Tag Archives: C. difficile research

Patients Diagnosed With C. difficile Infection (CDI) Have Higher Readmission Rates

Elijah Verheyen, MD'Correspondence information about the author MD Elijah Verheyen

,

Vijay Dalapathi, MD

,

Shilpkumar Arora, MD

,

Kalpesh Patel, MD

,

Pavan Kumar Mankal, MD

,

Varun Kumar, MD

,

Edward Lung, MD

,

Donald P. Kotler, MD

,

Ari Grinspan, MD

Highlights

  • One in five patients admitted with C. difficile is readmitted within 30 days.
  • Recurrent C difficile is the leading cause of readmission.
  • Female sex, renal disease, and anemia increase C difficile readmission risk.
  • Discharge home, as opposed to facility, increases C difficile readmission risk.

Background

Clostridium difficile infection (CDI) is a leading cause of community-onset and healthcare–associated infection, with high recurrence rates, and associated high morbidity and mortality. We report national rates, leading causes, and predictors of hospital readmission for CDI.

Methods

Retrospective study of data from the 2013 Nationwide Readmissions Database of patients with a primary diagnosis of CDI and re-hospitalization within 30-days. A multivariate regression model was used to identify predictors of readmission.

Results

Of 38,409 patients admitted with a primary diagnosis of CDI, 21% were readmitted within 30-days, and 27% of those patients were readmitted with a primary diagnosis of CDI. Infections accounted for 47% of all readmissions. Female sex, anemia/coagulation defects, renal failure/electrolyte abnormalities and discharge to home (versus facility) were 12%, 13%, 15%, 36%, respectively, more likely to be readmitted with CDI.

Conclusions

We found that 1-in-5 patients hospitalized with CDI were readmitted to the hospital within 30-days. Infection comprised nearly half of these readmissions, with CDI being the most common etiology.

Predictors of readmission with CDI include female sex, history of renal failure/electrolyte imbalances, anemia/coagulation defects, and being discharged home. CDI is associated with a high readmission risk, with evidence of several predictive risks for readmission.

SOURCEhttps://www.ajicjournal.org/article/S0196-6553(19)30026-4/fulltext?utm_source=dlvr.it&utm_medium=twitter

Ribotypes and Prevalence of Clostridium difficile (C. diff) Hypervirulent Strain: NAP1/B1/027

The Hypervirulent Strain of Clostridium Difficile: NAP1/B1/027

– A Brief Overview



Abstract

Clostridium difficile is a gram-positive bacterium notorious for causing epidemic diarrhea globally with a significant health burden. The pathogen is clinically challenging with increasing antibiotic resistance and recurrence rate. We provide here an in-depth review of one particular strain/ribotype 027, commonly known as NAP1/B1/027 or North American pulsed-field gel electrophoresis type 1, restriction endonuclease analysis type B1, polymerase chain reaction ribotype 027, which has shown a much higher recurrence rate than other strains.

Introduction & Background

Clostridium difficile (C. diff) is a gram-positive, anaerobic, motile, spore-forming, rod-shaped bacteria [1-2]. It has been isolated from almost all mammals, including pigs, cows, horses, elephants, and Kodiak bears, as well as in poultry and ostriches. It has also been found in the soil and feces of humans and animals. It is transmitted from person to person by the fecal-oral route. The C. diff isolates found in animals are similar to the ones found in humans, but according to Hensgens et al., this similarity does not mean that interspecies transmission occurs. However, immunocompromised people are still at risk for interspecies transmission [1]. Its pathogenicity is dependent on the two toxins that it produces: enterotoxin A (Toxin A or TcdA) and cytotoxin B (Toxin B or TcdB). Enterotoxin damages the actin in target cells which leads to neutrophil infiltration, inflammation, and necrosis of epithelial cells. Cytotoxin B has been shown to damage tight junctions of epithelial cells, which increases vascular permeability and causes hemorrhage [2-3]. These toxins form the basis of stool analysis when diagnosing people with the suspected infection. Despite all the virulence characters described, C. diff is a poor competitor against other gut flora in the human colon. In a healthy colon, this pathogen is not in sufficient quantity to produce a clinically significant disease. Risk factors that disrupt this balance include antibiotics exposure, health care environment, acid suppressants, and elemental diet. The bacterium can cause severe watery diarrhea that can progress to pseudomembranous colitis [3-8]. It has been named as one of the three microorganisms with an ‘urgent’ threat level by the Centers for Disease Control and Prevention (CDC) based on its public health impact in the United States (US) with an estimated $1.5 billion US in annual health care expenditures [8]. Patients who have more than three episodes of unexplained and new onset unformed stools in 24 hours should be referred for testing for a Clostridium difficile infection (CDI). Also, patients with risk factors described previously should undergo testing for this pathogen [9]. The ribotype 027 strain of C. diff is particularly noteworthy as contradicting evidence in the literature is present regarding the disease severity it causes. We provide here a brief overview of the epidemiology, pathophysiology, and treatment of this particular strain.

