Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients
© The Author(s). 2017
Received: 25 October 2016
Accepted: 11 February 2017
Published: 28 February 2017
Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy.
The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses.
Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002).
These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.
KeywordsMicrobiome Temporal variability Leukemia Chemotherapy Antibiotics
There is an increasing appreciation for the role the human microbiome plays in many aspects of human physiology, health, and disease. Several studies of healthy human cohorts have found that although each person has a relatively distinct gastrointestinal microbiome signature, a healthy individual’s microbiome remains relatively stable over time [1–4]. Although several factors, such as diet, drive normal levels of day-to-day microbiota variability, it appears that a steady-state equilibrium both ecologically and functionally is required for health. In contrast, acute perturbations of an individual’s microbiome stability within a temporal context can lead to an unhealthy status [5, 6]. Considering that one of the principal aims of the microbiome research community is to use the microbiome as either an indicator for morbidity or to improve human health, an enhanced understanding of the kinetics and taxonomic characterization of microbiome stability in acutely ill patients is of paramount importance [7–11].
Although several studies have been done in healthy subjects, relatively scant data are available as to the stability, resilience, and temporal dynamics of the gastrointestinal microbiome in acutely ill patients [1, 3, 4, 12–16]. Many of the previous investigations examining temporal variability of the microbiome using healthy participants have been limited by small numbers of volunteers [4, 12, 13], short periods of longitudinal sampling [2, 3, 16], or by being focused on only one site of collection [1, 4, 14, 17]. On the other hand, the limited number of temporal variability studies among ill patients have typically been in cohorts with chronic ailments such as atopic dermatitis or colitis [18–20]. A study of stool samples from 14 patients under intensive care described rapid shifts in microbiome composition to ultra-low diversity communities comprised of four or less taxa as a result of aggressive antibiotic treatment and other intensive care medication stresses, such as opioids . Similar dramatic changes in the microbiome were also observed in patients undergoing hematopoietic stem cell transplant, where increased microbial chaos early after transplant is thought to be a potential risk factor for subsequent graft versus host disease [21, 22]. However, quantitative measurements of longitudinal microbial variability among ill patients and an analysis of factors affecting microbiome temporal stability are lacking [21, 23–25]. Moreover, despite many reports associating low microbial diversity with different illnesses, most studies associate only one-time microbiome measurements with subsequent clinical outcomes, which could be potentially problematic in settings of significant temporal variability [24, 26].
Our group previously reported that a single measurement of baseline stool microbial diversity was associated with infectious risk for 34 patients during induction chemotherapy (IC) for acute myelogenous leukemia (AML) . Similar to other studies of ill patients, we observed instances of rapid and profound shifts in the microbiota in our AML cohort [11, 21, 22]. Thus, herein, we sought to quantify the overall intra-patient temporal variability of the oral and stool microbiome of this cohort expanded to 59 patients. In addition, we sought to determine the consequences of microbiome temporal instability on patient outcomes and clinical factors driving intra-patient temporal variability of the microbiome during IC. We chose to study such patients because of the opportunity to characterize the microbiome prior to receipt of chemotherapy and intense antibiotic exposure (i.e., prior to severe perturbations) and the capacity to obtain dense longitudinal sampling over the course of intensive treatment due to the extended inpatient nature of IC. Moreover, AML patients are at high risk for infection during IC and such infections are generally derived from the commensal microflora [21, 23]. We hypothesized that higher microbiome intra-patient temporal variability, driven by prolonged antibiotic exposure, would be associated with poorer clinical outcomes.
Patient recruitment and specimen collection
Study subjects included 59 newly diagnosed adult AML patients undergoing IC at MD Anderson Cancer Center (MDACC) in Houston, TX from September 2013 to October 2014. AML patients initiating inpatient IC at MDACC were approached for study inclusion unless they had systemic infection. AML patients receiving IC at MDACC are routinely prescribed a prophylactic fluoroquinolone or cephalosporin prior to the initiation of therapy. In this study, 100% of patients received routine prophylaxis, with 64% of baseline stool, and 55% of baseline oral samples taken after the patient had already started prophylactics. AML patients over 50 years receiving IC are treated in a laminar-air flow isolation until neutrophil counts recover to >500 cells/μL or until day 28. Patients aged under 50 years are admitted for the duration of the chemotherapy (approximately 4–5 days) and then followed as an outpatient with clinic visits three times a week until neutrophil recovery or 28 days.
