“Microbiota Dynamics in Patient Treated with Fecal Microbiota Transplantation for Recurrent Clostridium Difficile Infection” by Yang Song, Shashank Gard
I need summative article evaluation which consists of Introduction, Body-Discussion, Conclusion and Summary.
Introduction – identify topic, explain why it is important. State the problem as simple as possible taking into account the broader view of the discipline as a whole.
Body/discussion – review of research material, summarizes the material to support the theory of the research. Critically analyzed unbiased result of the research, present statistical data to judge the findings, may reference literature order to show how research builds upon previous work in the field of study.
Conclusion – explain in expert-like detail what has been learned, explained, decided, proven. Reveal the ways in which the paper’s thesis might have significance in society. A conclusion should strive to answer questions that readers logically raise-“Why are you telling me this?”
Summary: Point out importance or implication of the research on an area of societal concern. Mention the lack of conclusion in the field to demonstrate understanding of the subject’s complexity, may choose to propose what may be the natural next step to take in light of what the argument is attempting to convince.
Microbiota Dynamics in Patients Treated with Fecal
Microbiota Transplantation for Recurrent Clostridium
Yang Song1, Shashank Garg2, Mohit Girotra2, Cynthia Maddox1, Erik C. von Rosenvinge3, Anand Dutta2,
Sudhir Dutta2,4, W. Florian Fricke1*
1 Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, 2 Division of Gastroenterology, Sinai Hospital
of Baltimore, Baltimore, Maryland, United States of America, 3 Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore,
Maryland, United States of America, 4 Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
Clostridium difficile causes antibiotic-associated diarrhea and pseudomembraneous colitis and is responsible for a large and
increasing fraction of hospital-acquired infections. Fecal microbiota transplantation (FMT) is an alternate treatment option
for recurrent C. difficile infection (RCDI) refractory to antibiotic therapy. It has recently been discussed favorably in the
clinical and scientific communities and is receiving increasing public attention. However, short- and long-term health
consequences of FMT remain a concern, as the effects of the transplanted microbiota on the patient remain unknown. To
shed light on microbial events associated with RCDI and treatment by FMT, we performed fecal microbiota analysis by 16S
rRNA gene amplicon pyrosequencing of 14 pairs of healthy donors and RCDI patients treated successfully by FMT. Post-FMT
patient and healthy donor samples collected up to one year after FMT were studied longitudinally, including one post-FMT
patient with antibiotic-associated relapse three months after FMT. This analysis allowed us not only to confirm prior reports
that RCDI is associated with reduced diversity and compositional changes in the fecal microbiota, but also to characterize
previously undocumented post-FMT microbiota dynamics. Members of the Streptococcaceae, Enterococcaceae, or
Enterobacteriaceae were significantly increased and putative butyrate producers, such as Lachnospiraceae and
Ruminococcaceae were significantly reduced in samples from RCDI patients before FMT as compared to post-FMT patient
and healthy donor samples. RCDI patient samples showed more case-specific variations than post-FMT patient and healthy
donor samples. However, none of the bacterial groups were invariably associated with RCDI or successful treatment by FMT.
Overall microbiota compositions in post-FMT patients, specifically abundances of the above-mentioned Firmicutes,
continued to change for at least 16 weeks after FMT, suggesting that full microbiota recovery from RCDI may take much
longer than expected based on the disappearance of diarrheal symptoms immediately after FMT.
Citation: Song Y, Garg S, Girotra M, Maddox C, von Rosenvinge EC, et al. (2013) Microbiota Dynamics in Patients Treated with Fecal Microbiota Transplantation
for Recurrent Clostridium difficile Infection. PLoS ONE 8(11): e81330. doi:10.1371/journal.pone.0081330
Editor: Gabriele Berg, Graz University of Technology (TU Graz), Austria
Received August 29, 2013; Accepted October 20, 2013; Published November 26, 2013
Copyright: ! 2013 Song et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study or parts thereof were funded by the Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, MD and
Gastroenterology Research Funds from the Division of Gastroenterology, Department of Medicine, Sinai Hospital of Baltimore, Baltimore, MD. The funders had no
role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: [email protected]
Clostridium difficile, the pathogen associated with the majority of
infective antibiotic-associated diarrhea and causative agent of
pseudomembraneous colitis , is responsible for a large fraction
of nosocomial, or hospital-acquired, disease . Today, in parts of
the U.S., the incidence of infections with C. difficile is higher than
that of methicillin-resistant Staphylococcus aureus . C. difficile
infection (CDI) is believed to result from gastrointestinal dysbiosis,
i.e., the disruption of the resident microbiota, often caused by
antibiotic treatment, which enables C. difficile to establish an
infection. C. difficile can be acquired via fecal-oral transmission of
spores that survive atmospheric oxygen and gastric acid exposure
and germinate in the large intestine. However, carriage of C.
difficile is not always associated with disease, as asymptomatic C.
difficile colonization is well recognized , especially in newborns
and infants of ,1 year age .
Besides treatment with almost any antibiotic [6–14], other
factors associated with increased risk for C. difficile infection include
old age, recent hospitalization, tube feeding, use of gastric acidsuppressing
drugs and underlying chronic disease, including
inflammatory bowel disease [15–19]. Recent evidence suggests
that excessive inflammatory responses in the human host enhance
the severity of CDI .
Standard treatment for C. difficile infection consists of metronidazole
or vancomycin administration and, more recently, fidaxomicin.
However, the rate of recurrent C. difficile infection (RCDI)
after initial therapy is about 20%  and even higher after
subsequent antibiotic courses and recurrences [8,22]. Consequently,
despite current therapeutic options, RCDI treatment has
become increasingly challenging and the incidence of RCDI has
been rising during the past decade resulting in increased
healthcare cost and significant morbidity .
PLOS ONE | www.plosone.org 1 November 2013 | Volume 8 | Issue 11 | e81330
Fecal microbiota transplantation (FMT), which aims to restore a
normal, functional intestinal microbiota from a healthy donor in
the RCDI patient, has recently received increasing attention in
clinical and research communities [24–27] and has also become a
popular subject of discussion in other media. First documented in
the fourth century in China and in 1958 in the U.S., FMT was
shown in a recent systematic review of 317 patients in 27 separate
studies to have an overall success rate of 92% . The exact
mechanism of action responsible for the success of FMT to treat
RCDI remains unknown and there is no clinically validated set of
parameters to define a suitable donor or ideal donor microbiota,
although attempts in this direction have been made . Shortand
long-term effects of FMT on the recipient microbiota remain
a concern, especially in light of the growing body of literature that
implicates the gastrointestinal microbiota in a large number of
diseases . For the same reason, there is significant clinical
interest in therapeutic options to target the microbiota to treat
microbiota-associated health problems besides RCDI. As a result,
attempts to treat IBD [31–33], metabolic syndrome  and other
diseases [35,36] by FMT have been made.
