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R E S E A R C H Open Access
Members of the Candidate Phyla Radiation
are functionally differentiated by carbon-
and nitrogen-cycling capabilities
R. E. Danczak
1
, M. D. Johnston
2
, C. Kenah
3
, M. Slattery
3
, K. C. Wrighton
1
and M. J. Wilkins
1,2*
Abstract
Background: The Candidate Phyla Radiation (CPR) is a recently described expansion of the tree of life that represents
more than 15% of all bacterial diversity and potentially contains over 70 different phyla. Despite this broad phylogenetic
variation, these microorganisms appear to feature little functional diversity, with members generally characterized as
obligate fermenters. Additionally, much of the data describing CPR phyla has been generated from a limited number of
environments, constraining our knowledge of their functional roles and biogeographical distribution. To expand our
understanding of subsurface CPR microorganisms, we sampled four separate groundwater wells over 2 years across three
Ohio counties.
Results: Samples were analyzed using 16S rRNA gene amplicon and shotgun metagenomic sequencing. Amplicon results
indicated that CPR members comprised between 2 and 20% of the microbial communities with relative abundances stable
through time in Athens and Greene samples but dynamic in Licking groundwater. Shotgun metagenomic analyses
generated 71 putative CPR genomes, representing roughly 32 known phyla and 2 putative new phyla, Candidatus
Brownbacteria and Candidatus Hugbacteria. While these genomes largely mirrored metabolic characteristics of known
CPR members, some features were previously uncharacterized. For instance, nitrite reductase, encoded by nirK,was
found in four of our Parcubacteria genomes and multiple CPR genomes from other studies, indicating a potentially
undescribed role for these microorganisms in denitrification. Additionally, glycoside hydrolase (GH) family profiles for
our 71 genomes and over 2000 other CPR genomes were analyzed to characterize their carbon-processing potential.
Although common trends were present throughout the radiation, differences highlighted potential mechanisms that
could allow microorganisms across the CPR to occupy various subsurface niches. For example, members of the
Microgenomates superphylum appear to potentially degrade a wider range of carbon substrates than other CPR phyla.
Conclusions: CPR members are present across a range of environments and often constitute a significant fraction of
the microbial population in groundwater systems, particularly. Further sampling of such environments will resolve this
portion of the tree of life at finer taxonomic levels, which is essential to solidify functional differences between
members that populate this phylogenetically broad region of the tree of life.
Keywords: Candidate phyla, Groundwater, Subsurface, Carbon, Nitrogen
* Correspondence: wilkins.231@osu.edu
1
Department of Microbiology, The Ohio State University, Columbus, OH, USA
2
School of Earth Sciences, The Ohio State University, Columbus, OH, USA
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Danczak et al. Microbiome (2017) 5:112
DOI 10.1186/s40168-017-0331-1
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Background
The Candidate Phyla Radiation (CPR) is a recently
described expansion of the tree of life that lacked any
sequenced genomes until 2012 [1], with all prior know-
ledge derived from various marker gene studies [2, 3].
This absence of genomic information limited inferences
on the phylogenetic diversity and biogeochemical roles
of these organisms. Early work predicted that this radi-
ation represents roughly 15% of the bacterial domain [4].
More recently, extensive genomic sampling of this
radiation yielded over 2000 genomes, many of which
were closed, significantly expanding the phylogenetic
diversity within the radiation [4–6]. With this increased
metagenomic sampling, CPR membership has grown to
include over 70 phyla including two superphyla (Parcu-
bacteria and Microgenomates) [6, 7]. Despite this
radiation constituting a large portion of bacterial diver-
sity, the absence of cultured representatives means that
our current understanding is derived exclusively from
various omics datasets, with a large fraction derived
from a single field site near Rifle, CO. Since then,
additional genomes have been reconstructed from other
subsurface locations [8–10] and habitats [11, 12] around
the globe.
Based upon current data, there appear to be a number
of conserved traits throughout the entire CPR. Firstly,
despite representing a broad phylogenetic diversity, CPR
members are typically characterized by very limited
biosynthetic capabilities (i.e., the inability to produce
amino acids, lipids, some conserved genes, etc.) [4, 5].
Originally, these organisms were inferred to be obligate
fermenters based upon the complete absence of respira-
tory genes. Recently, however, CPR metabolic versatility
was expanded for members of the Parcubacteria with
new genomes that encode putative components of the
dissimilatory nitrate reduction to ammonia pathway
[12, 13] as well as potential hydroxylamine oxidation
[13]. Metaproteomic analyses also suggested that fermen-
tative CPR likely play a significant role in hydrogen and
carbon cycling in subsurface ecosystems [4, 14, 15].
Beyond metabolism, small genome size (~ 1 Mb) is
also generally conserved throughout the radiation [4].
Consistent with small genome sizes, high-resolution
cryo-TEM of the CPR revealed extremely small cell sizes
of 0.009 ± 0.002 mm
3
[16]. Taken together, phylogenetic,
metabolic, and cell biology inferences suggest that
members of the CPR may lead a symbiotic or syntrophic
lifestyle, dependent upon some partner (or partners) for
necessary metabolites while potentially providing labile
fermentation waste products such as acetate in return
[4, 5, 9, 11]. Such a co-dependent lifestyle could potentially
account for our current lack of cultured CPR members.