Review

Ribotypes and prevalence of Clostridium difficile (C. diff)

Clostridium difficile can be characterized according to its ribotyping which is performed using the polymerase chain reaction. Several different ribotypes have been associated with CDI. The ribotypes 001, 002, 014, 046, 078, 126, and 140 have been found to be prevalent in the Middle East [10-12]. In Asia, ribotypes 001, 002, 014, 017, and 018 are more prevalent [13-15]. The predominant strains in Europe and North America include ribotypes 001, 014, 020, 027, and 078 [6]. The ribotype 027 (also referred to as NAP1/B1/027) has emerged in the last decade. Studies have underlined antimicrobial resistance as one of the causes of its epidemic outbreaks. Capillary electrophoresis (CE) ribotyping is used as the standard for characterization of C. diff isolates. This method relies on the intergeneric region variability between 16S and 23S ribosomal deoxyribonucleic acid (DNA) [16]. Ribotype 027 was found to have reduced susceptibility to metronidazole, rifampicin, moxifloxacin, clindamycin, imipenem, and chloramphenicol [17-18]. It is clinically and financially concerning as it leads to severe disease presentation, as well as antimicrobial resistance with high morbidity and mortality rates as compared to other strains [19]. Strains, such as ribotype 027 (especially its spores), spread more easily within the hospital because they can resist the hospital environment, cleaning, and disinfectants [1]. An observational study conducted on patients admitted with diarrhea in a Veteran Affairs Medical Center showed that around 22% of the patients were positive for the NAP1/B1/027 strain out of all the people who tested positive for CDI. Further, a reduction in the rate of diarrhea caused by the NAP1/B1/027 strain was observed with a prevalence of 16.9% in 2016, down from 26.2% in 2013. An increase in the level of awareness and education was thought to be the reason for this decline [20]. The prevalence of this strain in North America is reportedly around 22% – 36%. Ribotype 027 was identified as the most prevalent strain causing CDI with recent outbreaks in North America [20-22]. The prevalence of this strain was shown to be 48% in hospitals in Poland with an outbreak of CDI during September 2011 to August 2013 [21].

NAP1/B1/027 strain

Toxigenicity and Pathogenesis

The North American pulsed-field gel electrophoresis type 1, restriction endonuclease analysis type B1, polymerase chain reaction ribotype 027 (NAP1/B1/027) strain has been shown to contain a gene locus, CdtLoc, that encodes for CD196 ADP-ribosyltransferase (CDT) or binary toxin. The bacterium also produces Toxin A and Toxin B, similar to non-027 ribotypes, through the PaLoc gene locus [23-24]. CDT was first isolated by Popoff et al. [25]. The toxin comprises two separate toxin components: CDTa and CDTb. CDTa, which is an ADP-ribosyltransferase enzyme, modifies actin which results in depolymerization and destruction of the actin cytoskeleton in the gut. CDTb binds to gut cells and increases uptake of CDTa. The destruction caused by CDT favors adherence of bacteria and increased uptake of Toxin A and Toxin B [26].

In addition to the toxins, this strain (along with few others) carries a base pair frameshift deletion at nucleotide 117 of the TcdC gene, which is a negative regulator of Toxins A and B. A mutation in this gene thus causes hyperexpression of toxins by this particular strain. Warny et al. showed that NAP1/B1/027 produces Toxin A approximately 16 times and Toxin B approximately 23 times more than the control strains [27]. One study also proposed that increased sporulation by this strain may also be associated with the increased spread of CDI [28]. The virulent factors associated with NAP1/B1/027 strain have been summarized in Table 1.

Virulent factor Mechanism
1. Toxin A (Enterotoxin A or TcdA) Damages the actin in target cells which leads to neutrophil infiltration, inflammation, and necrosis of epithelial cells [24].
2. Toxin B (Cytotoxin B or TcdB) Damages tight junctions of epithelial cells, which increases vascular permeability and causes hemorrhage [24].
3. CDTa toxin Modification of actin with ADP-ribosylation that results in actin depolymerization and destruction of the cytoskeleton that assists in adherence of bacteria to gut epithelial cells [25-26].
4. CDTb toxin Facilitates uptake of CDTa toxin into the gut epithelial lining [25-26].
5. Hypersporulation Increases reproduction and spread of bacteria [28].
6. TcdC gene mutation (18-bp deletion) Increases the production of Toxin A and Toxin B by down-regulation of feedback inhibitor involved in suppressing toxin production [27].

Previous studies have shown contradicting evidence regarding the severity of disease caused by this particular strain. A recent retrospective analysis by Bauer et al. concluded that NAP1/B1/027 was associated with a decreased odds of severe disease (odds ratio (OR): 0.35, 95% confidence interval (CI) 0.13 – 0.93) and did not increase in-hospital mortality (OR: 1.02, 95% CI 0.53 – 1.96) or recurrence rate (OR: 1.16, 95% CI 0.36 – 3.77) [23]. Several other studies conducted (including cross-sectional, case-control, and cohort studies) did not show any worse outcomes compared to other strains [29-31]. Sirad et al. demonstrated that although NAP1/B1/027 strain may produce more toxins compared to other strains, they produced fewer spores and were not always associated with severe disease [32]. On the contrary, Rao et al. conducted a cohort study and concluded that ribotype 027 was associated with severe CDI (OR: 1.73, 95% CI 1.03 – 2.89; p = 0.037) and increased mortality (OR: 2.02, 95% CI 1.19 – 3.43; p = 0.009) compared to other ribotypes [24]. Another study showed similar results with the North American pulsed-field gel electrophoresis type 1 (NAP1) strain. Multivariate regression analysis exhibited an increase in the severity of CDI with the NAP1 strain (OR: 1.66, 95% CI: 1.90 – 2.54) and increased mortality (OR: 2.12, 95% CI: 1.22 – 3.68) [33]. One study from Quebec labeled this strain to be responsible for severe diseases twice as frequently as compared to other strains [34].