Clinical concerns and the increasing number of FMT procedures
performed by U.S. physicians recently led the U.S. Food
and Drug Administration (FDA) to release new guidelines that
define FMT as a biologic therapy that requires physicians to
obtain an investigational new drug (IND) application . Shortly
after this guideline was a released, however, the FDA announced a
decision to exercise enforcement discretion in order to allow
physicians to perform FMT in patients with RCDI not responsive
to standard therapy. The urgency for further research into the
short- and long-term effects of FMT is highlighted by the fact that
the public awareness of FMT as a treatment option for RCDI has
increased to a degree where do-it-yourself protocols have become
available over the Internet and the procedure is being performed
without medical surveillance.
In this study, we applied 16S rRNA amplicon pyrosequencing
to analyze fecal samples from RCDI patients and their
corresponding donors before and after FMT. For the first time,
we included longitudinal simultaneous sampling of both post-FMT
patients and healthy donors for up to one year after FMT. This
unique sample set allowed us to describe previously undocumented
microbiota dynamics in post-FMT patients after resolution of
CDI. In addition, inclusion of a patient, who was initially treated
successfully by FMT but experienced relapse after new antibiotic
treatment, provided us with the unique opportunity to distinguish
microbiota changes seen in a previously asymptomatic patients
after relapse of CDI from those apparent in RCDI patients with
long-term disease and multiple courses of anti-C. difficile antibiotic
Materials and Methods
Study cohort and sample collection
The Institutional Review Board of Sinai Hospital Baltimore
approved the study under protocol number #1826 and all subjects
provided their written informed consent to participate in the study.
FMT was performed at Sinai Hospital of Baltimore, Baltimore,
MD by infusion of a fecal solution prepared by a predefined
protocol (Dutta et al., submitted) based on Aas et al. . Potential
donors were thoroughly clinically evaluated based on history,
physical examination and serological screening for HIV, syphilis,
hepatitis A, B and C and Helicobacter pylori infection. Fecal
specimens of patients and donors were tested 3–5 days before
FMT for the presence of pathogenic bacteria (salmonella, shigella,
yersinia), parasites (entamoeba, giardia, worms), and C. difficile.
Patients were admitted to the hospital the day before and bowel
prep administered the night before FMT. Patients were also
administered a proton pump inhibitor (omeprazole, 20 mg) on the
evening and morning before the procedure. Donor fecal samples
(25–30 g) were mixed with 250 ml of sterile saline buffer, mixed
into slurry and filtered once with surgical gauze for large particles
and twice with a coffee filter. The volume of the filtrate was
increased to 450 ml with sterile saline buffer and divided into 5
aliquots of 90 ml. For FMT, two aliquots (180 ml) were
endoscopically delivered by spray catheter into the jejunum. The
remaining three aliquots were instilled by colonoscopy into the
right colon (180 ml) and transverse and upper descending colon
The clinical aspects of this study, including a comprehensive
description and discussion of the FMT-treated patient population
and individual case metadata, are provided in a separate
publication (Dutta et al., submitted). Fecal samples were collected
from 14 patient-donor pairs and used for this study (Fig. 1; Table
1). All patients had at least three recurrences of C. difficile infection
and were treated with at least three courses of antibiotics. Fecal
samples were collected before and after FMT from patients and, at
corresponding time points, from their respective donors, which
included family members (spouses and children) and friends (Fig.
Sample collection and nucleic acid isolation
All fecal samples were self-collected by patients and donors
without bowel preps, stored in the freezer and within 24 hours
brought to Sinai Hospital, after which they were stored at –80uC.
Patients stopped antibiotic use 5 days before the FMT procedure;
RCDI patient samples were taken 1–2 days prior to FMT. For
processing, samples were thawed at 4uC and in aliquots of 0.15 g
per tube re-suspended in 1 ml of 1 6phosphate-buffered saline.
Cell lysis was initiated with two enzymatic incubations, first using
5 ml of lysozyme (10 mg ml21; Amresco, Solon, OH, USA), 13 ml
of mutanolysin (11.7 U ml21; Sigma-Aldrich) and 3 ml of lysostaphin
(4.5 U ml21; Sigma-Aldrich) for an incubation of 30 min at
37uC and, second, using 10 ml Proteinase K (20 mg ml21;
Research Products International, Mt Prospect, IL, USA), 50 ml
10% SDS and 2 ml RNase (10 mg ml21) for an incubation of 45
min at 56uC. After the enzyme treatments, cells were disrupted by
Figure 1. Overview of analyzed patient and donor samples.
RCDI patient samples are marked in red, post-FMT patient samples in
blue and donor samples in green. *Patient #6a experienced antibioticinduced
relapse of C. difficile infection and was treated successfully with
a second round of FMT as patient #6b. In the NCBI short read archive,
samples referred to as #6b are designated as #7 samples.
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 2 November 2013 | Volume 8 | Issue 11 | e81330
bead beating in tubes with Lysing Matrix B (0.1 mm silica spheres,
MP Biomedicals, Solon, OH, USA), at 6 m s21 for 40 s at room
temperature in a FastPrep-24 (MP Biomedicals). The resulting
crude lysate was processed using the ZR Fecal DNA mini-prep kit
(Zymo, Irvine, CA, USA) according to the manufacturer’s
recommendation. The samples were eluted with 100 ml of ultra
pure water into separate tubes. DNA concentrations in the
samples were measured using the Quant-iT PicoGreen dsDNA
assay kit (Molecular Probes, Invitrogen, Carlsbad, CA, USA).
Amplification and sequencing
In brief, hypervariable regions V1–V3 of the bacterial 16S
rRNA gene were amplified with primers 27F and 534R as
described previously . DNA amplification of 16S rRNA genes
was performed using AccuPrime Taq DNA polymerase High
Fidelity (Invitrogen) and 50 ng of template DNA in a total reaction
volume of 25 ml, following the AccuPrime product protocol.
Reactions were run in a PTC-100 thermal controller (MJ
Research, Waltham, MA, USA) using the following protocol: 3
min of denaturation at 94uC, followed by 30 cycles of 30 s at 94uC
(denaturation), 30 s at 52uC (annealing) and 45 ss at 68uC
(elongation), with a final extension at 68uC for 5 min.
Equimolar amounts (50 ng) of the PCR amplicons were mixed
in a single tube. Amplification primers and reaction buffer were
removed using the AMPure Kit (Beckman Coulter, Brea, CA,
USA) and purified amplicon mixtures sequenced at the Institute
for Genome Sciences, University of Maryland, using 454 primer A
and protocols recommended by the manufacturer (Roche,
Branford, CT, USA). Raw sequences were deposited in the Short
Read Archive Database (http://www.ncbi.nlm.nih.gov/sra; project
number SRP016902). In the NCBI short read archive, samples
referred to as #6a are designated as #6 samples and samples
referred to as #6b as #7 samples.
Sequence processing and analysis
16S rRNA sequence reads were processed with QIIME 
and CloVR , using the automated CloVR-16S pipeline as
described in the corresponding standard operating procedure .
Briefly, using the QIIME split_libraries.py tool sequences were
binned based on sample-specific barcodes, trimmed by removal of
barcode and primer sequences and filtered for quality, using the
default parameters, except for “—barcode-type “variable_length”.
