Leon Dent

Genomic and proteomic characterization of multi-drug resistant Acinetobacter baumannii

Background Multidrug resistant Acinetobacter baumanii (MRAB) is an emerging pathogen that is an important cause of hospital acquired infection and has been shown to increase mortality and length of hospital stay. MRAB is the predominant multidrug resistant bacteria at Nashville General Hospital at Meharry Medical College (NGHM). The goal of this study is to determine the major genomic and proteomics patterns of MRAB at NGHM.

Next-gen sequencing of multi-drug resistant Acinetobacter baumannii to determine antibiotic resistance genotypes

Background Multi-drug resistant (MDR) Acinetobacter baumannii is an important cause of hospital acquired infection and often increases mortality and length of stay[1-3]. The mechanisms of resistance include: (1) antimicrobialinactivating enzymes such as -lactamases, (2) alteration of membrane porin channels, and (3) mutations that change cellular functions [4]. Accurate genotyping and correlation to antimicrobial susceptibility will help prevent and treat outbreaks of Acinetobacter. The genome of A. baumannii ranges from 3.2 Megabases (Mb) in the drug sensitive SDF strain up to 3.9 Mb in the MDR AYE strain. A surprisingly high proportion of baumannii ORFs, (15%-20%), are located in resistance islands or “alien islands” long stretches of DNA acquired from a foreign source. The MDR AYE strain has an 86Kb island containing 45-50 drug resistance genes located in an insertion hotspot [5]. Our study aims to sequence several A. baumannii isolates from Metro Nashville General (NGH) Hospital and conduct a strain-to-reference genomic characterization of clinical virulence factors.

Mathematical Modeling/Problem Solving in Global Oxygen Transport

A simplified approach to mathematical modeling/problem solving in global oxygen transport is presented. In addition to standard oxygen transport formulae, it uses the S-Factor and a mathematical relationship relating SvO(2) to the ratio DO(2)/VO(2). This method allows the determination or specification of SvO(2), PvO(2), P(50), and systemic shunting in the context of this simplified approach. Heretofore this has not been possible. With this approach, essentially all clinical problems in global oxygen transport can be dealt with. This is illustrated by the broad scope of the five problems presented.

Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: proceedings

Fig. 1 (abstract P1). RAST server annotations for A. baumanii clinical isolate. Genes associated with virulence are highly represented. P1 Proteogenomic characterization of a clinical isolate of Acinetobacter baumanii from a case of fulminant sepsis: What does the data mean clinically? L Leon Dent, Sammed N Mandape, Siddharth Pratap, Jianan Dong, Jamaine Davis, Jennifer A Gaddy, Kofi Amoah, Steve Damo, Dana R Marshall Department of Surgery, Meharry Medical College, Nashville, TN 37208, USA; Bioinformatics Core, Meharry Medical College, Nashville, TN 37208, USA; Department of Biochemistry and Cancer Biology, Meharry Medical College, Nashville, TN 37208, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Life and Physical Sciences, Fisk University, Nashville, TN 37208, USA; Department of Pathology, Anatomy and Cell Biology, Meharry Medical College, Nashville, TN 37208, USA Correspondence: Dana R Marshall (dmarshall@mmc.edu) BMC Proceedings 2017, 11(Suppl 9):P1

The antibiotic resistance proteome of Acinetobacter baumanii MDR isolate MMC#4

Background Two hundred and forty-seven isolates of Acinetobacter baumanii (AB) were identified in the Nashville General Hospital at Meharry epidemiology database for a three year period. Of these isolates, 77% were multi-drug resistant (MRAB). Mechanical ventilation and multiple site recovery were associated with MRAB, and MRAB isolates were associated with increased mortality relative to sensitive AB isolates [1]. AB acquires resistance rapidly and the mechanisms are still being identified. Proteomic analysis can identify proteins that change in their expression levels in the presence of antibiotics, a possible mechanism of resistance for AB. No proteome databases exist for this organism so antibiotic sensitivity and resistance proteomes were generated for MRAB bronchial wash isolate MMC#4. Materials and methods AB MMC#4 was grown in plain LB broth or LB broth supplemented with MIC50 concentrations of levofloxacin, tobramycin, gentamicin, cefotaxime and meropenem. Cell pellets were lysed and total protein run on an SDS-PAGE gel. Protein bands were excised and in-gel digested with trypsin. Resulting peptides were analyzed using a Thermo Finnigan LTQ ion trap mass spectrometer equipped with a 1-D nanoLC pump (Eksigent), Nanospray source (James A Hill Company), and Xcalibur 2.0 SR2 instrument control (Thermo Scientific). Peptides were separated on a

Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016

Table of contentsI1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won ParkP1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpointsXueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder LambaP2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental powerBernie J Daigle, Jr.P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomesDeborah Burgess, Stephanie Gehrlich, John C CarmenP4 Tracking the molecular evolution of Pax geneNicholas Johnson; Chandrakanth EmaniP5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparisonStephanie Gehrlich, Deborah Burgess, John C CarmenP6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencingKalpani De Silva, Michael P Heaton, Theodore S KalbfleischP7 Mining physiological data to identify patients with similar medical events and phenotypesTeeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y HuangP8 Smart brief for home health monitoringMohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F NaberP9 Side-effect term matching for computational adverse drug reaction predictionsAKM Sabbir, Sally R EllingsonP10 Enrichment vs robustness: A comparison of transcriptomic data clustering metricsYuping Lu, Charles A Phillips, Michael A LangstonP11 Deep neural networks for transcriptome-based cancer classificationRahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan CuiP12 Motif discovery using K-means clusteringMohammed Sayed, Juw Won ParkP13 Large scale discovery of active enhancers from nascent RNA sequencingJing Wang, Qi Liu, Yu ShyrP14 Computationally characterizing genomic pipelines and benchmarking results using GATK best practices on the high performance computing cluster at the University of KentuckyXiaofei Zhang, Sally R EllingsonP15 Development of approaches enabling the identification of abnormal gene expression from RNA-Seq in personalized oncologyNaresh Prodduturi, Gavin R Oliver, Diane Grill, Jie Na, Jeanette Eckel-Passow, Eric W KleeP16 Processing RNA-Seq data of plants infected with coffee ringspot virusMichael M Goodin, Mark Farman, Harrison Inocencio, Chanyong Jang, Jerzy W Jaromczyk, Neil Moore, Kelly SovacoolP17 Comparative transcriptomics of three Acinetobacter baumanii clinical isolates with different antibiotic resistance patternsLeon Dent, Mike Izban, Sammed Mandape, Shruti Sakhare, Siddharth Pratap, Dana MarshallP18 Metagenomic assessment of possible microbial contamination in the equine reference genome assemblyM Scotty DePriest, James N MacLeod, Theodore S KalbfleischP19 Molecular evolution of cancer driver genesChandrakanth Emani, Hanady Adam, Ethan Blandford, Joel Campbell, Joshua Castlen, Brittany Dixon, Ginger Gilbert, Aaron Hall, Philip Kreisle, Jessica Lasher, Bethany Oakes, Allison Speer, Maximilian ValentineP20 Biorepository Laboratory Information Management SystemNaga Satya V Rao Nagisetty, Rony Jose, Teeradache Viangteeravat, Robert Rooney, David Hains

Draft Genome Sequence of Multidrug-Resistant Acinetobacter baumannii Strain MMC4, Isolated from a Patient in Tennessee

ABSTRACT Acinetobacter baumannii multidrug-resistant strain MMC4 was isolated from a bronchoalveolar lavage fluid sample from a patient in Nashville, TN, USA. Here, we report a draft genome sequence with a size of 3,985,367 bp, an average G+C content of 39.8%, and 3,863 predicted protein-coding sequences.

Next-gen sequencing of multi-drug resistant Acinetobacter baumanii at Nashville General Hospital at Meharry.

Background Acinetobacter baumannii is a nonfermentative Gramnegative bacillus, which easily acquires antibiotic resistance determinants and causes life-threatening nosocomial infections [1]. Multi-drug resistant (MDR) strains are common therefore, empirical treatment choices are limited. More knowledge is needed regarding genetic diversity patterns and resistance phenotypes in a given clinical setting. Our goal is to identify the resistance genotypes of A. baumanii at Nashville General Hospital and correlate them with MDR phenotypes [1].