Identification of Gram Negative Non-Fermentative Bacteria: How Hard Can It Be?

RESEARCH ARTICLE
Identification of Gram negative non-

fermentative bacteria: How hard can it be?
Toni Whistler ID1,2*, Ornuma Sangwichian2, Possawat Jorakate2, Pongpun Sawatwong ID2,
Uraiwan Surin3, Barameht Piralam3, Somsak Thamthitiwat2, Chidchanok Promkong3,
Leonard Peruski1
1 Division of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, Georgia, United
States of America, 2 Thailand Ministry of Public Health—US Centers for Disease Control and Prevention
Collaboration (TUC), Nonthaburi, Thailand, 3 Nakhon Phanom General Hospital, Nakhon Phanom Provincial
Health Office, Nakhon Phanom, Thailand
a1111111111
a1111111111 * taw6@cdc.gov
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Abstract
Introduction
The prevalence of bacteremia caused by Gram negative non-fermentative (GNNF) bacteria
OPEN ACCESS has been increasing globally over the past decade. Many studies have investigated their epi-
Citation: Whistler T, Sangwichian O, Jorakate P, demiology but focus on the common GNNF including Pseudomonas aeruginosa and Acine-
Sawatwong P, Surin U, Piralam B, et al. (2019) tobacter baumannii. Knowledge of the uncommon GNNF bacteremias is very limited. This
Identification of Gram negative non-fermentative
study explores invasive bloodstream infection GNNF isolates that were initially unidentified
bacteria: How hard can it be? PLoS Negl Trop Dis
13(9): e0007729. https://doi.org/10.1371/journal. after testing with standard microbiological techniques. All isolations were made during labo-
pntd.0007729 ratory-based surveillance activities in two rural provinces of Thailand between 2006 and
Editor: Alfredo G. Torres, University of Texas 2014.
Medical Branch, UNITED STATES
Received: May 20, 2019

Methods
Accepted: August 25, 2019
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 test-
Published: September 30, 2019
ing system. If an organism was not identified (12/204) DNA was extracted for whole genome
Copyright: This is an open access article, free of all sequencing (WGS) on a MiSeq platform and data analysis performed using 3 web-based
copyright, and may be freely reproduced,
distributed, transmitted, modified, built upon, or
platforms: Taxonomer, CGE KmerFinder and One Codex.
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative Results
Commons CC0 public domain dedication.
The BD Phoenix automated identification system recognized 92% (187/204) of the GNNF
Data Availability Statement: Sequences have isolates, and because of their taxonomic complexity and high phenotypic similarity 37% (69/
been deposited in the NCBI Sequence Read Archive
187) were only identified to the genus level. Five isolates grew too slowly for identification.
and can be accessed through Biosample identifiers
SAMN11784141-52. All relevant data are within the Antimicrobial sensitivity (AST) data was not obtained for 93/187 (50%) identified isolates
manuscript and its Supporting Information files. either because of their slow growth or their taxa were not in the AST database associated
Funding: Support for this project was provided by with the instrument. WGS identified the 12 remaining unknowns, four to genus level only.
the Global Disease Detection program of the U.S.
Centers for Disease Control and Prevention, and Conclusion
the Pneumococcal Vaccines Accelerated
Development and Introduction Plan (PneumoADIP) The GNNF bacteria are of increasing concern in the clinical setting, and our inability to iden-
funded by the GAVI Alliance based at the Johns tify these organisms and determine their AST profiles will impede treatment. Databases for
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007729 September 30, 2019 1 / 16

Identification of Gram negative non-fermentative bacteria: How hard can it be?
Hopkins Bloomberg School of Public Health. The automated identification systems and sequencing annotation need to be improved so that
funders had no role in study design, data collection opportunistic organisms are better covered.
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared

that no competing interests exist.
Author summary
Infections caused by Gram negative non fermentative bacteria (GNNF), other than the
more commonly described Pseudomonas aeruginosa and Acinetobacter baumannii, con-
stitute an emerging problem. They are not only isolated in nosocomial settings, mainly
affecting immunocomprised hosts, but also are opportunistic infections causing invasive
disease in rural communities. GNNF bacteria are ubiquitously distributed in the environ-
ment and have a high propensity for antimicrobial resistance. Thus, their accurate identi-
fication to the species level is important for appropriate patient management. These
bacteria are taxonomically heterogeneous, and many isolates are not satisfactorily identi-
fied by the standard biochemical assays because of overlapping phenotypic characteristics.
In this study we explored using the BD Phoenix automated identification system to
improve the identification of GNNF isolates and recognized 92% (187/204) of the previ-
ously unidentified isolates; 117 of which were to the species level. Antimicrobial resistance
profiles were identified for 50% (94/187) of the isolates identified by the BD Phoenix sys-
tem. For the remaining 12 unidentified organisms whole genome sequencing was per-
formed. Three web-based programs were used for organism identification and results
varied because of differences in the underlying databases used.
Introduction
The non-fermentative Gram-negative bacteria are widely distributed in the environment and
have become increasingly common isolates in the clinical laboratory. Being ubiquitous in
nature, they are often disregarded as contaminants. Medically, their pathogenic potential has
been proved beyond doubt by their frequent isolation from clinical material and their associa-
tion with disease. This group of bacteria has emerged as opportunistic pathogens, particularly
in immunocompromised hosts, and are difficult to treat because of widespread antibiotic resis-
tance. Due to their taxonomic complexity and phenotypic similarity accurate identification
represents a challenge for conventional microbiology.
Automated systems that perform organism identification and antimicrobial susceptibility
testing are now the mainstay of clinical microbiology laboratories. The recent implementation
of the BD Phoenix and a MiSeq next generation sequencing (NGS) instrument in our labora-
tory-based surveillance activities has allowed us to re-examine a subset of previously unidenti-
fied Gram negative bacilli and determine which genera are contributing to bloodstream
infections in rural, agrarian populations in Thailand. An understanding of the uncommon
Gram negative non-fermentative (GNNF) bacteria causing invasive disease in these communi-
ties should assist in diagnosis and treatment and possibly impact patient outcomes.
Methods
Ethical statement
The CDC Human Subjects Review Office determined this protocol to be a routine public
health activity not involving human subject research (CGH Determination and Approval
number 2014–273).

Isolate collection
Unidentified Gram negative bacterial isolates used in this study (Fig 1) were collected during
2006–2014 as part of a previously published surveillance system [1]. Organisms were taken
from the isolate culture collections of the Sa Kaeo and Nakhon Phanom Provincial Health Lab-
oratories that had been previously been characterized using standard biochemical testing
methods [2]. A random subset of approximately 20% (n = 204) of the 947 unidentified organ-
isms were used in this study.
BD Phoenix system procedures

Upon removal from the freezer, isolates were grown on tryptic soy agar with 5% sheep blood.
The inoculum for the Phoenix Gram-negative identification and susceptibility combination
panel NMIC/ID 55 was prepared according to the manufacturer’s instructions and panels
were sealed, logged, and loaded into the instrument for incubation at 35˚C (http://legacy.bd.
com/ds/technicalCenter/clsi/clsi-Phoenix_GramNegative_V5.15_V4.31.pdf). Kinetic, colori-
metric, and fluorescent signals were automatically collected by the instrument every 20 min-
utes until results were completed. The Phoenix system leads to an identification result when a
species or group of species is identified with a �90% confidence level [3]. Antimicrobial sus-
ceptibility testing (AST) breakpoints were interpreted using the Clinical and Laboratory Stan-
dards Institute (CLSI) guidelines from 2016 [4].
Whole genome sequencing

DNA was extracted from bacterial colonies using the manual Qiagen DNA Mini Kit. Libraries
were prepared employing the Illumina Nextera XT DNA Library Prep Kit according to the
manufacturer’s instructions. Whole genome sequencing was performed on the Illumina
MiSeq sequencer (Illumina, San Diego, CA) using Illumina MiSeq Reagent Kit version 2.0 and
300 cycles paired-end runs.
Sequences have been deposited in the NCBI Sequence Read Archive and can be accessed
through Biosample identifiers SAMN11784141–52.
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.
https://doi.org/10.1371/journal.pntd.0007729.g001