The basis for these contradictory findings can be explained by several reasons, including study design, study population, sample size, the method of detection for C. diff, study setting, and unmeasured confounders. Given these contradictory results, healthcare providers should focus on treating this infection based on their clinical judgment and markers of severe infection, including the number of diarrheal episodes, signs of dehydration, creatinine level, albumin level, white blood cell count, associated co-morbidities, immunocompromised state, etc.

Prevention

Preventive strategies employed for NAP1/B1/027 strain are similar to strategies taken for other strains. These include barrier methods (gloves and gown while examining patient), use of disposable equipment, handwashing with soap and water, disinfecting the environment, and antimicrobial stewardship [35]. Further vaccines are being developed targeting the toxins, including TcdA and TcdB, for simultaneous prevention and treatment of CDI. Actoxumab and bezlotoxumab, which are monoclonal antibodies against TcdA and TcdB, are being investigated for this purpose. A combined Phase III trial (MODIFY I (NCT01241552) and MODIFY II (NCT01513239)) showed benefit from bezlotoxumab, but the combination of actoxumab and bezlotoxumab did not yield any further benefit [36]. Bezlotoxumab has received Food and Drug Administration (FDA) approval in October 2016 and is to be used in patients more than 18 years of age, who are at high risk of recurrence from CDI, and are receiving antibiotics [37]. A novel tetravalent vaccine against TcdA, TcdB, CDTa, and CDTb has been proposed by Secore et al. using a hamster model which has shown promising results [38].

A novel drug, SYN-004 (ribaxamase), is under investigation that has shown promising results for preventing CDI. This drug, which is a β-lactamase, is excreted into the gut and degrades the excess antibiotic that prevents disruption of normal gut flora, ultimately preventing CDI [39]. The Phase IIa clinical trial of this drug showed that ribaxamase at a dose of 150 mg every six hours results in an undetectable concentration of ceftriaxone in the intestine which can potentially decrease the likelihood of a C. diff infection, given the less probability of disruption of the gut bacteria.

Resistance to Antibiotics and Treatment

Cases of NAP1/B1/027 reported in Panama were found to be highly resistant to clindamycin, moxifloxacin, levofloxacin, ciprofloxacin, and rifampin but were susceptible to metronidazole and vancomycin [40]. Susceptibility of ribotype 027 and non-027 ribotypes to different antibiotics was tested in a study in Canada. Ribotype 027 showed a resistance of 92.2% to moxifloxacin compared to 11.2% for other strains. Similarly, 78.2% of ribotype 027 strains were resistant to ceftriaxone compared to 15.7% of other strains. Ribotype 027 demonstrated a greater than four-fold higher minimum inhibitory concentration (MIC) to metronidazole (4 vs. 1 μg/ml) and two-fold higher MIC for fidaxomicin (1 vs. 2 μg/ml). For clindamycin and vancomycin, the resistance was similar in both groups [41].

Resistance to erythromycin is linked to mutations in the ribosomal methylase genes, whereas resistance to fluoroquinolones is due to a mutation in DNA gyrase. Resistance to rifamycin and fidaxomicin is attributed to ribonucleic acid (RNA) polymerase methylation. The presence of phenicol and lincosamide genes has been shown to cause resistance to linezolid. A study conducted in hospitals of Mexico showed some isolates of ribotype 027 to have reduced susceptibility to fidaxomicin despite the unavailability of this drug in Mexico and the patients being unexposed to it [42]. Antibiotics form the basis of treatment for the NAP1/B1/027 strain. Currently, no specific Infectious Diseases Society of America (IDSA) guidelines are available to guide treatment for this particular strain, and hence, the treatment is similar to a non-NAP1/B1/027 strain [9]. Based on the current guidelines for treating CDI overall, we propose the following table for treating infection caused by the NAP1/B1/027 strain (Table 2).