Buccal and fecal specimens were collected from each patient at baseline, continued approximately every 96 h as available, and stopped upon neutrophil recovery. Baseline samples were considered up to 8 days before and 24 h following IC initiation. As per availability of samples, 55 (93%) of the patients had oral samples collected before or at the same time as the initiation of chemotherapy, while 35 (59%) of the patients had stool samples collected before or at the same time as the initiation of chemotherapy. The buccal mucosa of each individual was swabbed three times on each side using a Catch-All™ Sample Collection Swab (Epicentre). Patient stool samples were either collected in a stool hat or using a BBL™ CultureSwab® (BD Diagnostics). All samples were placed in sterile 2-mL cryovials and stored immediately at −80 °C until further processing.
16S rRNA sequencing and data processing
Bacterial genomic DNA was extracted from buccal and stool specimens using the MO BIO PowerSoil DNA Isolation Kit (MO BIO Laboratories). The 16S rRNA V4 region was PCR amplified and sequenced on the Illumina MiSeq platform using a 2 × 250-bp paired-end protocol adapted from the Human Microbiome Project (HMP) methods [16, 27]. All samples from the same patient and site were processed and sequenced together to minimize batching issues. Amplification primers contained adapters for MiSeq sequencing and single-index barcodes resulting in PCR products that were pooled and sequenced directly. Read pairs were de-multiplexed based on barcodes and merged using USEARCH v7.0.100. 16S rRNA gene sequences were allocated to specific operational taxonomic units using a UPARSE pipeline and aligned to the V4 region within the SILVA SSURef_NR99_119 database . Analysis of microbiome communities was performed in R (R Core Team 2015, version 3.2.2, http://www.R-project.org), using phyloseq  to calculate α- and β-diversity metrics. The Shannon Diversity Index (SDI) was used for α-diversity calculations, and weighted and unweighted UniFrac for β-diversity distances . The 16S V3–V4 region HMP sequencing reads were obtained from http://hmpdacc.org/HMQCP, trimmed to match the region amplified by this study, and processed identically to AML patient samples.
Microbiome community and statistical analyses
Intra-patient temporal variability of microbial diversity was defined as the coefficient of variation (CV) of a longitudinal collection of α-diversity values, and was calculated for each patient’s set of oral and stool samples. Higher values were indicative of more variable microbial diversity. Temporal variability in community composition, or β-diversity, of each patient was determined for the oral and stool by calculating the CV of the weighted and unweighted UniFrac distances of longitudinal samples collected from each individual per site. Again, higher values were indicative of more variable communities. Pairwise differences in temporal variability across body sites were made using Mann–Whitney U test, whereas pairwise differences among infection or response groups was performed using Student’s t-test with Welch’s correction. Linear correlations between CVs at different body sites were determined using Pearson’s r and P values generated in GraphPad Prism 6.
Heatmaps analyzing genera abundance over time among patients with increasing temporal variability were generated with the publically available pheatmap R package version 1.0.8. (http://CRAN.R-project.org/package=pheatmap), and include correlation metrics calculated with R’s cor and cor.test stats package functions. P values were corrected for multiple comparisons using the Benjamini and Hochberg method.
For each body habitat the population was divided into quartiles based on CV of the weighted UniFrac distance values or SDI where the first quartile was defined as stable, second and third as average, and fourth as variable as previously described . To determine significant differences in genera abundance between stable, average, and variable individuals, we tested for differences between groups using non-parametric Kruskal–Wallis analysis of variance in R for genera across individuals, then corrected for the false discovery rate using the Benjamini and Hochberg method.
Multivariable regression analyses were performed using base R (R Core Team 2015, version 3.2.2, http://www.R-project.org ) and included age, antibiotic type, chemotherapy regimen, and exposure to antibiotics as covariates. Antibiotic types were subdivided into three major broad spectrum β-lactam antibiotics received by this cohort, namely, cefepime, carbapenems (primarily meropenem), and piperacillin-tazobactam. Chemotherapy regimens were subdivided into fludarabine-containing regimens, high intensity non-fludarabine-containing regimens, hypomethylators, or other. Fludarabine-containing regimens included fludarabine in combination with idarubicin and cytarabine , or fludarabine/idarubicin/cytarabine with G-CSF (FLAG-Ida). High intensity non-fludarabine-containing regimens were purine analog of clofarabine or cladrabine in combination with idarubicin and cytarabine. Hypomethylator-based combinations included decitabine and azacytidine .