While these conclusions are well supported by existing
genomic datasets, samples from only a limited range of
subsurface locations account for nearly all the phylogen-
etic diversity within the CPR. To better resolve both the
biogeography of CPR members and uncover new phylo-
genetic diversity within this radiation, we performed
time-resolved metagenomic sampling on planktonic
biomass from four aquifers across southern Ohio (Fig. 1a).
From the resulting data, we were able to obtain 71 near-
complete or incomplete CPR genomes. Furthermore, we
profiled glycoside hydrolases encoded across over 2000
separate CPR genomes and identified a potentially new
role for many of these microorganisms in the nitrogen
cycle via a critical step in denitrification (conversion
ofnitritetonitricoxide).Thesedataexpandour
current understanding of the phylogeny of the CPR
and suggest that these microorganisms are cosmopol-
itan in subsurface environments. Moreover, our re-
sults advance knowledge of functional differentiation
across the radiation, with new roles in nitrogen and
carbon cycling highlighted here.
Methods
Sample collection
Groundwater samples were collected from the Ohio
Department of Natural Resources (ODNR) Observation
Well Network used to monitor ground water level fluc-
tuations in three different counties (Fig. 1a). These three
wells are located within separate buried valley aquifers,
valleys that have been back filled with glacial sands and
gravels and some till. The observation wells were
sampled on a quarterly basis over a 2-year period from
July 2014 to July 2016. Additionally, one private drinking
water well, located in a sand and gravel aquifer within a
thick till sequence in western Licking County (Fig. 1a),
was sampled once in June 2016.
Wells were purged of more than 250 L of water to
ensure that aquifer-derived water was being sampled
(dedicated pumps were placed at the screened interval
for the ODNR wells). Approximately 38 L of post-purge
groundwater was pumped sequentially through a 0.2-μm
then 0.1-μm Supor PES Membrane Filter (Pall Corpor-
ation, NY, USA). The filters were then immediately flash
frozen and kept on dry ice until they were returned to the
Ohio State University where they were stored at −80 °C
until DNA extraction. During sampling, oxidation-
reduction potential (ORP), temperature, and pH were
measured using a handheld Myron Ultrameter II (Myron
L Company, CA, USA). General observational data for
each of these groundwater wells is provided in Additional
file 1: Table S1.
DNA extraction, sequencing, and processing
DNA was extracted from roughly a quarter of each Supor
PES membrane filter by using the PowerSoil DNA Isola-
tion Kit (Mo Bio Laboratories, Inc., Carlsbad, CA, USA).
Danczak et al. Microbiome (2017) 5:112 Page 2 of 14
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Final DNA concentrations were determined by using a
Qubit fluorometer (Invitrogen, Carlsbad, CA, USA).
To generate 16S rRNA gene data, the V4 region of 16S
rRNA genes was amplified and sequenced by using the
universal bacterial/archaeal primer set 515F/806R on an
Illumina MiSeq instrument at Argonne National Labora-
tory. The resulting reads were processed through the
QIIME pipeline (V1.7.0) and clustered into operational
taxonomic unit (OTU) classifications at 97% similarities.
Taxonomies were assigned using the SILVA database as
well as a CPR 16S rRNA gene database curated by
Brown et al. [4].
Metagenomic data for nine samples (0.2-μm filters from
July 2014, October 2014, and April 2016 for Greene and
Athens, October 2014 for Licking, July 2016 for the Lick-
ing Private Well and then a 0.1-μm filter from October
2014 for Greene) was collected by shotgun sequencing on
an Illumina HiSeq 2500 at the Genomics Shared Resource
at the Ohio State University. Raw reads were trimmed and
filtered based upon read quality using sickle pe with de-
fault parameters [17]. Resulting reads were subsequently
assembled into larger contigs and then scaffolds using
idba_ud with default parameters [18].
The data generated during 16S rRNA gene sequencings
can be obtained from NCBI using accession number
SRX2896383. The 71 genomes and annotated protein se-
quences are publicly hosted at http://chimera.asc.ohio-sta-
te.edu/Danczak_Genomes_and_Protein_sequences.html.
16S rRNA gene sequencing analysis
In order to obtain results specific to this radiation, non-
CPR taxonomies were first removed from the OTU
table. A stacked bar chart was generated in R v3.3.2 [19]
using ggplot2 to visualize differential relative abundances
(ggplot, ggplot2 package v2.2.1) [20]. Beta diversity was
calculated using Bray-Curtis dissimilarity [21] and
subsequently plotted using non-metric multidimensional
scaling (NMDS) to observe CPR community differences
(metaMDS, vegan package v2.4-2) [22]. Species scores
were then plotted as loading arrows in order to under-
stand which members were responsible for the observed
separation.