First line treatment Alternative treatment
Initial non-severe infection Oral vancomycin, 125 mg four times daily for 10 days Fidaxomicin, 200 mg twice daily for 10 days; If neither is available, then use metronidazole, 500 mg three times daily for 10 days
First non-severe recurrence Repeat oral vancomycin, 125 mg four times daily for 10 days Fidaxomicin, 200 mg twice daily for 10 days
Second non-severe recurrence Oral vancomycin taper as follow: 125 mg four times daily for seven to 14 days, 125 mg twice daily for seven days, 125 mg twice once daily for seven days, 125 mg once every other day for seven days, 125 mg once every three days for 14 days Fidaxomicin, 200 mg orally twice daily for 10 days, or a fecal microbiota transplant
Subsequent non-severe recurrence Fecal microbiota transplant Tapering oral vancomycin with probiotics, IVIG, fidaxomicin
Severe disease Oral vancomycin, 125 mg four times daily, increase to 500 mg four times daily if no improvement noted in 24-48 hours or associated complications, including renal failure, ileus, etc. Fidaxomicin if the patient cannot tolerate oral vancomycin for any reason
Ileus Add IV metronidazole, 500 mg every eight hours, to oral vancomycin or fidaxomicin therapy; consider general surgery consult as needed Intracolonic vancomycin, IVIG

This strain has not shown any resistance to fidaxomicin, but there has been some contradicting evidence to this. A case report was published in 2017 in which the NAP1 C. diff infection, resistant to treatment with fidaxomicin and fecal transplants, was effectively treated with intravenous immunoglobulin (IVIG) [43]. Given the emerging threat of antibiotic resistance, increasing awareness, controlling infections, and antimicrobial stewardship can be effective measures to reduce this threat [17].

Currently, several novel antibiotics are under investigation which have gone through various randomized controlled trials for CDI treatment. Ridinilazole and cadazolid have completed Phase II trials, while surotomycin has completed two Phase III trials which have shown promising results [44-47].

Conclusions

The data regarding the NAP1/B1/027 strain is inconclusive with ongoing debates whether this particular strain is associated with severe disease. Further research, including meta-analyses, are needed to solve this enigma. Clinicians should guide treatment based on their judgment and objective evidence of disease severity.


