Identification of Gram negative non-fermentative bacteria: How hard can it be?

Abstract

Introduction: The prevalence of bacteremia caused by Gram negative non-fermentative (GNNF) bacteria has been increasing globally over the past decade. Many studies have investigated their epidemiology but focus on the common GNNF including Pseudomonas aeruginosa and Acinetobacter baumannii. Knowledge of the uncommon GNNF bacteremias is very limited. This study explores invasive bloodstream infection GNNF isolates that were initially unidentified after testing with standard microbiological techniques. All isolations were made during laboratory-based surveillance activities in two rural provinces of Thailand between 2006 and 2014.

Methods: A subset of GNNF clinical isolates (204/947), not identified by standard manual biochemical methodologies were run on the BD Phoenix automated identification and susceptibility testing system. If an organism was not identified (12/204) DNA was extracted for whole genome sequencing (WGS) on a MiSeq platform and data analysis performed using 3 web-based platforms: Taxonomer, CGE KmerFinder and One Codex.

Results: The BD Phoenix automated identification system recognized 92% (187/204) of the GNNF isolates, and because of their taxonomic complexity and high phenotypic similarity 37% (69/187) were only identified to the genus level. Five isolates grew too slowly for identification. Antimicrobial sensitivity (AST) data was not obtained for 93/187 (50%) identified isolates either because of their slow growth or their taxa were not in the AST database associated with the instrument. WGS identified the 12 remaining unknowns, four to genus level only.

Conclusion: The GNNF bacteria are of increasing concern in the clinical setting, and our inability to identify these organisms and determine their AST profiles will impede treatment. Databases for automated identification systems and sequencing annotation need to be improved so that opportunistic organisms are better covered.

Fig 1. Flow diagram denoting blood culture results from blood-stream infection surveillance in two rural Thai provinces from 2006–2014.

*WGS–whole genome sequencing.

Fig 2. Distribution of previously unidentified isolates at the genus level using BD Phoenix automated identification system.

Fig 3. Antibiotic resistance levels by organism using the BD Phoenix identification system.

Antibiotic abbreviation by drug class: Aminoglycosides: AMK–Amikacin, GEN—Gentamicin; Beta-lactam: PIP–Piperacillin, ATM–Aztreonam, TZP–Piperacillin-Tazobactam; Carbapenem: IPM–Imipenem, MEM–Meroenem; Cephalosporin: CAZ–Ceftazidime, CTX–Cefotaxime, FEP–Cefepime; CHL–Chloramphenicol; Fluoroquinolone: CIP–Ciprofloxacin, LVX–Levofloxacin; TET- Tetracycline; Folate Antagonist: SXT—Trimethoprim-Sulfamethoxazole. In the figure legend n = number of isolates for which BD Phoenix antimicrobial sensitivity test (AST) data was available/total number of species specific isolates in this study. For several isolates where AST data was not recorded the isolate grew too slowly to meet the growth control criteria cutoff. Clinical and Laboratory Standards Institute (CLSI) 2016 minimum inhibitory concentration break points were applied. Moraxella (n = 14) and Pasteurella (n = 12) species are not included in the BD Phoenix AST database and therefore this data is not available.

Fig 4. Taxonomer bacterial composition classifier sunburst of isolate NA45737.

Reads are classified against 16S sequences and protein sequences from Uniref50. The size of a given sector represents the relative abundance at the read level. Taxonomic ranks are hierarchical with the highest level placed in the center. Reads not classified at the species level, either because they are shared between taxa or represent novel microorganisms, are collapsed to the lowest common ancestor and shown as part of slices that terminate at higher taxonomic ranks (e.g. genus).