Metagenomic binning and annotation
In order to determine average coverage across a scaffold,
trimmed reads were mapped to assembled scaffolds
using bowtie2 (bowtie2 –fast) [23]. Using the read-
mapping information, assembled scaffolds ≥2500 bp
were binned using MetaBAT (metabat –superspecific)
[24]. Genome completeness in each bin was determined
by analyzing the presence of 31 conserved bacterial
genes using AMPHORA2 [25]. Given that these
conserved genes are considered single copy, genomes
with multiple copies were considered to be contami-
nated (or “misbinned”). Bins with a genome completion
> 50% and a misbin < 10% were used in downstream
analyses. The open reading frames (ORFs) for the nine
ba
Fig. 1 Map of Ohio and CPR relative abundance through time. aA map of Ohio with studied colors indicated by various colors; colored circles
represent the approximate location of the Ohio Department of Natural Resources (ODNR) sampling wells and one private well. Columbus, OH, is
indicated as the yellow dot for reference. bStacked bar chart of CPR phyla relative abundances from the three ODNR sampling locations (Athens,
Greene, and Licking). Oxidation-reduction potential (ORP) is plotted atop the abundance graphs as line graphs
Danczak et al. Microbiome (2017) 5:112 Page 3 of 14
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entire metagenomes and resulting bins were predicted
using MetaProdigal [26] and subsequently annotated
by comparing predicted ORFs to the KEGG,
UniRef90, and InterProScan databases [27–31] using
USEARCH to scan for single and reverse best hit
(RBH) results [32]. The KEGG Automatic Annotation
Server (KAAS) [33] was also used to visualize path-
way completeness.
The presence of different glycoside hydrolase (GH)
families was determined in both the above bins and
2155 previously studied genomes obtained from
ggkbase [4, 6] by using the dbCAN hidden Markov
model (HMM) (hmmsearch –cpu 4 –tblout
result.hres –noali -E 0.00001 -o result_hmm.txt
dbCAN-fam-HMMs.hmm input.faa)[34–36]. Using
the hmmsearch output, GH family gene counts were
obtained for each genome by selecting the HMM results
with the lowest e-values. Differences in GH profiles for
both bins and genomes were visualized in R using both a
presence/absence heatmap (ggplot, ggplot2 package
v2.2.1) [20] and a redundancy analysis (rda, vegan package
v2.4-2) [22] with bins from this study collapsed into their
putative phylogenies. Given that putative nirK function in
Parcubacteria is newly proposed, a MUSCLE alignment
[37] of nirK sequences from this study, from previously
obtained Parcubacteria, and from other bacteria was
generated using default parameters to observe the pres-
ence/absence of conserved residues [38]. The nirK
sequences not from this study were obtained from NCBI
and represent diverse taxonomies capable of nitrite reduc-
tion. These alignments were then trimmed in Geneious
9.1.5 [39] to remove unaligned ends and portions with >
95% gaps. A RAxML tree with 100 bootstraps was then
generated to determine evolutionary relatedness and func-
tional potential (raxmlHPC-PTHREADS –fa–mPROT-
CAT –x 12345 –p 12345 –N100–T20–sinput–n
output) [40] and visualized in R using ggtree (ggtree,
ggtree package v1.6.9) [41].
Metagenomic abundance calculations
The rough microbial diversity within the nine metage-
nomic samples was determined using an approach
modified from Anantharaman et al. [6]. First, the
ribosomal protein S3 (rps3 or rpsC)waspulledfrom
each sample using an HMM derived from AM-
PHORA2 [25]. Ribosomal protein S3 sequences for
each metagenome were subsequently clustered to-
gether at 99% using USEARCH [32]. Reads were then
mapped to the resulting rps3 clusters using Bowtie2,
and coverage was subsequently determined as a proxy
for abundance. Taxonomic assignments for each rps3
cluster were obtained by comparing them against a
database of rps3 proteins derived from Hug et al. [7]
using blastp with an e-value cutoff of 10
−15
.
Phylogenetic analysis for bins
Given the inconsistent presence of various phylogenetic
marker genes in different bins, numerous genes were
analyzed to approximate phylogeny. The 16S rRNA gene
was extracted from bins using SSU-Align with default
parameters [42] and then aligned to two separate 16S
rRNA gene databases [4, 6] using SINA on the SILVA
website [43, 44]. The sequence identity to all genes
within the SILVA NR99 database was also determined
for potential phyla proposition [6]. Unaligned ends and
regions with 95% or greater gaps were then trimmed using
Geneious 9.1.5 [39] with a RAxML tree (100
bootstraps) subsequently generated (raxmlHPC-
PTHREADS –fa–m GTRGAMMA –x 12345 –p 12345 –
N100–T20–sinput–noutput)[40]andvisualizedusing
the ggtree package in R (ggtree, ggtree package v1.6.9) [41].
Phylogeny was further identified by building rps3 and
gyrA trees following the protocols outlined above for
nirK (i.e., pulled sequences using HMMs, aligned in
MUSCLE and generated a tree using RAxML). For
high-resolution phylogeny of 45 bins, a concatenated
ribosomal tree was generated from 16 ribosomal
proteins (rpL2,3,4,5,6,14,15,16,18,22,24,rps3,8,
10,17,19). First, each ribosomal protein sequence was
pulled from the bins using the appropriate HMM either
derived from AMPHORA2 or TIGRFAM release 15.0
in hmmsearch [25, 34, 45]. Alignments were then
performed independently for each sequence using
MUSCLE with default parameters [37]. Completed
alignments were concatenated using Geneious 9.1.5
and trimmed to remove unaligned ends and portions
with 95% gaps or greater [39]. RAxML was then used
inordertogenerateaphylogenetictreewith100
bootstraps with the same command given above,
again visualized in R with ggtree (ggtree, ggtree pack-
age v1.6.9) [40, 41].
