# Emerging Insights into Antibiotic-Associated Diarrhea andClostridium difficile Infection through the Lens of Microbial Ecology

Authors: Seth T.  Walk; Vincent B. Young
Journal: Interdisciplinary Perspectives on Infectious Diseases
 (2008)

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## Abstract

Antibiotics are the main, and often only, clinical intervention for prophylactic and active treatment of bacterial infections in humans.  Perhaps it is not surprising that these drugs also shift the composition of commensal bacteria inside our bodies, especially those within the gut microbial community (microbiota).  How these dynamics ultimately affect the function of the gut microbiota, however, is not fully appreciated.  Likewise, how antibiotic induced changes facilitate the outgrowth and pathogenicity of certain bacterial strains remains largely enigmatic.  Here, we discuss the merits of a microbial ecology approach toward understanding a common side effect of antibiotic use, antibiotic-associated diarrhea (AAD), and the opportunistic bacterial infections that sometimes underlie it.  As an example, we discuss how this approach is being used to address complex disease dynamics duringClostridium difficile infection.


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## Body

## 1. Introduction

The  human colon contains the most
abundant and diverse assemblage of bacteria in the body. Symbiotic interactions
with and within this complex community are now recognized as important
predictors of human health. Aberrant community structures are associated with
complex diseases like obesity, irritable bowel syndrome, and immune
dysfunction. Antibiotic administration can disrupt the colonic ecosystem, which,
in turn, leaves patients vulnerable to gastrointestinal disease. Diarrhea is a
common manifestation of antibiotic-mediated disturbance and can result from
altered function of the disrupted microbiota, direct effects on host tissue,
and colonization by opportunistic organisms that invade the altered microbial
community. Here, we review the relevant microbial ecology of
antibiotic-associated diarrhea with an emphasis on bacterial community dynamics
duringC. difficile infection.


## 2. Commonalities and Assumptions for GI Tract Microbial Ecology

When initiating a discussion of the
microbial ecology of the gastrointestinal (GI) tract, it is important to review
some of the common areas and assumptions investigators used when studying this
ecosystem.First, the proportion of
uncultivable bacteria in the GI tract is high (~60%–80%).Initially, culture-based surveys of the gut
microbial successfully isolated and characterized large numbers of the
bacterial morphotypes (i.e., distinct cellular forms) present in human feces [1, 2].
However, recent surveys based on DNA sequencing have indicated that the vast
majority of genetically distinct organisms have not been isolated by culture
techniques [3]. These relatively new sequence-based approaches in
combination with robust bioinformatics provide the framework to explore a vast
amount of genetic diversity. It is now feasible to survey nearly all of the
genetic information in a given system, and this ability has ushered in
a new area of research, referred to as metagenomics [4].The field is still in its infancy, and much
of the data continue to be open for interpretation. It is important to note
that the currency for GI tract microbial ecology in the metagenomic era is the
abundance and distribution of targeted DNA sequences and not actual organisms
or randomly sampled genomes of organisms. The amplification, cloning, and
sequencing of certain loci, such as the highly conserved 16S rRNA locus, are
the tools used to study the phylogenetic signal contained in the metagenome,
and this is different than classical metagenomics, where one seeks to analyze the
functional and sequence-based diversity contained in all microbial genomes of
communities [4, 5]. Lastly, we draw attention to an early few studies that
use culture-based approaches, but will put these data into a metagenomic
context.There are measurable, statistical, and real differences
(i.e., not all the detectable differences are biologically significant) between
the bacterial communities throughout the human body (skin, mouth, vagina, GI
tract, etc.). Studies have shown regional differences in microbial composition
throughout the mammalian GI tract in both the longitudinal (i.e., stomach to
small intestine to large intestine) and axial (i.e., mucosal associated to
mucus to lumen) directions [6–8]. For further discussion on this topic,
see the recent review by Peterson et al. [7]. Currently, most studies
circumvent the practical and ethical problems associated with direct intestinal
sampling (e.g., via colonoscopy and biopsy) by using feces as a proxy [9].
Many of the studies reviewed here do the same and regard the bacterial
community in feces as representative of the gut microbiota as a whole, with the
caveat that existing spatial community differences may result in a biased
representation. For example, total anaerobe counts
were found to be 100 times lower in the human cecum compared to feces [10].Lastly, it is generally assumed that the abundance
and distribution of an organism (16S rRNA gene sequence) and broader taxonomic
groups of organisms (sequences grouped based on percent similarity and called
operational taxonomic units or OTUs) are important. The abundance and
distribution of OTUs are often called community structure. As we will discuss in detail below, there are
observable patterns in the gut microbiota under certain conditions. Some
taxonomic groups are very abundant, while others are at such low abundance that
they can only be detected using highly sensitive and specific molecular
techniques. Most studies look for community structure and try to assess the
underlying mechanisms that caused it (disease, diet, drug effect, etc.). While
this may at first seem logical and perhaps trivial, it is currently not well
understood what these patterns really mean. For example, what OTUs should be
used to assess structure? At the phylum level, patterns may be clear, but at
the species level, where functional variation is driven by evolutionary
processes, the structure may not be statistically different from a random
assemblage (due, in part, to the lack of a universal bacterial species concept
[11]). Currently, a challenge for microbial ecologists is to understand
dynamics with respect to the functional attributes of bacterial communities and
not only through the lens of taxonomy.


