Tag Archives: Microbiome Research

Rebiotix In Partnership With BioRankings® Develops Microbiome Health Index™ (MHI™) to Identify Indicators for Microbiome Restoration

Rebiotix Inc., a clinical-stage microbiome company focused on harnessing the power of the human microbiome to treat debilitating diseases, announced  on February 12, 2018 — the development of the Microbiome Health Index™ (MHI™) to provide the microbiome research community with a standardized metric to quantify the rehabilitation of the human microbiome.

MHI was established in partnership with data analytics firm, BioRankings®, to enable a non-biased comparison of the efficacy of microbiome-based therapeutics.

New MHI data will be presented at the 28th European Congress of Clinical Microbiology and Infectious Diseases (ECCMID 2018) in April.

“In developing the Microbiome Health Index, our aim is to provide an objective, universal tool to measure the restoration of a dysbiotic microbiome across different trial designs, sequencing methods and across multiple drug technologies,” stated Ken F. Blount, Ph.D., Chief Scientific Officer of Rebiotix.  “Initial analyses using MHI in Clostridium difficile (C. diff) infections have demonstrated its significant potential to quantify and differentiate dysbiotic from healthier microbiomes.  As presented at ACG2017, MHI was able to quantify the relationship between four key bacterial classes into a single metric that can distinguish patients with dysbiosis resulting from C. diff. From this, we were able to gain valuable insight into the mechanism of action by which Rebiotix’s Phase 3 microbiota drug, RBX2660, is able to rehabilitate a dysbiotic microbiome to a healthier state.”

Blount continued, “MHI is now being employed to analyze microbiome profile data gathered in the ongoing Phase 1 clinical trial of RBX7455, Rebiotix’s lyophilized, non-frozen oral capsule formulation. The intent with this research is to further strengthen and refine MHI and confirm the RBX2660 analysis. Additionally, we will look to utilize MHI in new diseases states being studied.”

Bill Shannon, Ph.D., MBA, Co-Founder and Managing Partner of Analytics at BioRankings said, “The human microbiome is a new frontier where very little analytical methodology or rigorous statistical methods have been developed specifically for this type of data.  Analytical tools such as MHI will be critical to advance translational clinical microbiome research, and we are emboldened by the MHI data that have been reported and continuing to be collected.  Our vision is for MHI to become a standard measure for microbiome research, potentially serving as a validated endpoint for clinical trials and providing both a predictive measure and actionable data.”

MHI provides a unidimensional expression of changes in four taxonomic classes known to have relevance to microbiome health and colonization resistance – Bacteriodia, Clostridia, Gammaproteobacteria and Bacilli.

Utilizing microbiome profiles of patients from the PUNCH CD2 Phase 2b trial of RBX2660, researchers determined that MHI can effectively distinguish patients with dysbiosis from healthier patients, as defined by the RBX2660 product profile and the Human Microbiome Project.  Notably following RBX2660 treatment, MHI significantly increased as early as seven days in responders compared to baseline and continued to increase at day 30 and day 60.

About BioRankings®

BioRankings is a contract analytics firm that works with clients to extract actionable results from their data. Their business philosophy centers on providing clients and partners with the methods, software, and support they need to make full use of their data and design accurate, cost-efficient experiments.  For more information on BioRankings, please visit http://www.biorankings.com.

About Rebiotix Inc.

Rebiotix Inc. is a late-stage clinical microbiome company focused on harnessing the power of the human microbiome to revolutionize the treatment of challenging diseases. Rebiotix possesses a deep and diverse clinical pipeline, with its lead drug candidate, RBX2660, in Phase 3 clinical development for the prevention of recurrent Clostridium difficile (C. diff) infection.  RBX2660 has been granted Fast Track status, Orphan Drug and Breakthrough Therapy designation from the FDA for its potential to prevent recurrent C. diff. infection. Rebiotix’s clinical pipeline also features RBX7455, a lyophilized, non-frozen, oral capsule formulation, which is currently the subject of an investigator-sponsored Phase 1 trial for the prevention of recurrent C. diff. infection.  In addition, Rebiotix is targeting several other disease states with drug products built on its pioneering Microbiota Restoration Therapy™ (MRT™) platform.  MRT is a standardized, stabilized drug technology that is designed to rehabilitate the human microbiome by delivering a broad consortium of live microbes into a patient’s intestinal tract via a ready-to-use and easy-to-administer format. For more information on Rebiotix and its pipeline of human microbiome-directed therapies, visit http://www.rebiotix.com.

Researchers Utilize Deep Metagenomic Sequencing to Profile FMT ‘s Retracting the Gut Microbiome Features That Coincided With Successful Fecal Transplant Engraftment

A team led by investigators at the Broad Institute have started untangling the bacterial strains that influence successful fecal microbiota transplantation (FMT) engraftment in individuals treated for recurrent Clostridium difficile infection.

As they reported in Cell Host & Microbe today, researchers from the Broad Institute, Massachusetts Institute of Technology, Massachusetts General Hospital, and elsewhere used deep metagenomic sequencing to profile FMT in four FMT donors and 19 recipients with C. difficile infections, retracing the gut microbiome features that coincided with successful fecal transplant engraftment.

The initial gut microbial communities in both the donors and the recipients seemed to influence this process, the team noted, particularly bacterial abundance and strain phylogeny. The final gut microbe composition differed between donors and post-FMT recipients, though, with specific strains that originated in the host either taking hold or falling by the wayside in recipients in an “all-or-nothing” manner.

“This paper provides a context for understanding how to make these live biological therapeutics as an alternative to transferring raw fecal matter,” co-senior author Eric Alm, co-director of MIT’s Center for Microbiome Informatics and Therapeutics, said in a statement.

“We describe a model focused on three elements, including bacterial engraftment, growth, and mechanism of action, that need to be considered when developing these live therapies targeting the gut microorganisms, or microbiome,” added Alm, who is also affiliated with the Broad Institute and Finch Therapeutics.

Along with its use for treating recurrent C. difficile infection, the team noted that FMT has been proposed in other conditions such as inflammatory bowel disease and metabolic syndrome. Even so, there is a ways to go in understanding the factors influencing bacterial engraftment and effectiveness in the recipient gut — information needed to move the approach from a shotgun approach using fecal donor material to microbe-based treatments based on purified collections of specific bacteria.

“Although the success of FMT requires donor bacteria to engraft in the patient’s gut, the forces governing engraftment in humans are unknown,” the authors wrote.

To follow this process, the researchers used the Illumina GAIIx instrument to do deep metagenomic sequencing on seven stool samples from four healthy donors and 67 samples collected over time from 19 individuals treated for C. difficile infection with FMT.

With the help of statistical modeling and a new computational method dubbed Strain Finder, the team looked at the bacterial species that successfully engrafted in FMT recipients and followed strain genotypes over time. It also mapped the metagenomes to Human Microbiome Project reference genomes to take a look at bacterial taxa abundance.

Prior to treatment, for example, FMT recipients had lower-than-usual gut microbiome diversity. And while gut microbial community patterns shifted in recipients after FMT, the resulting gut microbiomes continued to differ from the original donor microbiomes, the researchers reported.

Even so, their analytical methods made it possible predict post-FMR metagenomic operational taxa unit abundance and incidence.

With nearly 1,100 bacterial strains in the 79 samples considered, the team traced transmission of certain strains from FMT donors to recipients, noting that bacterial strains tended to engraft in an “all-or-nothing” manner, “whereby no strains or complete sets of strains colonize the patients.”

“We find that engraftment can be predicted largely from the abundance and phylogeny of bacteria in the donor and the pre-FMT patient,” Alm and co-authors wrote. “Furthermore, donor strains within a species engraft in an all-or-nothing manner and previously undetected strains frequently colonize patients receiving FMT.”

Such patterns were supported by the researchers’ follow-up analyses on 16S ribosomal RNA sequence data for stool samples from 10 more FMT donors and 18 recipients, as well as an analysis of metagenomic sequence data for samples from five individuals treated with FMT for metabolic syndrome.

“Together,” they authors said, “these findings suggest that the principles of engraftment we discovered for recurrent C. difficile infection may generalize to other disease indications, including metabolic syndrome.”

 

To read article in its entirety please click on the following link to be redirected:

https://www.genomeweb.com/sequencing/donor-recipient-strain-analyses-offer-fecal-transplant-engraftment-clues

Gut Microbiome Research High-resolution Profiling Reveals the Extent of Clostridium difficile (C.diff.) Burden

Microbiome profiling through 16S rRNA gene sequencing has proven to be a valuable tool to characterize the diversity and composition of gut microbial communities, including in studies of CDI development and recurrence.8

 

Authors:

  • Ninalynn Daquigan,
  • Anna Maria Seekatz,
  • K. Leigh Greathouse,
  • Vincent B. Young &
  • James Robert White
Published online:

Abstract

Microbiome profiling through 16S rRNA gene sequence analysis has proven to be a useful research tool in the study of C. difficile infection (CDI); however, CDI microbiome studies typically report results at the genus level or higher, thus precluding identification of this pathogen relative to other members of the gut microbiota.

Accurate identification of C. difficile relative to the overall gut microbiome may be useful in assessments of colonization in research studies or as a prognostic indicator for patients with CDI.

