Elizabethkingia spp. is a gram-negative, non-fermenting rod bacterium that is frequently implicated in hospital outbreaks. Elizabethkingia has a high rate of resistance to antibiotics and a shortage of effective parenteral antibiotics usually occurs in intensive care units. Infection includes neonatal sepsis and meningitis. Recently, a new species of Elizabethkingia, which is closely related to E. meningoseptica ATCC 13253 and E. miricola GTC862, was reported as a human pathogen in Central Africa and named E. anophelis. Our investigation involved 27 Elizabethkingia clinical isolates, which were fully identified through phenotypic and genotypic typing. The isolates were identified as E. meningoseptica by VITEK 2 (bioMereux) and Phoneix (Beckton Dickinson) automated bacterial identification systems. We then re-identified the isolates by 16S rRNA gene sequencing; 23 of the 27 strains were identified as E. anophelis and one was identified as E. miricola instead of E. meningoseptica. Subsequently, we evaluated the performance of the Bruker MALDI-TOF MS system for identification of the E. anophelis strains; many were misidentified as E. meningoseptica or were unidentified. All of the strains were correctly re-identified as E. anophelis when the original Bruker database was expanded with the inclusion of 10 E. anophelis clinical isolates and a standard 〖R26 〗^T strain. We also analysed 23 E. anophelis clinical isolates by biochemical tests, antimicrobial susceptibilities tests and pulsed-field gel electrophoresis. From the biochemical investigation of all isolates and type strain, showing that the conventional biochemical tests are not reliable to differentiate E. anophelis from other Elizabethkingia spp. More than 75% of the isolates tested were susceptible to cotrimoxazole, ciprofloxacin, and cefoperazone-sulbactam, however they were all resistant to aminoglycosides and beta-lactam drugs except one strain. At the PFGE investigation all the strains were not clonally related as shown by PFGE and displayed distinct PFGE fingerprints. / published_or_final_version / Medicine / Master / Master of Medical Sciences

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published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

Developing diagnostic modalities that utilize nanomaterials and miniaturized detectors can have an impact in point-of-care diagnostics. Diagnostic systems that (i) are sensitive, robust, and portable, (ii) allow detection in clinical samples, (iii) require minimal sample preparation yielding results quickly, and (iv) can simultaneously quantify multiple targets, would have a great potential in biomedical research and public healthcare. Bacterial infections still cause pathogenesis throughout the world (Chapter I). The emergence of multi-drug resistant strains, the potential appearance of bacterial pandemics, the increased occurrence of bacterial nosocomial infections, the wide-scale food poisoning incidents and the use of bacteria in biowarfare highlight the need for designing novel bacterial-sensing modalities. Among the most prominent disease-causing bacteria are strains of Escherichia coli, like the E. coli O157:H7 that produces the Shiga-like toxin (Stx). Apart from diarrheagenic E. coli strains, others cause disease varying from hemolytic uremic syndrome and urinary tract infections to septicemia and meningitis. Therefore, the detection of E. coli needs to be performed fast and reliably in diverse environmental and clinical samples. Similarly, Mycobacterium avium spp. paratuberculosis (MAP), a fastidious microorganism that causes Johne's disease in cattle and has been implicated in Crohn's disease (CD) etiology, is found in products from infected animals and clinical samples from CD patients, making MAP an excellent proof-of-principle model. Recently, magnetic relaxation nanosensors (MRnS) provided the first applications of improved diagnostics with high sensitivity and specificity. Nucleic acids, proteins, viruses and enzymatic activity were probed, yet neither large targets (for instance iv bacterial and mammalian cells) nor multiple bacterial disease parameters have been simultaneously monitored, in order to provide thorough information for clinical decision making. Therefore, the goal of this study was to utilize MRnS for the sensitive identification of multiple targets associated with bacterial pathogenesis, while monitoring virulence factors at the microorganism, nucleic acid and virulence factor levels, to facilitate improved diagnosis and optimal treatment regimes. To demonstrate the versatility of MRnS, we used MAP as our model system, as well as several other pathogens and eukaryotic cell lines. In initial studies, we developed MRnS suitable for biomedical applications (Chapter II). The resulting MRnS were composed of an iron oxide core, which was caged within a biodegradable polymeric coating that could be further functionalized for the attachment of molecular probes. We demonstrated that depending on the polymer used the physical and chemical properties of the MRnS can be tailored. Furthermore, we investigated the role of polymer in the enzyme-mimicking activity of MRnS, which may lead to the development of optimized colorimetric in vitro diagnostic systems such as immunoassays and small-molecule-based screening platforms. Additionally, via facile conjugation chemistries, we prepared bacterium-specific MRnS for the detection of nucleic acid signatures (Chapter III). Considering that MAP DNA can be detected in clinical samples and isolates from CD patients via laborious isolation and amplification procedures requiring several days, MRnS detected MAP's IS900 nucleic acid marker up to a single MAP genome copy detection within 30 minutes. Furthermore, these MRnS achieved equally fast IS900 detection even in crude DNA extracts, outperforming the gold standard diagnostic method of nested Polymerase Chain v Reaction (nPCR). Likewise, the MRnS detected IS900 with unprecedented sensitivity and specificity in clinical isolates obtained from blood and biopsies of CD patients, indicating the clinical utility of these nanosensors. Subsequently, we designed MRnS for the detection of MAP via surface-marker recognition in complex matrices (Chapter III). Milk and blood samples containing various concentrations of MAP were screened and quantified without any processing via MRnS, obtaining dynamic concentration-dependent curves within an hour. The MAP MRnS were able not only to identify their target in the presence of interferences from other Gram positive and Gram negative bacteria, but could differentiate MAP among other mycobacteria including Mycobacterium tuberculosis. In addition, detection of MAP was performed in clinical isolates from CD patients and homogenized tissues from Johne's disease cattle, demonstrating for the first time the rapid identification of bacteria in produce, as well as clinical and environmental samples. However, comparing the unique MAP quantification patterns with literatureavailable trends of other targets, we were prompted to elucidate the underlying mechanism of this novel behavior (Chapter IV). We hypothesized that the nanoparticle valency – the amount of probe on the surface of the MRnS – may have modulated the changes in the relaxation times (ΔΤ2) upon MRnS – target association. To address this, we prepared MAP MRnS with high and low anti-MAP antibody levels using the same nanoparticle formulation. Results corroborated our hypothesis, but to further bolster it we investigated if this behavior is target-size-independent. Hence utilizing small-moleculeand antibody-carrying MRnS, we detected cancer cells in blood, observing similar detection patterns that resembled those of the bacterial studies. Notably, a single cancer vi cell was identified via high-valency small-molecule MRnS, having grave importance in cancer diagnostics because a single cancer cell progenitor in circulation can effectively initiate the metastatic process. Apart from cells, we also detected the Cholera Toxin B subunit with valencly-engineered MRnS, observing similar to the cellular targets' diagnostic profiling behavior. Finally, as bacterial drug resistance is of grave healthcare importance, we utilized MRnS for the assessment of bacterial metabolism and drug susceptibility (Chapter V). Contrary to spectophotometric and visual nanosensors, their magnetic counterparts were able to quickly assess bacterial carbohydrate uptake and sensitivity to antibiotics even in blood. Two MRnS-based assay formats were devised relying on either the Concanavalin A (Con A)-induced clustering of polysaccharide-coated nanoparticles or the association between free carbohydrates and Con A-carrying MRnS. Overall, taking together these results, as well as those on pathogen detection and the recent instrumentation advancements, the use of MRnS in the clinic, the lab and the field should be anticipated.