Adjustment of Modified Carbapenem Inactivation Method Conditions for Rapid Detection of Carbapenemase-Producing Acinetobacter baumannii

BACKGROUND: The existing modified carbapenem inactivation methods (mCIMs) recommended by the CLSI for detecting carbapenemase production have not been applicable for Acinetobacter baumannii. We evaluated the influence of matrices used in mCIMs and CIMTris on the stability of the disks for detecting carbapenemase producers and suggested optimal mCIM conditions for detecting carbapenemase-producing A. baumannii. METHODS: Seventy-three A. baumannii isolates characterized for antimicrobial susceptibility and carbapenemase encoding genes were tested for carbapenemase production using mCIM and CIMTris. The influence of the matrices (Tryptic soy broth [TSB] and Tris-HCl) used in these methods on the stability of the meropenem (MEM) disk was also evaluated. The mCIM conditions were adjusted to enhance screening sensitivity and specificity for detecting carbapenemase-producing A. baumannii. RESULTS: The matrices had an impact on the stability of the MEM disk after the incubation period (two or four hrs). TSB nutrient broth is an appropriate matrix for mCIM compared with Tris-HCl pH 7.6, which leads to the loss of MEM activity in CIMTris. The sensitivity and the specificity of the optimal mCIM were both 100%. CONCLUSIONS: We established optimal mCIM conditions for simple, accurate, and reproducible detection of carbapenemase-producing A. baumannii.

Prediction of Putative Resistance Islands in a Carbapenem-Resistant Acinetobacter baumannii Global Clone 2 Clinical Isolate

BACKGROUND: We investigated the whole genome sequence (WGS) of a carbapenem-resistant Acinetobacter baumannii isolate belonging to the global clone 2 (GC2) and predicted resistance islands using a software tool. METHODS: A. baumannii strain YU-R612 was isolated from the sputum of a 61-yr-old man with sepsis. The WGS of the YU-R612 strain was obtained by using the PacBio RS II Sequencing System (Pacific Biosciences Inc., USA). Antimicrobial resistance genes and resistance islands were analyzed by using ResFinder and Genomic Island Prediction software (GIPSy), respectively. RESULTS: The YU-R612 genome consisted of a circular chromosome (ca. 4,075 kb) and two plasmids (ca. 74 kb and 5 kb). Its sequence type (ST) under the Oxford scheme was ST191, consistent with assignment to GC2. ResFinder analysis showed that YU-R612 possessed the following resistance genes: four β-lactamase genes bla(ADC-30), bla(OXA-66), bla(OXA-23), and bla(TEM-1); armA, aadA1, and aacA4 as aminoglycoside resistance-encoding genes; aac(6')Ib-cr for fluoroquinolone resistance; msr(E) for macrolide, lincosamide, and streptogramin B resistance; catB8 for phenicol resistance; and sul1 for sulfonamide resistance. By GIPSy analysis, six putative resistant islands (PRIs) were determined on the YU-R612 chromosome. Among them, PRI1 possessed two copies of Tn2009 carrying bla(OXA-23), and PRI5 carried two copies of a class I integron carrying sul1 and armA genes. CONCLUSIONS: By prediction of resistance islands in the carbapenem-resistant A. baumannii YU-R612 GC2 strain isolated in Korea, PRIs were detected on the chromosome that possessed Tn2009 and class I integrons. The prediction of resistance islands using software tools was useful for analysis of the WGS.

Xpert CARBA-R Assay for the Detection of Carbapenemase-Producing Organisms in Intensive Care Unit Patients of a Korean Tertiary Care Hospital

Carbapenemase-producing organisms (CPO) are rapidly disseminating worldwide, and their presence in tertiary care hospitals poses a significant threat to the management of nosocomial infections. There is a need to control CPO, especially in intensive care unit (ICU) patients, because these organisms are resistant to most beta-lactam antibiotics and are easily transmitted. At present, the identification of CPO is time-consuming; hence, this study focused on the use of the Xpert CARBA-R assay (Cepheid, USA) to determine intestinal colonization rates of CPO in patients admitted to the ICU of a tertiary care hospital in Korea. Forty clinical stool samples were collected and inoculated both in a CARBA-R cartridge and in conventional culture plates. The CARBA-R assay required only ~one hour to screen CPO, while the time required for conventional culture was over three days. We also found that the prevalences of intestinal colonization by carbapenem-resistant organisms and Enterobacteriaceae were 17.5% (7 out of 40) and 7.5% (3 out of 40), respectively. Among the colonizing strains, three that contained carbapenemase, including Klebsiella pneumonia carbapenemase (KPC), and imipenem (IMP) and Verona integron-mediated metallo-beta-lactamase (VIM) were found. With its convenience, the Xpert CARBA-R assay can be included in CPO surveillance strategies.

The role of gut microbiota in the gut-brain axis: current challenges and perspectives

Brain and the gastrointestinal (GI) tract are intimately connected to form a bidirectional neurohumoral communication system. The communication between gut and brain, knows as the gut-brain axis, is so well established that the functional status of gut is always related to the condition of brain. The researches on the gut-brain axis were traditionally focused on the psychological status affecting the function of the GI tract. However, recent evidences showed that gut microbiota communicates with the brain via the gut-brain axis to modulate brain development and behavioral phenotypes. These recent findings on the new role of gut microbiota in the gut-brain axis implicate that gut microbiota could associate with brain functions as well as neurological diseases via the gut-brain axis. To elucidate the role of gut microbiota in the gut-brain axis, precise identification of the composition of microbes constituting gut microbiota is an essential step. However, identification of microbes constituting gut microbiota has been the main technological challenge currently due to massive amount of intestinal microbes and the difficulties in culture of gut microbes. Current methods for identification of microbes constituting gut microbiota are dependent on omics analysis methods by using advanced high tech equipment. Here, we review the association of gut microbiota with the gut-brain axis, including the pros and cons of the current high throughput methods for identification of microbes constituting gut microbiota to elucidate the role of gut microbiota in the gut-brain axis.