Infections were defined as microbiologically defined infections (MDIs) or clinically defined infections as described previously . Subsequent infectious episodes were defined as MDIs that occurred within 90 days of cessation of longitudinal sampling. Complete remission (CR) of AML was assessed using standard definitions .
AML patients undergoing IC exhibit temporal instability of the stool and oral microbiome diversity
Clinical features of 59 AML patients
Median age in years a
Median days on study
Median number of oral samples
Median number of stool samples
Non-fludarabine high intensityc
Complete remission after IC
Overall response ratef
Microbiologically documented infection
Clinically documented infection
Received treatment antibioticsh
Carbapenem >72 h
Piperacillin/tazobactam >72 h
Cefepime >72 h
Received prophylactic antibiotics
Median number of antibiotics administered
Median number of days exposed to all antibioticsi
Median number of days exposed to treatment antibiotics
Median number of days exposed to prophylactic antibiotics
High intra-patient temporal variability of oral and stool microbiome among AML patients is associated with increased pathogenic-associated genera abundance
Next we sought to determine the temporal variability in microbiome community structure and membership as represented by quantitative and qualitative measurements of β-diversity using weighted and unweighted UniFrac distance measurements, respectively. Here, we considered the CV of each patient’s samples per site in order to characterize the dispersion of β-diversity metrics. The mean CVs of weighted and unweighted UniFrac distances for the cohort were 0.24 ± 0.1 and 0.16 ± 0.04 for the oral samples and 0.32 ± 0.2 and 0.20 ± 0.08 for stool samples, respectively (Fig. 1c). Contrary to SDI CVs, the temporal variability of the weighted UniFrac distances between the oral and stool of patients was not significantly correlated (P = 0.10; Additional file 1: Figure S1d). Reports in healthy persons have observed associations between diversity and temporal stability, such as individuals with a more diverse microbiome are likely to have a more stable microbiome over time [4, 14, 15]. However, we did not find any statistically significant correlations between either the baseline or median SDI values of patients and their temporal variability as measured by the CV of the weighted UniFrac distances of their samples, suggesting microbiome structural variability does not appear to be affected by α-diversity in treated AML patients (Additional file 1: Figure S2).
Stabilizing and destabilizing taxa can be inferred by categorizing AML patients into stable, average, or variable microbiomes during IC
Intra-patient temporal instability of microbial diversity is linked to adverse infectious outcomes during and after IC
To evaluate the clinical consequences of the observed differences in temporal stability in our cohort, we sought to determine if intra-patient temporal variability of the microbiome could be linked with adverse outcomes during and following IC. Specifically, we analyzed if the measures of temporal variability (CVs of the SDI and weighted and unweighted Unifrac distances) were correlated with infection during IC, infection in the 90 days post-IC neutrophil recovery, or response of the leukemia to chemotherapy.
Duration of antibiotic treatment is associated with temporal instability of the oral microbiome during IC in AML patients
Multivariable regression analyses of potential clinical factors associated with the intra-patient temporal instability of the oral and stool microbiomes of AML patients
Oral SDI CV
Stool SDI CV
Received piperacillin/tazobactam >72 h
Received cefepime >72 h
Received carbapenem >72 h
Days on all antibioticsa
Days on treatment antibiotics
Number of antibiotics received
Non-fludarabine high intensity chemotherapy
Prolonged antibiotic exposure is associated with long-term infectious outcomes among AML patients undergoing IC
There is tremendous enthusiasm for using measurements and manipulation of the microbiome as a means to improve different aspects of human health. Indeed, multiple studies have shown that the microbiome can significantly impact a broad variety of pathophysiologic processes, from carcinogenesis to autoimmunity to serious infections [7, 9, 18, 24, 25, 38, 39]. As almost all of the existing datasets ascertaining longitudinal variability exist in healthy controls, the limited understanding of the variability in microbiota composition for sick patients impedes the capacity to readily translate microbiome measurements into the clinical setting. The urgent need to monitor and understand microbiome dysbiosis during critical illness has been expressed with recent initiatives such as the ICU Microbiome Project (http://americangut.org/the-icu-microbiome-project-is-there-a-better-way-to-treat-infections-than-antibiotics/), the National Microbiome Initiative (https://www.whitehouse.gov/the-press-office/2016/05/12/fact-sheet-announcing-national-microbiome-initiative), and the Center for Disease Control’s recent Broad Agency Announcement for Advanced and Innovative Solutions to Improve Public Health, which includes the request for microbiome assessment and intervention to address antibiotic resistance in both healthy individuals and in healthcare settings. Herein, we contribute to addressing this knowledge gap by analyzing the inter-patient variability of both the oral and stool microbiome for 59 AML patients using >800 samples collected over a median of 28 days, the factors driving differential variability in this cohort, and the association of inter-patient variability with clinical outcomes.