To investigate the burden of C. difficile at the species level relative to the overall gut microbiome, we applied a high-resolution method for 16S rRNA sequence assignment to previously published gut microbiome studies of CDI and other patient populations. We identified C. difficile in 131 of 156 index cases of CDI (average abundance 1.78%), and 18 of 211 healthy controls (average abundance 0.008%).

We further detected substantial levels of C. difficile in a subset of infants that persisted over the first two to 12 months of life. Correlation analysis of C. difficile burden compared to other detected species demonstrated consistent negative associations with C. scindens and multiple Blautia species.

These analyses contribute insight into the relative burden of C. difficile in the gut microbiome for multiple patient populations, and indicate that high-resolution 16S rRNA gene sequence analysis may prove useful in the development and evaluation of new therapies for CDI.

Introduction

Clostridium difficile infection (CDI) poses a major healthcare burden to the global population, with an estimated 450,000 cases and 29,000 deaths in the United States annually.1,2 CDI is often associated with antibiotic treatment and is frequently acquired by patients during hospitalization.

Multiple diagnostic tests for CDI are available and hospitals commonly use a combination of enzyme immunoassay (EIA) and glutamate dehydrogenase (GDH) testing in tandem with real-time polymerase chain reaction (PCR) for increased sensitivity and shorter turnaround time.3

After diagnosis, patients with CDI are typically treated with metronidazole and/or vancomycin depending on symptom severity.3 Treatment failure is estimated to occur in 20% of patients, resulting in a recurrent CDI population that may require other treatment strategies.4,5 The development of microbial-based therapeutics, such as fecal microbiota transplantation (FMT) and combinations of selected microbes for the treatment of recurrent CDI suggests that mixtures of commensal microbes may be routinely utilized in the future as an alternative to powerful antibiotics.6,7

Microbiome profiling through 16S rRNA gene sequencing has proven to be a valuable tool to characterize the diversity and composition of gut microbial communities, including in studies of CDI development and recurrence.8

Given the intricate relationship between the gut microbiota and CDI, accurate                                   identification of C. difficile directly from 16S rRNA profiles in patient populations could be a valuable measure in future studies. However, a fundamental challenge to studying C. difficile through these approaches has been the level of taxonomic resolution provided through short 16S rRNA sequences.

As a result, most microbiome sequencing studies of CDI utilize higher aggregate taxonomic categories (e.g., the Clostridium XI cluster, which encompasses many other organisms related to C. difficile) as a proxy for the organism itself or simply avoid quantification altogether.9,10,11,12,13,14,15,16,17

Here we utilize a high-resolution method (Resphera Insight) for assigning species-level context to 16S rRNA gene sequence data to estimate C. difficile burden in different patient populations. This method was recently validated for detection of Listeria monocytogenes18 and Salmonella enterica,19,20 and was applied in this study to determine the relative abundance of C. difficile in several clinically relevant patient groups. Re-examining published 16S rRNA gene sequence datasets has confirmed previous associations of C. difficile with C. scindens, and identified new positive and negative correlations of C. difficile with other species, both of which may help provide insight into community aspects of C. difficile colonization and resistance against CDI.

Results

Evaluation of sensitivity and specificity for C. difficile identification

One of the challenges of 16S rRNA gene sequencing is the limited information available in these short DNA fragments to distinguish related microbial members below the genus-level. To accurately assess C. difficile at the species level from 16S rRNA gene sequence data, we used a method developed specifically for species level characterization (Resphera Insight, see Methods). We first validated this approach by obtaining full-length 16S rRNA gene sequences from 804 novel C. difficile isolates derived from multiple sources, and subsequently simulated noisy 16S rRNA gene sequence reads for taxonomic assignment (see Methods). Performance was measured using the Diagnostic True Positive Rate (DTP), defined as the percentage of sequences with an unambiguous assignment to C. difficile. The method achieved an average DTP of 99.9% (ranging from 98.92 to 100% per isolate, Table S1), indicating sufficient sensitivity to detect C. difficile from short 16S rRNA gene sequence reads.

In addition to establishing sufficient sensitivity to detect C. difficile, we also sought to evaluate false positive rates in which the method incorrectly assigns a sequence to C. difficile. As this species is a member of the Clostridium XI cluster, a false positive assessment was performed based on in silico simulations of 22 other members of this group, including the very similar Clostridium irregulare. Simulating 10,000 16S rRNA gene sequence reads per species with a 0.5% error rate, 20 of 22 species resulted in zero false positive assignments to C. difficile, with the highest false positive rate (0.07%) attributed to Clostridium irregulare (Table 1).

Table 1: False positive rates for 22 related species

Representation of C. difficile relative to the microbiota in adult cases of CDI and healthy individuals

To examine the presence of C. difficile in different human populations, we re-examined existing published 16S rRNA gene sequencing datasets with our validated method. We first compared the relative abundance of C. difficile across a cohort of healthy individuals to two cohorts of patients diagnosed with CDI (symptomatic index cases) from Seekatz et al.10 and Khanna et al.21 (Table S2). The Seekatz protocol for CDI diagnosis followed a two-stage algorithm employing enzyme immunoassay for GDH antigen and toxins A and B, with confirmation of tcdB gene presence via PCR if toxin and GDH results were discordant; the Khanna et al. protocol for CDI diagnosis was not reported in the original publication. The healthy patient cohort and Seekatz CDI datasets were generated using equivalent processing and sequencing methods.10 Average analyzed sequencing depths per sample for CDI and healthy groups were 16,114 and 14,937, respectively.

Overall, C. difficile was detected in 58 of 70 CDI index patients (82.9%) in the Seekatz study with an average abundance of 3.04% (Fig. 1a). In the Khanna dataset, C. difficile was detected in 73 of 86 CDI index patients (84.9%) with an average abundance of 0.76% (Fig. 1b). Among healthy controls, only 18 of 211 (8.5%) harbored detectable levels of C. difficile, with an average abundance of 0.008%, significantly less than both Seekatz and Khanna index cases (P < 2e-16; Mann–Whitney test).

Fig. 1
Fig. 1

Relative burden of C. difficile in the gut microbiome of two cohorts of CDI index patients and healthy controls. a Index cases of recurrent CDI (Seekatz et al.) and b CDI index patients (Khanna et al.) frequently harbored moderate to high levels of C. difficile. c Healthy controls. Overall 91.5% of controls had no detectable C. difficile and 0.9% maintained C. difficile levels higher than 0.1%

We were further interested in determining whether the ability to detect C. difficile or varying levels of C. difficile relative abundance from 16S rRNA gene sequences was related to disease outcome. The Seekatz dataset included samples collected from patients that went on to develop recurrent CDI, a serious outcome following primary diagnosis, or from patients who were later reinfected with CDI beyond the standard time recurrence window.10 Additionally, a severity score22 was available for some of the patients. Across the full Seekatz CDI positive sample set, our method detected C. difficile above 0.1% abundance in 59.2% of samples (Table 2). On average, patients with CDI for index (at primary diagnosis), recurrence or reinfection events had C. difficile abundances greater than 1% regardless of the calculated severity status using Infectious Diseases Society of America (IDSA) standards. We found no significant associations of C. difficile abundance with IDSA severity status among index samples or at the time of recurrence or reinfection (P > 0.05, Mann–Whitney test).

Table 2: C. difficile relative abundances in cases of CDI from Seekatz et al.10 compared to healthy controls

Representation of C. difficile relative to the microbiota in infants

To assess the levels of C. difficile carriage among infants relative to the total gut microbiome, we re-examined 16S rRNA gene sequence datasets describing longitudinal studies of pre-term infants in the neonatal intensive care unit (NICU) by Zhou et al.23 and a single infant profiled during the first 18 months of life by Davis et al.16 In the Zhao dataset, 12 necrotizing enterocolitis (NEC) cases and 26 age-matched controls (all treated at Brigham and Women’s Hospital NICU, Boston, MA) were sequenced with an average of seven samples per subject. The Davis asymptomatic case study consisted of profiling 50 fecal samples over time, during which researchers noted colonization switching between toxigenic and non-toxigenic strains and observed 100,000-fold fluctuations of C. difficile spore counts.16

In these two 16S rRNA gene sequence datasets, moderate levels of C. difficile (>1.0% abundance) appeared consistently within infants over time. In the Zhao dataset, C. difficile was detected in 25 of 38 (66%) infants, including 6 of 12 (50%) infants with NEC, and 19 of 26 (73%) normal infants. There was no significant difference in overall C. difficile presence between NEC and normal infants (P = 0.27, Fisher’s exact test), and both groups maintained statistically similar C. difficile abundance distributions relative to their total gut microbial communities under multivariate regression after adjustment for patient source (Fig. 2a). As the original Davis case study determined C. difficile carriage using spore counts and GDH concentration, we detected substantial representation of C. difficile (up to 7.1% abundance) until the time of weaning and transition to cow’s milk (Fig. 2b). We further found a statistically significant correlation between our C. difficile relative abundance estimates and GDH concentration measurements from the Davis study (Spearman correlation = 0.817; P = 5e-13).