Downstream genome analysis

The whole genome data were analyzed using open access tools through web-based servers that
accept raw fastq files. Organism identification for the study isolates were determined using: 1)
Center for Genomic Epidemiology (CGE) KmerFinder 3.0 (https://cge.cbs.dtu.dk/services/
KmerFinder/) [5] with the selected database bacteria organisms (K: 16, P: ATG) updated July
2018. Run 10/16/2018. This method finds the unique 16-mers (k-mers) in the input sequence
and predicts species based on the number of overlapping k-mers between the query genome
and genomes in a reference database. The prediction is made at which it has the highest num-
ber of 16-mers in common despite of position. 2) Taxonomer (https://www.taxonomer.com/)
run in full analysis mode which analyzes the incoming read data in two steps: (a) binning,
which classifies reads into top categories, such as Human, Bacteria, Virus, etc. and (b) classifi-
cation, which further categorizes each read into a particular phylum or species, if sufficiently
unique. Bacterial reads are classified against 16S rRNA and bacterial transcriptomes compiled
from Greengenes and UniRef50 databases [6]. 3) One Codex (https://onecodex.com/) [7]
using the free research account that allows for analysis of 25 samples. One Codex classifies the
input files against three databases of microbial genomes and genes a) the RefSeq Complete
Genomes, b) the extended One Codex database and c) a targeted loci database.
Results are presented as absolute percentages regardless of phylogeny level so are presented
as the percent of total bacterial reads classified.
Results
Approximately 7% (947/14,507) of all clinical isolates could not be identified by standard bio-
chemical methods. A proportion of these, denoted as “unknown” (204/947 (21.5%); Fig 1),
were randomly selected and examined using the BD Phoenix automated identification and
susceptibility testing system. Identification to at least the genus level was achieved for 187/204
(91.7%) (Fig 2; S1 Tables). The largest number of isolates identified as Achromobacter species
(46/204; 22.5%), with nearly 70% of cases being in adults �50 years of age (32/46, Table 1).
One case appeared to be hospital onset (defined as positive blood cultures obtained >2 days
after hospital admission), with blood obtained for blood culture 5 days after admission. All
others were community-acquired infections. AST data was available for 36 isolates (78.3%)
and all showed resistance to ampicillin, amoxicillin-clavulanate, aztreonam, and cefazolin.
Cefepime resistance was determined in 14/36 isolates with the remaining 18 isolates scoring as
intermediate (Fig 3; S1 Tables). Ten Achromobacter isolates identified by Phoenix had no asso-
ciated AST data as isolate growth was too slow, preventing the control from reaching the
required cutoff value and terminating that portion of the panel. On re-test a similar result was
obtained.
Pseudomonas species were identified in 35/204 isolates (17.2%) of which the most common
were P. putida (11; 31.4%), and P. pseudoalcaligenes 8 (22.9%). P. oryzihabitans and P. aerugi-
nosa had 5 and 4 isolates respectively, and one isolation each of P. luteola, P. mendocina and P.
stutzeri. Three Pseudomonas isolates were not speciated. The majority of isolates were not able
to meet the growth criteria required in the AST control, even upon re-testing, and susceptibil-
ity profiles were only available for 9/35 (25.7%) isolates. These were 100% resistant to ampicil-
lin, amoxicillin/clavulanate (augmentin), cefazolin, and cefoxitin, as expected with intrinsic
resistance to these antibiotics. All 9 were sensitive to amikacin, cefepime, imipenem and piper-
acillin/tazobactam (Fig 3; S1 Tables). Twenty percent of cases (7/35) were in children <5 years
old (Table 1); and 3/4 likely hospital onset were in children <5 years old.
The third most common isolates were Moraxella species (14/204 cases (6.9%)), with 5 being
hospital onset infections, and most cases occurring in adults �50 years old (9/14; 64.3%). AST

Fig 2. Distribution of previously unidentified isolates at the genus level using BD Phoenix automated
identification system.
profiles were not available as Moraxella species are not included in the Phoenix AST database
[8].
Other genera identified are Sphingomonas paucimobilis (13/204 isolates), twelve isolations
of Pasteurella species (7 P. multocida, 2 P. aerogenes, and 3 P. pneumotropica), and 9 isolations
of Ochrobactrum anthropi.
Clinical outcome data was available for 126/204 (61.8%) of case-patients (Table 1), ninety
patients (71.4%) had a complete recovery, or their condition was improving at the time of dis-
charge. Twenty-three patients (18.3%) showed no improvement (13 were transferred to
another hospital and two discharged against medical advice). Thirteen patients died (10.3%).
AST profiles were not determined for 93/187 (50%) isolates: 44/187 isolates (23.5%) as they
were not included in the Phoenix AST taxa (15 different species; S1 Tables). A further 50 (26.7%)
isolates grew too slowly so the cutoff value required in the AST growth control was not reached.
Table 2 summarizes the time-to-positivity ((TTP) defined as the time from the start of
blood culture bottle incubation to a positive signal) and the patient self-reported antibiotic use
within 72 hours of hospital admission. Antibiotic use was extremely high among cases with
only 16.9% (31/183) reporting that no antibiotics were used. Blood culture TTP shows that
111/176 (63.1%) of cultures were called within 48 hours (Table 2), despite the high levels of
antibiotic usage.
A total of 17/204 (5.9%) isolates could not be identified by the Phoenix system, five because
of no growth. Characterization of the remaining 12 isolates was undertaken through WGS on
an Illumina MiSeq platform.
Isolate NA66303 was identified as Laribacter hongkongensis by all 3 programs (Table 3; S2
Tables). The One Codex software identified the sample as a low-complexity/isolate and a high

