A strain of C. difficile that was resistant to fluoroquinolones and had binary toxin and a partial deletion of the tcdC gene was responsible for this outbreak of C. difficile-associated diarrhea. Exposure to fluoroquinolones or cephalosporins was a risk factor.

In this study, health care-associated C. difficile infection and colonization were differentially associated with defined host and pathogen variables. The NAP1 strain was predominant among patients with C. difficile infection, whereas asymptomatic patients were more likely to be colonized with other strains. (Funded by the Consortium de Recherche sur le Clostridium difficile.).

This study helped to quantify the impact of strain NAP1 on the incidence and severity of C. difficile-associated disease in Québec in 2005. The identification of the geographic dissemination of this predominant strain may help to focus regional infection-control efforts.

During a 2-year surveillance program (1996 to 1998) in Quebec, Canada, 442 strains of Candida species were isolated from 415 patients in 51 hospitals. The distribution of species was as follows: Candida albicans, 54%; C. glabrata, 15%; C. parapsilosis, 12%; C. tropicalis, 9%; C. lusitaniae, 3%; C. krusei, 3%; and Candida spp., 3%. These data, compared to those of a 1985 survey, indicate variations in species distribution, with the proportions of C. glabrata and C. parapsilosis increasing by 9 and 4%, respectively, and those of C. albicans and C. tropicalis decreasing by 10 and 7%, respectively. However, these differences are statistically significant for C. glabrata and C. tropicalis only. MICs of amphotericin B were >4 g/ml for 3% of isolates, all of which were non-C. albicans species. Three percent of C. albicans isolates were resistant to flucytosine (>32 g/ml). Resistance to itraconazole (>1 g/ml) and fluconazole (>64 g/ml) was observed, respectively, in 1 and 1% of C. albicans, 14 and 9% of C. glabrata, 5 and 0% of C. tropicalis, and 0% of C. parapsilosis and C. lusitaniae isolates. Clinical data were obtained for 343 patients. The overall crude mortality rate was 38%, reflecting the multiple serious underlying illnesses found in these patients. Bloodstream infections were documented for 249 patients (73%). Overall, systemic triazoles had been administered to 10% of patients before the onset of candidiasis. The frequency of isolation of non-C. albicans species was significantly higher in this group of patients. Overall, only two C. albicans isolates were found to be resistant to fluconazole. These were obtained from an AIDS patient and a leukemia patient, both of whom had a history of previous exposure to fluconazole. At present, it appears that resistance to fluconazole in Quebec is rare and is restricted to patients with prior prolonged azole treatment.

BackgroundThe identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies.ResultsThe method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery.ConclusionsOur method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github.com/aldro61/kover/).Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2889-6) contains supplementary material, which is available to authorized users.

Background:The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies. Results: The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery. Conclusions: Our method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github. com/aldro61/kover/).

Clostridium difficile isolates from a 2004 outbreak in Québec, Canada, were all found to be susceptible to metronidazole, vancomycin, rifampin, and meropenem but resistant to bacitracin, cefotaxime, ciprofloxacin, and levofloxacin, and most (>80%) were resistant to ceftriaxone, clarithromycin, gatifloxacin, and moxifloxacin. The predominant NAP1 isolates were susceptible to clindamycin, while the NAP2 isolates were resistant.

Peptococcus magnus was recovered from 10% of anaerobic cultures collected from suspected clinical infections over a 3.5-year period. It was the commonest species of anaerobic gram-positive cocci isolated (30%). To evaluate the clinical significance of this organism, we retrospectively reviewed the charts of 222 patients from whom P. magnus was isolated. Twenty-five patients had no evidence of infection, 151 had mixed infections, and 32 had infections from which only P. magnus was isolated (pure cultures). Mixed infections involved the following sites: bone and joint (32 cases), soft tissue (57 cases), foot ulcers (29 cases), abdominal cavity (16 cases), and miscellaneous (17 cases). The average number of organisms was four (2.5 facultatives and 1.5 anaerobes). Eighteen patients with pure cultures of P. magnus had bone or joint infections, and foreign bodies were present in 15 of these. Other pure cultures of P magnus infections included 12 soft tissue, one vascular graft, and one infected sternotomy with persistent bacteremia. Pure culture infections were usually chronic, and serious sequelae often resulted. Peptococcus magnus is frequently isolated from significant infections and seems particularly pathogenic in infections of bones and joints or in association with the presence of foreign bodies, or both.