New phyla were proposed based upon three separate
parameters. Firstly, if the 16S rRNA gene sequence was
less than 75% similar to previously described sequences
and at least two new genomes formed a monophyletic
clade within the concatenated ribosomal tree, a new
phylum was proposed [6, 46]. However, given high
phylogenetic variability within the CPR, if a very obvi-
ous monophyletic clade was observed on the
concatenated tree, up to roughly 80% similarity was
tolerated. Following naming conventions for the CPR,
proposed names for the phyla were based upon re-
searchers who have made significant contributions to
our understanding of the diversity and physiology of
CPR microorganisms [6].
All alignments generated during the phylogenetic ana-
lysis (i.e., rps3,gyrA, 16S rRNA, and concatenated ribo-
somal proteins) can be found in the supplemental
information (Additional files 2, 3, 4, and 5).
Danczak et al. Microbiome (2017) 5:112 Page 4 of 14
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Results
CPR community member abundances are stable through
time
Quarterly groundwater samples were collected from four
wells across central and southern Ohio in Licking,
Athens, and Greene counties over the course of 2 years
(Fig. 1a). Field measurements revealed varying redox
conditions across these locations (Fig. 1b). CPR mem-
bers constituted modest portions of the overall microbial
community of each well, as inferred from 16S rRNA
gene analyses (Fig. 1b). Relative abundances ranged be-
tween 4 and 5% in Athens groundwater, between 1 and
4% in Greene groundwater, and between 5 and 20% in
Licking groundwater. Moreover, the relative abundance
of CPR was temporally stable in both the Athens and
Greene samples but more dynamic in Licking where
members of the CPR represented an increasing portion
of the overall community over the sampling period.
Multivariate ordination analyses of CPR community
members revealed that each location contained signifi-
cantly different CPR phyla (Additional file 6: Figure S1).
For example, Peregrinibacteria were almost entirely
unique to the Greene location (Fig. 1b), while a broad
diversity of phyla differentiated this well from the
Athens and Licking locations. Notably, groundwater
samples from the Greene location, which had the lowest
relative abundance but greatest diversity of CPR, were
the most oxidized (Fig. 1b). In contrast, the more re-
duced groundwater contained greater CPR-relative
abundances and shared more similar CPR membership
(Additional file 6: Figure S1).
To better profile the phylogeny and physiology of
these CPR lineages, we used assembly-based shotgun
metagenomic analyses of microbial communities in nine
of these samples. Briefly, between 300 and 900 Mbp of
sequence information was generated after assembly
(Additional file 1: Table S2). The relative abundances for
the CPR in each metagenomic sample were calculated
using sequencing read-depth for all ribosomal protein S3
(rps3) sequences (Fig. 2a). Although some differences
were observed between metagenomic and 16S rRNA
gene amplicon-inferred relative abundances, broad
patterns between samples were generally maintained.
For example, the Peregrinibacteria were the most
abundant CPR taxa in samples from the Greene well,
while the Microgenomates had pronounced representa-
tion in the Athens well samples.
Samples contain significant CPR phylogenetic diversity
Sizes of the genomes obtained during the binning
process ranged from 0.5 to 1.3 Mbp, in line with previ-
ous characterizations of the radiation [4]. Completeness
measurements, phylogeny, and putative function were
determined to identify and separate putative CPR bins.
Any bins less than 50% complete based upon single copy
gene presence, or containing greater than 10% misbin
based upon multiple copies of single copy genes, were
excluded from future analysis. In total, 71 putative CPR
bins were obtained across all samples. Of these 71 bins,
45 bins were considered “near complete”based on com-
pletion greater than 90%; the 26 remaining bins were
deemed “incomplete”(Fig. 2b). Taxonomy was initially
assigned to each bin using the rps3 marker gene and at
least one other single copy phylogenetic marker (16S
rRNA gene or gyrA) (Additional file 6: Figures S2–S4;
Additional files 7, 8 and 9). For increased phylogenetic
resolution, a 16-protein concatenated ribosomal tree was
also constructed that included a mix of 45 near-
complete and incomplete genomes (63%) from this study
(Fig. 3). A brief summary of the 71 bins is provided in
Additional file 1: Table S3.
Genomes obtained from this study occupied a broad
phylogenetic diversity with many different CPR phyla
sampled. Though most bins were from the Parcubacteria
superphylum, Berkelbacteria were the best-represented
individual phylum with six bins total. Many of the
Parcubacteria bins were from recently characterized
phyla [6], with Kaiserbacteria (five bins), Harrisonbac-
teria (four bins), Lloydbacteria (four bins), and Taylor-
bacteria (four bins) being the most represented. At these
high taxonomic levels (i.e., phyla), many of the spatial
differences between wells inferred from 16S rRNA
amplicon data were not recapitulated in the metage-
nomic data (Fig. 2b).