Fig. 2
Fig. 2

Distribution of C. difficile during longitudinal gut microbiome sampling of infants. a Pre-term infants in a NICU, including those developing necrotizing enterocolitis (purple) and normal (grey). Each boxplot reflects a single patient with multiple time points (total samples per patient shown along the x-axis). b A longitudinal case study of an infant before (red) and after (blue) weaning during the first 18 months of life. During the transition to cow’s milk, C. difficile relative abundance fell to undetectable levels

Correlations of C. difficile with other bacterial species

Recent studies in animal models have indicated that certain species may generate metabolites that inhibit C. difficile, such as the production of secondary bile acids by C. scindens.15 However, previous studies correlating the abundance of C. difficile with other taxa did not utilize the microbiome-based abundances directly, but rather quantified C. difficile abundance through other means such as real-time PCR, colony forming units through culture, measuring GDH concentration or spore counts.15,16,17

We sought to determine whether high-resolution analysis of the 16S rRNA gene sequence data itself could reveal the same associations, and perhaps other relevant species. Computing correlations using Compositionality Corrected by REnormalization and PErmutation (CCREPE)24 across our re-analyzed cohorts, we found a significant negative association between C. difficile and C. scindens for the Khanna CDI patient cohort and the Davis infant longitudinal study (P < 0.02 for both datasets), with a supporting trend in the other studies (Fig. 3, Table S3). Additionally, multiple members of Blautia spp. displayed a consistent negative correlation like that of C. scindens (Fig. 3, Table S3). In contrast, other Clostridia such as C. neonatale and C. paraputrificum and members of Veillonella showed strong positive associations with C. difficile abundance. In silico simulations of noisy 16S rRNA gene sequence reads from these species confirmed a low mis-assignment rate (average 0.08%; see Table S4).

Fig. 3
Fig. 3

Correlation analysis identifies species positively or negatively associated with C. difficile. The CCREPE N-dimensional checkerboard score (y-axis) incorporates the ratio of co-variation to co-exclusion patterns normalized to a range of (−1, +1). In addition to C. scindens, we identify significant negative correlations with C. difficile for members of Blautia and positive correlations with other Clostridia and Veillonella spp. (*P ≤ 0.05). Ambiguous species level assignments are denoted by slashes. Key for re-analyzed datasets from the following studies: Recurrent CDI=10, Index CDI=21, FMT=9, Infant longitudinal=16, NICU=23 (Table S2)

Discussion

In this study, we sought to identify species-level abundances of C. difficile in 16S rRNA gene sequence datasets from different patient populations using a validated algorithm (Resphera Insight). Similar to previous studies of Listeria monocytogenes18 and Salmonella enterica,19,20 validation using a high-resolution taxonomic assignment method from 804 novel C. difficile isolates established an overall sensitivity of 99.9% with a marginal false positive rate less than 0.1%, suggesting that C. difficile could be distinguished from other related microbiota members.

Compared to the microbiota of healthy individuals, we observed a higher presence and relative abundance of C. difficile in microbiota data collected from two CDI patient cohorts. 8.5% of healthy individuals were positive for C. difficile using our approach, supporting previous epidemiological assessments of asymptomatic carriage rates.25,26,27,28 Although analysis of CDI datasets revealed a wide distribution of C. difficile relative abundances (ranging from virtually undetectable to above 50% of total sequences), the relative abundance of detected C. difficile in relation to other members of the microbiota was significantly lower in healthy individuals than that of CDI patients. The ability to assess C. difficile levels as part of the microbiota community is potentially more important within population surveys compared to diagnosis using traditional PCR or GDH/EIA tests that merely account for the presence of C. difficile using toxin B or GDH as a proxy.

While detection of C. difficile from 16S rRNA gene sequence data is limited by sequencing depth, our results suggest that C. difficile does not generally reside in healthy adults. In contrast, we did not detect C. difficile in all patients with CDI. The relative presence of C. difficile in these patients is likely below the detection limit given the available sequencing depth, however some of the samples collected from patients in the Seekatz dataset were collected during antibiotic treatment, thus potentially limiting growth of C. difficile during those time points. Indeed, Seekatz et al. report that they were unable to retrieve C. difficile strains from all patient time points via anaerobic cultivation, generally the gold standard for C. difficile detection and diagnosis.

In a third cohort of 14 recurrent CDI patients receiving fecal microbiota transplantation from nine healthy donors (FMT; Table S2, Fig. 3), C. difficile was less frequently detected than the Seekatz and Khanna index CDI patient groups. Only 4 of 14 FMT patients had any detectable levels of C. difficile before treatment, and 3 of 14 had observations of C. difficile post-FMT. Notably, Resphera Insight detected C. difficile presence in both patients who went on to develop symptomatic CDI post-FMT (recipient IDs 005 and 006).9 Prior to FMT, all patients were treated with vancomycin (125 mg 4× per day) for at least 4 days before and the day of transplantation. Thus, we attribute the reduced detection of C. difficile in this cohort to differences in patient treatment before sampling.

Applying our approach to a longitudinal dataset of 38 premature infants in a single NICU, we identified C. difficile in two-thirds of this patient cohort. Asymptomatic carriage of C. difficile among infants has been observed to be higher than for adults, and it remains unknown whether infant cases of CDI represent true disease.29,30 While CDI testing of infants is not recommended,30 recent epidemiological studies indicate 26% of children hospitalized with CDI are infants under 12 months of age, and 5% are neonates.31 In one study of 753 pediatric patients 0 to 12 years of age, 2.9% of CDI outpatients, 4.6% of CDI inpatients, and 6.6% of healthy controls were positive for C. difficile toxin B.32 Another recent study of C. difficile in 338 healthy infants (<2 yrs) in the United Kingdom found 10% were colonized at enrollment with a toxigenic strain, and 49% became colonized with a toxigenic strain post-enrollment.33 Symptomatic Clostridium difficile infections are believed not to occur in infants due to the expected lack of specific toxin receptors and under-developed signaling pathways in the gut; however, these proposed mechanisms have not been rigorously evaluated in studies of humans.34,35,36 Multiple case studies have argued that CDI can occur in this patient population,36 and there is ongoing debate about the appropriate policy for treatment of symptomatic children who test positive for C. difficile.37,38

Our analysis of an infant case study of asymptomatic colonization during the first 18 months of life identified a reduction in C. difficile relative abundance after abrupt transition from human milk to cow’s milk. Yet in a large longitudinal study by Stoesser and colleagues, multivariate analysis demonstrated that breastfeeding (mixed with formula or exclusively) was protective against asymptomatic C. difficile colonization.33 As noted by Davis and colleagues,16C. difficile does not carry the functional capacity for cleaving monosaccharides from oligosaccharide side chains and thus depends on the generation of monomeric glucose by other commensal members of the gut microbiome.39 Additionally, C. difficile relies on sialic acid as a carbon source for expansion made available by other commensals such as Bifidobacterium species.40 Therefore, the reduction of C. difficile after transition to cow’s milk is potentially the result not of milk source alone, but shifting microbial community composition and the presence of substrates by which C. difficile may thrive.

We were also able to identify a significant negative correlation between the abundance of C. difficile and C. scindens in one of the CDI cohorts, confirming similar trends reported by Buffie et al.15C. scindens, a secondary bile acid producer of deoxycholic acid which has been shown to protect against CDI, may have important translational implications.13,41 New and consistent negative correlations were also identified between C. difficile and multiple species within the Blautia genus including B. faecis, B. luti, B. schinkii, and B. wexlerae. Notably, some members of the Blautia genus are known for 7α-dehydroxylating activity of primary bile acids,42,43,44 however this remains to be evaluated for the species we identified in this study. These data suggest that species other than C. scindens may provide relevant functional capabilities in the context of CDI and prove to be informative in the development of future microbial-based therapeutics. One exception to these findings was the lack of negative correlations identified within the NICU infant cohort, which can be attributed to the very limited observations of these Blautia species and C. scindens in the overall dataset (Table S3). Indeed, among the 322 NICU infant samples analyzed, only B. luti and B. wexlerae were observed at all, and only in 5 (1.6%) and 2 (0.6%) samples, respectively, which precluded their evaluation with the CCREPE method.

While microbiome profiling through 16S rRNA gene sequencing is unlikely to replace existing methods for routine diagnosis of CDI, sequence-based assessment of C. difficile levels in the context of microbiota profiling rather than presence alone may prove valuable in surveillance of C. difficile in patient populations, prediction of disease outcome, or the development of new therapies for CDI. Although our study is limited to 16S rRNA gene-based identification of C. difficile and cannot predict whether a strain produces toxin or carries a functional pathogenicity locus,45 consideration for accurate identification of C. difficile and related members may be useful in assessing clinical outcomes of new microbial therapies that rely on 16S rRNA gene sequencing to validate recovery of the microbiota.

Methods

Validation of Resphera Insight for identification of C. difficile

Whole-genome shotgun sequence datasets available from (i) The Wellcome Trust Sanger Institute and (ii) The University of Maryland Institute for Genome Sciences designated as novel C. difficile isolates were downloaded from the NCBI Sequence Read Archive (see Table S1 for accessions), trimmed for quality using Trimmomatic46 and assembled into contigs using Minia.47 Contigs containing portions of 16S rRNA genes were identified using BLASTN48 and extracted for amplicon simulations. For each isolate, we subsequently simulated 16S rRNA amplicon sequence reads (10,000 per isolate) from the V4 region (the primary amplicon region selected in the real datasets) with a random nucleotide error rate of 0.5%. The Diagnostic True Positive Rate was computed as the percentage of sequences unambiguously assigned by Resphera Insight to C. difficile.