Chimeric sequences were removed with UCHIME  using
MicrobiomeUtilities (http://microbiomeutil.sourceforge.net/) and
the rRNA16S.gold.fasta reference database. Reads were clustered
into operational taxonomic units (OTUs) using a similarity
threshold of 95%. On average, OTUs were classified using the
RDP Naive Bayesian Classifier  with a score filtering threshold
of 0.5. Rarefaction curves were calculated based on OTU counts
using the rarefaction.single routine of the Mothur package .
Hierarchical clustering, boxplots, and statistical calculations
(Wilcoxon rank sum tests, Jensen-Shannon divergence etc.) were
performed in R. Differentially abundant OTUs were determined
with Metastats . Phylogenetic trees were created with
FastTree2  using trimmed alignments generated with NAST.
Dot plots to evaluate phylogenetic distances and Jensen-Shannon
divergence between sample pairs and changes in relative
abundance of specific taxonomic families over time were
generated with Prism5 (version 6 for Mac, GraphPad Software,
San Diego CA, USA).
Results and Discussion
Patient population, sample set and sequence data
For this longitudinal study, fecal samples were collected from 14
pairs of RCDI patients, treated successfully by FMT, and their
respective donors (Fig. 1). In addition to the 14 donor samples used
for FMT, 11 samples from pre-FMT RCDI patients and 17
samples from eight post-FMT patient samples, as well as 14
samples from eight healthy donors collected after FMT were
Table 1. RCDI patient study population.
Case [#] Sex Age
Time to resolution of
symptoms [days] Follow up [months] Inciting antibiotic
1 F 65 18 Husband 2 26 Beta-lactam1 + lincosamide2
2 F 65 6 Husband 3 21 multiple
3 F 61 5 Friend 2 22 Lincosamide2
4 F 56 12 Friend 3 19 Fluoroquinolones
5 F 76 72 Friend 2 7 Fluoroquinolones
6a* F 57 8 Son 3 18 Fluoroquinolones
6b* 2 Brother 4 Fluoroquinolones3
8 F 72 5 Daughter 3 17 Unknown
9 F 63 6 Husband 2 17 Lincosamide2 +
10 F 61 11 Husband 3 17 Clindamycin
11 M 68 6 Wife 3 16 Unknown
12 F 41 12 Husband 2 16 Lincosamide2
13 F 79 12 Husband 3 12 Unknown
14 M 57 4.5 Wife 2 12 Unknown
*#6a had a relapse of RCDI one month after successful FMT and received a second FMT three months after the first (#6b). In the NCBI short read archive, samples
referred to as #6b are designated as #7 samples.
1Penicillin; 2 clindamycin; 3 ciprofloxacin; 4 levofloxacin.
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 3 November 2013 | Volume 8 | Issue 11 | e81330
analyzed, collected between one week and one year after the
procedure, (total number of samples: 56). This allowed us to
perform the first characterization of long-term microbiota changes
in patients after FMT. All treated RCDI patients experienced
resolution of diarrheal symptoms within 2–3 days after FMT
(Table 1), in accordance with previous reports . Of the post-
FMT samples collected from asymptomatic patients, 14 were
paired with donor samples collected at the same time points to
serve as a control for intra-individual, longitudinal variations not
associated with RCDI. RCDI patient #6a was successfully treated
by FMT but experienced recurrence of C. difficile infection one
month later, after being treated for a urinary tract infection with
ciprofloxacin. Subsequent oral vancomycin and intravenous
immunoglobulin therapy did not resolve the problem. The patient
#6a was treated successfully for a second time by FMT, three
months after the first FMT (designated as case #6b). Selected
characteristics of all cases for which samples were analyzed are
summarized in Table 1. Additional clinical aspects of this study
have been described in a separate publication  FMT donors
for this study were chosen by the RCDI patients and included
genetically unrelated individuals living in the same household (8x
spouses), as well as genetically related (2x children) or unrelated (3x
friends) individuals living in households separate from those of the
RCDI patients (Table 1). On average, 3,315 sequence reads were
obtained per sample using the Roche/454 GS FLX Titanium
platform (average sequence length: 527 bp). A list of read numbers
and identified operational taxonomic units (OTUs) for each of the
samples is part of the supplement (Table S1).
Reduced microbiota diversity in RCDI patients increases
Reduced microbiota diversity associated with C. difficile infection
is reported in humans [49-51] and mice [52,53]. This finding was
confirmed in our study with multiple post-FMT samples collected
up to one year after the procedure. Compared to healthy donors
the fecal microbiota diversity of RCDI patients was reduced, as
shown by rarefaction analysis of OTU counts (Fig. 2). Microbiota
diversity increased significantly in post-FMT patient samples, as
demonstrated by Shannon diversity index calculations (p,0.01,
Wilcoxon rank sum test) between RCDI (mean 1.686 0.75) and
post-FMT (mean 3.376 0.46) patient samples (Fig. 3). Microbial
richness was also increased in post-FMT compared to RCDI
patient samples, based on the comparison of mean ACE indices
(46%; p , 0.001). Interestingly, no significant difference in
microbial diversity or richness was noted between post-FMT
patient and donor samples as determined by Shannon and ACE
indices. Shannon diversity increased in all 17 post-FMT patients as
soon as one week after FMT and remained stable and comparable
among different patients for up to one year afterwards (Fig. S1).
Compared to the RCDI sample collected before the first FMT
treatment (#6a_P0), microbial diversity in the RCDI sample from
the same patient collected three months later after RCDI relapse
(#6b_P0) showed a 2-fold increase based on the Shannon index
but was still low compared to healthy donor samples (Fig. 3).
These results suggest that FMT restores the reduced microbiota
diversity associated with RCDI. Furthermore, diversity increases
immediately after FMT and remains stable over time.
FMT shifts fecal microbiota towards healthy donor
To gain further insights into the effects of FMT on the patient
microbiota, shared OTUs between RCDI patients, post-FMT
patients and healthy donor samples were determined (Fig. S2).
Using a threshold of at least five supporting reads across all 38
samples for OTUs to be considered in the comparison, a total of
1,321 OTUs were identified of which 876 (65%) were only
identified in post-FMT patient and healthy donor samples but
never in RCDI patient samples. This finding could be interpreted
to indicate that post-FMT patients acquired donor OTUs as a
consequence of FMT. However, the applied analysis has a
detection limit of approximately 0.03% and does not allow for
the distinction of different bacterial strains from the same OTU. It
is therefore impossible to distinguish between OTUs that might
have been present in RCDI patients below the detection limit and
those that were acquired from the donors.
Microbiota compositions were analyzed based on phylogenetic
distance calculations between samples using the unweighted, i.e.,
comparing OTU presences/absences, and weighted, i.e., including
quantitative information about detected OTUs, UniFrac metric
(Fig. 4). Principal coordinate analyses (PCoA) of the unweighted
UniFrac comparison showed that most of the compositional
variation among samples is accounted for by post-FMT patient
and healthy donor samples (Fig. 4A). In contrast, when OTU
abundance is also taken into consideration (weighted UniFrac
analysis) most of the variation within the entire sample set is
observed among RCDI patient samples (Fig. 4B), suggesting that
relative abundances of major microbiota members can vary
substantially not only between RCDI patient and healthy donor
samples but also among different RCDI patient samples.