From the placement of these new genomes in the
context of the CPR, we identified two new phylum-level
groups within the Parcubacteria superphylum. Following
naming conventions previously established for this radi-
ation, we propose that six of our bins belong to two new
phylum-level lineages, Candidatus Brownbacteria and
Candidatus Hugbacteria. These names were chosen due
to the significant contributions that Christopher Brown
and Laura Hug have made to our understanding of
CPR physiology and phylogeny [4, 7]. Candidatus
Brownbacteria is defined by four separate bins from
this study and five previously described Uhrbacteria
or unaffiliated members that now form a well-
supported, early branching, monophyletic clade in the
concatenated tree (Fig. 3). Candidatus Hugbacteria,in
contrast, represents a new clade most closely related
to Adlerbacteria, represented by genomes sampled
only within this study (Fig. 3; Additional file 6: Figure
S5; Additional file 10). Aside from these two candi-
date phyla, a number of other deep-branching and
unique lineages at lower phylogenetic levels were also
recovered here (Additional file 6: Figures S2–S5),
suggesting that additional CPR diversity remains to be
discovered.
Danczak et al. Microbiome (2017) 5:112 Page 5 of 14
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The CPR potentially catalyze diverse reactions in carbon
and nitrogen cycling
The functional annotation of each CPR bin in our
dataset indicated that our CPR genomes shared meta-
bolic features with prior descriptions [1, 4–6]. Mirroring
these trends, our CPR genomes appeared to be incapable
of synthesizing most of their own amino acids, lacked
lipopolysaccharide synthesis pathways, relied upon EMP
glycolysis, and lacked a complete tricarboxylic acid cycle.
Interestingly, genes encoding the pentose phosphate
pathway were present and near-complete in many
members of the CPR described here, despite lacking
downstream biosynthetic pathways or energy-capturing
mechanisms. Given that 34 out of 45 of the near-
complete bins were members of the Parcubacteria, the
metabolisms of these 34 genomes were summarized to
illustrate the potential functional capabilities of this
superphylum in the samples studied here (Fig. 4).
Additionally, while many prior studies concluded that
NiFe-hydrogenases were important to the physiology of
some CPR members [14], there appears to be little
evidence for these genes within the genomes recovered
from Ohio aquifers.
While many of the CPR lineages were inferred to lack
electron transport mechanisms, the putative capability
for nitrite reduction (nirK) was found in four separate
Parcubacteria bins from two different phyla, Kaiserbac-
teria and Harrisonbacteria (Fig. 5). This copper-containing
nitrite reductase is responsible for reducing nitrite to
nitric oxide, an important step in denitrification. Although
nirK genes from CPR genomes are present in public data-
bases, these sequences have not been previously analyzed
in a phylogenetic or metabolic context [6, 9]. Sequence
alignments using both CPR nirK and nirK sequences from
a
b
Fig. 2 CPR relative abundance derived from metagenomic rps3 sequences and bin counts. aRelative percentage of mapped rps3 sequence reads
from a given metagenome presented as a stacked bar chart; Inset: a pie chart illustrating the relative percentage of mapped reads for the 0.1-μm
filter from Greene 10/2014. bA count of the number of near-complete quality (NC; > 90% complete; solid colors) and incomplete (IC; 50–89%
complete; dashed colors) bins from each metagenomic sample (71 total)
Danczak et al. Microbiome (2017) 5:112 Page 6 of 14
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well-characterized nitrite-reducing microorganisms
revealed that the type 1 and type 2 Cu ligand binding sites
were conserved (Additional file 6: Figure S6; Additional
file 11) [38, 47, 48]. CPR nirK sequences formed an
individual clade, suggesting that potential nitrite reduction
is not a recently acquired function of these CPR genomes
(Fig. 5; Additional file 6: Figure S7; Additional file 12).
Adjacent to this nirK clade, the closest non-CPR sequences
in the tree were from known nitrogen-cycling organisms
such as Candidatus Brocadia and Rhodanobacter.
For nirK to catalyze nitrite reduction, the enzyme
must receive electrons from cellular metabolism. Each of
the nirK-containing CPR genome bins contained a puta-
tive cupredoxin protein, a class of proteins previously
shown to pass electrons onto nirK specifically [47].
These bins also contained a potential means of removing
electrons from reducing equivalents within the cell: the
two Harrisonbacteria encoded an NADH dehydrogenase
II-like (NDH II-like) gene whereas the two Kaiserbac-
teria contained genes for a putative membrane-bound,
six-subunit Nqo-like/hydrogenase similar to sequences
previously reported [1]. This latter gene was originally
annotated as a group 4 NiFe-hydrogenase, but upon
closer inspection, the near-complete two Kaiserbacteria
genome bins did not contain maturation enzymes.
Moreover, while the main subunit did not contain
Ni-binding sites, the presence of NADH-binding sites
suggests that NADH may act as an electron donor to
this complex, unsurprising given the evolutionary rela-
tionship of these subunits to NADH dehydrogenase [49].