References

  1. Hensgens MP, Keessen EC, Squire MM, et al.: Clostridium difficile infection in the community: a zoonotic disease?. Clin Microbiol Infect. 2012, 18:635-45. 10.1111/j.1469-0691.2012.03853.x
  2. Aziz M, Fatima R, Douglass L, Abughanimeh O, Raza S: Current updates in management of Clostridium difficile infection in cancer patients. Curr Med Res Opin. 2018, Epub ahead of print:1-6. 10.1080/03007995.2018.1487389
  3. Sachsenheimer FE, Yang I, Zimmermann O, et al.: Genomic and phenotypic diversity of Clostridium difficile during long-term sequential recurrences of infection. Int J Med Microbiol. 2018, 308:364-77. 10.1016/j.ijmm.2018.02.002
  4. Luciano JA, Zuckerbraun BS: Clostridium difficile infection: prevention, treatment, and surgical management. Surg Clin North Am. 2014, 94:1335-49. 10.1016/j.suc.2014.08.006
  5. Clabots CR, Johnson S, Olson MM, Peterson LR, Gerding DN: Acquisition of Clostridium difficile by hospitalized patients: evidence for colonized new admissions as a source of infection. J Infect Dis. 1992, 166:561-67. 10.1093/infdis/166.3.561
  6. Howell M, Novack V, Grgurich P, Soulliard D, Novack L, Pencina M, Talmor D: Iatrogenic gastric acid suppression and the risk of nosocomial Clostridium difficile infection. Arch Intern Med. 2010, 170:784-90. 10.1001/archinternmed.2010.89
  7. O’Keefe S: Tube feeding, the microbiota, and Clostridium difficile infection. World J Gastroenterol. 2010, 16:139-42. 10.3748/wjg.v16.i2.139
  8. Hampton T: Report reveals scope of US antibiotic resistance threat. JAMA. 2013, 310:1661-63. 10.1001/jama.2013.280695
  9. McDonald LC, Gerding DN, Johnson S, et al.: Clinical practice guidelines for Clostridium difficile infection in adults and children: 2017 update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA). Clin Infect Dis. 2018, 66:e1-e48. 10.1093/cid/cix1085
  10. Jamal W, Rotimi VO, Brazier J, Duerden BI: Analysis of prevalence, risk factors and molecular epidemiology of Clostridium difficile infection in Kuwait over a 3-year period. Anaerobe. 2010, 16:560-65. 10.1016/j.anaerobe.2010.09.003
  11. Jalali M, Khorvash F, Warriner K, Weese J: Clostridium difficile infection in an Iranian hospital. BMC Res Notes. 2012, 5:159. 10.1186/1756-0500-5-159
  12. Al-Thani AA, Hamdi WS, Al-Ansari NA, Doiphode SH, Wilson GJ: Polymerase chain reaction ribotyping of Clostridium difficile isolates in Qatar: a hospital-based study. BMC Infect Dis. 2014, 14:502. 10.1186/1471-2334-14-502
  13. Sawabe E, Kato H, Osawa K, Chida T, Tojo N, Arakawa Y, Okamura N: Molecular analysis of Clostridium difficile at a university teaching hospital in Japan: a shift in the predominant type over a five-year period. Eur J Clin Microbiol Infect Dis. 2007, 26:695-703. 10.1007/s10096-007-0355-8
  14. Cheng V, Yam W, Lam O, et al.: Clostridium difficile isolates with increased sporulation: emergence of PCR ribotype 002 in Hong Kong. Eur J Clin Microbiol Infect Dis. 2011, 30:1371-81. 10.1007/s10096-011-1231-0
  15. Kim H, Lee Y, Moon H, Lim C, Lee K, Chong Y: Emergence of Clostridium difficile ribotype 027 in Korea. Korean J Lab Med. 2011, 31:191-96. 10.3343/kjlm.2011.31.3.191
  16. Krutova M, Nyc O, Matejkova J, Kuijper E, Jalava J, Mentula S: The recognition and characterisation of Finnish Clostridium difficile isolates resembling PCR-ribotype 027. J Microbiol Immunol Infect. 2018, 51:344-51. 10.1016/j.jmii.2017.02.002
  17. Freeman J, Vernon J, Pilling S, et al.: The ClosER study: results from a three-year pan-European longitudinal surveillance of antibiotic resistance among prevalent Clostridium difficile ribotypes, 2011-2014. Clin Microbiol Infect. 2018, 24:724-31. 10.1016/j.cmi.2017.10.008
  18. Goldstein EJ, Citron DM, Sears P, Babakhani F, Sambol SP, Gerding DN: Comparative susceptibilities of fidaxomicin (OPT-80) of isolates collected at baseline, recurrence, and failure from patients in two fidaxomicin phase III trials of fidaxomicin against Clostridium difficile infection. Antimicrob Agents Chemother. 2011, 55:5194-99. 10.1128/AAC.00625-11
  19. Camacho-Ortiz A, López-Barrera D, Hernández-García R, et al.: Correction: First report of Clostridium difficile NAP1/027 in a Mexican hospital. PLoS One. 2015, 10:e0129079. 10.1371/journal.pone.0129079
  20. Giancola S, Williams R, Gentry C: Prevalence of the Clostridium difficile BI/NAP1/027 strain across the United States Veterans Health Administration. Clin Microbiol Infect. 2018, 24:877-81. 10.1016/j.cmi.2017.11.011
  21. Pituch H, Obuch-Woszczatyński P, Lachowicz D, et al.: Prevalence of Clostridium difficile infection in hospitalized patients with diarrhoea: results of a Polish multicenter, prospective, biannual point-prevalence study. Adv Med Sci. 2018, 63:290-95. 10.1016/j.advms.2018.03.003
  22. DePestel DD, Aronoff DM: Epidemiology of Clostridium difficile infection. J Pharm Pract. 2013, 26:464-75. 10.1177/0897190013499521
  23. Bauer KA, Johnston JEW, Wenzler E, et al.: Impact of the NAP-1 strain on disease severity, mortality, and recurrence of healthcare-associated Clostridium difficile infection. Anaerobe. 2017, 48:1-6. 10.1016/j.anaerobe.2017.06.009
  24. Rao K, Micic D, Natarajan M, et al.: Clostridium difficile ribotype 027: relationship to age, detectability of toxins A or B in stool with rapid testing, severe infection, and mortality. Clin Infect Dis. 2015, 61:233-41. 10.1093/cid/civ254