When considering how our cohort of leukemia patients compares to healthy individuals, we obtained, trimmed, and processed data from the Human Microbiome Project (HMP)  to match the V4 region of the 16S rRNA gene amplified by this study’s primers and processing protocols. Although a direct statistical comparison is not suitable due to differences between study methodologies, our mean SDI CV values for both oral and stool were two- to fourfold higher than both the HMP as well as a separate cohort of healthy subjects studied by Flores et al. [15, 16] (HMP mean SDI CV values were 0.2 for oral and 0.09 for stool, and mean SDI CV for stool and tongue samples for the Flores et al. data set was approximately 0.17 and 0.1, respectively), indicating that our cohort of treated AML patients had high intra-patient temporal variability of α-diversity compared to healthy subjects (Additional file 1: Figure S1c). A high level of intra-patient temporal variability found within our cohort is in concordance with observations of rapid fluctuations in microbiota composition reported in previous cohorts of intensive care unit and stem cell transplant patients [7, 11, 21].
Although the overall cohort had high levels of variability, there was a wide range, showing that many patients maintained a relatively stable microbiome despite the significant stress of AML therapy and a prolonged hospital stay. It has been previously demonstrated that specific enterotypes and the diversity of the microbiome influence the potential adverse impact of antibiotics on microbial communities . In our cohort, variability was associated with the predominance of potentially pathogenic genera such as Staphylococcus whereas more stable microbiomes were characterized by high levels of commensals such as Akkermansia, Subdilogranulum, and Pseudobutyrivibrio (Figs. 2, 3, and 5). Akkermansia spp. have been described to be important in host metabolic homeostasis, anti-inflammatory functions, such as interleukin secretion, and promoting intestinal epithelial integrity in murine models and human colonic cell lines [38, 41]. Moreover, butyrate-producing commensal microorganisms, like Akkermansia and Pseudobutyrivibrio, are important in maintaining the health of the intestinal epithelium, which in turn provides nutrients necessary for microbiota stability [9, 38, 42]. However, Akkermansia spp. have also been recently associated with loss of the colonic mucus layer and compromised intestinal barrier function in mice with graft versus host disease . More perplexing was the increased abundance of Lactobacillus and Lactococcus in the stool of variable individuals, as these organisms have often been associated with microbiome maintenance and mucosal integrity [39, 44–47]. These results suggest the importance of performing metagenomic shotgun sequencing in order to determine the specific species among these complex genera that are associated with maintenance and variability. Therefore, in combination with the aforementioned studies, our data appear to suggest that stable microbiomes with high levels of specific commensal microorganisms might be implicated in mechanisms protecting patients from intestinal domination and subsequent infection by pathogenic bacteria. These observations raise the question of whether fecal microbiota transplantation or targeted species repletion of patients with a microbiota dominated by pathogenic bacteria could restore intestinal homeostasis .
Interestingly, although antibiotic exposure is often associated with reduced stool microbial diversity [7, 35, 36], our results show total days on antibiotics was significantly correlated with temporal variability of the oral microbial diversity, but not the stool. These findings are in agreement with recent findings using generalized linear models which identified antibiotic use as a significant predictor of temporal variability of the tongue, but not the gut, in healthy subjects . Given previous literature associating other clinical factors, such as the age and chemotherapy, with microbiome variability and dysbiosis, it was surprising that total antibiotic exposure to all antibiotics, including prophylaxis, was the only clinical variable tested to be statistically correlated with any measure of temporal variability [2, 37]. This suggests the very treatment applied to protect the patient appears to predispose the patient to recurrent infectious-related issues. Thus, greater efforts need to be taken towards antibiotic stewardship as well as tailoring antimicrobial treatments within the context of the microbiome.
