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, [email protected]
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,
[email protected]
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
APHL.org
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.