Stool Microbiota at Neutrophil Recovery Is Predictive for Severe Acute Graft vs Host Disease After Hematopoietic Cell Transplantation
Stool Microbiota at Neutrophil Recovery Is Predictive for Severe Acute Graft vs Host Disease After Hematopoietic Cell Transplantation
Jonathan L. Golob 1 2
Steven A. Pergam 1 2
Sujatha Srinivasan 2
Tina L. Fiedler 2
Congzhou Liu 2
Kristina Garcia 2
Marco Mielcarek 0
Daisy Ko 2
Sarah Aker 2
Sara Marquis 2
Tillie Loeffelholz 2
Anna Plantinga 5
Michael C. Wu 4
Kevin Celustka 2
Alex Morrison 2
Maresa Woodfield 2
David N. Fredricks 1 2 3
0 Clinical Research Division, Fred Hutchinson Cancer Institute, Departments of
1 Division of Allergy and Infectious Diseases, University of Washington , USA
2 Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Institute
3 Department of Microbiology, University of Washington , Seattle , USA
4 Public Health Sciences Division, Fred Hutchinson Cancer Institute
5 Biostatistics, University of Washington , USA
Background. Graft-versus-host disease (GVHD) is common after allogeneic hematopoietic cell transplantation (HCT). Risk for death from GVHD has been associated with low bacterial diversity in the stool microbiota early after transplant; however, the specific species associated with GVHD risk remain poorly defined. Methods. We prospectively collected serial weekly stool samples from 66 patients who underwent HCT, starting pre-transplantation and continuing weekly until 100 days post-transplant, a total of 694 observations in HCT recipients. We used 16S rRNA gene polymerase chain reaction with degenerate primers, followed by high-throughput sequencing to assess the relative abundance of sequence reads from bacterial taxa in stool samples over time. Results. The gut microbiota was highly dynamic in HCT recipients, with loss and appearance of taxa common on short time scales. As in prior studies, GVHD was associated with lower alpha diversity of the stool microbiota. At neutrophil recovery post-HCT, the presence of oral Actinobacteria and oral Firmicutes in stool was positively correlated with subsequent GVHD; Lachnospiraceae were negatively correlated. A gradient of bacterial species (difference of the sum of the relative abundance of positive correlates minus the sum of the relative abundance of negative correlates) was most predictive (receiver operator characteristic area under the curve of 0.83) of subsequent severe acute GVHD. Conclusions. The stool microbiota around the time of neutrophil recovery post-HCT is predictive of subsequent development of severe acute GVHD in this study.
Acute graft-versus-host disease (GVHD) is a major cause of
morbidity and mortality in patients who undergo hematopoietic
cell transplantation (HCT). GVHD can present with enteritis,
dermatitis, and hepatitis following transplant [
]. An estimated
30%–80% of HCT recipients suffer from GVHD despite
matching for HLA and use of prophylactic immunosuppression (eg,
]. First-line treatment for GVHD is
glucocorticoid steroids, which are associated with an increased risk
for cytomegalovirus disease and fungal infections [
Death from GVHD in HCT has been previously associated
with low bacterial species diversity [
] and the lack of Blautia
] in the stool microbiota of human patients. These data
have suggested associations between increased bacterial
diversity at the time of engraftment with improved survival [
but do not focus on development of GVHD as the endpoint.
The use of antianaerobe antibiotics (piperacillin–tazobactam
and imipenem–cilastatin) was associated with higher rates of
GVHD and overgrowth of Akkermansia species in a murine
model of HCT [
]. Together these data indicate a need for
more detailed assessment of the relationship between specific
members of the gut microbiota and GVHD, including
assessments at different transplant centers where antibiotic use
Here, we prospectively collected weekly stool samples from
patients who underwent HCT, starting before transplant and
continuing for 100 days post-transplant. We show an
association between lower bacterial species diversity and severe acute
GVHD, with a modest predictive strength when tested at
recovery of neutrophils post-transplant. We also identify several
specific organisms whose relative abundance at neutrophil
recovery appears to predict severe acute GVHD of the gut.
