, Birukova AA, Birukov KG. Alterations of lung
microbiota in a mouse model of LPS-induced lung injury. Am J Physiol
Lung Cell Mol Physiol 309: L76 –L83, 2015.
45. Quinn LH, Meyer OO. The relationship of sinusitis and bronchiectasis.
Arch Otolaryngol 10: 152–165, 1929.
46. Redfield AC. On the proportions of organic derivatives in sea water and
their relation to the composition of plankton. In: James Johnstone Memorial Volume. Liverpool, UK: University Press of Liverpool, 1934.
47. Sandrini S, Aldriwesh M, Alruways M, Freestone P. Microbial endocrinology: host-bacteria communication within the gut microbiome. J
Endocrinol 225: R21–R34, 2015.
48. Segal LN, Alekseyenko AV, Clemente JC, Kulkarni R, Wu B, Chen H,
Berger KI, Goldring RM, Rom WN, Blaser MJ, Weiden MD. Enrich-
L1055