In most cases, FMT resulted in the adoption of a fecal
microbiota composition in post-FMT samples that was similar to
that of healthy donors. This is apparent in the clustering of post-
FMT patient and healthy donor samples in unweighted UniFrac
analysis (Fig. 4A). However, several patients appeared to at least
temporarily return to pre-FMT fecal microbiota composition
states (e.g., Patient #8 at 5 months and Patient #14 at 3 weeks
after FMT), although all treated patients were reported to be
symptom-free within 2–3 days after FMT. The adoption of a fecal
microbiota composition in post-FMT patient samples similar to
Figure 2. Microbiota rarefaction curves showing fecal microbiota
diversity in RCDI (red) and post-FMT (blue) patient and
donor (green) samples. Each curve shows the average number of
OTUs found in a given number of sampled sequences. OTUs can be
treated as equivalent to taxonomic species in the sequence space. RCDI
samples are marked from patient #6a (*), who experienced antibioticinduced
relapse and was treated by FMT again as patient #6b (**).
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 4 November 2013 | Volume 8 | Issue 11 | e81330
that of healthy donors was also supported by comparing mean
phylogenetic UniFrac distances. These were significantly larger
between RCDI and post-FMT patient samples than between post-
FMT and donor samples both in unweighted (p,0.05) and
weighted (p,0.01) UniFrac analysis.
Interestingly, the RCDI sample from the patient (#6a/b), who
relapsed after unrelated antibiotic treatment, showed a microbiota
composition that was similar to that of other post-FMT and
healthy donor samples, especially in the weighted UniFrac analysis
(Fig. 4). This second RCDI episode lasted only two months and
included treatment with a single antibiotic (vancomycin) compared
to 4.5–72 months duration and at least three different antibiotic
treatments in other RCDI patients, It is therefore possible that
several of the phenotypes observed in other RCDI samples are
reflective of long-term disease and multiple antibiotic treatment
courses. The data presented here suggest that RCDI is associated
with the presence or absence of specific fecal microbiota members
(i.e., co-clustering of all RCDI samples in unweighted UniFrac
analysis, including #6b_P0), rather than significant changes in the
relative abundance of major microbiome components (i.e.,
separate clustering of different RCDI samples and of #6b_P0
with healthy donor samples in weighted UniFrac analysis), which
could represent a consequence of long-term disease.
FMT affects predominantly Firmicutes and
The identification of specific microbiota members associated
with RCDI and successful FMT treatment bears the potential to
identify new diagnostic markers to predict susceptibility to C.
difficile infection or infection relapse in at-risk populations. In
addition, this knowledge may provide the insights required to
assemble culture-based “probiotic” bacterial mixtures as substitutes
for transplantation of fecal samples, as has recently been
demonstrated in humans  and the mouse model . Towards
this goal, the relative abundances of all identified microbial taxa
were compared between RCDI and post-FMT patient and healthy
donor sample groups using Metastats . Among these three
groups, bacteria from only three taxonomic orders, belonging to
two phyla, showed significant changes, i.e., Clostridiales and
Figure 3. Microbiota diversity (Shannon) and richness (ACE) of
RCDI and post-FMT patient and donor samples. (A) Shannon
index; (B) ACE index. Significant differences are shown (*, p,0.01; **,
p,0.001) as measured by Wilcoxon rank sum test. RCDI samples from
patient #6a (+), who experienced antibiotic-induced relapse and was
treated by FMT again as patient #6b (++) are marked.
Figure 4. Unscaled principal coordinate analysis (PCoA) plots showing unweighted (A) and weighted (B) UniFrac analysis of RCDI
(red) and post-FMT (blue) patient and healthy donor (green) samples. RCDI patient samples are circled in red. RCDI samples from patient
#6a (*), who experienced antibiotic-induced relapse and was treated by FMT again as patient #6b (**) are marked in dark red. Sample names
indicate case numbers, patient or donor source and time point of collection (“0” time point refers to pre-FMT sampling time points; other time points
are abbreviated as weeks [w], months [m] and year [y]).
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 5 November 2013 | Volume 8 | Issue 11 | e81330
Lactobacillales (both from phylum Firmicutes) and Enterobacteriales
(phylum Proteobacteria) (Fig. 5). Clostridiales, which include
the species C. difficile, were present at only 12.8% in RCDI patient
samples and significantly increased in post-FMT samples (55%)
but still remained lower compared to healthy donor samples (70%)
(p,0.001, unpaired t-test with unequal variance). Lactobacillales,
which were present at high abundance in RCDI patient samples
(mean: 58%), were significantly decreased in post-FMT patient
(22%) and healthy donor (5%) samples. However, abundance of
Lactobacillales remained higher in post-FMT patient compared to
donor samples (p,0.01). Enterobacteriales, present at 6.5% in
RCDI patient samples, were less than 1% in post-FMT patient
and donor samples (p,0.001).
Three taxonomic families within the order Clostridiales
(phylum: Firmicutes) significantly increased in relative abundance
between RCDI and post-FMT patient samples (p,0.01), Lachnospiraceae,
Peptostreptococcaceae, and Ruminococcaceae (Fig. 6). Most
prominently, an uncharacterized genus within the Lachnospiraceae
family (Lachnospiraceae Incertae Sedis) increased from on average
3% in RCDI patient samples to 30% in post-FMT patient samples
and was 39% in healthy donor samples (p,0.01). The dominant
OTU within this genus (99% identical to GenBank Acc.-No.:
EF399262) was identified in all 28 donor samples (27 samples with
.4 reads), 15 out of 17 post-FMT patient samples (14 samples
with .4 reads), and 8 out of 11 RCDI patient samples (#6b was
the only sample with .4 reads). C. difficile is a member of the
Peptostreptococcaceae , which increased in patients after FMT.
Moreover, an unknown genus within this family accounts for .2%
of the fecal microbiota in healthy donor samples (Fig. 6),
demonstrating that taxonomically close relatives of C. difficile exert
non-pathogenic or even beneficial functions in the healthy
Within the orders Lactobacillales (phylum: Firmicutes) and
Enterobacteriales (phylum: Proteobacteria), the genera Enterococcus
and Klebsiella, which were present on average at 18% and 4% in
RCDI patient samples, respectively, were significantly reduced to
less than 0.1% in post-FMT patient samples (p,0.01). Members of
the Streptococcaceae (phylum: Firmicutes), the dominant taxonomic
family in RCDI patient samples (mean: 30.1%), were reduced on
average by more than 10% after FMT, although this change was
not statistically significant due to large variations between RCDI
patients. With the exception of the genus Streptococcus, none of these
families or genera showed significant differences in relative
abundance between post-FMT patient and healthy donor samples
(p,0.05). Streptococcus was the only genus with a significant
difference in relative abundance between both RCDI patient
and donor samples and between post-FMT patient and donor
samples. As post-FMT patients appear to show increased
susceptibility to C. difficile infection compared to healthy donors,
if additional antibiotic medication to treat unrelated infections
becomes necessary , the increased abundance of the Streptococcus
genus in this population could play a role for this
susceptibility. However, not all RCDI samples contained high
counts of Streptococcus sequences (range: 0.1% to 82.4%). In
general, different RCDI samples showed more variation in the
abundance of microbiota members that were increased relative to
healthy donors (e.g., Enterococcaceae and Streptococcaceae) than of
microbiota members that were reduced (see error bars in Fig. 6).