Microbiome Sample Collection
Weekly kits that contained polyurethane foam swabs (Epicenter
Biotechnologies, Madison, Wisconsin) were provided to
participants to self-collect stool samples at home; nurses collected
swabs from inpatients. Swabs were returned to our laboratory by
courier or regular mail at room temperature without special
preservative. Upon arrival, samples were tagged and stored at −80oC
until retrieved for DNA extraction. The collection date and
arrival date were noted; if it was longer than 72 hours between
sample collection and storage, the samples were discarded.
Several studies have demonstrated that the stool microbiota is
not greatly impacted by these storage conditions [
donors who consented to the study collected 1 stool sample.
All clinical data including GVHD grade [
] onset date, and
neutrophil recovery were determined by retrospective review
of the clinical charts by individuals blinded to the microbiome
results of the participants. Severe GVHD was defined as grade
≥3. All patients had stage IIb to IV gut GVHD to be called
severe acute GVHD in our study. The acute GVHD start date is
defined as the onset of any symptoms, while the stage is
determined by the worst conditions observed in the patient, typically
after the onset of symptoms. See the Supplementary Appendix
for further details.
Neutrophil recovery was defined as the third day of an
absolute neutrophil count >500 neutrophils/mm3. We selected
neutrophil recovery as our primary time point because it is present
in almost all HCT recipients at or before the onset of GVHD
and at a point at which one could consider microbial
interventions such as fecal microbiota transplant with less concern for
Patients received prophylactic antibiotics including
levofloxacin when their absolute neutrophil count was below 500 cells/
mm3. For comprehensive details of the antibiotic protocols,
see the supplement Antibiotic Protocols in Hematopoietic Cell
Transplant at the Fred Hutch.
DNA Extraction and Quality Control
DNA was extracted using the BiOstic Bacteremia DNA Isolation
Kit (MoBio, Carlsbad, California) and eluted into 150 μL of
0.5 mM Tris–0.05 mM EDTA (TE) buffer. Sham swabs were
extracted in the same manner to monitor for contamination of
the extraction materials. All extracts were assessed for the
presence of polymerase chain reaction (PCR) inhibitors via spike-in
of jellyfish gDNA [
]. Bacterial DNA concentrations were
measured using a quantitative PCR assay that target the V3–V4
region of the 16S rRNA gene [
Broad-Range Polymerase Chain Reaction and High-Throughput
Sequencing of 16S rRNA Gene Amplicons
Broad-range PCR targeting of the V3–V4 hypervariable region
of the 16S rRNA gene was performed (see Supplementary
Materials for primer details and PCR conditions). Amplicons
were cleaned using Agencourt AMPure XP beads (Beckman
Coulter, Indianapolis, Indiana) per the 16S Metagenomic
Sequencing Library Preparation [
]. Cleaned amplicons were
subjected to Index PCR using NexteraXT index kits v2 set A,
set B, set C, and set D to multiplex 384 samples per
sequencing run. Following index PCR, the DNA was cleaned using
Agencourt AMPure XP beads (Beckman Coulter) per the 16S
Metagenomic Sequencing Library Preparation [
], air dried
for 8 minutes, and eluted in 30 µL 1× TE buffer. DNA
concentration of each sample was determined using the Quant-iT
dsDNA assay kit-high sensitivity (Thermo Fisher Scientific,
Waltham, Massachusetts), and equimolar quantities of samples
Sequencing was performed on the Illumina MiSeq
instrument (Illumina, San Diego, California) with the MiSeq reagent
kit v3-600 cycle to capture paired-end reads (2 × 300). PhiX
Control Library v3 (Illumina) was combined with the
amplicon library at 15%. The combined library was denatured with
sodium hydroxide, diluted with hybridization buffer, and heat
denatured. The library was sequenced as PE300 reads on the
MiSeq using standard Illumina primers. Raw sequence reads
were demultiplexed using Illumina’s bcl2fastq conversion
software, v1.8.4, with zero mismatches to forward and reverse
Bacterial Species Classification Reference Set Creation
] was used to quality control (QC) filter, pair, and
cluster the amplicon reads. The filtered and paired 16S rRNA
gene amplicons were used to recruit full-length 16S rRNA gene
sequences from Ribosomal Database Project release 11.4 [
The collected reference sequences were assembled into a
phylogenetic tree using FastTree; leaves with only 1 representative
sequence were used to recruit additional sequences.