Several limitations of our study bear mentioning. First, all of our patients had a single disease and were recruited from a single center, which means we do not know how these data represent the broader array of hospitalized patients. However, the relative homogeneity of the cohort was chosen in order to facilitate comparative analyses of the complex data generated in microbiome studies and to be able to characterize the microbiota prior to the patient becoming seriously ill. With this in mind, while the majority of samples were true baselines, a number of baseline samples, particularly fecal samples, were taken within the 24 h after initiation of chemotherapy due to sample availability. To our knowledge, there are no time-wise studies showing the effects of chemotherapy on the microbiome as the studies published to date comparing the microbiome before and after chemotherapy consider a wide range of days post-chemotherapy analyzed together . Moreover, it is also known that the impact of chemotherapy on host factors is not typically observed until 7–10 days following chemotherapy initiation (e.g., neutropenia, mucositis, etc.). So, although we believe 24 h provides a reasonable window to collect baseline samples from patients urgently receiving chemotherapy, we cannot exclude the concern that chemotherapy could affect the microbiome within 24 h as this is currently unknown. Second, we used 16S rRNA analyses to determine microbial composition, limiting our classification of bacteria to the genus level. A species level analysis could be performed via shotgun metagenomic sequencing, which would also permit functional metabolomics studies that could help to elucidate mechanistic bases for our observations. However, applying such methodology to the very large number of samples in our study is currently cost prohibitive. Finally, although we had >800 microbiome measurements in our cohort, the complexity of the clinical course of our patients meant that we were limited in our ability to detect certain associations between clinical factors and microbiota variability. For example, most of our patients received various durations and combinations of antimicrobial administration that were problematic to reduce to discrete categories that could be incorporated into multivariable regression analyses. However, clinical studies of the microbiota in the acute care setting will face this same challenge which mandates large sample sizes and careful collection and analyses of both clinical and microbiome data.
We have provided the largest dataset to date quantifying the longitudinal variability of the oral and stool microbiome in ill, hospitalized patients. We have identified particular bacterial taxa that are positively and negatively correlated with microbiome instability and demonstrated that high temporal variability is associated with increased rates of infectious outcomes. The characterization of microbiome temporal fluctuations described herein contribute to the first steps towards advancing microbiome-based diagnostic and therapeutic interventions that can be applicable in a wide range of ailments such as cancer, critical illness, and other immune-compromised individuals. Our previous data revealed low baseline stool α-diversity was associated with infectious risk during IC while decreases in both the oral and stool α-diversity between baseline and last samples were associated with infection post-IC. . These previous findings, combined with the data herein, indicate that both microbial diversity as well as intra-patient temporal variability have infectious implications when studying acutely ill patients. Our finding of high intra-patient variability having clinical implications in these patients signifies that making conclusions based on single microbiome measurements in ill patients is likely to be problematic and suggests that statistical mechanisms that capture both the composition and the overall trajectory of a patient’s microbiota during illness will likely be required to fully integrate microbiome measurements into the clinical arena.
Acute myelogenous leukemia
Coefficient of variation
Human Microbiome Project
MD Anderson Cancer Center
Microbiologically defined infection
Shannon diversity index.
We thank Lisa Marsh and Lily Carlin for assistance with sample collection, and any other clinical or nursing staff that assisted with patient enrollment or sample collection. We thank members of the Center for Metagenomics and Microbiome Research for processing and sequencing of samples, specifically Tulin Ayvaz.
This work was supported by The University of Texas MD Anderson Cancer Center AML MoonShot Knowledge Gap Project (to Dimitrios P. Kontoyiannis), and the Odyssey Program and CFP Foundation at The University of Texas MD Anderson Cancer Center (to Jessica Galloway-Peña). W. Duncan Wadsworth is supported by NIH grant NCI T32 CA096520 at Rice University. Supported by the NIH/NCI under award number P30CA016672 and used the Bioinformatics Shared Resource.