For false positive assessment, simulated V4 sequences were generated from reference 16S rRNA genes for 22 unique species within the Clostridium XI cluster (10,000 per species, 0.5% nucleotide error rate). False positives were defined as unambiguous assignments to C. difficile.


Processing of 16S rRNA gene sequence datasets

Raw 16S rRNA gene sequence datasets were processed as follows: Raw overlapping paired-end reads were merged into consensus fragments by FLASH49 requiring a minimum 20 bp overlap with 5% maximum mismatch density, and subsequently filtered for quality (targeting error rates < 1%) and length (minimum 200 bp) using Trimmomatic46 and QIIME.50 Spurious hits to the PhiX control genome were identified using BLASTN and removed. Sequences were then trimmed of their associated primers, evaluated for chimeras with UCLUST (de novo mode),51 and screened for human-associated contaminants using Bowtie252 searches of NCBI Homo sapiens Annotation Release 106. Mitochondrial contaminants were detected and filtered using the RDP classifier53 with a confidence threshold of 50%, and passing high-quality 16S rRNA gene sequences were subsequently assigned to a high-resolution taxonomic lineage using Resphera Insight (Baltimore, MD).18,19,20,54,55 Briefly, the method relies on (i) a manually curated 16S rRNA gene database including 11,000 unique species and (ii) a hybrid global-local alignment strategy to assign sequences a species-level taxonomic lineage. While the method attempts to achieve species-level resolution, if the internal statistical model indicates uncertainty in final species membership, the tool minimizes false positives by providing “ambiguous assignments” i.e., a list of species reflecting all relevant candidates. For example, if a 16S rRNA gene fragment is ambiguous between Veillonella atypica and Veillonella dispar, the algorithm will provide the ambiguous assignment: “Veillonella_atypica:Veillonella_dispar.”


Statistical analyses

Correlations between C. difficile and other species were computed using CCREPE (v.1.10.0)24 (http://huttenhower.sph.harvard.edu/ccrepe). CCREPE (Compositionality Corrected by REnormalization and PErmutation) utilizes an N-dimensional extension of the checkerboard score particularly suited to similarity score calculations between compositions derived from ecological relative abundance measurements of co-occurrence or co-exclusion. Two sample statistical comparisons utilized the Mann-Whitney U test unless otherwise noted.

In silico evaluation for species identified in CCREPE analysis

For single species reported in CCREPE correlation analysis, we simulated noisy 16S rRNA gene sequences (V4 region; 0.5% error rate; 1000 seqs per species), and calculated the frequency of (1) assignments that included the correct species (allowing for ambiguous assignments), (2) unambiguous assignments to the correct species, and (iii) mis-assignments that did not include the correct species (Table S4).


Ethics approvals and consent to participate

IRB approval and patient consent statements from each study: Recurrent CDI (Seekatz et al.10)—All subjects signed written consent to participate in this study. This study was approved by the University of Michigan Institutional Review Board (Study HUM33286; originally approved 8/26/2009).

Index CDI (Khanna et al.21)—We prospectively recruited 88 patients (median age 52.7 years, interquartile range 36.9–65.1; 60.2% female) with their first CDI episode (from 3/2012–9/2013) as identified from the Clinical Microbiology Laboratory at Mayo Clinic, Rochester, Minnesota and collected an aliquot from the stool samples that led to the diagnosis. Clinical data including demographics, hospitalization status, concomitant medications, CDI severity, laboratory parameters, prior and concomitant antibiotic use, initial CDI treatment, treatment response and recurrent CDI were obtained by a review of the electronic medical record.

NICU Infants (Zhou et al.23)—Samples were collected following a protocol that was approved by the Partner’ s Human Research Committee (IRB) for Brigham and Women’ s Hospital. All study procedures were approved by the IRBs at both Brigham and Women’ s Hospital in Boston, MA and at The Genome Institute in St. Louis, MO. The IRB deemed this study to be of minimal risk with no interaction and no intervention with human subjects and thus, was exempt from consent.

Infant Longitudinal (Davis et al.16)—The study was approved by the TechLab Institutional Review Board and included informed consent obtained from the mother.

FMT (Seekatz et al.9)—Informed consent was received from all participants under an approved Institutional Review Board (IRB) protocol at Essentia Health Duluth Clinic (IRB no. SMDC-09068; principal investigator, Timothy Rubin, FDA Investigational New Drug [IND] no. 15460).

Healthy Controls (Seekatz et al. submitted)—All subjects signed written consent to participate in this study. This study was approved by the University of Michigan Institutional Review Board (Study HUM33286; originally approved 8/26/2009).


Data availability

NCBI BioProject accessions of publicly available 16S rRNA gene sequence datasets used in this study: PRJNA307992, PRJNA342347, PRJNA264177, PRJNA331150, PRJNA238042, and PRJNA386260 (Table S2).

Additional Information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Leffler, D. A. & Lamont, J. T. Clostridium difficile Infection. N. Engl. J. Med. 373, 287–288 (2015).

 

  • 2.

    Lessa, F. C. et al. Burden of Clostridium difficile infection in the United States. N. Engl. J. Med. 372, 825–834 (2015).

  • 3.

    Surawicz, C. M. et al. Guidelines for diagnosis, treatment, and prevention of Clostridium difficile infections. Am. J. Gastroenterol. 108, 478–498 (2013).

  • 4.

    Pepin, J. et al. Increasing risk of relapse after treatment of Clostridium difficile colitis in Quebec, Canada. Clin. Infect. Dis. 40, 1591–1597 (2005).

  • 5.

    Vincent, Y., Manji, A., Gregory-Miller, K. & Lee, C. A review of management of Clostridium difficile Infection: primary and recurrence. Antibiotics 4, 411–423 (2015).

  • 6.

    Seekatz, A. M. & Young, V. B. Clostridium difficile and the microbiota. J. Clin. Invest. 124, 4182–4189 (2014).

  • 7.

    Kelly, C. R. et al. Update on fecal microbiota transplantation 2015: indications, methodologies, mechanisms, and outlook. Gastroenterology 149, 223–237 (2015).

  • 8.

    Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).

  • 9.

    Seekatz, A. M. et al. Recovery of the gut microbiome following fecal microbiota transplantation. MBio 5, e00893–00814 (2014).

  • 10.

    Seekatz, A. M., Rao, K., Santhosh, K. & Young, V. B. Dynamics of the fecal microbiome in patients with recurrent and nonrecurrent Clostridium difficile infection. Genome Med. 8, 47 (2016).

  • 11.

    Seekatz, A. M. et al. Fecal microbiota transplantation eliminates Clostridium difficile in a murine model of relapsing disease. Infect. Immun. 83, 3838–3846 (2015).

  • 12.

    Zackular, J. P. et al. Dietary zinc alters the microbiota and decreases resistance to Clostridium difficile infection. Nat. Med. 22, 1330–1334 (2016).

  • 13.

    Theriot, C. M. et al. Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection. Nat. Commun. 5, 3114 (2014).

  • 14.

    Weingarden, A. R. et al. Microbiota transplantation restores normal fecal bile acid composition in recurrent Clostridium difficile infection. Am. J. Physiol. Gastrointest. Liver Physiol. 306, G310–G319 (2014).

  • 15.

    Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).

  • 16.

    Davis, M. Y., Zhang, H., Brannan, L. E., Carman, R. J. & Boone, J. H. Rapid change of fecal microbiome and disappearance of Clostridium difficile in a colonized infant after transition from breast milk to cow milk. Microbiome 4, 53 (2016).

  • 17.

    Schubert, A. M., Sinani, H. & Schloss, P. D. Antibiotic-induced alterations of the murine gut microbiota and subsequent effects on colonization resistance against Clostridium difficile. MBio 6, e00974 (2015).

  • 18.

    Ottesen, A. et al. Enrichment dynamics of Listeria monocytogenes and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak. BMC Microbiol. 16, 275 (2016).

  • 19.

    Daquigan, N., Grim, C. J., White, J. R., Hanes, D. E. & Jarvis, K. G. Early recovery of Salmonella from food using a 6-hour non-selective pre-enrichment and reformulation of tetrathionate broth. Front. Microbiol. 7, 2103 (2016).

  • 20.

    Grim, C. J. et al. High-resolution microbiome profiling for detection and tracking of Salmonella enterica. Front. Microbiol. 8, 1587 (2017).

  • 21.

    Khanna, S. et al. Gut microbiome predictors of treatment response and recurrence in primary Clostridium difficile infection. Aliment. Pharmacol. Ther. 44, 715–727 (2016).

  • 22.

    Cohen, S. H. et al. Clinical practice guidelines for Clostridium difficile infection in adults: 2010 update by the society for healthcare epidemiology of America (SHEA) and the infectious diseases society of America (IDSA). Infect. Control Hosp. Epidemiol. 31, 431–455 (2010).

  • 23.

    Zhou, Y. et al. Longitudinal analysis of the premature infant intestinal microbiome prior to necrotizing enterocolitis: a case-control study. PLoS One 10, e0118632 (2015).

