Tag Archives: C. difficile research

There Are Smart Antibiotics to treat C.difficile infections being developed by Researchers

Cationic amphiphilic bolaamphiphile-based delivery of antisense oligonucleotides provides a potentially microbiome sparing treatment

for C. difficile

The Journal of Antibiotics (2018) | Download Citation

Abstract

Conventional antibiotics for C. difficile infection (CDI) have mechanisms of action without organismal specificity, potentially perpetuating the dysbiosis contributing to CDI, making antisense approaches an attractive alternative. Here, three (APDE-8, CODE-9, and CYDE-21) novel cationic amphiphilic bolaamphiphiles (CABs) were synthesized and tested for their ability to form nano-sized vesicles or vesicle-like aggregates (CABVs), which were characterized based on their physiochemical properties, their antibacterial activities, and their toxicity toward colonocyte (Caco-2) cell cultures. The antibacterial activity of empty CABVs was tested against cultures of E. coli, B. fragilis, and E. faecalis, and against C. difficile by “loading” CABVs with 25-mer antisense oligonucleotides (ASO) targeting dnaE. Our results demonstrate that empty CABVs have minimal colonocyte toxicity until concentrations of 71 µM, with CODE-9 demonstrating the least toxicity. Empty CABVs had little effect on C. difficile growth in culture (MIC90 ≥ 160 µM). While APDE-8 and CODE-9 nanocomplexes demonstrated high MIC90 against C. difficile cultures (>300 µM), CYDE-21 nanocomplexes demonstrated MIC90 at CABV concentrations of 19 µM. Empty CABVs formed from APDE-8 and CODE-9 had virtually no effect on E. coli, B. fragilis, and E. faecalis across all tested concentrations, while empty CYDE-21 demonstrated MIC90 of >160 µM against E. coli and >40 µM against B. fragilisand E. faecalis. Empty CABVs have limited antibacterial activity and they can deliver an amount of ASO effective against C. difficile at CABV concentrations associated with limited colonocyte toxicity, while sparing other bacteria. With further refinement, antisense therapies for CDI may become a viable alternative to conventional antibiotic treatment.

Introduction

C. difficile infection (CDI) is the most frequently reported nosocomial bacterial infection [1] in the United States, accounting for more than 450,000 new cases annually and for more than four billion dollars in CDI-attributable annual health care costs [2]. CDI has a strong reliance on intestinal dysbiotic states, which, when combined with the presence of C. difficile in the human gut, represents the most common pathogenesis for CDI. The high prevalence of this infection is, in large part, due to formidable recurrence rates of 15–25% following first treatment [3] with conventional antibiotics (CAs). CAs have long been recognized as the most important risk factor for the development of CDI [4], due to their mechanisms of action lacking organismal specificity, leading to widespread changes in gut ecology [5], which can lead to CDI by disrupting the gut microbial community. Given the important role of intestinal dysbiosis in the development of CDI, there has also been recent interest in studying the effects of difficile-directed conventional antibiotics on the bacterial and fungal communities of human subjects being treated for CDI, as a way of potentially explaining the high persistence and recurrence rates of this disease. These more recent data [6] suggest that even difficile-directed conventional antibiotics could potentially contribute to the perpetuation of dysbiotic states, which in turn could perpetuate CDI, potentially leading to even primary treatment failures.

There has been previous [7, 8] interest in the development of antisense therapies to treat bacterial infections, in part due to concerns regarding antibiotic resistance to traditional drugs. Given the dependence of CDI on dysbiotic states, approaches using therapeutic antisense oligonucleotides (ASO) complimentary to specific C. difficile mRNAs could limit or prevent the expression of important bacterial genes leading to bacterial death, all while sparing other organisms. This approach would offer significant advantages over CAs, especially in terms of a more limited impact on gut microbial communities. Developing clinically effective antisense therapies targeting a Gram-positive organism requires several elements. Since antisense oligonucleotides will not be efficiently introduced into bacteria without assistance given the presence of both a cell membrane and a thick cell wall, a carrier molecule must be used to deliver the ASO. This carrier must complex with the ASO strongly enough to concentrate it, to protect it from degradation in the extracellular environment, and to focus its delivery on its target cell. In order to accomplish these activities, the carrier-ASO complex itself must be stable in the in vivo environment of the gut. Once at the cell, the carrier must be able to release its cargo. Simultaneously, the carrier must demonstrate both limited gut toxicity and limited antibacterial activity at the doses required to effectively treat the target bacteria.