This may suggest that the second group provides a better target for
the identification of diagnostic markers for RCDI (e.g., among the
Lachnospiraceae, Peptostreptococcaceae, and Ruminococcaceae).
In contrast to all other cases, the fecal RCDI microbiota from
patient #6b, who experienced antibiotic-induced relapse of C.
difficile infection, contained large fractions of Lachnospiraceae (11%
compared to no detection before the first FMT and on average 1%
in other RCDI samples) and Akkermansia (60% compared to on
average 0.1% in other RCDI samples and 1.8% in healthy donor
samples) (Fig. S3). This atypical composition could be responsible
for the clustering of this sample with healthy donor and post-FMT
patient samples in the weighted UniFrac analysis (Fig. 4B). It is
therefore possible that the reductions in Lachnospiraceae characteristic
of the other RCDI samples, rather than being a cause of
disease susceptibility, represent an effect of disease duration and
number of antibiotic treatment regimens exceeding those that
patient #6b experienced after recurrence. Interestingly, Akkermansia
spp. have recently received special attention in human
microbiome research because of their ability to colonize the
intestinal mucosa and to utilize mucus as a sole carbon and
nitrogen source [57,58]. While A. municiphila has been proposed as
a marker of a healthy intestine, due to its production of short chain
fatty acids and its negative correlation with inflammatory bowel
diseases, appendicitis and obesity (reviewed here:), its high
abundance in the fecal sample of patient #6b might also be an
indicator of high concentrations of mucus in the stool, which could
be the result of acute diarrhea.
The fecal microbiota continues to change in
asymptomatic post-FMT patients
Asymptomatic post-FMT patients appear to be at higher risk for
recurrence of C. difficile infection compared to patients without a
history of RCDI, if additional antibiotic medication to treat
unrelated infections becomes necessary . Whether specific
microbiota features, such as the increased abundance of Streptococcus
in post-FMT patient compared to healthy donor samples,
are responsible for this susceptibility is unknown, but the
susceptibility of post-FMT patients to RCDI may decrease over
time and little is known about the long-term dynamics of FMTinduced
microbiota changes. In order to characterize microbiota
changes after FMT over time, fecal samples from post-FMT
patients, all of which were asymptomatic with respect to RCDI,
were compared longitudinally. Microbiota diversity in post-FMT
patient samples did not change significantly over time, as
measured by comparing the Shannon diversity index (Fig. S1).
Figure 5. Microbiota changes between RCDI and post-FMT
patient and healthy donor sample groups at the taxonomic
order level. Significant differences between sample groups as
calculated with the Metastats tool are marked with asterisks (p,0.01).
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 6 November 2013 | Volume 8 | Issue 11 | e81330
To study changes in microbiota composition over time, weighted
and unweighted UniFrac distances and the Jensen-Shannon
divergence were calculated between (i) RCDI and post-FMT
patient sample pairs, (ii) donor and post-FMT patient samples
pairs and, as a control for temporal variations in healthy
individuals, between (iii) sample pairs collected from the same
donor before and after FMT (Fig. 7). For the comparison of post-
FMT and RCDI patient samples, both unweighted UniFrac and
Jensen-Shannon distance metrics displayed a significant linear
change over time when plotted on a logarithmic scale. However,
comparison of post-FMT patient and donor samples or of donor
samples collected before and after FMT did not. That this
correlation is only apparent if temporal changes are plotted on a
logarithmic scale shows that the most significant changes happen
immediately after FMT and that the microbiota continues to
evolve over time albeit at a decreasing rate.
Individual taxonomic families showed similar trends in post-
FMT patients over time, if compared case-by-case, i.e. increases in
Lachnospiraceae and Ruminococcaceae and decreases in Streptococcaceae
(Fig. 8). However, in contrast to changes in relative abundance
between the pre- and post-FMT patient microbiota (Fig. 6),
changes in post-FMT patients over time were not significant for
the three studied Firmicutes families. This suggests that, while
changes in the abundance of Lachnospiraceae and/or Streptococcaceae
might play important roles for RCDI or successful recovery after
FMT in some patients, general post-FMT microbiota dynamics
across the entire patient population are better described using
metrics that take account of the microbiota as a whole, i.e.,
UniFrac distances and Jensen-Shannon divergence.
‘Keystone’ species are not identified in RCDI or FMT
The concept of keystone species has been used to describe the
disproportionate importance of a single or a few organisms for the
structure or function of an entire environment [59,60], e.g. in the
oral cavity where colonization with the commensal bacterium
Porphyromonas gingivalis even at low abundance can play a major
role for microbiota changes associated with periodontitis . In
the context of RCDI and FMT, keystone bacteria could be crucial
for the identification of diagnostic markers to predict susceptibility
to C. difficile infection and as substitutes for fecal samples of largely
unknown composition to be used in transplantation. That RCDI
can principally be treated by transplantation of in vitro-assembled
microbial communities instead of fecal material was shown
recently in humans  and mice , although little justification
was provided for the selection of specific bacterial species or
strains. While, based on our findings and previous data, members
of the Lachnospiraceae family, for example, might present themselves
as keystone candidates [50,62,63], at least one case was found in
our cohort where RCDI was associated with relatively high counts
of Lachnospiraceae (i.e., #6b). In another case (#9), Lachnospiraceae
did only increase temporarily six weeks after FMT but dropped to
pre-FMT levels 12 weeks after FMT. Khoruts et al. found a
Figure 6. Microbiota changes between RCDI and post-FMT patient and healthy donor sample groups at the taxonomic family and
genus levels. Significant differences between sample groups as calculated with the Metastats tool are marked with asterisks (p,0.01). Note that
standard deviations are smaller for genera that increased in post-FMT relative to RCDI patient samples (e.g., Lachnospiraceae Incertae Sedis)
compared to those that decreased (e.g. Streptococcus), which reflects differences in the relative abundances of major microbiota members among
RCDI patient samples.
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 7 November 2013 | Volume 8 | Issue 11 | e81330
relatively large proportion (.5%) of Lachnospiraceae Inc. Sed. in an
RCDI sample before FMT treatment . Interestingly, the
dominant representative of the genus Lachnospiraceae Inc. Sed.
associated with successful FMT treatment, which was identified in
the Canadian study by Shahinas et al. , is different from the
one identified here (Shahinas: 97% identical to GenBank Acc.-No.
JX230866, compared to this study: 99% identical to EF399262).
This difference could either result from variations in the applied
pyrosequencing protocols (e.g., Shahinas et al. used primers
specific for hypervariable regions V5–V6 instead of primers
specific for V1–V3 used here) or indicate that different species or
strains of the genus Lachnospiraceae Inc. Sed. circulate in U.S. and
Canadian human populations. In any case, it seems as if neither
RCDI nor FMT are associated with the presence or absence of a
single specific microbiota fraction.
