Tag Archives: c diff research

CspC Plays a Critical Role in Regulating C. diff. Spore Germination in Response to Multiple Environmental Signals.


Abstract

The gastrointestinal pathogen, Clostridioides difficile, initiates infection when its metabolically dormant spore form germinates in the mammalian gut. While most spore-forming bacteria use transmembrane germinant receptors to sense nutrient germinants, C. difficile is thought to use the soluble pseudoprotease, CspC, to detect bile acid germinants. To gain insight into CspC’s unique mechanism of action, we solved its crystal structure. Guided by this structure, we identified CspC mutations that confer either hypo- or hyper-sensitivity to bile acid germinant. Surprisingly, hyper-sensitive CspC variants exhibited bile acid-independent germination as well as increased sensitivity to amino acid and/or calcium co-germinants. Since mutations in specific residues altered CspC’s responsiveness to these different signals, CspC plays a critical role in regulating C. difficile spore germination in response to multiple environmental signals. Taken together, these studies implicate CspC as being intimately involved in the detection of distinct classes of co-germinants in addition to bile acids and thus raises the possibility that CspC functions as a signaling node rather than a ligand-binding receptor

Author summary

The major nosocomial pathogen Clostridioides difficile depends on spore germination to initiate infection. Interestingly, C. difficile’s germinant sensing mechanism differs markedly from other spore-forming bacteria, since it uses bile acids to induce germination and lacks the transmembrane germinant receptors conserved in almost all spore-forming organisms. Instead, C. difficile is thought to use CspC, a soluble pseudoprotease, to sense these unique bile acid germinants. To gain insight into how a pseudoprotease senses germinant and propagates this signal, we solved the crystal structure of C. difficile CspC. Guided by this structure, we identified mutations that alter the sensitivity of C. difficile spores to not only bile acid germinant but also to amino acid and calcium co-germinants. Taken together, our study implicates CspC in either directly or indirectly sensing these diverse small molecules and thus raises new questions regarding how C. difficile spores physically detect bile acid germinants and co-germinants.

Authors:

  • Amy E. Rohlfing ,
  • Brian E. Eckenroth ,
  • Emily R. Forster,
  • Yuzo Kevorkian,
  • M. Lauren Donnelly,
  • Hector Benito de la Puebla,
  • Sylvie Doublié,
  • Aimee Shen

To view the Abstract in its entirety – please click on the link provided below:

https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008224

  • Published: July 5, 2019

Researchers Find Sulfated glycosaminoglycans and Low-Density Lipoprotein Receptor Contribute To Clostridioides difficile Toxin A Cell Entry

 

Abstract

Clostridium difficile toxin A (TcdA) is a major exotoxin contributing to disruption of the colonic epithelium during C. difficile infection. TcdA contains a carbohydrate-binding combined repetitive oligopeptides (CROPs) domain that mediates its attachment to cell surfaces, but recent data suggest the existence of CROPs-independent receptors. Here, we carried out genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9)-mediated screens using a truncated TcdA lacking the CROPs, and identified sulfated glycosaminoglycans (sGAGs) and low-density lipoprotein receptor (LDLR) as host factors contributing to binding and entry of TcdA. TcdA recognizes the sulfation group in sGAGs. Blocking sulfation and glycosaminoglycan synthesis reduces TcdA binding and entry into cells. Binding of TcdA to the colonic epithelium can be reduced by surfen, a small molecule that masks sGAGs, by GM-1111, a sulfated heparan sulfate analogue, and by sulfated cyclodextrin, a sulfated small molecule. Cells lacking LDLR also show reduced sensitivity to TcdA, although binding between LDLR and TcdA are not detected, suggesting that LDLR may facilitate endocytosis of TcdA. Finally, GM-1111 reduces TcdA-induced fluid accumulation and tissue damage in the colon in a mouse model in which TcdA is injected into the caecum. These data demonstrate in vivo and pathological relevance of TcdA-sGAGs interactions, and reveal a potential therapeutic approach of protecting colonic tissues by blocking these interactions.

To view abstract in its entirety please click on the following link to be redirected:  https://www.ncbi.nlm.nih.gov/pubmed/31160825?dopt=Abstract&utm_source=dlvr.it&utm_medium=twitter

A Systematic Review Evaluates the Diagnostic Accuracy of Laboratory Testing Algorithms that Include Nucleic Acid Amplification Tests (NAATs) to Detect the Presence of C. difficile

SUMMARY

The evidence base for the optimal laboratory diagnosis of Clostridioides (Clostridium) difficile in adults is currently unresolved due to the uncertain performance characteristics and various combinations of tests.