Table 1. Demographic data associated with the 204 unidentified isolates processed using the BD Phoenix automated identification system, focusing on commonly
identified genera.
GNNF Phoenix ID Achromobacter spp. Pseudomonas spp. Moraxella
(n = 204) (n = 46) (n = 35) spp.
(n = 14)
n % n % n % n %
Age Group n 188 92.2 44 95.7 33 94.3 13 92.9
(years) 0–4 20 10.6 0 0 7 21.2 1 7.7
5–19 6 3.2 1 2.3 2 6.1 0 0
20–49 42 22.3 11 25.0 4 12.1 3 23.1
50–74 89 47.3 23 52.3 18 54.5 4 30.8
75+ 31 16.5 9 20.4 2 6.1 5 38.5
Sex n 73 35.8 19 41.3 11 31.4 4 28.6
Male 37 50.7 8 42.1 6 54.5 3 75.0
Female 36 49.3 11 57.9 5 45.5 1 25.0
a
Onset n 188 92.2 41 89.1 32 91.4 13 92.9
Community Onset 110 58.5 40 97.6 28 87.5 8 61.5
Hospital Onset 78 41.5 1 2.4 4 12.5 5 38.5
Outcomeb n 126 61.8 37 84.1 22 62.6 9 64.3
Complete recovery 14 11.1 3 8.1 3 13.6
Improved 76 60.3 22 59.5 12 54.5 7 77.8
Not improved 23 18.3 7 18.9 6 27.3 1 11.1
Death 13 10.3 5 13.5 1 4.5 1 11.1
Site n 204 100 46 100 35 100 14 100
Nakhon Phanom 140 68.6 41 89.1 20 57.1 11 78.6
Sa Kaeo 64 31.4 5 10.9 15 42.9 3 21.4
a
Infection classified as community onset when positive cultures were obtained �48 hours after hospital admission; hospital onset after that.
b
Outcome data was derived from several variables in the surveillance database including “outcome”, “discharge status” and “discharge type”. If the patient was
discharged with consent, the classification was “improved”; discharged against advice was recorded as “not improved”.
https://doi.org/10.1371/journal.pntd.0007729.t001
confidence identification with 88.8% of reads (n = 960,230) being specific to L. hongkongensis.

CGE KmerFinder mapped 68.4% of Kmers to the strains HLHK9 and LHGZ1. The Taxono-
mer software classified 977,524/1,081,322 (90.4%) reads as bacterial, with 252,896 (34.1%)
being classified to L. hongkongensis HLHK9.
The One Codex database was the only program to call isolate SA27898 with high confi-
dence. It classified 81.4% of reads (n = 548,861) of which 79.6% (n = 436,853) were specific to
Moraxella atlantae, with an estimated depth of coverage at 45X. CGE KmerFinder identified
M. osloensis strain CCUG 350 with a query coverage of 3.1% to this template of 91,679 k-mers.
No other significant results were obtained. Taxonomer was able to classify 275,794/548,852
reads (50.2%) as bacterial, but of these only 16,909 (6.1% bacterial or 3.1% of the total number
of reads) were assigned to Enhydrobacter aerosaccus SK60. A further 8,505 reads were assigned
to other Moraxella species (S2 Tables). Of note 267,145 reads (48.7%) were unclassified in
Taxonomer.
NA45737 was identified by both One Codex and CGE KmerFinder as M. osloensis. In One
Codex the isolate was characterized as having no single dominant species and M. osloensis was
a medium abundant species (defined as 5–25% of classified reads) with 13.3% (n = 236,195)
of classified reads. Table 4 shows greater detail of the read classifications for NA45737 by phy-
logeny. At the genus level, even though Enhydrobacter has approximately 2.6X more reads
than the Moraxella, it was classified as the latter because no species was identified to the

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.
Enhydrobacter. Taxonomer binned 46.6% of reads as bacterial (1,773,485) and a larger propor-
tion as unclassified (2,003,568; (52.7%)), with 29.0% of the bacterial reads assigned to E. aero-
saccus SK60 (Fig 4).
SC13199 was an Acinetobacter species isolate as corroborated by all 3 programs. One Codex
counted 69.5% (1,386,621/ 1,994,443) classified reads as belonging to the Acinetobacter genus.
Table 2. Summary of patient antibiotic use prior to admission and time-to-positivity (TTP) for blood culture.
GNNF Phoenix ID Achromobacter spp. Pseudomonas spp. Moraxella
(n = 204) (n = 46) (n = 35) spp.
(n = 14)
n % n % n % n %
Antibiotic use within 72h admission n 183 89.7 44 95.6 31 88.6 12 85.7
(Self-report) Yes 133 71.1 26 59.1 23 74.2 10 83.3
No 31 16.9 12 27.3 7 22.6 2 16.7
Not sure 19 10.3 6 13.6 1 3.2 0 0
Blood culture TTP� n 176 86.3 44 95.6 31 86.1 12 65.7
(hours) � 24 25 14.2 1 2.3 4 12.9 3 25.0
25–48 86 48.9 22 50.0 17 54.8 4 33.3
> 48 65 36.9 21 47.7 11 35.5 5 41.7
�
TTP defined as the time from the start of blood culture bottle incubation to a positive signal.