Availability of data and materials
The dataset supporting the conclusions of this article is available in the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra). The datasets generated during the current study are available in the NCBI Sequence Read Archive under BioProject ID PRJNA352060. BioSample accession numbers for the data generated are listed in Additional file 1: Table S7.
JGP: conceptualization, methodology, validation, formal analysis, investigation, data curation, writing (original draft), writing (review and editing), visualization, project administration, and funding acquisition. DPS: methodology, software, validation, formal analysis, resources, data curation, writing (review and editing), visualization, and project administration. PS: methodology, investigation, resources, and writing (review) and editing. WDW: methodology, software, validation, formal analysis, data curation, writing (review and editing), and visualization. BF: methodology, software, validation, formal analysis, data curation, writing (review and editing), and visualization. NJA: methodology, software, validation, formal analysis, resources, data curation, writing (review and editing), visualization, and project administration. EJS: writing (review and editing) and funding acquisition. NGD: investigation, resources, writing (review and editing), and visualization. MG: methodology, software, validation, formal analysis, data curation, writing (original draft), writing (review and editing), and visualization. JFP: software, validation, formal analysis, resources, data curation, writing (review and editing), visualization, and project administration. DPK: conceptualization, methodology, resources, writing (original draft), writing (review and editing), and funding acquisition. SAS: conceptualization, methodology, formal analysis, investigation, data curation, writing (review and editing), visualization, supervision, project administration, and funding acquisition. All authors read and approved the final manuscript.
Nadim J. Ajami and Joseph F. Petrosino are the Project Director and Founder/Chief Science Officer, respectively, of Diversgen. Dimitrios P. Kontoyiannis reports research support from Merck, Pfizer, and Astellas; service on the Merck Advisory Board; acting as a consultant for Astellas and F2G; and honoraria from Gilead, T2 Biosystems, and Mylan, Inc., during the course of the study. The other authors declare that they have no competing interests.
Consent for publication
All patient information was anonymized at source and unique ID codes were used to identify cases. Publication of de-identified results from all consenting participants was approved in the protocol below (MDACC IRB PA13-0339).
Ethics approval and consent to participate
The study protocol was approved by the MDACC Institutional Review Board (PA13-0339) and was conducted in compliance with the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment.
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- Cameron SJ, Huws SA, Hegarty MJ, Smith DP, Mur LA. The human salivary microbiome exhibits temporal stability in bacterial diversity. FEMS Microbiol Ecol. 2015;91(9):fiv091.
- Claesson MJ, Cusack S, O’Sullivan O, Greene-Diniz R, de Weerd H, Flannery E, et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc Natl Acad Sci U S A. 2011;108 Suppl 1:4586–91.View ArticlePubMedGoogle Scholar
- Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. Bacterial community variation in human body habitats across space and time. Science. 2009;326(5960):1694–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Oh J, Byrd AL, Park M, Program NCS, Kong HH, Segre JA. Temporal stability of the human skin microbiome. Cell. 2016;165(4):854–66.View ArticlePubMedGoogle Scholar
- Moya A, Ferrer M. Functional redundancy-induced stability of gut microbiota subjected to disturbance. Trends Microbiol. 2016;24(5):402–13.View ArticlePubMedGoogle Scholar
- Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature. 2012;489(7415):220–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Halpin AL, de Man TJ, Kraft CS, Perry KA, Chan AW, Lieu S, et al. Intestinal microbiome disruption in patients in a long-term acute care hospital: a case for development of microbiome disruption indices to improve infection prevention. Am J Infect Control. 2016;44(7):830–6.View ArticlePubMedGoogle Scholar