This systematic review evaluates the diagnostic accuracy of laboratory testing algorithms that include nucleic acid amplification tests (NAATs) to detect the presence of C. difficile. The systematic review and meta-analysis included eligible studies (those that had PICO [population, intervention, comparison, outcome] elements) that assessed the diagnostic accuracy of NAAT alone or following glutamate dehydrogenase (GDH) enzyme immunoassays (EIAs) or GDH EIAs plus C. difficile toxin EIAs (toxin). The diagnostic yield of NAAT for repeat testing after an initial negative result was also assessed.

Two hundred thirty-eight studies met inclusion criteria. Seventy-two of these studies had sufficient data for meta-analysis. The strength of evidence ranged from high to insufficient. The uses of NAAT only, GDH-positive EIA followed by NAAT, and GDH-positive/toxin-negative EIA followed by NAAT are all recommended as American Society for Microbiology (ASM) best practices for the detection of the C. difficile toxin gene or organism. Meta-analysis of published evidence supports the use of testing algorithms that use NAAT alone or in combination with GDH or GDH plus toxin EIA to detect the presence of C. difficile in adults. There is insufficient evidence to recommend against repeat testing of the sample using NAAT after an initial negative result due to a lack of evidence of harm (i.e., financial, length of stay, or delay of treatment) as specified by the Laboratory Medicine Best Practices (LMBP) systematic review method in making such an assessment. Findings from this systematic review provide clarity to diagnostic testing strategies and highlight gaps, such as low numbers of GDH/toxin/PCR studies, in existing evidence on diagnostic performance, which can be used to guide future clinical research studies.

SOURCE:  To Learn More:  https://cmr.asm.org/content/32/3/e00032-18.long?utm_source=dlvr.it&utm_medium=twitter

INTRODUCTION

Clostridioides (Clostridium) difficile infection (CDI) is the leading cause of health care-associated infections in the United States (1, 2). It accounts for 15% to 25% of health care-associated diarrhea cases in all health care settings, with 453,000 documented cases of CDI and 29,000 deaths in the United States in 2015 (3). Acquisition of C. difficile as a health care-associated infection (HAI) is associated with increased morbidity and mortality. This adds a significant burden to the health care system by increasing the length of hospital stay and readmission rates, with significant financial implications. The cost of hospital-associated CDI ranges from $10,000 to $20,000 per case (47) and $500 million to $1.5 billion per year nationally (1, 4, 5, 810).

Accurate diagnosis of CDI is critical for effective patient management and implementation of infection control measures to prevent transmission (11). The diagnosis of CDI requires the combination of appropriate test ordering and accurate laboratory testing to differentiate CDI from non-CDI diarrheal cases, including non-CDI diarrhea in a C. difficile-colonized patient (8). Accurate diagnosis of CDI is critical for appropriate patient management and reduction of harms that may arise from diagnostic error (12) and is critical for implementation of infection control measures to prevent transmission (11). Consequently, among patients presenting with diarrhea, there is significant potential for underdiagnosis or overdiagnosis as can arise from incorrect diagnostic workups (13).

Quality Gap: Factors Associated with the Laboratory Diagnosis of C. difficile

Best practices for laboratory diagnosis of CDI remain controversial (14). Current laboratory practice is not standardized, with wide variation in test methods and diagnostic algorithms. Several laboratory assays are available to support CDI diagnosis in combination with clinical presentation. These include toxigenic culture (TC); the cell cytotoxicity neutralization assay (CCNA); enzyme immunoassays (EIAs) and immunochromatographic assays for the detection of glutamate dehydrogenase (GDH), toxin A or B, or both toxins; and, within the last 10 years, nucleic acid amplification tests (NAATs). Currently, two tests, TC and the CCNA, serve as reference methods for the diagnosis of C. difficile infection (15). The principle of the TC test is to detect strains of C. difficile that produce a toxin(s) following culture on an appropriate medium. CCNA detects fecal protein toxins contained within the stool and is often referred to as fecal toxin detection (16). Unfortunately, both tests are slow and labor-intensive.

Commercially available NAATs for C. difficile detection include those based on PCR or loop-mediated or helicase-dependent isothermal amplification (1720). The performance of NAATs and non-NAAT tests is commonly assessed using diagnostic accuracy measures for the presence of the organism (e.g., diagnostic sensitivity, diagnostic specificity, positive predictive value [PPV], and negative predictive value [NPV]). However, these measures may not directly link to the clinical definition of CDI or clinical outcomes, and some measures (e.g., PPV and NPV) are dependent on disease prevalence in the patient population being tested (8, 17, 19, 20). Finally, in addition to diagnostic sensitivity and specificity, other factors influence the choice of testing strategy, such as cost and turnaround time.