Table 3. Summary of isolate identification using 3 different web-based platforms on whole genome sequencing short read raw data files.
ID CGE: KmerFinder Taxonomer One Codex
https://cge.cbs.dtu.dk/services/KmerFinder/ https://www.taxonomer.com/ https://onecodex.com/
NA45072 Acinetobacter nosocomialis Wohlfahrtiimonas chitiniclastica Wohlfahrtiimonas chitiniclastica1
NA45230 Genus: Acinetobacter Genus: Acinetobacter Genus: Acinetobacter
NA45737 Moraxella osloensis Genus: Enhydrobacter Genus: Enhydrobacter
NA48754 Delftia acidovorans Genus: Delftia Genus: Delftia
NA55273 Burkholderia cenocepacia Genus: Burkholderia Burkholderia cenocepacia1
NA62451 Roseomonas gilardii Family: Acetobacteraceae Roseomonas gilardii1
NA62784 Roseomonas gilardii Family: Acetobacteraceae Roseomonas gilardii
NA64181 Pseudomonas oryzihabitans Genus: Pseudomonas Genus: Pseudomonas
NA66303 Laribacter hongkongensis Laribacter hongkongensis Laribacter hongkongensis1
�
NA69389 Genus: Chryseobacterium Genus: Chryseobacterium Flavobacteriales bacterium
SA27898 Moraxella osloensis� Enhydrobacter aerosaccus Moraxella atlantae1
SC13199 Acinetobacter indicus Genus: Acinetobacter Acinetobacter indicus
�
Low confidence identification: only 1–5% of reads map to the designated organism.
1
One Codex high confidence identification. The sample contains >50% unique genomic content of that organism
The two highest abundance species were Acinetobacter sp. CIP 53.82 (365,836 reads (25.3%))
and A. indicus (349,113 (24.1%)). Taxonomer classified 61.7% of reads (n = 1,230,514) as bac-
terial with 15% (190,138/1,230,514) categorize as Acinetobacter with no dominant species.
CGE KmerFinder identified A. indicus with 58.8% query coverage. A second isolate, NA45230,
was identified as Acinetobacter with no speciation. One Codex classified 83.5% of reads of
which 97.8% were Acinetobacter. Taxonomer mapped 49% of reads and 51.4% were Acineto-
bacter species; CGE KmerFinder had 24.7% of reads mapped to 6 Acinetobacter species.
One Codex classified NA69389 as a mixed sample with 2 high abundance species from
69.7% of classified reads: Flavobacteriales bacterium (56.8% of classified reads, split between
strains UBA896 and UBA3891 (S2 Tables)) and Chryseobacterium hominis (18.1%). Taxono-
mer binned fewer reads as bacterial (56.8%) with 38.5% aligned to the family Flavobacteriaceae
and 17.0% associated with the genus Chrysobacterium. The dominant species being C. orani-
mense (2.2%) and C. gleum (1.1%). CGE KmerFinder identified C. indologenes and C. taklima-
kanense, each with very low template coverages (3.0% and 1.7% respectively; S2 Tables).
Table 4. Details of One Codex analysis of taxonomic grouping for classified reads from isolate NA45737� .
Rank Taxonomy of classified reads
(n = 1,772,214 (46.6%))
Phylum Proteobacteria
1,490,804 (84.1%)
Class Alphaproteobacteria Gammaproteobacteria
655,284 (37.0%) 296,499 (16.7%)
Order Rhodospirillales Pseudomonadales
655,069 (37.0%) 280,234 (15.8%)
Family Unclassified Rhodospirillales Moraxellaceae
654,543 (37.0%) 279,939 (15.8%)
Genus Enhydrobacter Moraxella
654,553 (37.0%) 249,022 (14.0%)
Species - osloensis
236,195 (13.3%)
�
Isolate NA45737 characterized as a mixed/metagenomics sample.