- Laufer Halpin A, McDonald LC. The dawning of microbiome remediation for addressing antibiotic resistance. Clin Infect Dis. 2016;62(12):1487–8.View ArticleGoogle Scholar
- Lei YM, Nair L, Alegre ML. The interplay between the intestinal microbiota and the immune system. Clin Res Hepatol Gastroenterol. 2015;39(1):9–19.View ArticlePubMedGoogle Scholar
- Parfrey LW, Knight R. Spatial and temporal variability of the human microbiota. Clin Microbiol Infect. 2012;18 Suppl 4:8–11.PubMedGoogle Scholar
- Zaborin A, Smith D, Garfield K, Quensen J, Shakhsheer B, Kade M, et al. Membership and behavior of ultra-low-diversity pathogen communities present in the gut of humans during prolonged critical illness. MBio. 2014;5(5):e01361–01314.View ArticlePubMedPubMed CentralGoogle Scholar
- Belstrom D, Holmstrup P, Bardow A, Kokaras A, Fiehn NE, Paster BJ. Temporal stability of the salivary microbiota in oral health. PLoS One. 2016;11(1):e0147472.View ArticlePubMedPubMed CentralGoogle Scholar
- Caporaso JG, Lauber CL, Costello EK, Berg-Lyons D, Gonzalez A, Stombaugh J, et al. Moving pictures of the human microbiome. Genome Biol. 2011;12(5):R50.View ArticlePubMedPubMed CentralGoogle Scholar
- de Meij TG, Budding AE, de Groot EF, Jansen FM, Frank Kneepkens CM, Benninga MA, et al. Composition and stability of intestinal microbiota of healthy children within a Dutch population. FASEB J. 2016;30(4):1512–22.View ArticlePubMedGoogle Scholar
- Flores GE, Caporaso JG, Henley JB, Rideout JR, Domogala D, Chase J, et al. Temporal variability is a personalized feature of the human microbiome. Genome Biol. 2014;15(12):531.View ArticlePubMedPubMed CentralGoogle Scholar
- Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207–14.View ArticleGoogle Scholar
- Falony G, Joossens M, Vieira-Silva S, Wang J, Darzi Y, Faust K, et al. Population-level analysis of gut microbiome variation. Science. 2016;352(6285):560–4.View ArticlePubMedGoogle Scholar
- Kong HH, Oh J, Deming C, Conlan S, Grice EA, Beatson MA, et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 2012;22(5):850–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Angelberger S, Reinisch W, Makristathis A, Lichtenberger C, Dejaco C, Papay P, et al. Temporal bacterial community dynamics vary among ulcerative colitis patients after fecal microbiota transplantation. Am J Gastroenterol. 2013;108(10):1620–30.View ArticlePubMedGoogle Scholar
- Martinez C, Antolin M, Santos J, Torrejon A, Casellas F, Borruel N, et al. Unstable composition of the fecal microbiota in ulcerative colitis during clinical remission. Am J Gastroenterol. 2008;103(3):643–8.View ArticlePubMedGoogle Scholar
- Taur Y, Xavier JB, Lipuma L, Ubeda C, Goldberg J, Gobourne A, et al. Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation. Clin Infect Dis. 2012;55(7):905–14.View ArticlePubMedPubMed CentralGoogle Scholar
- Jenq RR, Ubeda C, Taur Y, Menezes CC, Khanin R, Dudakov JA, et al. Regulation of intestinal inflammation by microbiota following allogeneic bone marrow transplantation. J Exp Med. 2012;209(5):903–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Montassier E, Al-Ghalith GA, Ward T, Corvec S, Gastinne T, Potel G, et al. Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection. Genome Med. 2016;8(1):49.View ArticlePubMedPubMed CentralGoogle Scholar
- Taur Y, Jenq RR, Perales MA, Littmann ER, Morjaria S, Ling L, et al. The effects of intestinal tract bacterial diversity on mortality following allogeneic hematopoietic stem cell transplantation. Blood. 2014;124(7):1174–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Galloway-Pena JR, Smith DP, Sahasrabhojane P, Ajami NJ, Wadsworth WD, Daver NG, et al. The role of the gastrointestinal microbiome in infectious complications during induction chemotherapy for acute myeloid leukemia. Cancer. 2016;122(14):2186–96.View ArticlePubMedGoogle Scholar
- Chang JY, Antonopoulos DA, Kalra A, Tonelli A, Khalife WT, Schmidt TM, et al. Decreased diversity of the fecal microbiome in recurrent Clostridium difficile-associated diarrhea. J Infect Dis. 2008;197(3):435–8.View ArticlePubMedGoogle Scholar
- Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012;486(7402):215–21.View ArticleGoogle Scholar
- Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590–6.View ArticlePubMedGoogle Scholar
- McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217.