Classification of Sequences into Taxonomic Profiles
QC-filtered amplicon sequences were placed onto the
classification reference tree using the pplacer tool, as previously
], and validated in a separate study from our
The PANDAS [
] package (version 0.19.2) of Python was
used for data organization and normalization. The Python
statsmodels package [
] (version 0.8.0) was used for all
statistical analyses. The correlation between alpha diversity and
severe acute GVHD was as follows: when all time points
considered for an individual were used, generalized estimating
equations (GEEs) using an independence working correlation
Stool Microbiota Predicts Graft Versus Host Disease • CID 2017:65 (15 December) • 1985
and a binomial family with a logit link function were used.
When 1 measurement was used per individual (ie, the sample
closest to neutrophil recovery), logistic regression was used.
For the correlation between organisms and severe (grade 3–4)
acute GVHD: similar to analysis of the composition of
microbiomes (ANCOM) [
], for each observed organism identified
to the genus or species level and each genus identified, a log
ratio of the species count plus 1 over the genus count plus 1
was calculated. Logistic regression was performed, with these
ratios as the independent variable and severe acute GVHD as
the dependent variable. The estimated coefficient (β) was
normalized to its standard error of the mean to generate the Wald
statistic. The distribution of the Wald statistic for each species
was considered, with the Student T distribution used to
determine a P value, which underwent the Bonferroni correction.
Participants had a wide variety of indications for transplant,
as well as transplant protocols (Table 1). For each participant
an average of 11 weekly samples were assessed, spanning from
before transplant through 100 days post-transplant, for a total
of 694 stool microbiota profiles. Additionally, 36 HCT donors’
stool microbiota was assessed.
Stool Microbiota of Healthy Donors
Healthy human stool microbiota varies from individual to
individual, with only about one third of bacterial species being
common between any 2 individuals. Members of the Bacteroidaceae
and Lachnospiracae families are dominant in most individuals
Stool Microbiota in Hematopoietic Cell Transplantation Transplant
Patients Over Time
In general, individuals without severe acute GVHD of the gut
maintained a stool microbiota broadly similar to those we
observed in healthy donors (Figure 1B); there was dominance
with a combination of Bacteroidaceae species and/or
members of the Lachnospiraceae. One exception was a patient who
underwent partial colectomy for diverticular disease prior to
transplant (Figure 1C). Some of those with severe acute GVHD
(Figure 1D) had a deficit of Lachnospiraceae. Two of the 3
individuals with steroid-refractory severe acute GVHD (Figure 1E)
had a persistent lack of Bacteroidaceae and Lachnospiracae
species after the onset of GVHD. We did not see a consistent trend
in the rate of change over time (Supplementary Figure S1) with
grade of GVHD. For recipients for whom we had paired stool
microbiota samples from their donors, we saw a trend toward
higher donor-to-recipient microbiota distance (an indicator of
dissimilarity of the bacterial communities) as grade of acute
GVHD increased, through grade 3 (Supplementary Figure S2);
the one grade 4 patient for whom we had a donor pair was an
outlier for this trend. The difference between grade 3 acute
GVHD and no acute GVHD was significant (P < 0.05) as per
Diversity of the Stool Microbiota of Hematopoietic Cell Transplantation
Recipients and Donors
Figure 2A shows the alpha diversity of HCT donors and HCT
recipients stratified by acute GVHD grade. We used the
following 2 measures of diversity: balanced weighted phylogenetic
diversity (BWPD) [
], which is a measure that accounts for
the phylogenetic relationships between organisms in the
community when calculating diversity, and Inverse Shannon [
(InvSh), which is a commonly used measure of species diversity.