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).
Wohlfahrtiimonas chitiniclastica was the dominant species identified in NA45072 by both

Taxonomer and One Codex. The former classified 59% of reads as bacterial and 65.1% of
those as W. chitiniclastica. One Codex classified 77.2% of classified reads as W. chitiniclastica.
CGE KmerFinder was unable to identify a dominant organism.
Isolates NA48754, NA55273 and NA64181 showed consensus across the 3 programs at the
genus level: Delftia, Burkholderia (most likely B. cenocepacia) and Pseudomonas respectively
(S2 Tables).
The organism associated with isolates NA62451 and NA62784 was clearly identified as
Roseomonas gilardii by One Codex, with >80% of classified reads linked to R. gilardii (S2
Tables). In both isolates approximately 16% of reads mapped to R. mucosa. KmerFinder also
identified R. gilardii as the dominant species with high coverage of chromosome 1 and 2, how-
ever Taxonomer was not able to provide a confident identification– 33% of reads were classi-
fied to the class Alphaproteobacteria and 10% to the family Acetobacteraceae. Only about 2%
or reads were assigned to any particular species.
One Codex was the only program to identify high confidence calls–where the sample had
>50% of unique genomic content of that organism (Table 3), and 5/12 (42%) unknowns were
such.
Discussion
The correct and rapid identification of bacteria in a clinical microbiology laboratory, along
with antimicrobial sensitivity testing, is an essential step towards the correct treatment of
patients. Our analysis of 9 years of blood culture surveillance data shows that traditional diag-
nostic methodologies resulted in a positive identification in 93.5% of isolates (13,506/14,507).
A fair proportion of the unidentified isolates were GNNF bacteria which often present as col-
orless/pale colonies that lack key metabolic characteristics, which impairs their identification
by conventional methods. In 2015, the BD Phoenix automated identification and susceptibility
testing system was installed in our laboratory and we retrospectively ran 204 previously

unidentified GNNF isolates through the system. This allowed us to identify a further 91.7%
(187/204) of isolates to at least the genus level. Due to their taxonomic complexity and high
phenotypic similarity, identification to the species level represents a challenge even for the
automated systems. In the majority of cases isolates were ubiquitous environmental organisms
such as Achromobacter and Sphingomonas that are likely opportunistic pathogens [9–11].
There are an increasing number of case reports and reviews published suggesting a global
increase in achromobacterial disease [10, 12], even so most clinicians remain unclear to their
significance (and that of other environmental organisms) when clinically isolated. Addition-
ally, effective treatment can be challenging due to these organism’s intrinsic and acquired mul-
tidrug resistance patterns. Our surveillance populations were from rural, agrarian provinces so
infections with environmental organisms is not surprising in people who are occupationally
exposed. Organisms from over 30 genera were identified. Moraxella (14/204) and Pasteurella
(12/204) species are considered zoonotic pathogens and infection in humans is commonly
associated with animal bite, scratch or lick, but infection without epidemiologic evidence of
animal contact may occur [13].
Common skin and environmental organisms are often considered likely contaminants
[14], however it is difficult to decide if their presence represents a clinically important infection
or a false-positive result of no clinical consequence. In our study the contamination rate was
around 4.7% (Table 1) and throughout our study efforts were made to reduce contamination
through on-going training for nurses on percutaneous blood collection, use of chlorhexidine
and alcohol for sterilization of the venipuncture site in adults [15] and tincture of iodine in
children [16] using appropriate contact times, disinfection of culture bottle tops and single
needle use for bottle inoculation. TTP has been suggested as a marker of bacterial load and
used to distinguish bacteremia from contamination. Some studies suggest that cultures posi-
tive 3 to 5 days after incubation are more likely to represent contaminants [17, 18]. In our
study 63% of cultures were positive within 48 hours and 81.3% (143/176) within 72 hours; and
several other factors should be considered i.e. blood volume inoculated and antibiotic presence
which make TTP difficult to interpret. It is critical to recognize that these isolates may repre-
sent true bacteremias and if untreated due to misinterpretation as contaminants could result
in devastating consequences. Isolations of GNNF bacteria have long been disregarded as prob-
able contaminants, but have recently emerged as important healthcare associated pathogens
[19]. Our data however, suggests that few of these were nosocomial infections, but likely com-
munity-acquired, with many occurring in adults > 50 years old possibly associated with weak-
ened immune systems. Lack of data on the disease epidemiology is a great obstacle to improve
patient quality of care, which is further compounded by the lack of antimicrobial resistance
data. For example both Moraxella and Pasteurella species are not included in the Phoenix AST
database [8], either because of the low probability of occurrence or special growth require-
ments. With the significant increase in the incidence of multidrug-resistant pathogens in
recent years, the high resistance rates seen in several of the GNNF organisms is concerning.
Traditional diagnostic technologies are insufficient for the identification of organisms not
usually considered pathogenic. This limited scope has created bias in what we know about
infection and the microbes capable of causing human disease. Our study illustrates that a
small, but significant portion of ubiquitous environmental bacterial species can be true patho-
gens. In our laboratory, these limitations were overcome through the use of an automated
microbial identification system, the BD Phoenix.
There was an even smaller proportion of isolates not identified through this system, and we
attempted to use WGS to fill this gap. This is possible as small, affordable instrumentation such
as the Illumina iSeq or MiSeq, along with decreasing sequencing costs, make WGS an attractive
option for lower throughput clinical microbiology and public health laboratories. This does not