Fig. 3 Sixteen-protein concatenated ribosomal tree. Concatenated ribosomal protein tree generated from 16 ribosomal proteins (rpL2,3,4,5,6,
14,15,16,18,22,24,rpS3,8,10,17,19). Each of the major CPR classifications and phyla are provided as text. Each point represents a binned
genome from a given location in Ohio, indicated by point shape and color. Newly proposed phyla are indicated by blue text
Danczak et al. Microbiome (2017) 5:112 Page 7 of 14
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This six-subunit enzyme contains four putative proton-
translocating, transmembrane domains that may offer a
mechanism via which electrons are transported across
the inner membrane to a periplasmic cupredoxin which
could ultimately shuttle electrons to nirK (Fig. 4).
Within the Harrisonbacteria, the NDH-like gene may
function similarly to our proposed Nqo-like model,
although this gene does not contain transmembrane
domains. The linked electron transport components and
the potential for nitrite reduction represent previously
undescribed functions within this radiation.
The CPR have often been broadly characterized as a
group of organisms responsible for generating fermenta-
tion products through the degradation of more complex
carbon substrates [4, 15]. To better understand this
potential role, over 2000 previously described genomes
and the 71 genomes from this study were analyzed for
the presence and count of 135 different glycoside hydro-
lase (GH) families (Fig. 6; Additional file 1: Table S4).
Across all CPR phyla, while the capacity to degrade
broad substrate classes (e.g., amylose, cellulose) was rela-
tively similar, the GH genes encoding these capacities
Fig. 4 Summary of Parcubacteria genomes obtained from Ohio groundwater. A metabolic map summarizing the genomic potential of 34
separate Parcubacteria bins. Arrow thickness represents the frequency a given gene occurs throughout the 34 genomes. Numbers adjacent to
the arrows correspond to the gene represented by the arrow; the legend can be found in the supplemental information. Functions within the
box were not broadly found throughout Ohio and indicate newly described functions. Inset within the metabolic map are the frequencies (as pie
charts) of 12 of the most common glycoside hydrolase families. Colors in the pie chart represent the primary activity of the given GH family:
green typically operates on cellulolytic material, brown is active on alpha-glycosides/starches, purple operates on peptidoglycan/chitin, and red
represents GH families that operate on other carbon substrates
Danczak et al. Microbiome (2017) 5:112 Page 8 of 14
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differed. While nearly all CPR genomes encoded en-
zymes for hemicellulose side-chain degradation, the
exact GH genes responsible differed between phyla. For
example, the Uhrbacteria contain a broad cassette of
different GH families (GH2, GH3, GH10, etc.) while
many other Parcubacteria primarily encode only GH39
and GH74. Additionally, many phyla within the Micro-
genomates appear to contain a wider array of GH family
sequences than other CPR members, indicating a
broader role for these organisms in carbon degradation
(Fig. 6). Certain phyla within the CPR were exceptions
however; KAZAN and Niyogibacteria lack the capacity
for amylose degradation, and CPR2 lack the capacity for
mannose degradation. Through the sampling of CPR
genomes from a new geographic location (Ohio ground-
water), additional functional capacity was uncovered. For
instance, our data revealed the presence of genes
encoding GH1 enzymes within the Harrisonbacteria,
representing a potential metabolic expansion of this
phylum into cellulose degradation (Fig. 6).
RDA analyses of GH profiles for every genome (based
on gene abundance) indicated that the capacity for
carbon degradation within the CPR is complex
(Additional file 6: Figures S8–S11). Within the Parcubac-
teria, for example, Lloydbacteria and Yonathbacteria ap-
pear to be differentiated primarily due to their putative
amylose degradation capacity (i.e., GH15 and 119) but
they localize near to Niyogibacteria and Azambacteria
(devoid of amylose-active GH families). This is likely a
result of the shared presence of the chitin-active GH18
(Additional file 6: Figure S8). Outside of the Parcubac-
teria, similar trends can be observed. While genes en-
coding chitin-active GH23 enzymes are present in both
the Doudnabacteria and Berkelbacteria, the presence of
additional cellulolytic GH families in the former repre-
sent a key difference in carbon-processing potential in
Fig. 5 nirK tree. A maximum-likelihood tree of nirK (nitrite reductase) amino acid sequences with bootstrap support indicated by the shaded circles on
the nodes
Danczak et al. Microbiome (2017) 5:112 Page 9 of 14
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
this phylum (Additional file 6: Figure S9). Additionally,
GH profiles for many of these CPR phyla occupy a large
ordination space (Additional file 6: Figures S10–S11).
Taken together with the general trends outlined above,
this suggests metabolic heterogeneity at finer taxonomic
resolution.
Discussion
The CPR is a recently described expansion of the tree of
life that continues to grow as metagenomic analyses are
performed from different ecosystems [1, 4, 7, 10–13, 50].
Despite their diminutive size, members of this radiation
are often abundant members of the microbial commu-
nity in subsurface environments [51, 52]. This trend is
apparent in the samples studied here, with members of
the CPR constituting up to 20% of the overall commu-
nity (Fig. 1b), although these abundances may be under-
estimates due to limitations of broad specificity 16S
rRNA gene primers [4]. An additional factor contribut-
ing to underestimates of CPR abundances is the
common utilization of 0.2-μm filters to retain microbial
biomass when filtering aqueous media. Due to small cell
sizes, CPR microorganisms can easily pass through such
membranes and are only retained when the filter is more
clogged or when a 0.1-μm pore size is used instead [16].