When using all samples for a given patient, or just the sample
closest to neutrophil recovery, there was a trend to lower alpha
diversity (InvSh or BWPD) at higher grades of acute GVHD.
By GEE regression with an independence correlation structure,
both BWPD and InvSh alpha diversity were statistically
significantly lower in patients with grade 3 or 4 GVHD when
compared to those with no acute GVHD or grade 1–2 acute GVHD
Stool Microbiota Predicts Graft Versus Host Disease • CID 2017:65 (15 December) • 1987
(P < 0.05). Figure 2B shows the receiver operator characteristic
(ROC) curve for BWPD (area under the curve [AUC] of 0.56)
and InvSh diversity (AUC of 0.63) at neutrophil recovery as
a predictor for eventual development of severe acute GVHD
Specific Bacteria and Development of Severe Acute Graft-versus-Host
To test for correlations between specific bacteria (identified to
species or genus level) at neutrophil recovery and the subsequent
development of severe acute GVHD, we used an ANCOM-like
analysis in which we calculated the log of the ratio of counts of
each species or genus to each other genus and then performed
logistic regression for each ratio to the endpoint (acute severe
GVHD or not) [
]. We then selected the organisms that were
consistently significantly positively or negatively correlated
with subsequent severe acute GVHD (Table 2). Members of the
Bacteroides genus (B. thetaiotaomicron, B. ovatus, and B. caccae)
were negatively correlated with subsequent acute severe GVHD,
while others (B. dorei) were positively correlated. Among the
gram-positive bacteria, oral bacteria such as Rothia
mucilaginosa, Solobacterium moorei, and Veillonella parvula were
positively correlated; several Lachnospiraceae (including B. luti)
and a Butyricicoccus species were negatively correlated.
A Gradient of Positively to Negatively Correlated Organisms at Neutrophil
Recovery Can be Used as a Metric to Assess Risk for Subsequent Severe
For each microbial community we calculated a gradient of the
sum of relative abundance of the positively correlated
bacteria minus the sum of the relative abundance of the negative
1988 • CID 2017:65 (15 December) • Golob et al
correlates. Figure 3A shows the gradient for all samples for
our recipients stratified by acute GVHD grade; there is a slight
trend to a higher gradient in those with higher grades of acute
GVHD. Figure 3B shows the gradient for the microbial
communities closest to neutrophil recovery; the trend is clearer
toward a higher gradient being associated with higher grade
of acute GVHD. Figure 3C is the ROC for this gradient as a
test for development of severe acute GVHD (grade 3 or 4);
the AUC of the ROC for this test is 0.83 (significantly better
than the null hypothesis value of 0.5, P < 0.05 by
permutation over 1000 iterations). Figure 3D shows the relationship
between specificity and sensitivity for this metric at various
In this study, we were able to directly correlate characteristics
of the stool microbiome with acute GVHD, further
establishing the microbiome as a predictor of GVHD. We noted a trend
toward greater phylogenetic distance between donor and
recipient stool microbiota with higher grades of acute GVHD. We
found a trend toward lower alpha diversity in patients with
severe acute GVHD. We described several distinct bacterial
species positively or negatively correlated with the subsequent
development of severe acute GVHD when present around
neutrophil recovery and used these organisms in a metric to predict
subsequent severe acute GVHD. Our results are broadly
comparable with the prior published work [
] but point to specific
species rather than alpha diversity as a more predictive
microbiome factor for eventual development of severe acute GVHD.