allow for comprehensive detection of pathogens from clinical samples (metagenomics analysis)
but expands on conventional diagnostic testing where it fails to detect the etiologic agent. Tar-
geted 16S rRNA NGS kits are recently available for specimen microbiome composition profiling
and offer a cost-effective method for bacterial taxonomic classification, similarly viral genera
can be targeted by combining VirCapSeq-VERT and unbiased NGS workflows [20, 21].
Our study illustrates the value of isolate WGS: 12 previously unidentified isolates by con-
ventional or automated methods were classified at least to the genus level. Of note, WGS also
allows for examination of antimicrobial resistance genotypes, but this was beyond the scope of
this study. The biggest challenge we faced with the introduction of NGS to our laboratory was
the data analyses. Laboratory scientists generally lack experience in NGS short-read sequence
bioinformatics and in low- or middle- income countries, laboratory computing resources are
often limited and unable to handle large data sets. Fortunately, there are rapid, user-friendly
web-based tools that can be applied without large investments in trained personnel or compu-
tational infrastructure. We chose three services accessible through personal computers with no
requirements for computational infrastructure on the user side. For both the Phoenix auto-
mated identification system and the NGS web-based tools, the bacterial identification is highly
dependent on the reference databases used. As long as there are similar microbes in the data-
base unknowns can be identified. We believe the differences in performance between the
selected platforms is a function of the reference database used. For bacterial identification Tax-
onomer uses 16S rRNA gene sequences from the Greengenes database and the bacterial subset
of UniRef50 [6]. The KmerFinder database is a monthly updated extraction from the National
Center for Biotechnology Information of whole bacterial genomes, and only contains genomes
that have registered taxonomy associated [22]. One Codex has a database collected from public
and private sources that is curated through manual and automated steps to remove low quality
or mislabeled records [7]. Sequence reference databases are heavily biased toward common
pathogens at the expense of environmental microbes and commensals. These biases, sequence
inaccuracies and incompleteness are challenges for moving the field forward. Databases should
include accurate annotations and high-quality reference sequences that provide a true diversity
of strains. Kirstahler et al. [23] found that 43% of bacterial reference genomes, particularly
incomplete ones, contained ambiguous sequences and removing these from databases reduced
the number of false positive hits. This supports the need for curated microbial genome data-
bases. Our results illustrate that reads derived from taxa that are absent from databases can
result in false-negative and false-positive classifications, especially at the genus and species
level. Different measures are provided in the outputs from the three programs and no parame-
ters have been clearly defined to qualify an identification. Consistent application of identifica-
tion parameters would help move this approach forward.
The advantages of web-based analysis for laboratories is obvious. The intensive computing
happens on a server located anywhere in the world, which reduces the necessity to invest in
expensive computers and bioinformatics resources, and lets scientists interact with their data
immediately and directly. Results are obtained rapidly and can be presented in a user-friendly
interface such as a sunburst chart (Fig 4) with a dynamic graphic view that presents corre-
sponding species’ proportion. The choice of an appropriate analytical tool is crucial and not
trivial. Results still require interpretation by a microbiologist, and an understanding of the
analytical limitations is essential, as is clearly evident in our data. Even with these limitations
NGS technology should be considered an essential supplement to culture-based methods, par-
ticularly where standard diagnostic tests consistently fail to identify the causative pathogen.
The development of targeted sequencing kits will enable organism identification from speci-
mens on lower throughput instruments such as the iSeq and MiSeq, a crucial step in any clini-
cal/public health microbiology investigation.