Many of the OTUs obtained during 16S rRNA gene
amplicon sequencing that were present exclusively on
the 0.1 μm were members of the CPR, primarily in the
Fig. 6 Glycoside hydrolase (GH) heat map. A heat map illustrating the presence/absence of various different GH families with putative substrates
illustrated by color.
Danczak et al. Microbiome (2017) 5:112 Page 10 of 14
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Athens and Greene locations, mirroring a trend ob-
served in previous studies [16]. Additionally, many of
our CPR genomes (~ 34) were assembled from a 0.1-μm
filter. Although our sampling locations were all ground-
water aquifers, geochemical differences (displayed as
ORP values) were apparent between sites (Fig. 1b), and
these differences were reflected in 16S rRNA gene
datasets (Additional file 6: Figure S1). Samples from the
Greene location were consistently the most oxidized and
contained the most different population of CPR bacteria
relative to samples from the Athens and Licking wells,
suggesting that as-yet unknown functional differences
between CPR members may account for heterogeneity
in the spatial distribution of different taxa.
The CPR represents a large portion of total bacterial
phylogenetic diversity [6, 7]; in addition to capturing
much of this diversity in this survey, we were also able
to expand the CPR through detection of two new phyla,
Candidatus Brownbacteria and Candidatus Hugbacteria
(Fig. 3). Given that these putative new phyla were found
from 71 genomes derived from one ecosystem type (sat-
urated subsurface), our results suggest that continued
studies across a range of environments will reveal
additional diversity within this radiation. Such discovery
is necessary; the spatiotemporal trends we observed
within our 16S rRNA gene amplicon data were not al-
ways recapitulated in binned genomes, likely due to the
wide phylogenetic breadth (but not depth) that our
library of CPR genomes currently accounts for. This
results in a situation whereby we can classify genomes at
the phyla level, but not at lower taxonomic levels (i.e.,
class, order). As more CPR genomes are available for
analysis, these lower taxonomic levels will resolve,
resulting in an improved ability to link genome bins with
environmental niches and observe temporal and spatial
trends in microorganism abundance.
Despite the CPR containing over 70 different phyla, pre-
vious work indicates that there is limited functional diver-
sity within the radiation [4, 14], although some recent
studies have described additional processes catalyzed by
CPR members [12, 13]. The 71 CPR genomes described in
this study span roughly 32 phyla and largely support these
previous conclusions at such a broad phylogenetic scale.
At the genome level, however, functional differences
between organisms are more apparent. Conserved traits
across the Parcubacteria superphylum include a near-
complete pentose phosphate pathway and consistent
patterns of genes involved in glycolysis/gluconeogenesis
(Fig. 4). Differentiating characteristics include the inferred
production of various fermentation end products, ranging
from D-lactate in some organisms to ethanol or acetate in
others. Furthermore, while some genomes encode partial
non-reductive and reductive TCA cycles, no individual
genome has every necessary component.
While the potential for these microorganisms to de-
grade more recalcitrant carbon compounds into labile
substrates is relatively well appreciated, little in-depth
analysis has been performed on their specific capabilities
[15]. To better characterize the carbon-processing
potential encoded with the CPR, we analyzed the glyco-
side hydrolase profiles of our 71 genomes and ~ 2000
previously obtained genomes. By looking at the differences
across these profiles, our analyses revealed functional dif-
ferentiation with regard to carbon processing. Despite
substrate redundancy among many of these families of
enzymes, variations exist in the bonds these proteins may
act upon (i.e., endo- versus exo-acting enzymes). We
suggest that these differences in carbon processing enable
these microorganisms to access a wide range of carbon
substrates and respond to fluctuating geochemical condi-
tions that might influence carbon type and availability.
Supporting this inference, prior studies have revealed
dynamic behavior of different CPR phyla in response to
varying hydrologic and geochemical conditions in a ripar-
ian aquifer [51].
A copper-containing nitrite reductase encoded by nirK
was found in 4 of the 34 high-resolution genomes,
representing an undescribed function within the Parcu-
bacteria that highlights a potential role for these
microorganisms in denitrification. Similarly, recent
studies implicated other members of the Parcubacteria
in the nitrogen cycle [12, 13], although these functions
were within novel genomes rather than widespread
throughout the superphyla. Despite being previously
undescribed in this lineage, nirK not only appears to be
widely dispersed throughout the Parcubacteria and po-
tentially other CPR groups [6, 9] but also appears to be
distinct from previously described sequences, suggesting
that these genes were not obtained by recent horizontal
gene transfer (Fig. 5). Even though the CPR nirK
sequences form a distinct clade, all of the residues ne-
cessary for function were conserved. Furthermore, there
was a potential path for electrons to be transferred from
a putative NDH or NDH-like protein to a cupredoxin by
an unknown means and lastly to the reductase. Overall,
these results suggest that some members of the CPR
may play a previously unknown role in nitrogen cycling
in the subsurface, reducing nitrite to NO, which is
subsequently available for utilization by microorganisms
with additional reductive machinery.