Figure 7. Post-FMT microbiota changes. Unweighted (A) and
weighted (B) UniFrac distances and Jensen-Shannon divergence (C)
metrics were calculated between post-FMT and RCDI patient sample
pairs (red), post-FMT patient and donor sample pairs (green) and
between donor sample pairs collected over time (blue) and plotted on
logarithmic scales. R2 values and p-values to establish whether the
slope of the curve was significantly different from zero are shown with
asterisks indicating significance (p,0.05, F-test). The 20-week data
point of patient #8 was classified as outlier and not included in the
analyses, based on the Bonferroni-adjusted outlier test, and is shown
with parentheses. One-year time points (patient and donor #1) were
also classified as outliers and omitted from the analysis and plot. A plot
showing all data points including those omitted is part of the
supplement (Fig. S4).
Figure 8. Post-FMT changes in selected microbiota members
by case (genus level). (A) Lachnospira Incertae Sedis; (B) Ruminococcus;
(C) Streptococcus. Genus-specific changes in relative abundance
over time were not significant (p.0.05)when samples were grouped by
time periods (1 week, 2–4 weeks, 6–8 weeks, 12–20 weeks) and groups
compared with a non-parametric statistical test (Wilcoxon rank sum
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 8 November 2013 | Volume 8 | Issue 11 | e81330
Instead of bacterial keystone taxa, specific microbial microbiota
genes or transcripts could be associated with health and disease
and, thus, serve as “keystone functions” with potential as
diagnostic markers. A redundancy and similarity of functional
microbiota compositions between individuals despite significant
taxonomic variation has previously been demonstrated for the
healthy human microbiota . These functions could be
predominantly but not exclusively associated with certain members
of the fecal microbiota, which would then still show statistical
correlations with health and disease states. Short-chain fatty acid
(SCFA) production plays an important role in the regulation of
intestinal inflammatory processes  and intestinal barrier
maintenance [66–68] and has been discussed in the context of
RCDI, as C. difficile infection in the mouse model was shown to
alter SCFA profiles . Consequently, the reduction of
Lachnospiraceae and Ruminococcaceae has been interpreted as a
depletion in butyrate-producing bacteria . Shotgun sequencing
of total metagenomic DNA and/or metatranscriptomic RNA
isolates will be needed to confirm the lack of butyrate production
in the fecal RCDI microbiota or to associated other “keystone
functions” with RCDI and FMT.
Concomitant effects of antibiotics and diarrhea
Previous RCDI microbiota studies have had difficulty determining
the chain of events leading to disease as well as the
relationship between observed microbiota phenotypes and disease.
C. difficile infection is typically initiated by antibiotic treatment and
phenotypically characterized by severe diarrhea. Both events by
themselves have a massive impact on the fecal microbiota
independent of the disease caused by the C. difficile infection
[69,70]. It is therefore difficult to distinguish between microbiota
changes that play a causative role in RCDI and those that simply
co-occur. The data presented here also include an RCDI patient
with successful FMT and subsequent relapse of CDI after
antibiotic treatment, whose fecal microbiota showed characteristics
described for healthy individuals as opposed to RCDI patients
(e.g. relatively high Lachnospiraceae abundance). This single patient
may therefore suggest that multiple rounds of antibiotic treatment
and/or long-term duration of the disease are needed to induce
some of the microbiota changes previously reported to be
associated with CDI. In order to determine the exact time line
of events, prospective studies are needed starting before antibiotic
treatment and following patients during the onset and course of
In accordance with previous reports, we found a reduction in
microbiota diversity and richness in fecal samples from RCDI
patients compared to healthy donors, which was restored after
FMT. Similarly, our results confirm previous findings that FMT
changes the RCDI fecal microbiota to become more similar to
that of healthy donors. We extend current knowledge by
demonstrating that there are different varieties of dysbiosis in
RCDI patient samples, that FMT predominantly affects Firmicutes
and Proteobacteria, and that the fecal microbiota continues
to change in post-FMT patients. We did not identify a ‘keystone’
species in RCDI or FMT, but our findings suggest that butyrate
producing bacteria may be important. We believe that additional
longitudinal studies, ideally beginning before initial infection and
including metagenomic and metatranscriptomic analyses, will lead
to improved outcomes in C. difficile infection.
Figure S1 Fecal microbiota diversity in patient and donor
samples depending on collection time points. The Shannon index
of all samples is plotted over time, split into donor (A, blue) and
patient (B, red) samples.
Figure S2 Venn diagram showing shared OTUs between RCDI
and post-FMT patient and donor samples. Only OTUs represented
by at least 5 reads across all 56 samples are shown.
Figure S3 Microbiota changes between RCDI samples collected
from the same patient before the first FMT (#6a) and, after
antibiotic-induced relapse, before the second FMT (#6b). Relative
abundances of all taxonomic genera (.1%) are shown.
Figure S4 Post-FMT microbiota changes. Unweighted (A) and
weighted (B) UniFrac distances and Jensen-Shannon divergence
(C) metrics were calculated between post-FMT and RCDI patient
sample pairs (red), post-FMT patient and donor sample pairs
(green) and between donor sample pairs collected over time (blue).
This figures shows that both patient and donor samples from case
#1 collected one year aft FMT show an unusual small divergence
(Unweighted/weighted UniFrac distances and Jensen-Shannon
divergence) from the donor sample collected before FMT.
Table S1 Numbers of reads and identified operational taxonomic
units (OTUs) by sample.
We are grateful for generous support from the Weinberg Foundation, the
Friedman and Friedman Group and Eric Cowan.
Conceived and designed the experiments: YS SD WFF. Performed the
experiments: YS SG MG CM AD SD. Analyzed the data: YS WFF.
Contributed reagents/materials/analysis tools: SD WFF. Wrote the paper:
YS ECvR SD WFF.
1. Bartlett JG (2002) Clinical practice. Antibiotic-associated diarrhea. N Engl J
Med 346: 334–339.
2. Jobe BA, Grasley A, Deveney KE, Deveney CW, Sheppard BC (1995)
Clostridium difficile colitis: an increasing hospital-acquired illness. Am J Surg
3. Miller BA, Chen LF, Sexton DJ, Anderson DJ (2011) Comparison of the
burdens of hospital-onset, healthcare facility-associated Clostridium difficile
Infection and of healthcare-associated infection due to methicillin-resistant
Staphylococcus aureus in community hospitals. Infect Control Hosp Epidemiol
4. Rivera EV, Woods S (2003) Prevalence of asymptomatic Clostridium difficile
colonization in a nursing home population: a cross-sectional study. J Gend Specif
Med 6: 27–30.
5. Jangi S, Lamont JT (2010) Asymptomatic colonization by Clostridium difficile in
infants: implications for disease in later life. J Pediatr Gastroenterol Nutr 51: 2–7.
6. McFarland LV, Elmer GW, Surawicz CM (2002) Breaking the cycle: treatment
strategies for 163 cases of recurrent Clostridium difficile disease. Am J
Gastroenterol 97: 1769–1775.