## 3. Normal Gut Microbiota

The human colon is typically
associated with1011 to 1012 bacterial cells per gram of
contents, and new estimates using genetic diversity suggest that the gut
ecosystem holds 15000–36000 different
species [9, 12, 13]. Colonization normally begins at birth, and a variety
of bacteria can be detected in infant stools within the first few days after
vaginal delivery [14]. Among the first gut bacteria to colonize infants were Escherichia coli and Staphylococcus aureus [15, 16]. These studies used
culture-based methods to show that abundance was highest at about one week
after birth and decreased 1–3 orders of
magnitude within the first year, suggesting that the abundance of early
bacterial colonizers is subsequently shifted by the growing biologic complexity
of this system. Recently, nonculture-based data supported these findings and
showed that multiple shifts occur among different taxonomic groups over the
first 200 days of life [17]. Also, the gamma-Proteobacteria, to which E. coli belongs, appear to be the
dominant members in these infant’s GI tract. It is interesting to note that E. coli was initially discovered in 1884
and studied by the famous German pediatrician Theodor Escherich because of its
presence in “normal” infant microbiota and because of its beneficial effects on
digestion [18].Defining normal gut microbiota is
challenging because of the compositional heterogeneity that exists between
hosts [19]. Most phylotypes (suspected
species) are unique to the individual being sampled [3]. At broader taxonomic
levels, a consistent community structure is often observed, leading to the
conclusion that the gut is dominated by members of a few bacterial phyla
(Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria). The human gut
is described as “exclusive” because there are more divisions (phyla) of
bacteria and archaea known to exist on earth than what is typically sampled
from human subjects (currently the Silva 16S rRNA database has 115 bacterial divisions
of which only 10 have been sampled from humans) [7].The bacteria in our GI tract are important for
certain aspects of human health, and there are clear mutualisms between human
and bacterial cells [20]. Not surprisingly, our immune system defends against
negative symbiotic interactions based on prior exposure and also on stimulating
mechanisms like breast feeding and vaccinations (prior exposure to living cells
is not always necessary for an effective immune response). Some of the traits
that make us human also dictate the structure of the gut community, as the
microbiota of conspecific relatives (same species of humans, primates, and
nonprimates) was most similar to each other in a recent study [21]. There are few data that describe the community structure of the GI tract microbiota in healthy individuals and this limits our ability to formulate generalities on the normal state. However, if we are to consider the healthy
human gut as a theoretically-based community, where a consistent structure is
defined and used to test hypotheses, then the microbiota of individuals should
converge upon a similar structure under similar conditions [22]. In the
absence of convergence, we are left to the study of stochastic events and
patterns that are best explained by random walk models, where species traits do
not correlate with the abundances along environmental gradients (for more on
the theoretical issues concerning community analysis, see Tilman [22]).Because of the low degree of similarity between
individuals, changes in the gut microbiota are typically measured by shifts in
structure. For example, a cohort study of 1032 infants showed that breast-fed
infants have a consistently different bacterial composition than bottle-fed
infants [23]. Based on real-time PCR and OTU specific probes, formula-fed infants
(n=232) were colonized by E. coli, C. difficile, B. fragilis, and lactobacilli more often than breast-fed infants
(n=700). Similar comparative studies have shown associations between an altered
gut microbiota and a number of human diseases, including obesity [24], Crohn’s disease [25], irritable bowel syndrome [26], and allergies [27]. It is clear that our understanding of the normal
gut microbiota is limited and just beginning, but comparative studies like these
illustrate a novel ability to describe the microbial ecology that underlies
many complex diseases.