  • 24.

    Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell. Host. Microbe 15, 382–392 (2014).

  • 25.

    Furuya-Kanamori, L. et al. Asymptomatic Clostridium difficile colonization: epidemiology and clinical implications. BMC Infect. Dis. 15, 516 (2015).

  • 26.

    McNamara, S. E. et al. Carriage of Clostridium difficile and other enteric pathogens among a 4-H avocational cohort. Zoonoses Public Health 58, 192–199 (2011).

  • 27.

    Miyajima, F. et al. Characterisation and carriage ratio of Clostridium difficile strains isolated from a community-dwelling elderly population in the United Kingdom. PLoS One 6, e22804 (2011).

  • 28.

    Ozaki, E. et al. Clostridium difficile colonization in healthy adults: transient colonization and correlation with enterococcal colonization. J. Med. Microbiol. 53, 167–172 (2004).

  • 29.

    Rousseau, C. et al. Clostridium difficile carriage in healthy infants in the community: a potential reservoir for pathogenic strains. Clin. Infect. Dis. 55, 1209–1215 (2012).

  • 30.

    Schutze, G. E. & Willoughby, R. E. Committee on infectious diseases and American academy of pediatrics. Clostridium difficile infection in infants and children. Pediatrics 131, 196–200 (2013).

  • 31.

    Kim, J. et al. Epidemiological features of Clostridium difficile-associated disease among inpatients at children’s hospitals in the United States, 2001–2006. Pediatrics 122, 1266–1270 (2008).

  • 32.

    Cerquetti, M., Luzzi, I., Caprioli, A., Sebastianelli, A. & Mastrantonio, P. Role of Clostridium difficile in childhood diarrhea. Pediatr. Infect. Dis. J. 14, 598–603 (1995).

  • 33.

    Stoesser, N. et al. Epidemiology of Clostridium difficile in infants in Oxfordshire, UK: Risk factors for colonization and carriage, and genetic overlap with regional C. difficile infection strains. PLoS One 12, e0182307 (2017).

  • 34.

    Chang, T. W., Sullivan, N. M. & Wilkins, T. D. Insusceptibility of fetal intestinal mucosa and fetal cells to Clostridium difficile toxins. Zhongguo Yao Li Xue Bao 7, 448–453 (1986).

  • 35.

    Eglow, R. et al. Diminished Clostridium difficile toxin A sensitivity in newborn rabbit ileum is associated with decreased toxin A receptor. J. Clin. Invest. 90, 822–829 (1992).

  • 36.

    Kuiper, G. A. et al. Clostridium difficile infections in young infants: case presentations and literature review. IDCases 10, 7–11 (2017).

  • 37.

    Nicholson, M. R., Thomsen, I. P. & Edwards, K. M. Controversies surrounding Clostridium difficile infection ininfants and young children. Children. 1, 40–47 (2014).

  • 38.

    El Feghaly, R. E., Stauber, J. L., Tarr, P. I. & Haslam, D. B. Intestinal inflammatory biomarkers and outcome in pediatric Clostridium difficile infections. J. Pediatr. 163, 1697–1704 (2013).

  • 39.

    Wilson, K. H. & Perini, F. Role of competition for nutrients in suppression of Clostridium difficile by the colonic microflora. Infect. Immun. 56, 2610–2614 (1988).

  • 40.

    Baumler, A. J. & Sperandio, V. Interactions between the microbiota and pathogenic bacteria in the gut. Nature 535, 85–93 (2016).

  • 41.

    Greathouse, K. L., Harris, C. C. & Bultman, S. J. Dysfunctional families: Clostridium scindens and secondary bile acids inhibit the growth of Clostridium difficile. Cell. Metab. 21, 9–10 (2015).

  • 42.

    Ridlon, J. M., Alves, J. M., Hylemon, P. B. & Bajaj, J. S. Cirrhosis, bile acids and gut microbiota: unraveling a complex relationship. Gut Microbes 4, 382–387 (2013).

  • 43.

    Kakiyama, G. et al. Modulation of the fecal bile acid profile by gut microbiota in cirrhosis. J. Hepatol. 58, 949–955 (2013).

  • 44.

    Theriot, C. M., Bowman, A. A. & Young, V. B. Antibiotic-induced alterations of the gut microbiota alter secondary bile acid production and allow for Clostridium difficile spore germination and outgrowth in the large intestine. mSphere 1, e00045-15 (2016).

  • 45.

    Natarajan, M., Walk, S. T., Young, V. B. & Aronoff, D. M. A clinical and epidemiological review of non-toxigenic Clostridium difficile. Anaerobe 22, 1–5 (2013).

  • 46.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

  • 47.

    Chikhi, R. & Rizk, G. Space-efficient and exact de Bruijn graph representation based on a Bloom filter. Algorithms Mol. Biol. 8, 22 (2013).

  • 48.

    Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

  • 49.

    Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

  • 50.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7, 335–336 (2010).

  • 51.

    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

  • 52.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  • 53.

    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

  • 54.

    Abernethy, M. G. et al. Urinary microbiome and cytokine levels in women with interstitial cystitis. Obstet. Gynecol. 129, 500–506 (2017).

  • 55.

    Guerrero-Preston, R. et al. High-resolution microbiome profiling uncovers Fusobacterium nucleatum, Lactobacillus gasseri/johnsonii, and Lactobacillus vaginalis associated to oral and oropharyngeal cancer in saliva from HPV positive and HPV negative patients treated with surgery and chemo-radiation. Oncotarget. https://doi.org/10.18632/oncotarget.20677 (2017).

 

Acknowledgements

We thank Cynthia Sears, Karen Carroll, and David Cook for helpful suggestions on this work. This work was supported in part by the ERIN CRC (Enteric Research Investigative Network Cooperative Research Center), (U19AI09087, NIAID), awarded to V.B.Y. A.M.S. supported by the National Center for Advancing Translational Sciences (UL1TR000433).

To review the article in its entirety please click on the following link:

https://www.nature.com/articles/s41522-017-0043-0

Clifford McDonald, MD and Alison Laufer-Halpin, Ph.D., of the CDC Discuss the Human Microbiome on C. diff. Spores and More

C Diff Foundation’s “C. diff. Spores and More Global Broadcasting Network” is honored to announce Doctors McDonald and Laufer-Halpin as our guest speakers on

Tuesday, July 25, 2017 at 10 a.m. PT / 1 p.m. ET

(www.cdiffradio.com)

These two leading topic experts will be discussing significant ways to unlock the mysteries of the human microbiome; how it affects our health, the immune system, and why it is so important to protect it.

As part of the Centers for Disease Control and Prevention (CDC) efforts to protect patients and slow antibiotic-resistance, the CDC is investing in research to discover and develop new ways to prevent antibiotic-resistant infections.

To Listen To the Podcast – click on the following link:

https://www.voiceamerica.com/episode/100322/the-human-microbiome-how-it-works-how-it-affects-your-health-your-immune-system-and-why-it-is

 

Learn more about C Diff Radio at: http://www.cdiffradio.com/.

Researchers Explore Effects of Probiotics Supplements on Intestinal Microbiota of Food Allergic Mice

Exploration of the effect of probiotics supplementation on intestinal microbiota of food allergic mice

Abstract

Environmental factor-induced alterations in intestinal microbiota have been demonstrated to be associated with increasing prevalence of food allergy. However, it is not clear to what extent oral administration of probiotics can affect gut microbiota composition, thus inhibiting food allergy development. Using ovalbumin (OVA)-sensitized murine model, it was demonstrated that probiotics ameliorated allergic symptoms, including reducing OVA specific-IgE, and -IgG1 levels in the serum, Th2 cytokines release in spleen, and occurrence of diarrhea. Moreover, 16S rRNA analysis showed that the probiotics-mediated protection was conferred by an enrichment of Coprococcus and Rikenella. The present study supports the theory that probiotics can treat food allergy by modulating specific genera of the gut microbiota.

Introduction

Food allergy is an adverse immune response to certain kinds of food. It is estimated that food allergy affects about 8% of children and 4% of adults [1,2]. The rapid increase in the prevalence of food allergy over past several decades cannot be explained by genetic variation alone. In current, avoidance of dietary allergens is the only proven remedy available for food allergic suffers.

Growing evidence suggests that gut microbiota exerts profound influence on immune system maturation and tolerance acquisition. Intestinal microflora alteration, caused by environmental factors (e.g., mode of birth, antibiotics, diet, vaccination, sanitation), has been observed to be associated with many gastrointestinal diseases, including food allergy [3], inflammatory bowel diseases [4], or colorectal cancer [58]. Of note, intestinal microflora has been demonstrated to play an important role in maintaining the Th1/Th2 balance [9], which is the key mechanism involved in allergic diseases.

The role of probiotics in allergic disease has been highlighted recently. Bifidobacteria and lactobacilli, which are common species of probiotics existing in most people, can affect immune function by various pathways. In many cases, probiotics supplementation was demonstrated to induce TGF-β expression, which ameliorates food allergy by suppressing Th2 response, and inducing Foxp3+ Treg production [1015]. A microarray analysis of intestinal epithelial cells from gnotobiotic mice revealed a mechanism that Clostridia facilitated immune cells to produce interleukin-22 (IL-22), regulated innate lymphoid cell function and intestinal epithelial permeability to protect against allergen sensitization [3]. Besides, the suppressive effect of probiotics on Th17 response has been shown both in murine asthma [16] and atopic dermatitis model [17]. However, whether probiotics treatment elicited changes in the composition of the intestinal microbiota, thereby regulating allergic disease remains poorly understood.