The diagnostic accuracies of current commercially available assays (GDH EIAs, toxin A/B EIAs, and NAATs) are based on comparison with one or both of the currently accepted reference methods (TC and CCNA) for the detection of toxigenic C. difficile, and these comparisons are generally made to inform potential replacement of these reference methods. Although a definitive reference “gold standard” is lacking, both TC and CCNA are regarded as acceptable reference methods (15). However, some view the gold standard to be TC of a stool specimen combined with colonic histopathology of pseudomembranous colitis in patients with symptoms, but it is known that there is a spectrum of disease wherein not all patients with C. difficile infection have pseudomembranes (21). Finally, less frequently, colonoscopic or histopathologic findings demonstrating pseudomembranous colitis can be used in diagnostic workups to increase the diagnostic specificity for CDI diagnosis (14).

In contrasting the two reference methods (TC and CCNA), TC, while infrequently performed in clinical laboratories, is regarded as being more analytically sensitive than CCNA for detecting C. difficile in fecal specimens but may have lower diagnostic specificity (and, therefore, a greater likelihood of false-positive [FP] test results). CCNA has been shown to have high diagnostic sensitivity, ranging from 80 to 100%. In addition, CCNA has high diagnostic specificity and positive predictive values as well as having greater clinical utility based upon clinical outcomes (2226). Furthermore, each reference method differs by the target detected: TC detects the presence of C. difficile strains that produce toxins A and/or B in vitro to confirm a toxigenic strain, whereas CCNA detects the presence of free toxin A or B in clinical specimens. Given these contrasting characteristics, there is potential for diagnostic discrepancy between the reference standards. Therefore, observed diagnostic performance may vary according to which reference standard is used.

Given the variety of test methods and diagnostic algorithms, there is disagreement in the laboratory community on whether best practices for the diagnosis of CDI consist of NAAT only or algorithmic testing that includes NAAT (GDH EIA followed by NAAT [GDH/NAAT] or GDH and toxin EIAs followed by NAAT [GDH/toxin/NAAT]) (20). At the initiation of these guidelines, this was the clinical quandary facing individuals who decide on a C. difficile testing strategy for their health care system, particularly as there is limited high-quality evidence to support which diagnostic testing strategy best supports the laboratory diagnosis of CDI (8, 22). Additionally, it remains to be determined if the potential differences in the accuracy of NAAT only or an algorithmic strategy would impact patient management or patient outcomes (27). There are few studies that encompass the nuances of laboratory CDI diagnosis as it occurs in the clinical context, for example, that evaluate the effect of preanalytic testing considerations on outcomes, to include clinical outcomes. This limitation is evident from the recent Infectious Diseases Society of America (IDSA)/Society for Healthcare Epidemiology of America (SHEA) systematic review, which included only studies that encompassed C. difficile testing within its clinical context, including preanalytic and postanalytic aspects (11).

Given these practice issues, and related diagnostic quality and patient safety concerns, the goal of this systematic review was to determine which laboratory testing strategies, with the inclusion of NAAT, had the best diagnostic accuracy for CDI. While it is clear that laboratory testing alone without taking into consideration the entire clinical picture is not appropriate for the diagnosis of CDI, the available literature has limited evidence linking laboratory diagnosis with clinical outcomes. Therefore, the questions for this systematic review were refined to be based only on the intermediate outcome of diagnostic accuracy for detecting the presence of the C. difficile organism or toxin. Although the reference standard in these studies defines what is meant by the target condition, this systematic review compares the diagnostic accuracies of these tests, including GDH detection by EIA, toxin detection by EIA, and NAAT, to those of CCNA and TC. It has been clear that preanalytical factors are crucial for NAAT specifically, and many of the studies did not include a preanalytical component, which limits whether this review can answer the question, Does this patient have C. difficile infection?

The questions that guided this systematic review were the following: (i) What is the diagnostic accuracy of NAAT only versus either TC or CCNA for detection of the C. difficile toxin gene?, (ii) What is the diagnostic accuracy of a GDH-positive EIA followed by NAAT versus either TC or CCNA for detection of the C. difficile organism/toxin gene?, (iii) What is the diagnostic accuracy of a GDH-positive/toxin-negative EIA followed by NAAT versus either TC or CCNA for detection of the C. difficile organism/toxin/toxin gene?, and (iv) What is the increased diagnostic yield of repeat testing using NAAT after an initial negative result for C. difficile detection of the toxin gene?