- Morgan XC, Huttenhower C. Human microbiome analysis. PLoS Comput Biol. 2012;8(12):e1002808.View ArticlePubMedPubMed CentralGoogle Scholar
- Nazha A, Kantarjian H, Ravandi F, Huang X, Choi S, Garcia-Manero G, et al. Clofarabine, idarubicin, and cytarabine (CIA) as frontline therapy for patients </=60 years with newly diagnosed acute myeloid leukemia. Am J Hematol. 2013;88(11):961–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Montalban-Bravo G, Garcia-Manero G. Novel drugs for older patients with acute myeloid leukemia. Leukemia. 2015;29(4):760–9.View ArticlePubMedGoogle Scholar
- Cheson BD, Bennett JM, Kopecky KJ, Buchner T, Willman CL, Estey EH, et al. Revised recommendations of the International Working Group for Diagnosis, Standardization of Response Criteria, Treatment Outcomes, and Reporting Standards for Therapeutic Trials in Acute Myeloid Leukemia. J Clin Oncol. 2003;21(24):4642–9.View ArticlePubMedGoogle Scholar
- The Integrative Human Microbiome Project. Dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe. 2014;16(3):276–89.View ArticleGoogle Scholar
- Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U S A. 2011;108 Suppl 1:4554–61.View ArticlePubMedGoogle Scholar
- Modi SR, Collins JJ, Relman DA. Antibiotics and the gut microbiota. J Clin Invest. 2014;124(10):4212–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Montassier E, Gastinne T, Vangay P, Al-Ghalith GA, Bruley des Varannes S, Massart S, et al. Chemotherapy-driven dysbiosis in the intestinal microbiome. Aliment Pharmacol Ther. 2015;42(5):515–28.View ArticlePubMedGoogle Scholar
- Derrien M, Belzer C, de Vos WM. Akkermansia muciniphila and its role in regulating host functions. Microb Pathog. 2016. doi: 10.1016/j.micpath.2016.02.005. http://www.sciencedirect.com/science/article/pii/S0882401015301789. [Epub ahead of print].
- Kozakova H, Schwarzer M, Tuckova L, Srutkova D, Czarnowska E, Rosiak I, et al. Colonization of germ-free mice with a mixture of three Lactobacillus strains enhances the integrity of gut mucosa and ameliorates allergic sensitization. Cell Mol Immunol. 2016;13(2):251–62.View ArticlePubMedGoogle Scholar
- Raymond F, Ouameur AA, Deraspe M, Iqbal N, Gingras H, Dridi B, et al. The initial state of the human gut microbiome determines its reshaping by antibiotics. ISME J. 2016;10(3):707–20.View ArticlePubMedGoogle Scholar
- Reunanen J, Kainulainen V, Huuskonen L, Ottman N, Belzer C, Huhtinen H, et al. Akkermansia muciniphila adheres to enterocytes and strengthens the integrity of the epithelial cell layer. Appl Environ Microbiol. 2015;81(11):3655–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Mathewson ND, Jenq R, Mathew AV, Koenigsknecht M, Hanash A, Toubai T, et al. Gut microbiome-derived metabolites modulate intestinal epithelial cell damage and mitigate graft-versus-host disease. Nat Immunol. 2016;17(5):505–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Shono Y, Docampo MD, Peled JU, Perobelli SM, Velardi E, Tsai JJ, 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(339):339ra371.
- Lam EK, Tai EK, Koo MW, Wong HP, Wu WK, Yu L, et al. Enhancement of gastric mucosal integrity by Lactobacillus rhamnosus GG. Life Sci. 2007;80(23):2128–36.View ArticlePubMedGoogle Scholar
- Yu Q, Yuan L, Deng J, Yang Q. Lactobacillus protects the integrity of intestinal epithelial barrier damaged by pathogenic bacteria. Front Cell Infect Microbiol. 2015;5:26.View ArticlePubMedPubMed CentralGoogle Scholar
- Bernbom N, Licht TR, Brogren CH, Jelle B, Johansen AH, Badiola I, et al. Effects of Lactococcus lactis on composition of intestinal microbiota: role of nisin. Appl Environ Microbiol. 2006;72(1):239–44.View ArticlePubMedPubMed CentralGoogle Scholar
- Araos R, Tai AK, Snyder GM, Blaser MJ, D’Agata EM. Predominance of Lactobacillus spp. among patients who do not acquire multidrug-resistant organisms. Clin Infect Dis. 2016;63(7):937–43.View ArticlePubMedGoogle Scholar