Given that these organisms lack typical energy conser-
vation mechanisms, the potential role of nirK within
these cells is questionable. This gene may function as
means of protection against nitrite in the environment,
given its toxicity [53]. At the single organism level, this
mechanism is unlikely, given that nitric oxide, the result
of nirK activity, is potentially more inhibitory than ni-
trite [54]. Such a role for nirK could be more viable in a
Danczak et al. Microbiome (2017) 5:112 Page 11 of 14
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
symbiotic relationship between the Parcubacteria and a
microorganism capable of reducing nitric oxide, thus re-
moving it from the local environment. Such relation-
ships have previously been hypothesized as a mechanism
for members of the CPR to obtain critical amino acids
and other nutrients that are not encoded within their ge-
nomes [5, 9]. An alternative role for nirK is for capturing
energy through the formation of a proton motive force
(PMF) [55]. This function may be possible for the two
Kaiserbacteria in this study given that they encode a pu-
tative proton-translocating hydrogenase and an ATP
synthetase. However, the two Harrisonbacteria described
here lack a proton-translocation mechanism and there-
fore could not generate a PMF from nitrite reduction. In
summary, while some members might be able to capture
energy by nitrite reduction, this is clearly not a universal
function in the superphylum and cannot explain the broad
distribution of the nirK gene. However, these results do
highlight a potentially novel function within a metabolic-
ally limited lineage.
Conclusions
The Candidate Phyla Radiation (CPR) represents a re-
cently discovered portion of the tree of life that spans a
large phylogenetic space that until a few years ago was
known only from marker gene studies [2, 3]. As the radi-
ation grows and we are able to populate phyla with rep-
resentative genomes, we increase our understanding of
different functional roles for CPR microorganisms and
the environmental niches that they might occupy. Such
efforts are critical for accurately placing CPR member
functions in subsurface biogeochemical networks. While
Wrighton et al. [14] linked the fermentative activity of
CPR bacteria with respiratory microorganisms, the ana-
lyses presented here (coupled with increased future sam-
pling of CPR genomes) enable us to better determine
how the CPR members interface with their local envir-
onment through the processing of varied complex car-
bon substrates. Additionally, recent studies (including
this one) have suggested new roles for these microorgan-
isms in nitrogen cycling in the environment. Through
further sampling of subsurface systems, the opportunity
exists to both improve the phylogeny of the CPR and
better understand their biogeochemical role in complex
environmental systems. The seeming ubiquity of these
microorganisms suggests that they are key members of
communities that drive critical elemental cycles across
the globe in a variety of subsurface environments.
Additional files
Additional file 1: Table S1. Table of field measurements taken during well
sampling. Table S2. Summary of metagenomic data. Table S3.Summaryof
bins derived from metagenomic data. Table S4. Glycoside hydrolase (GH)
profiles for over 2000 CPR genomes. Values indicate the number of positive
hits per GH family found within a given genome during H MM alignment.
Table S5. Legend used to identify the genes in Figure 4. (XLSX 934 kb)
Additional file 2: Trimmed MUSCLE alignment of rps3 sequences.
(AFA 911 kb)
Additional file 3: Trimmed SINA alignment of 16S rRNA sequences.
(AFA 841 kb)
Additional file 4: Trimmed MUSCLE alignment of gyrA sequences.
(AFA 1832 kb)
Additional file 5: Trimmed MUSCLE alignment of 16 concatenated
ribosomal proteins. (AFA 1964 kb)
Additional file 6: Figure S1-S11. (PDF 20.0 mb)
Additional file 7: Maximum-likelihood rps3 tree file. (TREE 419 kb)
Additional file 8: Maximum-likelihood 16S rRNA tree file. (TREE 102 kb)
Additional file 9: Maximum-likelihood gyrA tree file. (TREE 294 kb)
Additional file 10: Maximum-likelihood concatenated ribosomal tree
file. (TREE 72 kb)
Additional file 11: Trimmed MUSCLE alignment of nirK sequences
(AFA 32 kb)
Additional file 12: Maximum-likelihood nirK tree file. (TREE 8 kb)
Acknowledgements
Not applicable.
Funding
This work was supported through a grant to MJW from the Ohio Water
Development Authority (#7171).
Availability of data and materials
The data generated during the 16S rRNA gene sequencings can be accessed
at NCBI using accession number SRX2896383. The 71 genomes and
annotated protein sequences are publicly hosted at http://chimera.asc.ohio-
state.edu/Danczak_Genomes_and_Protein_sequences.html.
Authors’contributions
RD helped design the project, carried out 16S rRNA amplicon and bioinformatic
analysis, assisted in collecting samples, and drafted the manuscript. MJ, CK, and
MS assisted in sample collection and coordination. KW assisted in data analysis
and helped draft the manuscript. MW conceived and helped design the study,
aided in data analysis, and helped draft the manuscript. All authors have read
and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Department of Microbiology, The Ohio State University, Columbus, OH, USA.
2
School of Earth Sciences, The Ohio State University, Columbus, OH, USA.
3
Ohio Environmental Protection Agency, Columbus, OH, USA.
Danczak et al. Microbiome (2017) 5:112 Page 12 of 14
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Received: 11 July 2017 Accepted: 23 August 2017
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