7. Pepin J, Routhier S, Gagnon S, Brazeau I (2006) Management and outcomes of
a first recurrence of Clostridium difficile-associated disease in Quebec, Canada.
Clin Infect Dis 42: 758–764.
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 9 November 2013 | Volume 8 | Issue 11 | e81330
8. Surawicz CM, McFarland LV, Greenberg RN, Rubin M, Fekety R, et al. (2000)
The search for a better treatment for recurrent Clostridium difficile disease: use
of high-dose vancomycin combined with Saccharomyces boulardii. Clin Infect
Dis 31: 1012–1017.
9. Musher DM, Logan N, Mehendiratta V, Melgarejo NA, Garud S, et al. (2007)
Clostridium difficile colitis that fails conventional metronidazole therapy:
response to nitazoxanide. J Antimicrob Chemother 59: 705–710.
10. Buggy BP, Fekety R, Silva J Jr (1987) Therapy of relapsing Clostridium difficileassociated
diarrhea and colitis with the combination of vancomycin and
rifampin. J Clin Gastroenterol 9: 155–159.
11. Berman AL (2007) Efficacy of rifaximin and vancomycin combination therapy in
a patient with refractory Clostridium difficile-associated diarrhea. J Clin
Gastroenterol 41: 932–933.
12. Johnson S, Schriever C, Galang M, Kelly CP, Gerding DN (2007) Interruption
of recurrent Clostridium difficile-associated diarrhea episodes by serial therapy
with vancomycin and rifaximin. Clin Infect Dis 44: 846–848.
13. Garey KW, Jiang ZD, Bellard A, Dupont HL (2009) Rifaximin in treatment of
recurrent Clostridium difficile-associated diarrhea: an uncontrolled pilot study. J
Clin Gastroenterol 43: 91–93.
14. Johnson S, Schriever C, Patel U, Patel T, Hecht DW, et al. (2009) Rifaximin
Redux: treatment of recurrent Clostridium difficile infections with rifaximin
immediately post-vancomycin treatment. Anaerobe 15: 290–291.
15. Navaneethan U, Venkatesh PG, Shen B (2010) Clostridium difficile infection
and inflammatory bowel disease: understanding the evolving relationship. World
J Gastroenterol 16: 4892–4904.
16. Forster AJ, Taljaard M, Oake N, Wilson K, Roth V, et al. (2012) The effect of
hospital-acquired infection with Clostridium difficile on length of stay in hospital.
CMAJ 184: 37–42.
17. Daneman N, Stukel TA, Ma X, Vermeulen M, Guttmann A (2012) Reduction
in Clostridium difficile infection rates after mandatory hospital public reporting:
findings from a longitudinal cohort study in Canada. PLoS Med 9: e1001268.
18. O’Keefe SJ (2010) Tube feeding, the microbiota, and Clostridium difficile
infection. World J Gastroenterol 16: 139–142.
19. Pohl JF (2012) Clostridium difficile infection and proton pump inhibitors. Curr
Opin Pediatr 24: 627–631.
20. Sartor RB (2008) Microbial influences in inflammatory bowel diseases.
Gastroenterology 134: 577–594.
21. Kelly CP, LaMont JT (2008) Clostridium difficile—more difficult than ever. N
Engl J Med 359: 1932–1940.
22. Maroo S, Lamont JT (2006) Recurrent clostridium difficile. Gastroenterology
23. McDonald LC, Killgore GE, Thompson A, Owens RC Jr, Kazakova SV, et al.
(2005) An epidemic, toxin gene-variant strain of Clostridium difficile. N Engl J
Med 353: 2433–2441.
24. Lund-Tonnesen S, Berstad A, Schreiner A, Midtvedt T (1998) [Clostridium
difficile-associated diarrhea treated with homologous feces]. Tidsskr Nor
Laegeforen 118: 1027–1030.
25. Khoruts A, Dicksved J, Jansson JK, Sadowsky MJ (2010) Changes in the
composition of the human fecal microbiome after bacteriotherapy for recurrent
Clostridium difficile-associated diarrhea. J Clin Gastroenterol 44: 354–360.
26. Wettstein A, Borody TJ, Leis S, Chongan J, Torres M, et al. (2007) Faecal
bacteriotherapy – an effective treatment for relapsing symptomatic Clostridium
difficile infection. 15th Gut United European Gastroenterology Week (France)
39(Suppl 1): A303 Vienna: United European Gastroenterology Federation.
27. Brandt LJ, Aroniadis OC, Mellow M, Kanatzar A, Kelly C, et al. (2012) Longterm
follow-up of colonoscopic fecal microbiota transplant for recurrent
Clostridium difficile infection. Am J Gastroenterol 107: 1079–1087.
28. Gough E, Shaikh H, Manges AR (2011) Systematic review of intestinal
microbiota transplantation (fecal bacteriotherapy) for recurrent Clostridium
difficile infection. Clin Infect Dis 53: 994–1002.
29. Bakken JS, Borody T, Brandt LJ, Brill JV, Demarco DC, et al. (2011) Treating
Clostridium difficile infection with fecal microbiota transplantation. Clin
Gastroenterol Hepatol 9: 1044–1049.
30. Vrieze A, de Groot PF, Kootte RS, Knaapen M, van Nood E, et al. (2013) Fecal
transplant: a safe and sustainable clinical therapy for restoring intestinal
microbial balance in human disease? Best Pract Res Clin Gastroenterol 27: 127–
31. Anderson JL, Edney RJ, Whelan K (2012) Systematic review: faecal microbiota
transplantation in the management of inflammatory bowel disease. Aliment
Pharmacol Ther 36: 503–516.
32. Musa S, Thomson S, Cowan M, Rahman T (2010) Clostridium difficile
infection and inflammatory bowel disease. Scand J Gastroenterol 45: 261–272.
33. Kump PK, Grochenig HP, Lackner S, Trajanoski S, Reicht G, et al. (2013)
Alteration of Intestinal Dysbiosis by Fecal Microbiota Transplantation Does not
Induce Remission in Patients with Chronic Active Ulcerative Colitis. Inflamm
Bowel Dis 19: 2155–2165.
34. Vrieze A, Van Nood E, Holleman F, Salojarvi J, Kootte RS, et al. (2012)
Transfer of intestinal microbiota from lean donors increases insulin sensitivity in
individuals with metabolic syndrome. Gastroenterology 143: 913–916 e917.
35. Borody TJ, Paramsothy S, Agrawal G (2013) Fecal microbiota transplantation:
indications, methods, evidence, and future directions. Curr Gastroenterol Rep
36. Borody TJ, Khoruts A (2012) Fecal microbiota transplantation and emerging
applications. Nat Rev Gastroenterol Hepatol 9: 88–96.
37. U.S. Department of Health and Human Services FaDA, Center for Biologics
Evaluation and Research (2013) Guidance for Industry: Enforcement Policy
Regarding Investigational New Drug Requirements for Use of Fecal Microbiota
for Transplantation to Treat Clostridium difficile Infection Not Responsive to
Standard Therapies. 1–4.