## 4. Antibiotics Increase Host Susceptibility to Pathogens

One measure of ecosystem stability, in terms of
maintaining function [28], is the ability to resist invasion and subsequent
dominance by immigrating organisms. For the gut ecosystem, antibiotic therapy
represents a strong perturbation that shifts the relative proportion of
community members, allowing opportunists to establish [29–32]. Antibiotic therapies exclude members of the community by eradicating
them directly or indirectly by breaking necessary mutualistic interactions [33]. During such events in murine models, the community structure was
disrupted and enteric pathogens reached high numbers [34, 35]. Similar observations underlie the proposed colonization resistance or
barrier function, provided to the host by the gut microbiota [32, 36, 37], preventing the ingress of pathogens into the gut ecosystem.Many details about the colonization resistance function
of the microbiota have yet to be tested, but it is clear that shifts in the gut
microbial community structure are permissive to the establishment of certain
pathogens. For example,Vibrio cholerae does not normally cause disease in conventional guinea pigs, but it established
and caused severe disease after disruption of the microbiota by pretreatment
with streptomycin [38]. Similarly, it has been shown that mice with a
conventional gut microbiota require a much higher infective dose (109 colony forming units per mL, CFU/mL) for colonization by a gram-negative
bacterium compared to antibiotic treated mice (102 CFU/mL) [39]. The mechanisms behind colonization resistance in
humans are topics of ongoing research, but the gut microbiota in animal models
has been shown to (i) utilize essential nutrients before they are available to
invading bacteria (resource limitation), (ii) limit access to attachment sites
(space limitation), and (iii) produce inhibitory substances [40].Many factors, including drug dose, route of
administration, absorption, and host inactivation, dictate the intensity of
antibiotic effects on the gut microbiota (see review by Sullivan et al. [32] for specific effects of commonly used drugs). A
number of culture-based and nonculture-based molecular techniques have been
used to follow bacterial community dynamics in humans upon exposure to
antibiotics. Often, specific groups of OTUs are singled out with specific
probes. Temporal effects of antibiotic treatment were recently shown among  members of the
Bacteroidetes division using culture techniques and genetic fingerprinting
(rep-PCR) [30]. During a case-control study of subjects taking capsules of 150 mg
clindamycin (orally), each individual was sampled prior to antibiotic treatment
and at set time points throughout the following 2-year posttreatment. The
overall diversity of this division decreased upon antibiotic treatment and remained
reduced during the entire 2 years of the study. The authors also show that the
dominant community members changed markedly in relative abundance during the
first 3 weeks of the posttreatment, suggesting that these effects were not
exclusive to the rest of the microbiota.We draw attention to these dynamics here to simply
point out that the gut microbiota changes markedly during and after normal therapeutic
courses of antibiotics and that host susceptibility to subsequent infection is
increased as a result. We now turn to specific clinical presentations that
result from antibiotic treatment of human patients and follow with a discussion
on a microbial ecology approach to these diseases.