The current study investigated the beneficial effect of Bifidobacterium Infantis (BB) in a murine model of food allergy at the level of commensal microbiota. Sequencing of the V4-V5 regions of 16S rRNA genes revealed that BB could modulate specific genera of intestinal microbiota in mice, which may induce immune responses in gastrointestinal tract to defend against food allergens.

Materials and methods

Animals

All the animal experimental procedures were conducted according to the guidelines approved by the Experimental Animal Ethic Committee at Shenzhen University, and were carried out in accordance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH publication no. 85-23, revised 1996). 6-8 weeks old female Balb/c mice were housed in a SPF animal facility with a 12 h light-dark cycle and were free to access standard diet and water.

Food allergic animal model

Mice were intragastrically administered with 100 mg OVA plus 20 mg cholera toxin (CT) in a final volume of 300 ml using a ball-end mouse feeding tube once a week for 4 consecutive weeks. At the end of sensitization, mice were challenged with 5 mg OVA orally. After 24 h, the mice were killed and serum and splenocytes were collected for the following analysis as reported previously (referred to as FA group) [25].

BB preparation and supplementation

BB was kindly provided by Shenzhen Kexing Biotech CO., LTD (Shenzhen, China) as lyophilized powder and inoculated before giving to mice. From Day 15 to Day 28, sensitized mice were orally administered with 200 ml/mouse of normal saline containing 108 cfu/ml as previously described (referred to as FAPro group) [13]. On day 29, the mice were challenged as described above.

Serum immunoglobulin levels

Serum was collected, and OVA-specific IgE was detected by commercial ELISA kit (Biolegend, USA) according to the manufacturer’s instructions. OVA-specific IgG1 was measured by an in-house ELISA as previously described [26].

DNA extraction, amplification and sequencing

During the process of food allergy model establishment, fecal samples (up to ~1 g) were collected on Day 0, 7, 14, 28, 29, and stored at -80°C. The total DNA from fecal samples was extracted by reported method [27]. The 16S rRNA was amplified and sequenced on the Ion Torrent Personal Genome Machine as reported in previous study [28].

Bioinformatics analysis

The data was treated with in-house pipeline developed based on mothur v.1.33.3 [29]. The community structure was calculated based on the membership and relative abundance of taxonomic groups in the sample. In this study, the Permutational multivariate analysis of variance (PERMANOVA) was used to assess the effect of BB (covariate) on operational taxonomic units (OTUs) profiles. A two-tailed Wilcoxon rank-sum test was used in the profile to identify the different OTUs and KEGG Orthologs (KOs). In addition, we used PICRUSt [30] to produce predicted KOs from the 16S rRNA gene sequence data.

Statistical analysis

In Figure 1, all values are presented as the means ± SEM. Differences between two groups were evaluated with the Student t test, while data among three or more groups were evaluated with one-way ANOVA (Prism version 5, GraphPad Software; CA, USA). A P value less than 0.05 was considered to indicate significant differences.

Figure 1

Allergic reactions in the mouse intestine were attenuated by BB. Balb/c mice were treated with PBS (Naïve group), OVA/CT (FA group), OVA/CT+BB (FAPro group). The bars indicate the levels of serum OVA-specific IgE (A), -IgG1 (B), IL-4, -5, and

Results

BB showed significant protective effect on food allergic mice

Food allergic mice model was established using OVA as allergen, CT as adjuvant. As shown in Figure 1A and and1B,1B, treatment with BB for two weeks attenuated sIgE and sIgG1 by 33% and 32% respectively, when compared with FA group. Moreover, spleneocytes were harvested from all the three groups of mice and incubated with OVA for 3 days. The levels of typical Th2-type cytokines in supernatant were determined by commercial ELISA. Intragastrically administered with BB significantly reduced IL-4, -5, and -13 by 31%, 24%, and 50% respectively in FA mice (Figure 1C). In addition, after challenge with OVA, the FA mice showed significant diarrhea (Figure 1D), which could be ameliorated by BB.

BB-induced phenotypic improvement was associated with specific OTUs

Next, to investigate the effect of BB on gut microbiome, we carried out metagenomic sequencing of fecal samples from FA and FAPro mice. All sequencing reads were finally classified into 1195 operational taxonomic units (OTUs). The correlation between food allergic phenotypes and OTUs was calculated. It was found that 61 OTUs were significantly related to sIgE, sIgG1, IL-4, IL-5, and IL-13. Among them, 45 OTUs were positively correlated with these phenotypes and 16 OTUs were negatively correlated (Figure 2). For instance, Otu0724, annotated to the family S24-7, was significantly positive correlated with allergic phenotypes. On the contrary, Otu0543, annotated to the genus Bacteroides, was significantly negatively correlated. Upregulation or downregulation of the relative abundances of these OTUs could trigger certain immune responses. The results indicated that BB treatment may change immune indexes of food allergy through modulation of these OTUs.

Figure 2

The heatmap of correlation between five phenotypes and OTUs profile. Red means positive correlation, while blue represents negative correlation.

Treatment with BB shows no effect on alpha-diversity of intestinal microflora

Chao [18] and ACE [19] are usually used to compute community richness; the higher score, the more richness. Shannon and Simpson metrics are commonly used to calculate community diversity [20]. The higher Shannon index indicates the greater community diversity, while the higher Simpson index indicates the lesser community diversity. We used these 4 kinds of alpha diversity parameters to describe the microbiologic species diversity changes between FA group and FAPro group (Figure 3). Student’s t-test showed that there were no significant differences of these four indexes (Figure 3). The results indicated that BB was not strong enough to change population diversity and richness of intestinal microbiota.

Figure 3

Boxplot of 4 kinds of alpha diversity between FA and FAPro group. Chao, ACE, Shannon, simpson are the four kinds of alpha diversity metrics. FA (n=27), FAPro (n=34). Mean values ± SEM are plotted.

BB didn’t alter intestinal microbiota compositon in mice

In order to investigate whether probiotics treatment change the composition of intestinal microbiota, we used principal coordinate analysis (PCoA) to compare FA and FAPro group. As shown in Figure 4, there was no significant difference between FA and FAPro group. Thus, it was implied that BB showed no effect on modulation of microbiota composition.

Figure 4

The PCoA of OTU profile between FA and FAPro mice. 16S rRNA gene surveys (analyzed by JSD-based PCoA) from mice fed PBS (red) or probiotics (blue) diets are presented in a different clustering pattern. Principal coordinate1 (PC)1 and PC2 are the x axis

The taxonomic classification of gut microbiota in mice

We found that Bacteroidetes and Firmicutes were two most prevalent phyla present in food allergic mice treated with or without probiotics, the same as that under physiological status [3]. Furthermore, Lachnospiraceae, S24-7, Rikenellaceae, and Ruminococcaceae accounted for four major components at family levels (Figure 5A). Further analysis revealed that 2-wk of BB treatment resulted in a significant change in fecal microbiota composition at genus level. As shown in Figure 5B, the levels of Coprococcus and Rikenella were significantly increased by 66% and 60% respectively, after BB treatment. Thus, the relative abundances of Coprococcus and Rikenella may be used as microbial biomarkers to diagnose food allergy.

Figure 5

A. Taxonomic distributions in gut communities. Values represent the relative abundance of bacteria at family level across all samples within FA group and FAPro group. A small amount of microorganism is unknown. B. Comparision of 12 major genera between

Comparison of OTUs levels between FA and FAPro mice

Next, Wilcoxon rank test showed that 92 OTUs were significantly different between FA group and FAPro group. Among them, 40 OTUs (43.5%) were enriched in FA group. 33 OTUs were picked out through a FDR adjust and make a heatmap with the OTU percentage profile (Figure 6). Moreover, we found that probiotics administration could enrich more bacteria assigned to Coprococcus, Rikenella and Bacteroides in the mice gut (Figure 6).

Figure 6

Heatmap of gut bacteria in FA and FAPro group at OTU level. Blue regions represent relatively low OUT abundance, while red regions means relatively high OTU abundance.

Mice gut microflora changed across time

In order to monitor the change of gut bacteria during the period of probiotics administration, we collected fecal samples at 5 time points: before oral treatment of probiotics (FAPro1), after one week’s probiotics administration (FAPro2), after two weeks’ administration (FAPro3), 1 h after allergen challenge (FAPro4), 24 h after allergen challenge (FAPro5). Intriguingly, we selected 12 most abundant genera and found that at least 6 genera of gut bacteria, including Odoribacter, Bacteroides, Coprococcus, Blautia, Eubacterium, Prevotella changed with time after probiotics treatment (Figure 7). For example, the levels of Odoribacter were significantly increased by 3.3 fold at the time point of 24 h after challenge compared to the time point of 1 h after challenge.