  25. Popoff MR, Rubin EJ, Gill DM, Boquet P: Actin-specific ADP-ribosyltransferase produced by a Clostridium difficile strain. Infect Immun. 1988, 56:2299-306.
  26. Gerding DN, Johnson S, Rupnik M, Aktories K: Clostridium difficile binary toxin CDT: mechanism, epidemiology, and potential clinical importance. Gut Microbes. 2014, 5:15-27. 10.4161/gmic.26854
  27. Warny M, Pepin J, Fang A, et al.: Toxin production by an emerging strain of Clostridium difficile associated with outbreaks of severe disease in North America and Europe. Lancet. 2005, 366:P1079-84. 10.1016/s0140-6736(05)67420-x
  28. Akerlund T, Persson I, Unemo M, Norén T, Svenungsson B, Wullt M, Burman LG: Increased sporulation rate of epidemic Clostridium difficile Type 027/NAP1. J Clin Microbiol. 2008, 46:1530-33. 10.1128/jcm.01964-07
  29. Cloud J, Noddin L, Pressman A, Hu M, Kelly C: Clostridium difficile strain NAP-1 is not associated with severe disease in a nonepidemic setting. Clin Gastroenterol Hepatol. 2009, 7:868-873.e2. 10.1016/j.cgh.2009.05.018
  30. Morgan OW, Rodrigues B, Elston T, Verlander NQ, Brown DF, Brazier J, Reacher M: Clinical severity of Clostridium difficile PCR ribotype 027: a case-case study. PLoS One. 2008, 3:e1812-10. 10.1371/journal.pone.0001812
  31. Walk ST, Micic D, Jain R, et al.: Clostridium difficile ribotype does not predict severe infection. Clin Infect Dis. 2012, 55:1661-68. 10.1093/cid/cis786
  32. Sirard S, Valiquette L, Fortier LC: Lack of association between clinical outcome of Clostridium difficile infections, strain type, and virulence-associated phenotypes. J Clin Microbiol. 2011, 49:4040-46. 10.1128/jcm.05053-11
  33. See I, Mu Y, Cohen J, et al.: NAP1 strain type predicts outcomes from Clostridium difficile infection. Clin Infect Dis. 2014, 58:1394-400. 10.1093/cid/ciu125
  34. Hubert B, Loo VG, Bourgault AM, et al.: A portrait of the geographic dissemination of the Clostridium difficile North American pulsed-field type 1 strain and the epidemiology of C. difficile-associated disease in Québec. Clin Infect Dis. 2007, 44:238-44. 10.1086/510391
  35. Hsu J, Abad C, Dinh M, Safdar N: Prevention of endemic healthcare-associated Clostridium difficile infection: reviewing the evidence. Am J Gastroenterol. 2010, 105:2327-39. 10.1038/ajg.2010.254
  36. Wilcox MH, Gerding DN, Poxton IR, et al.: Bezlotoxumab for prevention of recurrent Clostridium difficile infection. N Engl J Med. 2017, 376:305-17. 10.1056/nejmoa1602615
  37. FDA Approval of Bezlotoxumab in Prevention of Recurrent Clostridium difficile Infection. (2017). Accessed: January 12, 2019: http://www.jwatch.org/na43666/2017/04/24/fda-approval-bezlotoxumab-prevention-recurrent-clostridium.
  38. Secore S, Wang S, Doughtry J, et al.: Development of a novel vaccine containing binary toxin for the prevention of Clostridium difficile disease with enhanced efficacy against NAP1 strains. PLoS One. 2017, 12:e0170640. 10.1371/journal.pone.0170640
  39. Kokai-Kun JF, Roberts T, Coughlin O, et al.: The oral β-lactamase SYN-004 (ribaxamase) degrades ceftriaxone excreted into the intestine in phase 2a clinical studies. Antimicrob Agents Chemother. 2017, 61:pii: e02197-16. 10.1128/AAC.02197-16
  40. López-Ureña D, Quesada-Gómez C, Miranda E, Fonseca M, Rodríguez-Cavallini E: Spread of epidemic Clostridium difficile NAP1/027 in Latin America: case reports in Panama. J Med Microbiol. 2014, 63:322-24. 10.1099/jmm.0.066399-0
  41. Karlowsky JA, Adam HJ, Kosowan T, et al.: PCR ribotyping and antimicrobial susceptibility testing of isolates of Clostridium difficile cultured from toxin-positive diarrheal stools of patients receiving medical care in Canadian hospitals: the Canadian Clostridium difficile Surveillance Study (CAN-DIFF) 2013-2015. Diagn Microbiol Infect Dis. 2018, 91:105-11. 10.1016/j.diagmicrobio.2018.01.017
  42. Martínez-Meléndez A, Tijerina-Rodríguez L, Morfin-Otero R, et al.: Circulation of highly drug-resistant Clostridium difficile ribotypes 027 and 001 in two tertiary-care hospitals in Mexico. Microb Drug Resist. 2018, 24:386-92. 10.1089/mdr.2017.0323
  43. Coffman K, Chen XJC, Okamura C, Louie E: IVIG – A cure to severe refractory NAP-1 Clostridium difficile colitis? A case of successful treatment of severe infection, which failed standard therapy including fecal microbiota transplants and fidaxomicin. IDCases. 2017, 8:27-28. 10.1016/j.idcr.2017.03.002
  44. Vickers RJ, Tillotson GS, Nathan R, et al.: Efficacy and safety of ridinilazole compared with vancomycin for the treatment of Clostridium difficile infection: a phase 2, randomised, double-blind, active-controlled, non-inferiority study. Lancet Infect Dis. 2017, 17:735-44. 10.1016/S1473-3099(17)30235-9
  45. Louie T, Nord CE, Talbot GH, et al.: Multicenter, double-blind, randomized, phase 2 study evaluating the novel antibiotic, cadazolid, in patients with Clostridium difficile infection. Antimicrob Agents Chemother. 2015, 59:6266-73. 10.1128/AAC.00504-15
  46. Daley P, Louie T, Lutz JE, et al.: Surotomycin versus vancomycin in adults with Clostridium difficile infection: primary clinical outcomes from the second pivotal, randomized, double-blind, phase 3 trial. J Antimicrob Chemother. 2017, 72:3462-70. 10.1093/jac/dkx299
  47. Aziz M, Chandrasekar VT, Desai M, Fatima R, Jackson M, Sharma P: Sa1858 – surotomycin (a novel antibiotic) vs vancomycin for Clostridium difficile infection: a systematic review and meta analysis. Gastroenterology. 2018, 154:S421.