Severe graft-versus-host disease is grade 3–4.
aAn ANCOM (analysis of composition of microbiomes)–style  analysis was performed. β/SEM(β) is the Wald statistic.The Bonferroni correction was used to correct P. Neutrophil recovery
is the third consecutive day of an absolute neutrophil count >500 cells per mm3.
We note that the onset of acute GVHD can be hyperacute,
occurring right as neutrophil recovery occurs. In 5 of the 14
patients who went on to develop severe acute GVHD, the onset
of 14 GVHD symptoms was with the recovery of neutrophils,
with the more severe GVHD symptoms occurring later. Thus,
our metric remains useful for predicting the subsequent
development of severe GVHD.
While this study is focused on correlations between the stool
microbiota and acute GVHD and their potential to be used
as a biomarker of disease risk, our results and those of other
] point to several possible mechanisms by which
the gut microbiome could play a direct role in modulating
GVHD. Typically oral Actinobacteria and oral Firmicutes in
the gut were positively correlated with subsequent
development of severe GVHD in our study (a novel association with
GVHD, not previously published). Others have implicated the
same family of organism with gut inflammation in colorectal
] and to gut damage [
] in a model system
of HCT. Several studies have shown a correlation between
antibiotic exposure and risk for death from severe GVHD [
particularly antibiotics with an anaerobic spectrum [
agents may open new ecologic niches for these typically oral
organisms in the gut.
Different species in the Bacteroides genus were associated
with different effects, with B. dorei positively correlated with
GVHD and B. ovatus, B. caccae, and B. thetaiotaomicron
negatively correlated in our study. These differences could be
explained by differences in metabolic capability, virulence, or
ability to promote inflammation among these organisms in the
same genus. Metabolites have also been considered as a
correlate with severe GVHD [
], specifically with changes in mucin
metabolism. A better understanding of the metabolic
capability and activity of the gut microbiome in HCT may offer
insight into how the microbiome can affect immune recovery
Our findings include a possible relationship between the
HCT donor and recipient microbiota, as measured by
phylogenetic distance and risk of GVHD [
]. Both the composition of
the microbiota and closeness to the HCT donor’s microbiota
may be important factors to consider when attempting future
interventions such as fecal microbiota transplantation [
This observation requires confirmation.
Stool Microbiota Predicts Graft Versus Host Disease • CID 2017:65 (15 December) • 1989
benefit from antibiotic decontamination of the gut prior to
transplant; decontamination became the standard of care [
the late 1990s, after the introduction of immunosuppressant
prophylaxis, clinical studies revealed a more mixed effect from
aggressive gut decontamination, even the possibility of increased
]. Understanding the mechanisms by which the
microbiota affect risk of GVHD will be needed to reconcile these
results with the current studies on the microbiome and GVHD.
This study is limited by the intrinsic constraints of 16S-SSU
profiles of a microbiota where we assess relative abundance
of sequence reads, not absolute concentrations of bacteria.
Certain clades of organisms (including the Clostridiales and
Enterobacteraceae) can be difficult to resolve via 16S rRNA
gene amplicon-based profiling [
]. Samples were at room
temperature without preservative for up to 72 hours prior to
being stored at −80oC. This makes the study conditions easier to
reproduce in the clinical setting but could have resulted in shifts
in the composition of the microbiota samples due to blooms
or loss of organisms in that time window [
]. In future
studies, we would like to explore the effect of enteral vs parenteral
nutrition, antacid use, and other factors that may correlate with
the shift to oral organisms we observed in severe GVHD. As
with other published studies, this was a single-center study, and
therefore our findings may not be generalized to all centers. We
provide a novel metric to assess risk of GVHD so that other
centers can validate or refute this approach to risk assessment.
In conclusion, we demonstrate that the stool microbiota
can be a useful predictor for subsequent development of acute
severe GVHD when measured around the time of neutrophil
recovery post-transplant. Determining which HCT patients are
most likely to develop severe acute GVHD would allow
clinicians to target these patients for more aggressive interventions,
including use of preemptive immunomodulatory agents or
efforts to modify gut microbial communities.