Figure 7

Time-dependent manner of gut bacteria changes at genus level. During the period of probiotics administration, we collected fecal samples at 5 time points: before oral treatment of probiotics (FAPro1), after one week’s probiotics administration

Metabolic pathways of gut microbiota was altered by BB supplementation

We used PICRUSt to produce predicted metagenomes from 16S rRNA gene sequence database. 143 KOs were found to be significantly different between FA and FAPro mice, using Wilcoxon rank test, p value < 0.05. Among them, only 4 KOs were enriched in FAPro group (Table 1). The results implied that BB supplementation significantly modified metabolic pathways of gut microbiota.

Table 1

Four KEGG Orthologs were enriched in FAPro group

Discussion

Gut microbiota plays an important role in the pathogenesis of food allergy. In this study, we found that oral administration of BB induced significant improvement on allergic symptoms in mice. Furthermore, the results demonstrated that BB conferred a protective effect on food allergic mice through up-regulation of the relative abundance of Coprococcus and Rikenella at genus level. Furthermore, the genera of gut microflora were presented in a time-dependent pattern after BB treatment.

Growing evidence suggests that the relationship among diet, probiotics, immune system and gut microbiota ecology determines the disease susceptibility to allergy [21]. Thus, it is very likely that intragastrical administration of probiotics may treat food allergy by restoring the unbalanced indigenous microbiota and controlling the inflammatory responses. Until now, there is no investigation targeting the direct effect of probiotic supplementation on intestinal microbiota. Although there are more than 1000 species of intestinal bacteria, most of them belong to just a few phyla. Bacteroidetes and Firmicutes phyla dominate the adult intestine. The intestinal microbiota is of high variation from people to people at species-level, but bifidobacteria and lactobacilli are common species existing in most people [22]. Thus, in the present study we chose BB to treat a classical animal model sensitized by OVA. In this study, animals treated with probiotics for two weeks showed improvement in all major indicators of experimental mucosal allergy, in line with the results previously reported [23].

When use traditional culture based techniques to determine the composition of the gut microbiota, there are only ~10% of gut bacteria possibly to be studied since others are not culturable [24]. Therefore, in order to further determine the different components of intestinal microbiota caused by probiotics, we chose state-of-the-art next-generation sequencing method to detect the 16S rRNA of faces samples and determine the frequency of microbes and its metabolic pathway in gastrointestinal tract. We found that there were 12 genera of gut bacteria existing in both FA and FAPro groups. After supplementation with BB for two weeks, each genus changed periodically. Based on their relative abundances, BB administration could up-regulate Rikenlla and down-regulate Eubacterium. These two genera of bacteria have never been highlighted by other related researches. Instead, Stefka [3] et al demonstrated that a Clostridia-containing microbiota was associated with innate lymphoid cell function and intestinal epithelial permeability. The divergence may be attributed to that they didn’t use a kind of probiotics to treat allergic mice.

In conclusion, this is the first study to explore microbial population changes in food allergic animal model, in case of probiotics administration. Likely, specific gut bacterial changes contributed to disease process altered by probiotics. Still, patients study are warranted in the future to determine whether the findings herein reported can be validated and correlated with the clinical features.

Acknowledgements

This work was supported by grants from the Natural Science Foundation of China (No. 81300292 to B.Y., No. 81271950 to Q.M.J., and 81460252 to X.Y.L.), Guangdong Foreign Scientific Technology Cooperative Project (No. 2013B051000088 to Z.G.L.), Shenzhen Scientific Technology Basic Research Projects (No. 005177 to Q.M.J., JCYJ20140418095735538 to Z.G.L., and JCYJ20130402151227168 to S.G.H.).

Disclosure of conflict of interest

None.

Authors’ contribution

B.Y., L.X. and S.L. performed experiments and analyzed data. B.Y. wrote the manuscript. X.Y.L. and Y.L. performed experiments. Q.M.J, P.C.Y. and Z.G.L. organized the project and supervised the experiments. P.C.Y. revised the manuscript.

References

1. Gupta R, Sheikh A, Strachan DP, Anderson HR. Time trends in allergic disorders in the UK. Thorax. 2007;62:91–96. [PMC free article] [PubMed]
2. Liew WK, Williamson E, Tang ML. Anaphylaxis fatalities and admissions in Australia. J Allergy Clin Immunol. 2009;123:434–442. [PubMed]
3. Stefka AT, Feehley T, Tripathi P, Qiu J, McCoy K, Mazmanian SK, Tjota MY, Seo GY, Cao S, Theriault BR, Antonopoulos DA, Zhou L, Chang EB, Fu YX, Nagler CR. Commensal bacteria protect against food allergen sensitization. Proc Natl Acad Sci U S A. 2014;111:13145–13150. [PMC free article] [PubMed]
4. Kim MH, Kang SG, Park JH, Yanagisawa M, Kim CH. Short-chain fatty acids activate GPR41 and GPR43 on intestinal epithelial cells to promote inflammatory responses in mice. Gastroenterology. 2013;145:396–406. e391–310. [PubMed]
5. Tojo R, Suarez A, Clemente MG, de los Reyes-Gavilan CG, Margolles A, Gueimonde M, Ruas-Madiedo P. Intestinal microbiota in health and disease: role of bifidobacteria in gut homeostasis. World J Gastroenterol. 2014;20:15163–15176. [PMC free article] [PubMed]
6. Wills-Karp M, Santeliz J, Karp CL. The germless theory of allergic disease: revisiting the hygiene hypothesis. Nat Rev Immunol. 2001;1:69–75. [PubMed]
7. Prioult G, Nagler-Anderson C. Mucosal immunity and allergic responses: lack of regulation and/or lack of microbial stimulation? Immunol Rev. 2005;206:204–218. [PubMed]
8. Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012;13:260–270. [PMC free article] [PubMed]
9. Gigante G, Tortora A, Ianiro G, Ojetti V, Purchiaroni F, Campanale M, Cesario V, Scarpellini E, Gasbarrini A. Role of gut microbiota in food tolerance and allergies. Dig Dis. 2011;29:540–549. [PubMed]
10. Barletta B, Rossi G, Schiavi E, Butteroni C, Corinti S, Boirivant M, Di Felice G. Probiotic VSL#3-induced TGF-beta ameliorates food allergy inflammation in a mouse model of peanut sensitization through the induction of regulatory T cells in the gut mucosa. Mol Nutr Food Res. 2013;57:2233–2244. [PubMed]
11. Lyons A, O’Mahony D, O’Brien F, MacSharry J, Sheil B, Ceddia M, Russell WM, Forsythe P, Bienenstock J, Kiely B, Shanahan F, O’Mahony L. Bacterial strain-specific induction of Foxp3+ T regulatory cells is protective in murine allergy models. Clin Exp Allergy. 2010;40:811–819. [PubMed]
12. Toomer OT, Ferguson M, Pereira M, Do A, Bigley E, Gaines D, Williams K. Maternal and postnatal dietary probiotic supplementation enhances splenic regulatory T helper cell population and reduces ovalbumin allergen-induced hypersensitivity responses in mice. Immunobiology. 2014;219:367–376. [PubMed]
13. Zhang LL, Chen X, Zheng PY, Luo Y, Lu GF, Liu ZQ, Huang H, Yang PC. Oral Bifidobacterium modulates intestinal immune inflammation in mice with food allergy. J Gastroenterol Hepatol. 2010;25:928–934. [PubMed]
14. Yoshida T, Fujiwara W, Enomoto M, Nakayama S, Matsuda H, Sugiyama H, Shimojoh M, Okada S, Hattori M. An increased number of CD4+CD25+ cells induced by an oral administration of Lactobacillus plantarum NRIC0380 are involved in antiallergic activity. Int Arch Allergy Immunol. 2013;162:283–289. [PubMed]
15. Kim HJ, Kim YJ, Lee SH, Yu J, Jeong SK, Hong SJ. Effects of Lactobacillus rhamnosus on allergic march model by suppressing Th2, Th17, and TSLP responses via CD4(+)CD25(+)Foxp3(+) Tregs. Clin Immunol. 2014;153:178–186. [PubMed]
16. Kwon HK, Lee CG, So JS, Chae CS, Hwang JS, Sahoo A, Nam JH, Rhee JH, Hwang KC, Im SH. Generation of regulatory dendritic cells and CD4+Foxp3+ T cells by probiotics administration suppresses immune disorders. Proc Natl Acad Sci U S A. 2010;107:2159–2164. [PMC free article] [PubMed]
17. Kim HJ, Kim HY, Lee SY, Seo JH, Lee E, Hong SJ. Clinical efficacy and mechanism of probiotics in allergic diseases. Korean J Pediatr. 2013;56:369–376. [PMC free article] [PubMed]
18. Chiu CH, Wang YT, Walther BA, Chao A. An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics. 2014;70:671–682. [PubMed]
19. Chao A, Bunge J. Estimating the number of species in a stochastic abundance model. Biometrics. 2002;58:531–539. [PubMed]
20. Xu H, Hao W, Zhou Q, Wang W, Xia Z, Liu C, Chen X, Qin M, Chen F. Plaque bacterial microbiome diversity in children younger than 30 months with or without caries prior to eruption of second primary molars. PLoS One. 2014;9:e89269. [PMC free article] [PubMed]
21. Berni Canani R, Gilbert JA, Nagler CR. The role of the commensal microbiota in the regulation of tolerance to dietary allergens. Curr Opin Allergy Clin Immunol. 2015;15:243–249. [PMC free article] [PubMed]
22. Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature. 2012;489:220–230. [PMC free article] [PubMed]
23. Vernocchi P, Del Chierico F, Fiocchi AG, El Hachem M, Dallapiccola B, Rossi P, Putignani L. Understanding probiotics’ role in allergic children: the clue of gut microbiota profiling. Curr Opin Allergy Clin Immunol. 2015;15:495–503. [PubMed]
24. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, Gill SR, Nelson KE, Relman DA. Diversity of the human intestinal microbial flora. Science. 2005;308:1635–1638. [PMC free article] [PubMed]
25. Ganeshan K, Neilsen CV, Hadsaitong A, Schleimer RP, Luo X, Bryce PJ. Impairing oral tolerance promotes allergy and anaphylaxis: a new murine food allergy model. J Allergy Clin Immunol. 2009;123:231–238. e234. [PMC free article] [PubMed]
26. Jiang C, Fan X, Li M, Xing P, Liu X, Wu Y, Zhang M, Yang P, Liu Z. Characterization of Der f 29, a new allergen from dermatophagoides farinae. Am J Transl Res. 2015;7:1303–1313. [PMC free article] [PubMed]
27. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Jian M, Zhou Y, Li Y, Zhang X, Qin N, Yang H, Wang J, Brunak S, Dore J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J, Bork P, Ehrlich SD. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65. [PMC free article] [PubMed]
28. Junemann S, Prior K, Szczepanowski R, Harks I, Ehmke B, Goesmann A, Stoye J, Harmsen D. Bacterial community shift in treated periodontitis patients revealed by ion torrent 16S rRNA gene amplicon sequencing. PLoS One. 2012;7:e41606. [PMC free article] [PubMed]
29. Schloss PD. A high-throughput DNA sequence aligner for microbial ecology studies. PLoS One. 2009;4:e8230. [PMC free article] [PubMed]
30. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–821. [PMC free article] [PubMed]