The commonest GNNF bacteria (Acinetobacter spp. and P. aeruginosa) are widely consid-
ered common nosocomial infections [24–26], and this was evident in the two Thai rural prov-
inces under surveillance here; Rhodes et al. [1] showed these were the second and sixth
commonest hospital onset infections respectively. In this same study they were the 7th and 10th
commonest pathogens causing community onset bloodstream infections. Our data also illus-
trates that the less commonly identified GNNF isolates are predominantly community onset
(Table 1). This is a concern as the phenotypic similarity and taxonomic complexity makes
these organisms frequently difficult to identify, and their clinical significance may be difficult
to determine as these organisms rarely cause invasive infections. From the literature we dis-
cover that L. hongkongensis causes non-bloody acute diarrhea with cases linked to eating fresh-
water fish [27]. It can cause invasive [28, 29] and even fatal disease [30], and has mainly been
described in East Asia [28, 31] but a worldwide distribution is suggested by case reports from
Europe [32] and North America [33]. The few reported cases of W. chitiniclastica infection
globally show septicemia and skin and soft tissue infections [34–36]. Numerous cases are doc-
umented in relation to maggot infestations and several affected were homeless [34, 37, 38].
Bacteremic cases have been reported from North and South America [34, 38], Europe [37], the
United Kingdom [39]. As with our unidentified isolates, Roseomonas species are considered
opportunistic pathogens because of their low pathogenic potential in humans, and R. gilardii is
most frequently related to human infections [40–42]. It is significantly associated with septice-
mia and underlying immunocompromised conditions [40, 43]. Several cases have been pre-
sented of catheter-related bacteremia with immunosuppression raising [41, 43] the possibility
that Roseomonas species may be a part of the normal skin flora of humans. Finally M. atlantae
infections are rarely reported in the literature and the few cases of bacteremia appear to all
have underlying conditions predisposing them to infection [44, 45]. These organisms are rare,
difficult to identify and are easily overlooked so it is likely that their occurrence is underesti-
mated. The clinical significance and appropriate therapy for patients with these bacteremias
are not well studied. This together with the emerging challenge of multi-drug resistance, is of
serious concern for treatment.
The use of WGS in public health or clinical microbiology laboratories would provide
unparalleled improvements in pathogen identification, antibiotic resistance detection, and
outbreak investigations, however, this capacity is severely hindered by lack of common stan-
dards. Regulatory agencies have not yet provided (or even proposed) standard guidelines, test-
ing has not been standardized, and benchmarks have not been set. External quality assurance
and proficiency testing programs are in progress. Analytical pipelines with well-curated, con-
tinually updated reference database are also important components to implementation. Once
these hurdles have been achieved the incorporation of NGS into clinical and public health rou-
tine workflows is achievable.
Disclaimers
Findings and conclusions presented in this paper represent the views of the authors and do
not necessarily official position of the U.S. Centers for Disease Control and Prevention or the
institutions with which the authors are affiliated.
Supporting information
S1 Tables. BD Phoenix summary data. Summary data extracted from the BD Phoenix auto-
mated identification system reports for each isolate run on the combination panel NMIC/ID
55.
(XLSX)

S2 Tables. WGS analysis summaries. Collated data from results generated by the 3 web-based
platforms: KmerFinder, Taxonomer and One Codex for the whole genome sequence data
from 12 unidentified isolates.
(XLSX)
Acknowledgments
We are grateful for the contributions of the many study collaborators, including Nakhon Pha-
nom and Sa Kaeo Provincial Health Offices, surveillance officers, and the district and provin-
cial hospital staff. The authors would like to acknowledge the contributions of TUC staff
Thantapat Akarachotpong and Apiwat Lapamnouysup for their continued support of our sur-
veillance, data and laboratory activities.
Author Contributions
Conceptualization: Toni Whistler.
Data curation: Toni Whistler, Ornuma Sangwichian, Possawat Jorakate.
Formal analysis: Toni Whistler.
Investigation: Chidchanok Promkong.
Methodology: Toni Whistler, Ornuma Sangwichian, Possawat Jorakate, Pongpun Sawatwong,
Uraiwan Surin.
Project administration: Barameht Piralam, Chidchanok Promkong.
Resources: Ornuma Sangwichian.
Supervision: Toni Whistler, Uraiwan Surin, Chidchanok Promkong, Leonard Peruski.
Validation: Ornuma Sangwichian.
Writing – original draft: Toni Whistler.
Writing – review & editing: Toni Whistler, Ornuma Sangwichian, Possawat Jorakate, Pong-
pun Sawatwong, Uraiwan Surin, Barameht Piralam, Somsak Thamthitiwat, Chidchanok
Promkong, Leonard Peruski.
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Identification of Gram Negative Non-Fermentative Bacteria: How Hard Can It Be?

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RESEARCH ARTICLE

Identification of Gram negative non-

Received: May 20, 2019

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Competing interests: The authors have declared

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BD Phoenix system procedures

Whole genome sequencing

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Downstream genome analysis

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confidence identification with 88.8% of reads (n = 960,230) being specific to L. hongkongensis.

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Wohlfahrtiimonas chitiniclastica was the dominant species identified in NA45072 by both

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