The goals of analysis based on these questions were specifically to evaluate the effectiveness of the following: (i) the diagnostic accuracies of NAAT-only and algorithmic (“two-step” or “three-step”) testing strategies, including detection of toxin or GDH in addition to NAAT, and (ii) the diagnostic yield of repeat testing after an initial negative NAAT result. The evidence supporting these two important issues was evaluated by applying the Centers for Disease Control and Prevention (CDC) Laboratory Medicine Best Practices (LMBP) Initiative’s systematic review method for translating results into evidence-based recommendations (28). The method has recently been used to evaluate practices for improving blood culture contamination (29), blood sample hemolysis (30), urine culture sample quality (31), timeliness of providing targeted therapy for bloodstream infections (32), and laboratory test utilization (33), in addition to others, and can be found at the CDC LMBP website (https://www.cdc.gov/labbestpractices/our-findings.html).

Dr. Michael Pride, a Pfizer Scientist, Leads a Team Searching For Ways to Improve Diagnosis, Prevention and Treatment of Clostridium difficile Infections

Dr. Pride of Pfizer leads a team that is searching for ways to improve diagnoses & treatment of C. difficile,

Dr. Michael Pride is the Executive Director, Vaccine Research and Development at Pfizer

Challenges, Chance and Looking Forward. Historically, a difficult diagnosis process has posed challenges to treatment for C. difficile infections, as detection is not straightforward. Dr. Pride and his team are working to tackle this issue by developing better ways to diagnose this infection, which will aid efforts to develop a vaccine. Additionally, he is encouraged by recent work that has demonstrated how an antibody can help prevent recurrent diseases, offering insight that an antibody-mediated response, raised by vaccines, may be a way to help reduce a primary episode of a C. difficile infection.

“If our vaccine is successful, we could help have a great impact on global health, reducing morbidity and even mortality worldwide,” he says. “I’m confident in our team, who is working tirelessly so that hopefully no one must suffer from these horrible symptoms again.”

Today, Dr. Pride leads a team of scientists responsible for the development, qualification and validation of various assays that support Pfizer’s vaccine programs.

 

 

Click on the link below to learn more about Dr. Michael Pride’s Work:

http://innovation.org/about-us/innovation-faces/researcher-profiles/michaelpride?utm_source=Twitter&utm_medium=Social&utm_campaign=NCAC&utm_term=02030501050201&utm_content=DrMichaelPride&sf200705754=1

 

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

C.difficile Study Using C. difficile Conditioned Medium of Six Different C. difficile Strains

 

 

 

 

Abstract

Clostridium difficile infection (CDI) is typically associated with disturbed gut microbiota and changes related to decreased colonization resistance against C. difficile are well described.

However, nothing is known about possible effects of C. difficile on gut microbiota restoration during or after CDI.

In this study, we have mimicked such a situation by using C. difficile conditioned medium of six different C. difficile strains belonging to PCR ribotypes 027 and 014/020 for cultivation of fecal microbiota.

A marked decrease of microbial diversity was observed in conditioned medium of both tested ribotypes. The majority of differences occurred within the phylum Firmicutes, with a general decrease of gut commensals with putative protective functions (i.e. Lactobacillus, Clostridium_XIVa) and an increase in opportunistic pathogens (i.e. Enterococcus). Bacterial populations in conditioned medium differed between the two C. difficile ribotypes, 027 and 014/020 and are likely associated with nutrient availability. Fecal microbiota cultivated in medium conditioned by E. coli, Salmonella Enteritidis or Staphylococcus epidermidis grouped together and was clearly different from microbiota cultivated in C. difficile conditioned medium suggesting that C. difficile effects are specific.

Our results show that the changes observed in microbiota of CDI patients are partially directly influenced by C. difficile.

https://www.ncbi.nlm.nih.gov/pubmed/29180685?dopt=Abstract&utm_source=dlvr.it&utm_medium=twitter

Researchers Find Key Role of Excess Calcium In the Gut In C. difficile infections (CDI)

New research shows, it can’t make this last, crucial move without enough of a humble nutrient: calcium.

And that new knowledge about Clostridium difficile (a bacterium also known as “C. diff“) may lead to better treatment for the most vulnerable patients.