Our group published the first [9] in vitro data for antisense therapies against CDI by complexing cyclohexyl dequalinium analogs to various ASO-targeting essential C. difficile genes. However, since dequalinium has both antibacterial activity as well as toxicity at higher doses, a better delivery compound for ASO is required if antisense approaches to CDI are to be further developed. Here, we report our data on vesicles formed from novel cationic amphiphilic bolaamphiphiles (CABs) as carriers for chimeric 25-mer 2′-O-methyl phosphorothioate ASO. CABs, characteristic of all bola-like compounds, have hydrophilic, positively charged end groups separated by a hydrophobic linker chain. This molecular structure enables CABs to form nano-sized vesicle-like aggregates (CABVs), which in turn allow them to complex with negatively charged oligonucleotides in addition to promoting electrostatic interactions with bacterial cell membranes for intracellular delivery of ASO. The synthesis, physiochemical properties, toxicity, and antibacterial properties of three novel CABs and their respective CABVs are described, and their specificity for C. difficile compared to several other organisms is also provided.

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https://www.nature.com/articles/s41429-018-0056-9

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

Clostridium difficile Research Suggests That Positively Selected Sites In the Surface Layer Proteins May Play A Role In Driving the Emergence of Hyper-virulent Strains

  • Mark Lynch,
  • Thomas A. Walsh,
  • Izabela Marszalowska,
  • Andrew E. Webb,
  • Micheál MacAogain,
  • Thomas R. Rogers,
  • Henry Windle,
  • Dermot Kelleher,
  • Mary J. O’ConnellEmail author and
  • Christine E. Loscher

To Read Abstract/Article In Its Entirety Please Click On the Following Link:

https://bmcevolbiol.biomedcentral.com/articles/10.1186/s12862-017-0937-8

 

Abstract

Background

Clostridium difficile is a nosocomial pathogen prevalent in hospitals worldwide and increasingly common in the community.

Sequence differences have been shown to be present in the Surface Layer Proteins (SLPs) from different C. difficile ribotypes (RT) however whether these differences influence severity of infection is still not clear.

Results

We used a molecular evolutionary approach to analyse SLPs from twenty-six C. difficile RTs representing different slpA sequences. We demonstrate that SLPs from RT 027 and 078 exhibit evidence of positive selection (PS).

We compared the effect of these SLPs to those purified from RT 001 and 014, which did not exhibit PS, and demonstrate that the presence of sites under positive selection correlates with ability to activate macrophages.

SLPs from RTs 027 and 078 induced a more potent response in macrophages, with increased levels of IL-6, IL-12p40, IL-10, MIP-1α, MIP-2 production relative to RT 001 and 014. Furthermore, RTs 027 and 078 induced higher expression of CD40, CD80 and MHC II on macrophages with decreased ability to phagocytose relative to LPS.

Conclusions

These results tightly link sequence differences in C. difficile SLPs to disease susceptibility and severity, and suggest that positively selected sites in the SLPs may play a role in driving the emergence of hyper-virulent strains.

 

Clostidium difficile Most Recent Research Discussed By ASM and Dr. Alice Guh, MD, MPH of the CDC

Clostridium difficile is an increasingly important problem being faced by clinical microbiologists. From 1993 to 2009, incidence of C. difficile increased fourfold (85,700 cases increased to 336,600 cases) in the United States. Because of this, it has become a significant area of research, as researchers search for better antimicrobial therapies, diagnostic assays, and prevention tactics.   ASM recently invited Alice Guh, MD, MPH, of the Centers for Disease Control and Prevention, to present the most recent C. difficile research as part of the Hot Topics in Clinical Microbiology series*. In her presentation, ‘Update on Clostridium difficile Infection’, Guh first describes the changing epidemiology of C. difficile infections (CDI), updating the data from the CDC’s Emerging Infections Program (EIP) and their long-term surveillance of CDI within the United States.

Guh further reviews current CDI diagnostic testing and its associated challenges. She highlights the benefits and downfalls of traditional enzyme immunoassay to detect C. difficile toxins compared to the nucleic acid amplification tests (NAAT) first put to widespread use in 2009.

Finally, Guh describes the role of asymptomatic carriers in C. difficile transmission. Her review of the literature presents best practices to trace transmission from asymptomatic carriers as well as suggested strategies to stop this transmission.