Researchers Present New Data that Brief NSAIDs Exposure Prior to a C.difficile Infection (CDI) Increases the Severity of the Infectious Colitis

ABSTRACT

Clostridium difficile infection (CDI) is a major public health threat worldwide. The use of nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with enhanced susceptibility to and severity of CDI; however, the mechanisms driving this phenomenon have not been elucidated. NSAIDs alter prostaglandin (PG) metabolism by inhibiting cyclooxygenase (COX) enzymes. Here, we found that treatment with the NSAID indomethacin prior to infection altered the microbiota and dramatically increased mortality and the intestinal pathology associated with CDI in mice. We demonstrated that in C. difficile-infected animals, indomethacin treatment led to PG deregulation, an altered proinflammatory transcriptional and protein profile, and perturbed epithelial cell junctions. These effects were paralleled by increased recruitment of intestinal neutrophils and CD4+ cells and also by a perturbation of the gut microbiota. Together, these data implicate NSAIDs in the disruption of protective COX-mediated PG production during CDI, resulting in altered epithelial integrity and associated immune responses.

IMPORTANCE Clostridium difficile infection (CDI) is a spore-forming anaerobic bacterium and leading cause of antibiotic-associated colitis. Epidemiological data suggest that use of nonsteroidal anti-inflammatory drugs (NSAIDs) increases the risk for CDI in humans, a potentially important observation given the widespread use of NSAIDs. Prior studies in rodent models of CDI found that NSAID exposure following infection increases the severity of CDI, but mechanisms to explain this are lacking. Here we present new data from a mouse model of antibiotic-associated CDI suggesting that brief NSAID exposure prior to CDI increases the severity of the infectious colitis. These data shed new light on potential mechanisms linking NSAID use to worsened CDI, including drug-induced disturbances to the gut microbiome and colonic epithelial integrity. Studies were limited to a single NSAID (indomethacin), so future studies are needed to assess the generalizability of our findings and to establish a direct link to the human condition.

INTRODUCTION

Clostridium difficile is the most commonly reported nosocomial pathogen in the United States and an urgent public health threat worldwide (1). C. difficile infection (CDI) manifests as a spectrum of gastrointestinal disorders ranging from mild diarrhea to toxic megacolon and/or death, particularly in older adults (2). The primary risk factor for CDI is antibiotic treatment, which perturbs the resident gut microbiota and abolishes colonization resistance (3). However, factors other than antibiotic exposure increase the risk for CDI and the incidence of cases not associated with the use of antimicrobials has been on the rise (4). Defining mechanisms whereby nonantibiotic factors impact CDI pathogenesis promises to reveal actionable targets for preventing or treating this infection.

Recently, several previously unappreciated immune system, host, microbiota, and dietary factors have emerged as modulators of CDI severity and risk. The food additive trehalose, for example, was recently shown to increase C. difficile virulence in mice, and the widespread adoption of trehalose in food products was implicated in the emergence of hypervirulent strains of C. difficile (5). Similarly, excess dietary zinc had a profound impact on severity of C. difficile disease in mice, and high levels of zinc altered the gut microbiota and increased susceptibility to CDI (6). Importantly, there is a growing body of evidence of the essential role of the innate immune response and inflammation in both protection against and pathology of CDI (79). Mounting a proper and robust inflammatory response is critical for successful clearance of C. difficile, and the immune response can be a key predictor of prognosis (3, 10). In this context, specific immune mediators can facilitate both protective and pathogenic responses through the activity of molecules such as interleukin-23 (IL-23) and IL-22, and an excessive and dysregulated immune response is believed to be one of the main factors behind postinfection complications.

Epidemiological data have established an association between the use of nonsteroidal anti-inflammatory drugs (NSAIDs) and CDI (11). Muñoz-Miralles and colleagues demonstrated that the NSAID indomethacin (Indo) significantly increased the severity of CDI in antibiotic-treated mice when the NSAID was applied following inoculation and throughout the infection (12), and indomethacin exposure is associated with alterations in the structure of the intestinal microbiota (13, 14). NSAIDs are among the most highly prescribed and most widely consumed drugs in the United States (15), particularly among older adults (16), and have been implicated in causing spontaneous colitis in humans (17, 18). They act by inhibiting cyclooxygenase (COX) enzymatic activity, which prevents the generation of prostaglandins (PGs) and alters the outcome of subsequent inflammatory events. Prostaglandins, especially PGE2, are important lipid mediators that are highly abundant at sites of inflammation and infection and that support gastrointestinal homeostasis and epithelial cell (EC) health (19). NSAID use has been associated with shifts in the gut microbiota, in both rodents and humans (2023), but these shifts have not been explored in the context of CDI.

In this report, we deployed a mouse model of antibiotic-associated CDI to examine the impact of exposure to indomethacin prior to infection with C. difficile on disease severity, immune response, intestinal epithelial integrity, and the gut microbiota. These investigations revealed that even a brief exposure to an NSAID prior to C. difficile inoculation dramatically increased CDI severity, reduced survival, and increased pathological evidence of disease. Inhibition of PG biosynthesis by indomethacin altered the cytokine response and immune cell recruitment following CDI, enhancing intestinal tissue histopathology and allowing partial systemic bacterial dissemination by dismantling intestinal epithelial tight junctions (TJs). Additionally, indomethacin treatment alone significantly perturbed the structure of the gut microbiota. These findings support epidemiological data linking NSAID use and CDI and caution against the overuse of NSAIDs in patients at high risk for C. difficile, such as older adults.