Supplementary materials are available at Clinical Infectious Diseases online.
Consisting of data provided by the authors to benefit the reader, the posted
materials are not copyedited and are the sole responsibility of the authors,
so questions or comments should be addressed to the corresponding author.
Acknowledgments. The authors thank Noah Hoffman for providing
code and tools to refine our classification pipeline and Timothy Randolph
for advice on statistical analysis.
Financial support. This work was supported in part by an
institutional grant from the Vaccine and Infectious Disease Division at the Fred
Hutchinson Cancer Research Center and through a generous donation. J. L.
G. is supported by the Joel Meyers Endowment.
Potential conflicts of interest. S. A. P. has served as consultant and has
participated in clinical trials with Merck, Chimerix, and Optimer/Cubist.
All remaining authors: No reported conflicts. All authors have submitted
the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts
that the editors consider relevant to the content of the manuscript have been
1. Glucksberg H , Storb R , Fefer A , et al. Clinical manifestations of graft-versushost disease in human recipients of marrow from HL-A-matched sibling donors . Transplantation 1974 ; 18 : 295 - 304 .
2. Storb R , Deeg HJ , Whitehead J , et al. Methotrexate and cyclosporine compared with cyclosporine alone for prophylaxis of acute graft versus host disease after marrow transplantation for leukemia . N Engl J Med 1986 ; 314 : 729 - 35 .
3. Socié G , Stone JV , Wingard JR , et al. Long-term survival and late deaths after allogeneic bone marrow transplantation . Late Effects Working Committee of the International Bone Marrow Transplant Registry. N Engl J Med 1999 ; 341 : 14 - 21 .
4. Nichols WG , Corey L , Gooley T , et al. Rising pp65 antigenemia during preemptive anticytomegalovirus therapy after allogeneic hematopoietic stem cell transplantation: risk factors, correlation with DNA load, and outcomes . Blood 2001 ; 97 : 867 - 74 .
5. Marr KA , Carter RA , Boeckh M , Martin P , Corey L . Invasive aspergillosis in allogeneic stem cell transplant recipients: changes in epidemiology and risk factors . Blood 2002 ; 100 : 4358 - 66 .
6. Taur Y , Jenq RR , Perales MA , et al. The effects of intestinal tract bacterial diversity on mortality following allogeneic hematopoietic stem cell transplantation . Blood 2014 ; 124 : 1174 - 82 .
7. Jenq R , Taur Y , Devlin S , et al. Intestinal blautia is associated with reduced death from graft-versus-host disease . Biol Blood Marrow Transplant J Am Soc Blood Marrow Transplant 2015 ;
8. Shono Y , Docampo MD , Peled JU , et al. Increased GVHD-related mortality with broad-spectrum antibiotic use after allogeneic hematopoietic stem cell transplantation in human patients and mice . Sci Transl Med 2016 ; 8 : 339ra71 .
9. Shaw AG , Sim K , Powell E , et al. Latitude in sample handling and storage for infant faecal microbiota studies: the elephant in the room ? Microbiome 2016 ; 4 . Available at: http://microbiomejournal.biomedcentral.com/articles/10.1186/ s40168-016-0186-x. Accessed 19 July 2017 .
10. Guo Y , Li S-H , Kuang Y-S , et al. Effect of short-term room temperature storage on the microbial community in infant fecal samples . Sci Rep 2016 ; 6 . Available at: http://www.nature. com/articles/srep26648. Accessed 19 July 2017 .
11. Carroll IM , Ringel-Kulka T , Siddle JP , Klaenhammer TR , Ringel Y. Characterization of the fecal microbiota using high-throughput sequencing reveals a stable microbial community during storage . PLoS One 2012 ; 7 : e46953 .