38. Aas J, Gessert CE, Bakken JS (2003) Recurrent Clostridium difficile colitis: case
series involving 18 patients treated with donor stool administered via a
nasogastric tube. Clin Infect Dis 36: 580–585.
39. Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SS, et al. (2011) Vaginal
microbiome of reproductive-age women. Proc Natl Acad Sci U S A 108 Suppl 1:
40. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, et al. (2010)
QIIME allows analysis of high-throughput community sequencing data. Nat
Methods 7: 335–336.
41. Angiuoli SV, Matalka M, Gussman A, Galens K, Vangala M, et al. (2011)
CloVR: a virtual machine for automated and portable sequence analysis from
the desktop using cloud computing. BMC Bioinformatics 12: 356.
42. James White WFF, Cesar Arze, Malcolm Matalka, The CloVR Team, Owen
White, et al. (2011) CloVR-16S: Phylogenetic microbial community composition
analysis based on 16S ribosomal RNA amplicon sequencing – standard
operating procedure, version1.0. Nature Precedings.
43. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME
improves sensitivity and speed of chimera detection. Bioinformatics 27: 2194–
44. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for
rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl
Environ Microbiol 73: 5261–5267.
45. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, et al. (2009)
Introducing mothur: open-source, platform-independent, community-supported
software for describing and comparing microbial communities. Appl Environ
Microbiol 75: 7537–7541.
46. White JR, Nagarajan N, Pop M (2009) Statistical methods for detecting
differentially abundant features in clinical metagenomic samples. PLoS Comput
Biol 5: e1000352.
47. Price MN, Dehal PS, Arkin AP (2010) FastTree 2—approximately maximumlikelihood
trees for large alignments. PLoS One 5: e9490.
48. Dutta SK, Garg S, Dutta A, von Rosenvinge EC, Maddox C, et al. (2013)
Jejunal and Colonic Fecal Microbiota Transplantation for Recurrent Clostridium
difficile Infection with genomic analysis. Gastroenterology ID: AJG-13-
49. Skraban J, Dzeroski S, Zenko B, Mongus D, Gangl S, et al. (2013) Gut
microbiota patterns associated with colonization of different Clostridium difficile
ribotypes. PLoS One 8: e58005.
50. Shahinas D, Silverman M, Sittler T, Chiu C, Kim P, et al. (2012) Toward an
understanding of changes in diversity associated with fecal microbiome
transplantation based on 16S rRNA gene deep sequencing. MBio 3.
51. Antharam VC, Li E, Ishmael A, Sharma A, Mai V, et al. (2013) Intestinal
dysbiosis and depletion of butyrogenic bacteria in Clostridium difficile infection
and nosocomial diarrhea. J Clin Microbiol.
52. Lawley TD, Clare S, Walker AW, Stares MD, Connor TR, et al. (2012)
Targeted restoration of the intestinal microbiota with a simple, defined
bacteriotherapy resolves relapsing Clostridium difficile disease in mice. PLoS
Pathog 8: e1002995.
53. Lawley TD, Clare S, Walker AW, Goulding D, Stabler RA, et al. (2009)
Antibiotic treatment of clostridium difficile carrier mice triggers a supershedder
state, spore-mediated transmission, and severe disease in immunocompromised
hosts. Infect Immun 77: 3661–3669.
54. Petrof EO, Gloor GB, Vanner SJ, Weese SJ, Carter D, et al. (2012) Stool
substitute transplant therapy for the eradication of Clostridium difficile infection:
‘RePOOPulating’ the gut. Microbiome 1.
55. Lawley B, Tannock GW (2012) Nucleic acid-based methods to assess the
composition and function of the bowel microbiota. Gastroenterol Clin North
Am 41: 855–868.
56. Ludwig W, Schleifer K-H, Whitman WB (2009) Revised road map to the
phylum Firmicutes. In: Bergey’s Manual of Systematic Bacteriology, ed., vol. 3
(The Firmicutes) (P. De Vos, G. Garrity, D. Jones, N.R. Krieg, W. Ludwig, F.A.
Rainey, K.-H. Schleifer, and W.B. Whitman, eds.). Springer-Verlag, New York:
57. Derrien M, Vaughan EE, Plugge CM, de Vos WM (2004) Akkermansia
muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium.
Int J Syst Evol Microbiol 54: 1469–1476.
58. Belzer C, de Vos WM (2012) Microbes inside– –from diversity to function: the
case of Akkermansia. ISME J 6: 1449–1458.
59. Power ME, Tilman D, Estes JA, Menge BA, Bond WJ, et al. (1996) Challenges
in the quest for keystones. Bioscience 46: 609–620.
60. Ebenman B, Jonsson T (2005) Using community viability analysis to identify
fragile systems and keystone species. Trends Ecol Evol 20: 568–575.
61. Hajishengallis G, Liang S, Payne MA, Hashim A, Jotwani R, et al. (2011) Lowabundance
biofilm species orchestrates inflammatory periodontal disease
through the commensal microbiota and complement. Cell Host Microbe 10:
62. Hamilton MJ, Weingarden AR, Unno T, Khoruts A, Sadowsky MJ (2013) Highthroughput
DNA sequence analysis reveals stable engraftment of gut microbiota
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 10 November 2013 | Volume 8 | Issue 11 | e81330
following transplantation of previously frozen fecal bacteria. Gut Microbes 4:
63. Reeves AE, Koenigsknecht MJ, Bergin IL, Young VB (2012) Suppression of
Clostridium difficile in the gastrointestinal tracts of germfree mice inoculated
with a murine isolate from the family Lachnospiraceae. Infect Immun 80: 3786–
64. Human Microbiome Project C (2012) Structure, function and diversity of the
healthy human microbiome. Nature 486: 207–214.
65. Maslowski KM, Vieira AT, Ng A, Kranich J, Sierro F, et al. (2009) Regulation
of inflammatory responses by gut microbiota and chemoattractant receptor
GPR43. Nature 461: 1282–1286.
66. Roediger WE (1982) Utilization of nutrients by isolated epithelial cells of the rat
colon. Gastroenterology 83: 424–429.
67. Koruda MJ, Rolandelli RH, Bliss DZ, Hastings J, Rombeau JL, et al. (1990)
Parenteral nutrition supplemented with short-chain fatty acids: effect on the
small-bowel mucosa in normal rats. Am J Clin Nutr 51: 685–689.
68. Peng L, He Z, Chen W, Holzman IR, Lin J (2007) Effects of butyrate on
intestinal barrier function in a Caco-2 cell monolayer model of intestinal barrier.
Pediatr Res 61: 37–41.
69. Dethlefsen L, Relman DA (2011) Incomplete recovery and individualized
responses of the human distal gut microbiota to repeated antibiotic perturbation.
Proc Natl Acad Sci U S A 108 Suppl 1: 4554–4561.
70. Gorkiewicz G, Thallinger GG, Trajanoski S, Lackner S, Stocker G, et al. (2013)
Alterations in the colonic microbiota in response to osmotic diarrhea. PLoS One
Post-Fecal Transplant Microbiota Characterization
PLOS ONE | www.plosone.org 11 November 2013 | Volume 8 | Issue 11 | e81330