## 5. Antibiotic-Associated Diarrhea andC. Difficile

Patients undergoing antibiotic
treatment often develop diarrhea (antibiotic-associated diarrhea or AAD) as a
side effect of therapy. Approximately, 5%–25% of patients
on antibiotic therapy develop AAD, which can range from a mild, self-limiting
illness to a serious and progressive pseudomembranous colitis [41, 42]. The risk of developing disease is highly variable and depends on host
factors (age, diet, immune system function, etc.), the type and dose of
antibiotic, and the duration of treatment. In a cohort study, Beaugerie et al.
found that 17.6% (46 out of 262) of adult (≥18 years old) outpatients developed
diarrhea within 14 days after the start of treatment [43]. Patients that remain in the hospital are similarly
affected. According to a prospective study of hospitalized patients in Sweden,
12% (294 out of 2 462) of
patients ≥12 years old developed diarrhea within 45 days after the start of
treatment [44]. However, certain patient populations in the
hospital appear to be at an elevated risk as 60% (9 out of 15) of individuals
(ages 37–79) enrolled in a
cohort study of intensive care units developed diarrhea within the first week
after of antibiotic treatment [45]. These data illustrate that diarrhea is a common
complication of antibiotic use and suggest that critically ill patients are
exquisitely susceptible to AAD.An etiologic agent is not necessary
for AAD, as certain drugs can cause gastrointestinal dysfunction directly [42]. A distinction can, then, be made between
pathogen-associated and pathogen-independent AAD in that Koch’s postulates are
not met in the classical sense. For example, if the bacteria responsible for
breaking down fermentable starches in the colon are eliminated by the effect of
an antibiotic, an osmotic diarrhea may present. In this scenario, the community
and not a defined pathogen is responsible for the disease etiology. To our
knowledge, however, replicating the disease in an otherwise naïve individual by
establishing the “pathogenic community” has not been shown.A number of opportunistic pathogens can cause disease
during antibiotic therapy, includingSalmonella spp., Clostridium perfringens, Klebsiella oxytoca, S. aureus, Candida albicans,
and C. difficile. Of these, C. difficile is the most common cause of
pathogen-associated AAD (15%–25%), the most
common cause of severe disease, and it causes nearly all cases of nosocomial
pseudomembranous colitis [46]. C. difficile is an anaerobic, spore
forming bacterium that is commonly found in soil, humans, and animals [47]. This toxigenic gram-positive bacillus is
asymptomatically carried by 1%–3% of the human
population, but is more prevalent among infants [23], hospitalized patients (55.4% of the hospital population in the Swedish
AAD cohort study mentioned above [44] were positive for C. difficile toxin), older (≥60 years) patients [47–49], and healthcare personnel that care for patients being treated with
antibiotics [50]. This pathogen can cause disease in nonhospitalized
patients [51], where the main risk factors are antibiotic therapy,
proton pump inhibitors, and the use of histamine-2-receptor antagonists [52].Pseudomembranous colitis in the distal colon and rectum
is fatal in 6%–30% of cases [47]. Disease onset occurs several days to several weeks
after initial antibiotic treatment and certain drugs, such as clindamycin,
cephalosporins, fluoroquinolones, and β-lactams, are associated with greater
risk of CDAD [46, 53]. Oral antibiotic therapies with vancomycin, metronidazole, bacitracin,
teicoplanin, and fucidin have been shown to be an effective initial treatments
for CDAD [54]. A significant number (20%–35%) of patients
develop recurrent illness caused by the same or different C. difficile strains and symptoms arise several days (usually
>4) to several weeks after the apparent success of the initial antibiotic therapy
[55–57].CDAD has been a recognized health problem in the
United States and many industrialized countries for more than 30 years [58], but the epidemiology of the disease is changing.
The prevalence and severity (case fatality rate) of CDAD continue to increase
in spite of numerous discoveries concerning its epidemiology, pathogenicity,
and treatment [53, 59]. This increasing trend is associated with the emergence and spread of
an epidemic strain referred to as NAP1/BI (North American pulsed-field type 1,
ribotype 027, restriction endonuclease analysis type BI, toxinotype III) [47, 60]. As a result, the average inhospital cost of CDAD patients is estimated
to be 54% more than non-CDAD patients in the United States, adding an overall
$1.1 billion to national health care costs [61]. Length of hospital stay also increases with CDAD
patients and ranges from an average of 3.6 days for the total inpatient
population to 16 days for surgical inpatients [62].