12. Tedjo DI , Jonkers DM , Savelkoul PH , et al. The effect of sampling and storage on the fecal microbiota composition in healthy and diseased subjects . PLoS One 2015 ; 10 : e0126685 .
13. Cardona S , Eck A , Cassellas M , et al. Storage conditions of intestinal microbiota matter in metagenomic analysis . BMC Microbiol 2012 ; 12 : 158 .
14. Przepiorka D , Weisdorf D , Martin P , et al. 1994 Consensus Conference on Acute GVHD Grading. Bone Marrow Transplant 1995 ; 15 : 825 - 28 .
15. Khot PD , Ko DL , Hackman RC , Fredricks DN . Development and optimization of quantitative PCR for the diagnosis of invasive aspergillosis with bronchoalveolar lavage fluid . BMC Infect Dis 2008 ; 8 : 73 .
16. Srinivasan S , Hoffman NG , Morgan MT , et al. Bacterial communities in women with bacterial vaginosis: high resolution phylogenetic analyses reveal relationships of microbiota to clinical criteria . PLoS One 2012 ; 7 : e37818 .
17. 16S Metagenomic Sequencing Library Preparation. 15044223 Rev. B; Available at: https://www.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/16s/16s -metagenomic-library-prep-guide-15044223-b .pdf.
18. bcl2fastq Conversion Software . Available at: https://support.illumina.com/downloads/bcl2fastq_conversion_software_184.html.
19. Callahan BJ , McMurdie PJ , Rosen MJ , Han AW , Johnson AJ , Holmes SP . DADA2: High-resolution sample inference from Illumina amplicon data . Nat Methods 2016 ; 13 : 581 - 3 .
20. Cole JR , Wang Q , Fish JA , et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis . Nucleic Acids Res 2014 ; 42 : D633 - 42 .
21. Matsen FA , Kodner RB , Armbrust EV . pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree . BMC Bioinformatics 2010 ; 11 : 538 .
22. Golob JL , Margolis E , Hoffman NG , Fredricks DN . Evaluating the accuracy of amplicon-based microbiome computational pipelines on simulated human gut microbial communities . BMC Bioinformatics 2017 ; 18 : 283 .
23. Python Data Analysis Library . Available at: http://pandas.pydata.org/.
24. statsmodels. Available at: http://statsmodels.sourceforge.net/stable/.
25. Mandal S , Van Treuren W , White RA , Eggesbø M , Knight R , Peddada SD . Analysis of composition of microbiomes: a novel method for studying microbial composition . Microb Ecol Health Dis 2015 ; 26 . Available at: http://www.microbecolhealthdis.net/index.php/mehd/article/view/27663. Accessed 26 April 2017 .
26. McCoy CO , Matsen FA 4th. Abundance-weighted phylogenetic diversity measures distinguish microbial community states and are robust to sampling depth . PeerJ 2013 ; 1 : e157 .
27. Shannon CE . A mathematical theory of communication . Bell Syst Tech J 1948 ; 27 : 379 - 423 -656.
28. Keku TO , Dulal S , Deveaux A , Jovov B , Han X. The gastrointestinal microbiota and colorectal cancer . Am J Physiol Gastrointest Liver Physiol 2015 ; 308 : G351 - 63 .
29. Flynn KJ , Baxter NT , Schloss PD . Metabolic and community synergy of oral bacteria in colorectal cancer . mSphere 2016 ; 1 : e00102 - 16 .
30. Mathewson ND , Jenq R , Mathew AV , et al. Gut microbiome-derived metabolites modulate intestinal epithelial cell damage and mitigate graft-versus-host disease . Nat Immunol 2016 ; 17 : 505 - 13 .
31. Weber D , Jenq RR , Peled JU , et al. Microbiota disruption induced by early use of broad spectrum antibiotics is an independent risk factor of outcome after allogeneic stem cell transplantation . Biol Blood Marrow Transplant . 2017 ; Available at: http://linkinghub.elsevier.com/retrieve/pii/S1083879117302756. Accessed 21 February 2017 .