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

https://www.asm.org/index.php/clinmicro-blog/item/6264-hot-topics-in-clinical-microbiology-clostridium-difficile

 

Clostridium difficile Infection Incidence In Mainland China – A Systematic Review

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 Abstract

It has been widely reported that the incidence and severity of Clostridium difficile infection (CDI) have increased dramatically in North America and Europe. However, little is known about CDI in Mainland China. In this study, we aimed to investigate the incidence of CDI and the main epidemic and drug-resistant strains of C. difficile in Mainland China through meta-analysis of related studies published after the year 2010. A total of 51 eligible studies were included. The pooled incidence of toxigenic C. difficile among patients with diarrhoea was 14% (95% CI = 12-16%). In Mainland China, ST-37 and ST-3 were the most prevalent strains; fortunately, hypervirulent strains, such as ST-1 (BI/NAP1/027) and ST-11 (RT 078), have only occurred sporadically to date.

The rates of C. difficile resistance to ciprofloxacin (98.3%; 95% CI = 96.9-99.7%), clindamycin (81.7%; 95% CI = 76.1-87.3%) and erythromycin (80.2%; 95% CI = 73.5-86.9%) are higher than in other counties; however, none of the C. difficile isolates reported in Mainland China were resistant to metronidazole (n/N = 0/960), vancomycin (n/N = 0/960), tigecycline (n/N = 0/41) or piperacillin/tazobactam(n/N = 0/288).

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https://www.ncbi.nlm.nih.gov/pubmed/27897206?dopt=Abstract&utm_source=dlvr.it&utm_medium=twitter

Researchers At University of Texas Health Science Center and Graduate School of Biomedical Sciences in Houston Have Uncovered How C. difficile Produces the Toxins A and B That Are Responsible For Causing Disease

laboratorybeakers3

In the battle against drug-resistant pathogens, genetic research holds promising answers to our toughest threats. A new study shows that the best tool for
treating Clostridium difficile
infections could be within the genome of the bacteria itself.

Researchers at the University of Texas Health Science Center and the Graduate School of Biomedical Sciences in Houston have uncovered an important new finding to learn just how the C. difficile bacteria produces toxins, offering some new direction for the development of non-antibiotic drugs to fight dangerous C. difficile infections (CDI).

Strain on already stressed healthcare industry —–   The bacteria are one of the more virulent and widespread drug-resistant pathogens responsible for healthcare associated infections around the world, costing acute care facilities nearly $4.8 billion dollars a year in excess healthcare costs in the United States.

HOW is C. diff. Acquired?   —-   CDIs are linked to the use of broad spectrum antibiotics, which when used to treat infections can also suppress the beneficial bacteria that live in our guts and protect us from infections.   When that intestinal microflora is compromised, individuals become more susceptible to CDIs when exposed to C. difficile bacteria on contaminated surfaces or other individuals who are carrying the bacteria.

CDC Report —    C. difficile works by producing two toxins, toxin A and toxin B, that cause life-threatening diarrhea as well as pseudomembranous colitis, toxic megacolon, perforations in the colon, sepsis and — death. According to a 2015 study from the Centers for Disease Control and Prevention (CDC), there are nearly half a million CDIs in the United States each year, and about 15,000 of those cases result in deaths. The CDC considers C. difficile a public threat needing urgent and aggressive action.

The authors of the new study from the University of Texas have uncovered just how                 C. difficile produces the toxins A and B that are responsible for causing disease.

They studied several strains of the bacteria and found that some encode two Agr loci in their genomes, designated agr1 and agr2. The agr1 locus is present in all of the C. difficile strains sequenced to date, whereas the agr2 locus is present in a few strains.

Until recently, the function of these loci were not known. To understand their roles in toxin regulation and pathogenesis, the researchers used allelic exchange to delete components of agr1 and agr2 and then examined the mutants for toxin production. In their results, they found that the agr1 mutant cannot produce toxins A and B – in their model the mutant was able to colonize but could not produce disease.

These findings offer a potential new approach to treating CDIs for a global medical community vexed by the dangerous pathogen and in need of a novel solution.

“The toxins have become promising non-antibiotic treatment targets,” write the authors. “Here, we have identified a pathway responsible for activating the production of the toxins.

This important finding opens up a unique therapeutic target for the development of a novel non-antibiotic therapy for C. difficile infections.”

Study author Charles Darkoh, PhD, explains how his team plans on building on their research findings. “By crippling their toxin-making machinery, C. diff cannot make toxins and thus cannot cause disease. My laboratory is already working on this and was awarded a 5-year National Institutes of Health grant to investigate and develop an oral compound we have identified that inactivate the toxins and block the toxin-making machinery
of C. diff by targeting this pathway.”

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