## 6. The Microbial Ecology Approach to AAD and CDAD

There are few data that assess changes in the human gut microbiota during the course of AAD. The
only sequence-based, microbial ecology study to date followed a 39-year-old
male throughout an amoxicillin-clavulanic acid treatment (875 and 125 mg,
resp., 2 times daily for 10 days) for acute sinusitis [63]. The patient developed non-CDAD within 24 hours of
the first dose and symptoms persisted until 4 days after the final dose. Stool
samples were taken 12 hours after the first dose (day 0), 4 days into the 10-day
regime (day 4), and at 2 weeks following the final dose (day 24). A total of
84, 74, and 84 randomly cloned 16S rRNA genes were sequenced from each sample,
respectively.At 4 days into the
amoxicillin-clavulanic acid therapy, the gut microbiota of this individual was
markedly shifted. Representation of the Bacteroides group went from exclusivelyB. fragilis on day 0 to almost all B. distasonis on day 4. There was also a
dramatic outgrowth of Enterobacteriaciae (most likely E. coli). Lastly, all members of the Clostridial rRNA cluster XIVa
and Bifidobacteria groups (32% of the all sequences on day 0) were lost or
below the detection limit.Two weeks after the last dose of antibiotic, the
microbiota appeared to be recovering to day 0 composition. TheB. fragilis and Clostridial rRNA cluster
XIVa groups rebounded, while B.
distasonis and Enterobacteriacea groups were drastically decreased or
undetected. Interestingly, members of the Clostridial rRNA cluster IV group
were relatively unaffected by the antibiotic treatment and were sampled at roughly
even numbers on all 3 sampling days. In contrast, members of the Bifidobacteria
group were lost or below detection by day 4 and remained so at day 24. These data suggest that (i) the composition
of the gut bacterial community is dramatically shifted during antibiotic
therapy, (ii) that resiliency to this drug’s effects is group specific, and (iii)
that it may require an extended period of time for the microbiota to recover to
the prestressed composition, if at all. More data are needed to adequately
assess the rate and extent of recovery from this and other antibiotics and to
assess how variable these effects are in the human population.The association between CDAD and perturbations of the
gut microbiota is well established but poorly understood. For example, animal
(hamster and mouse) and in vitro models show antagonism between conventional
microbiota andC. difficile population growth [64]. These findings help to explain the success of
bacteriotherapy for recurrent-CDAD, where the disease was resolved by rectal
instillation of donor stool [65, 66]. However, the use of probiotics and synthetic mixtures of bacteria has
had limited success [67] and is not currently efficacious as alternative
therapies. The hope is that a better understanding of the complexity of this
system during CDAD infection will lead to defined manipulations of patient
microbiota that will
both prevent
establishment of this pathogen and treat acute disease.To this end, Chang et al. recently applied the same
approach discussed above (16S rRNA gene sequencing) to 7 patients with initial
(n=3) and recurrent (n=4) CDAD and 3 control individuals from an outpatient
clinic [68]. Species level identity based on 97% nucleotide
similarity was determined for 125–184 16S rRNA
genes per individual. To gain insight
into the overall bacterial diversity of each patient’s fecal microbiota,
rarefaction curves were generated from these sequence data. Rarefaction is a
method of generating idealized taxonomic “collectors curves” from community
data through data resampling [69]. The shape of the rarefaction curves is then
indicative of the overall complexity of the microbiota in each community,
allowing comparison of the diversity of each patient’s fecal microbiota.At this level of community sampling, inferences were
restricted to the most abundant members. However, and without exception, the
microbiota from control and initial CDAD patients was more complex than the
microbiota from recurrent CDAD patients. Furthermore, the authors were able to
combine these data with those from the non-CDAD-AAD patient [63] to show a clear association between microbiota
complexity and disease outcome (i.e., Controls > AAD > initial CDAD ≫ recurrent CDAD). This study not only provides a support for the
barrier function against C. difficile establishment and disease, but also because the sequences represent actual
organisms, these data can be used to identify potentially useful antagonistic
relationships in the community.The 16S rRNA clone library approach is useful to
study interesting symbiotic associations in bacterial communities. This and
other techniques may also be useful in predicting clinical outcomes based on
their association with specific consortia of bacteria. To do so requires a novel conceptualization of
the disease process in that one particular organism is not necessarily defined
as the causative agent, but
rather the entire community is involved in causing the outcome. There is little
information available to generate these types of risk models, but the clinical
potential in using microbial ecological inferences to guide therapies (i.e.,
tapering antibiotic treatments, probiotics, etc.) and prevention certainly
warrants further investigation.


## 7. Conclusions and Future Direction

Comparative studies that use microbial ecology
techniques to analyze temporally sampled patients and control individuals are a
promising approach to complex disease research. Traditional culture-based
methods continue to be the gold standard for disease diagnostics, but this
approach can only detect organisms that are easy to isolate and have simple
metabolic requirements. Since the vast majority of the human gut microbiota is
currently noncultivable, a nonculture-based approach may be more useful for the
diagnosis and prediction of clinical outcomes [70]. Analyzing the metagenome is such an approach and
can be used to identify members of complex bacterial communities based on
nucleotide variability in conserved genes [70, 71]. New technologies, such as pyrosequencing, have recently become
available and attain the high throughput and resolution required to make
detailed community comparisons based on more than one locus. An added benefit
of these technologies is that reagents and chemistries are constantly being
re-engineered so that efficiency is maximized at lower cost.



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