32. Routy B , Letendre C , Enot D , et al. The influence of gut-decontamination prophylactic antibiotics on acute graft-versus-host disease and survival following allogeneic hematopoietic stem cell transplantation . Oncoimmunology 2017 ; 6 : e1258506 .
33. Holler E , Butzhammer P , Schmid K , et al. Metagenomic analysis of the stool microbiome in patients receiving allogeneic stem cell transplantation: loss of diversity is associated with use of systemic antibiotics and more pronounced in gastrointestinal graft-versus-host disease . Biol Blood Marrow Transplant 2014 ; 20 : 640 - 5 .
34. Weber D , Oefner PJ , Hiergeist A , et al. Low urinary indoxyl sulfate levels early after transplantation reflect a disrupted microbiome and are associated with poor outcome . Blood 2015 ; 126 : 1723 - 8 .
35. Atarashi K , Tanoue T , Shima T , et al. Induction of colonic regulatory T cells by indigenous Clostridium species . Science 2011 ; 331 : 337 - 41 .
36. Atarashi K , Tanoue T , Oshima K , et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota . Nature 2013 ; 500 : 232 - 6 .
37. Varelias A , Ormerod KL , Bunting MD , et al. Acute graft-versus-host disease is regulated by an IL-17-sensitive microbiome . Blood 2017 ; 129 : 2172 - 85 .
38. Tawara I , Liu C , Tamaki H , et al. Influence of donor microbiota on the severity of experimental graft-versus-host-disease . Biol Blood Marrow Transplant 2013 ; 19 : 164 - 8 .
39. Kakihana K , Fujioka Y , Suda W , et al. Fecal microbiota transplantation for patients with steroid-resistant acute graft-versus-host disease of the gut . Blood 2016 ; 128 : 2083 - 8 .
40. Spindelboeck W , Schulz E , Uhl B , et al. Repeated fecal microbiota transplantations attenuate diarrhea and lead to sustained changes in the fecal microbiota in acute, refractory gastrointestinal graft-versus-host-disease . Haematologica 2017 ; 102 : e210 - 3 .
41. Storb R , Prentice RL , Buckner CD , et al. Graft-versus-host disease and survival in patients with aplastic anemia treated by marrow grafts from HLA-identical siblings. Beneficial effect of a protective environment . N Engl J Med 1983 ; 308 : 302 - 7 .
42. Vossen JM , Heidt PJ , van den Berg H , Gerritsen EJ , Hermans J , Dooren LJ . Prevention of infection and graft-versus-host disease by suppression of intestinal microflora in children treated with allogeneic bone marrow transplantation . Eur J Clin Microbiol Infect Dis 1990 ; 9 : 14 - 23 .
43. Beelen DW , Elmaagacli A , Müller KD , Hirche H , Schaefer UW . Influence of intestinal bacterial decontamination using metronidazole and ciprofloxacin or ciprofloxacin alone on the development of acute graft-versus-host disease after marrow transplantation in patients with hematologic malignancies: final results and long-term follow-up of an open-label prospective randomized trial . Blood 1999 ; 93 : 3267 - 75 .
44. Guthery SL , Heubi JE , Filipovich A . Enteral metronidazole for the prevention of graft versus host disease in pediatric marrow transplant recipients: results of a pilot study . Bone Marrow Transplant 2004 ; 33 : 1235 - 9 .
45. Vossen JM , Guiot HF , Lankester AC , et al. Complete suppression of the gut microbiome prevents acute graft-versus-host disease following allogeneic bone marrow transplantation . PLoS One 2014 ; 9 : e105706 .
46. Amir A , McDonald D , Navas-Molina JA , et al. Correcting for microbial blooms in fecal samples during room-temperature shipping . mSystems 2017 ; 2 .