Lab Matters Summer 2019 - Page 64

APHL 2019 POSTER ABSTRACTS 15 NTM isolates were obtained from University of Colorado Health and National Jewish Health respectively for the validation of colony recognition and MALDI-TOF identification. These isolates represent the most often clinically recovered NTMs. Internal colony recognition competencies were set up for all staff. Bruker MALDI-TOF was then validated for NTM identification. Our second phase includes lesser known NTM to ensure our repertoire and library are robust. Identification competencies and algorithms will be expanded as our experience continues. Colorado Department of Public Health and Environment (CDPHE) laboratory and our engagement with the Association of Public Health Laboratories (APHL) and the Centers for Disease Control and Prevention (CDC) make this laboratory a significant resource to the state and underserved communities within our region. Presenter: Sarah Elizabeth Totten, Colorado Department of Public Health and Environment, Denver, CO, Influenza A Virus Multiple Infection Dependence is Determined Through Virus-host Interactions K. Phipps, K. Ganti and A. Lowen, Emory University 62 LAB MATTERS Summer 2019 Presenter: Kara Phipps, Emory University, Atlanta, GA, Identification of Candida auris and Other Pathogenic Yeasts by MALDI-TOF Mass Spectrometry of Membrane Lipids L. Leung 1 , M. Sorensen 2 , E. Nilsson 2 , C. Chandler 3 , D. Goodlett 3 , R. Ernst 3 , R. Myers 1 ; 1 Maryland Department of Health, 2 Pataigin, 3 University of Maryland, Baltimore Candida species are the most common invasive fungal pathogens and the fourth most common cause of healthcare-associated bloodstream infections in the United States. The Centers for Disease Control and Prevention estimates that 46,000 healthcare- associated Candida infections occur annually. Among pathogenic Candida, Candida auris represents an emerging global health threat due to the high incidences of multidrug-resistant and health care-associated infections; however, accurately diagnosing C. auris infections is challenging. Recently, protein-based identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis has had some success in identifying C. auris. To improve Candida species identification, we developed a novel and complementary diagnostic platform that utilizes essential microbial membrane lipids as chemical fingerprints for identification of pathogenic fungi, including C. auris. A fungal library of lipid mass spectral profiles was constructed containing fifty-five yeast and fungal species, thirty of which were Candida species. Individual isolates were grown at 30˚C for 48 hours on Sabouraud-dextrose agar and harvested by centrifugation. Lipids were extracted by heat-assisted, ammonium-isobutyrate reaction of cell pellets. Mass spectra were acquired by MALDI- TOF-MS in negative ion mode on a microflex LRF using the matrix norharmane. To evaluate the fungal library for identifying Candida, 50% of spectra were randomly selected and designated as the testing set from the top five pathogenic Candida – C. albicans, C. glabrata, C. tropicalis, C. parapsilosis, and C. krusei – and C. auris. Using the PostalService™ platform, mass spectra were transformed and analyzed to extract unique mass difference features. Testing set isolates were classified using a Support Vector Machine algorithm, and identification rates of accuracy were determined by multiple analyses of different training set iterations. With this novel, in-house computational model, we achieved 87-95% mean accuracy for identification of the pathogenic Candida with 92% mean accuracy for identifying C. auris. Importantly, experiments are ongoing to determine sub-species differences, namely between C. auris isolates with different antifungal susceptibilities or within isolates in response to environmental cues, and whether these differences are diagnostic or can offer insight into virulence and drug resistance mechanisms. Overall this study demonstrates the potential of this platform to reduce time and improve accuracy of diagnosis during an infection. Our novel diagnostic platform identifies Candida species based on their lipid mass spectra including the newly emergent pathogen, C. auris. PublicHealthLabs @APHL Influenza A viruses (IAV) poses a substantial and continued public health threat due to the ability of the virus to evolve and escape pre-existing immunity. The IAV genome is comprised of eight essential, negative-sense RNA gene segments. Segmentation facilitates genetic diversification through reassortment, which occurs when multiple virions co-infect the same cell and exchange gene segments. Reassortment between IAV subtypes has underlied the formation of multiple pandemic strains and also can enable coupling of beneficial mutations and purging of deleterious mutations within the same strain background. Previous studies from our lab and others suggest IAVs exhibit a dependence on co-infection for productive infection. Due to the requirement of multiple infection to produce reassortant genotypes, we assessed multiple infection dependence by assaying reassortment levels between phenotypically similar, genetically barcoded viruses derived from the same strain background in various cell lines. Notably, A/guinea fowl/HK/WF10/1999 (WF10) viruses resulted in extremely high levels of reassortment in MDCK cells, indicative of an acute dependence on co-infection for productive infection, but this dependence was ameliorated in DF-1 chicken fibroblasts. Reassortment frequency also differed with strain: co-infection with A/mallard/Minnesota/199106/99 (MN99) viruses yielded less reassortment than WF10 viruses in MDCK cells. In vitro phenotypes were corroborated by animal studies using the same virus strains. Quail and guinea pigs were infected with barcoded WF10 at a species determined 10^2 ID50. Upper respiratory samples collected over the course of infection revealed much higher prevalence of reassortment in the guinea pigs as compared to quail. Guinea pigs infected with 10^2 ID50 of the MN99 viruses resulted in lower levels of reassortment than WF10 viruses in the same animal model. These findings demonstrate the degree of multiple infection dependence is determined through virus-host interactions. To investigate if the multiple infection dependence is attributable to complementation of incomplete viral genomes, incomplete genomes were quantified by a single cell based assay. Measurements taken from infection of WF10 in MDCK cells revealed that incomplete viral genomes were present, but the frequency of missing segments was not sufficient to explain the reassortment levels observed. RNA quantification studies comparing viral RNA production under low and high multiple infection conditions revealed that polymerase function, particularly of WF10 in MDCKs, is enhanced under multiple infection conditions. Overall these findings reveal multiple infection dependence as a frequent and host dependent feature of IAV infection and point to a role for incomplete viral genomes and enhancement of polymerase activity in determining multiple infection and reassortment levels.