The discovery, made in research laboratories at the University of Michigan Medical School and the U.S. Food and Drug Administration, is published in the online journal PLoS Pathogens.

It helps solve a key mystery about C. diff: What triggers it to germinate, or break its dormancy, from its hard spore form when it reaches the gut.

Though the findings were made in mice, not humans, the researchers say the crucial role of calcium may help explain another mystery: Why some hospital patients and nursing home residents have a much higher risk of contracting C. diff infections and the resulting diarrhea that carries its spores out of the body.

That group includes people whose guts are flooded with extra calcium because they’re taking certain medications or supplements, have low levels of Vitamin D in their blood or have gut diseases that keep them from absorbing calcium.

The new discovery shows that C. diff can recognize this extra calcium, along with a substance called bile salt produced in the liver, to trigger its awakening and the breaking of its shell.

Previous research had suggested it couldn’t do this without another key component, an amino acid called glycine. But the new findings show calcium and the bile salt called taurochlorate alone are enough. Mouse gut contents that were depleted of gut calcium had a 90 percent lower rate of C. diff spore germination.

“These spores are like armored seeds, and they can pass through the gut’s acidic environment intact,” says Philip Hanna, Ph.D., senior author of the new paper and a professor of microbiology and immunology at U-M. “Much of the spore’s own weight is made of calcium, but we’ve shown that calcium from the gut can work with bile salts to trigger the enzyme needed to activate the spore and start the germination process.”

Ironically, the researchers say, one way to use this new knowledge in human patients might be to add even more calcium to the system.

That could awaken all the dormant C. diff spores in a patient’s gut at once, and make them vulnerable to antibiotics that can only kill the germinated form. That could also prevent the transmission of more spores through diarrhea to the patient’s room. That could slow or stop the cycle of transmission that could threaten them or other patients in the future.

Hanna’s graduate student, Travis Kochan, made a key observation that led to the discovery. He noted that the fluid “growth medium” that the researchers typically grow C. diff in for their studies had calcium in it. He realized this could artificially alter the results of their experiments about what caused C. diff spores to germinate.

So, he used a chemical to remove the calcium while leaving all the other nutrients that                  keep C. diff growing. The result: no new spore germination happened in the calcium-free growth medium.

FDA’s Center for Biologics Evaluation and Research conducted further research in laboratory dishes and in the guts of mice. FDA’s Paul Carlson, Ph.D., a former U-M research fellow, and fellow FDA scientists in his laboratory found that C. diff spores that were mutated so that glycine couldn’t act on them could still germinate and colonize mice. This suggested that calcium, and not glycine, was critical for this process.

Both mutant and regular forms of the bacteria could still activate an enzyme inside the C. diff spore that led the bacteria to start dissolving their hard shell. This released the store of calcium that the spore had been harboring inside itself, and increases the local level of the nutrient even further.

“These spores don’t want to germinate in the wrong place,” says Kochan, whose grandfather suffered from a severe C. diff infection which ultimately led to his death. “C. diff spores have specialized to germinate in the gut environment, especially in the environment of the small intestine, where calcium and the bile salt injection from the liver comes in.”

Hanna notes that the bile salt connection to C. diff spore germination was first discovered at U-M in 1982 by a team led by Ken Wilson, M.D.

Calcium and the gut

Certain ailments and treatments cause defects in calcium absorption, but are also risk factors for C. diff infections. For example, patients with vitamin D deficiency are five times more likely to get C. diff.

Medications aimed at calming acid reflux – such as proton pump inhibitors – and steroids can increase the amount of calcium in the gut. A Vitamin D deficiency can keep the body from reabsorbing calcium through the gut wall, allowing it to build up.

And people with inflammatory bowel diseases such as Crohn’s and colitis also have a harder time absorbing calcium from food through their gut walls.

Older adults are also often counseled to take calcium supplements to compensate for lower calcium levels and protect their bones from fracturing.

Hanna cautions that the new findings should not cause any patients to stop taking their medications or doctor-recommended supplements, or to start taking new ones. But he hopes to work with clinicians at U-M and beyond to test the new knowledge in a clinical setting. Meanwhile, he and Kochan and their FDA and U-M colleagues will continue to study C. diff germination in mice and look for ways to block the enzymes crucial to spore germination.

 

To read the article in its entirety – please click on the following link to be re-directed:

http://www.news-medical.net/news/20170713/Scientists-reveal-key-role-of-excess-gut-calcium-in-C-diffc2a0infections.aspx?utm_source=dlvr.it&utm_medium=twitter