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5340674/

Study shows Microbiome Differences Between Intensive Care Unit Patients Hospitalized From Healthy Patients

laboratorystill

 

The microbiome of patients admitted to the intensive care unit (ICU) at a hospital differs dramatically from that of healthy patients, according to a new study published in mSphere.

 

Researchers analyzing microbial taxa in ICU patients’ guts, mouth and skin reported finding dysbiosis, or a bacterial imbalance, that worsened during a patient’s stay in the hospital. Compared to healthy people, ICU patients had depleted populations of commensal, health-promoting microbes and higher counts of bacterial taxa with pathogenic strains – leaving patients vulnerable to hospital-acquired infections that may lead to sepsis, organ failure and potentially death.

What is dysbiosis?  Pathogens, antibiotic use, diet, inflammation, and other forces can cause dysbiosis, a disruption in these microbial ecosystems that can lead to or perpetuate disease  (1)

What makes a gut microbiome healthy or not remains poorly defined in the field. Nonetheless, researchers suspect that critical illness requiring a stay in the ICU is associated with the the loss of bacteria that help keep a person healthy. The new study, which prospectively monitored and tracked changes in bacterial makeup, delivers evidence for that hypothesis.
“The results were what we feared them to be,” says study leader Paul Wischmeyer, an anesthesiologist at the University of Colorado School of Medicine. “We saw a massive depletion of normal, health-promoting species.”
Wischmeyer, who will move to Duke University in the fall, runs a lab that focuses on nutrition-related interventions to improve outcomes for critically ill patients.

He notes that treatments used in the ICU – including courses of powerful antibiotics, medicines to sustain blood pressure, and lack of nutrition – can reduce the population of known healthy bacteria. An understanding of how those changes affect patient outcomes could guide the development of targeted interventions to restore bacterial balance, which in turn could reduce the risk of infection by dangerous pathogens.
Previous studies have tracked microbiome changes in individual or small numbers of critically ill patients, but Wischmeyer and his collaborators analyzed skin, stool, and oral samples from 115 ICU patients across four hospitals in the United States and Canada. They analyzed bacterial populations in the samples twice – once 48 hours after admission, and again after 10 days in the ICU (or when the patient was discharged). They also recorded what the patients ate, what treatments patients received, and what infections patients incurred.
The researchers compared their data to data collected from a healthy subset of people who participated in the American Gut project dataset. (American Gut is a crowd-sourced project aimed at characterizing the human microbiome by the Rob Knight Lab at the University of California San Diego.) They reported that samples from ICU patients showed lower levels of Firmicutes and Bacteroidetes bacteria, two of the largest groups of microbes in the gut, and higher abundances of Proteobacteria, which include many pathogens.
Wischmeyer was surprised by how quickly the microbiome changed in the patients. “We saw the rapid rise of organisms clearly associated with disease,” he says. “In some cases, those organisms became 95 percent of the entire gut flora – all made up of one pathogenic taxa – within days of admission to the ICU. That was really striking.” Notably, the researchers reported that some of the patient microbiomes, even at the time of admission, resembled the microbiomes of corpses. “That happened in more people than we would like to have seen,” he says.
Wischmeyer suggests the microbiome could be tracked like other vital signs and could potentially be used to identify patient problems and risks before they become symptomatic. In addition, now that researchers have begun to understand how the microbiome changes in the ICU, Wischmeyer says the next step is to use the data to identify therapies – perhaps including probiotics – to restore a healthy bacterial balance to patients.
Everyone who collaborated on the project – including dietitians, pharmacists, statisticians, critical care physicians, and computer scientists – participated on a largely voluntary basis without significant funding to explore the role of the microbiome in ICU medicine, says Wischmeyer.

 

To read this article in its entirety please click on the following link:

https://www.asm.org/index.php/journal-press-releases/94540-icu-patients-lose-helpful-gut-bacteria-within-days-of-hospital-admission?platform=hootsuite

Sources:

(1)  http://www.serestherapeutics.com

 

July 19th Join C. diff. Spores and More With Dr. Matthew Henn – Discussing The Role Of the Microbiome In Health and Disease: The Basics

 

Listen To the Live Broadcast

On  July 19th,  2016

cdiffRadioLogoMarch2015CLICK ON THE LOGO TO BE REDIRECTED TO LISTEN TO THE BROADCAST

Listen in to the live broadcast at 10a PT,   11a MT,   12p CT,   1p ET     6p UK


C. diff. Spores and More,”™ Global Broadcasting Network – innovative and educational interactive healthcare talk radio program discusses

This Episode:  

The Role of the Microbiome in Health and Disease: The Basics

With Our Guest

Dr. Matthew Henn,  Senior Vice President, Head of Drug Discovery and Bioinformatics

Matthew Henn is the Senior Vice President and Head of Drug Discovery & Bioinformatics of Seres Therapeutics, Inc. He has more than 16 years of combined research experience in microbial ecology, genomics, and bioinformatics that spans both environmental and infectious disease applications.

Dr. Henn’s research has focused on the development, implementation, and application of genomic technologies in the area of microbial populations and their metabolic functions. Prior to joining Seres, he was the Director of Viral Genomics and Assistant Director of the Genome Sequencing Center for Infectious Diseases at the Broad Institute of MIT and Harvard.

Join us on Tuesday, July 19th as Dr. Henn provides the foundation educational information about the microbiome by answering the fundamental questions of what is it, why is it important, how does it impact patients with C. difficile infections, and what are the possibilities of the microbiome as a therapeutic target for future drugs.  This interview will solely be with Dr. Matthew Henn, Senior Vice President and Head of Drug Discovery & Bioinformatics at Seres Therapeutics, Inc,.

Seres Therapeutics is a leading microbiome therapeutics company dedicated to creating a new class of medicines to treat diseases resulting from imbalances in the microbiome.  These first-in-class drugs, called Ecobiotics®, are ecological compositions of beneficial organisms that are designed to restore a healthy human microbiome. The discovery efforts at Seres Therapeutics currently span metabolic, inflammatory, and infectious diseases.

♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦♦

C. diff. Spores and More ™“ Global Broadcasting Network spotlights world renowned topic experts, research scientists, healthcare professionals, organization representatives,C. diff. survivors, board members, and C Diff Foundation volunteers who are all creating positive changes in the C. diff. community worldwide.

Through their interviews, the C Diff Foundation mission will connect, educate, and empower many worldwide.

Questions received through the show page portal will be reviewed and addressed  by the show’s Medical Correspondent, Dr. Fred Zar, MD, FACP,  Dr. Fred Zar is a Professor of Clinical Medicine, Vice HeZarPhotoWebsiteTop (2)ad for Education in the Department of Medicine, and Program Director of the Internal Medicine Residency at the University of Illinois at Chicago.  Over the last two decades he has been a pioneer in the study of the treatment of
Clostridium difficile disease and the need to stratify patients by disease severity.

To access the C. diff. Spores and More program page and library, please click on the following link:    www.voiceamerica.com/show/2441/c-diff-spores-and-more

Take our show on the go…………..download a mobile app today

http://www.voiceamerica.com/company/mobileapps

Programming for C. diff. Spores and More ™  is made possible through our official  Sponsor;  Clorox Healthcare

CloroxHealthcare_72