RESULTS

Indomethacin worsens C. difficile Infection in Mice and Increases Mortality.To determine the extent to which preexposure to NSAIDs influences the natural course of CDI, mice were treated with indomethacin for 2 days prior to inoculation with C. difficile (Fig. 1A). We infected C57BL/6 female mice with 1 × 104 spores of C. difficile NAP1/BI/027 strain M7404 following 5 days of pretreatment with a broad-spectrum antibiotic, cefoperazone (Fig. 1A). This brief indomethacin treatment prior to CDI dramatically decreased cecum size and increased the mortality rate from 20% to 80% (Fig. 1C) but did not significantly impact weight loss (Fig. 1D). Mice pretreated with indomethacin and infected with C. difficile also displayed histopathological evidence of more-severe cecal tissue damage compared to mice infected with C. difficile that were not exposed to the drug (Fig. 1E). Indomethacin-exposed and infected mice exhibited no change in the burden of C. difficile in the cecum (Fig. 1F), but their livers harbored significantly greater loads of mixed aerobic and anaerobic bacteria (Fig. 1G), suggesting that indomethacin pretreatment compromised intestinal barrier function during CDI and fostered microbiota translocation to the liver.

FIG 1

Indomethacin worsens the effects of C. difficile infection in mice. (A) C57BL/6 mice were treated with cefoperazone for 5 days followed by 2 days of recovery and then challenged by gavage with 1 × 104 spores of NAP1 strain M7404. Animals received 2 doses of 10 mg/kg of body weight of indomethacin by gavage daily as indicated by the top arrows. (B) Representative picture illustrating the macroscopic effects of the different treatments in the cecum. Indo, indomethacin; Abx, antibiotic; C. diff, C. difficile. (C to E) Mice were monitored for survival (Kaplan-Meier curve) (C), weight loss (D), and histopathologic severity of colitis (E) (n = 13 to 15/group). (F and G) C. difficile bacterial burden was evaluated in the ceca of 12 mice/group (F) and total aerobic bacterial burden plus anaerobic bacterial burden in the liver of 5 mice/group (G) also at day 3 after infection, with the discontinuous line indicating the limit of detection. Path., pathology. **, P < 0.01 (by log rank [Mantel-Cox] test for survival [panel C] and by unpaired t test for weights [panel D]); *, P < 0.05 (1-way analysis of variance [ANOVA] test for histopathological scores [panel E]); **, P < 0.01 (Wilcoxon test with Bonferroni correction [panel G]). I, indomethacin; A, antibiotic.

Indomethacin alters the proportions of neutrophils and CD4+ T cells in mucosal-associated tissues during C. difficile infection…………………………………

 

Damian Maseda, Joseph P. Zackular, Bruno Trindade, Leslie Kirk, Jennifer Lising Roxas, Lisa M. Rogers, Mary K. Washington, Liping Du, Tatsuki Koyama, V. K. Viswanathan, Gayatri Vedantam, Patrick D. Schloss, Leslie J. Crofford, Eric P. Skaar, David M. Aronoff
Jimmy D. Ballard, Editor

TO READ THE ARTICLE IN ITS ENTIRETY PLEASE CLICK ON THE FOLLOWING LINK TO BE REDIRECTED:

https://mbio.asm.org/content/10/1/e02282-18

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

 

Veteran Affairs Patients with Recurrent C.difficile Infections Participate In Study

 

 

 

 

Though recurrent Clostridium difficile infections (CDI) are common and pose a major clinical concern, data are lacking regarding mortality among patients who survive their initial CDI and have subsequent recurrences. Risk factors for mortality in patients with recurrent CDI are largely unknown.

Methods

Veterans Affairs patients with a first CDI (positive C. difficile toxin(s) stool sample and ≥ 2 days CDI treatment) were included (2010–2014). Subsequent recurrences were defined as additional CDI episodes ≥ 14 days after the stool test date and within 30 days of end of treatment. A matched (1:4) case-control analysis was conducted using multivariable conditional logistic regression to identify predictors of all-cause mortality within 30 days of the first recurrence.

Results

Crude 30-day all-cause mortality rates were 10.6% for the initial CDI episode, 8.3% for first recurrence, 4.2% for second recurrence, and 5.9% for third recurrence. Among 110 cases and 440 controls six predictors of mortality were identified: use of proton pump inhibitors (PPIs, odds ratio [OR] 3.86, 95% confidence interval [CI] 2.14–6.96), any antibiotic (OR 3.33, 95% CI 1.79–6.17), respiratory failure (OR 8.26, 95% CI 1.71–39.92), congitive dysfunction (OR 2.41, 95% CI 1.02–5.72), nutrition deficiency (OR 2.91, 95% CI 1.37–6.21), and age (OR 1.04, 95% CI 1.01–1.07).

Conclusion

In our national cohort of Veterans, crude mortality decreased by 44% from the initial episode to the third recurrence. Treatment with antibiotics, PPIs, and underlying co-morbidities were important predictors of mortality in recurrent CDI. Our study assists healthcare providers in identifying patients at high risk of death after CDI recurrence.

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

https://academic.oup.com/ofid/advance-article/doi/10.1093/ofid/ofy175/5056240

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.

Please click on the following link to view graphs and read this article in its entirety:

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