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Microbes and Environments
DIGEST 2021
CONTENTS
MOST VALUABLE PAPAR OF THE YEAR 2019
Antimicrobial Activities of Cysteine-rich Peptides Specific to Bacteriocytes of the Pea Aphid
Acyrthosiphon pisum
Nahoko Uchi, MitsUtaka FUkUdoMe, NarUMi Nozaki, MiyUzU sUzUki, keN-ichi osUki,
shUji shigeNobU, and toshUki UchiUMi
34(2), 155–160, 2019

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kaMoNlUck teaMtisoNg, PaNlada tittabUtr, NaNtakorN booNkerd, Masayoshi kawagUchi,
toshiki UchiUMi, and NeUNg teaUMrooNg
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haMUel jaMes doUghari, Patrick alois NdakideMi, izaNNe sUsaN hUMaN, and sPiNNey beNade
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*Times cited at the end of 2020

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Microbes Environ. Vol. 34, No. 2, 155-160, 2019https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME18148

MOST VALUABLE PAPER OF THE YEAR 2019

Antimicrobial Activities of Cysteine-rich Peptides Specific to Bacteriocytes of the
Pea Aphid Acyrthosiphon pisum
Nahoko Uchi1†, Mitsutaka Fukudome1, Narumi Nozaki1, Miyuzu Suzuki2, Ken-ichi Osuki1, Shuji Shigenobu2, andToshiki Uchiumi1*
1Graduate School of Science and Engineering, Kagoshima University, 1–21–35 Korimoto, Kagoshima, Kagoshima 890–0065,

Japan; and 2National Institute for Basic Biology, 38 Nishigonaka, Myodaiji, Okazaki, Aichi 444–8585, Japan(Received October 12, 2018—Accepted January 23, 2019—Published online March 21, 2019)

Aphids have a mutualistic relationship with the bacterial endosymbiont Buchnera aphidicola. We previously reported sevencysteine-rich peptides in the pea aphid Acyrthosiphon pisum and named them Bacteriocyte-specific Cysteine-Rich (BCR)
peptides; these peptides are exclusively expressed in bacteriocytes, special aphid cells that harbor symbionts. Similar symbioticorgan-specific cysteine-rich peptides identified in the root nodules of leguminous plants are named Nodule-specific Cysteine-Rich(NCR) peptides. NCR peptides target rhizobia in the nodules and are essential for symbiotic nitrogen fixation. A BacA (membraneprotein) mutant of Sinorhizobium is sensitive to NCR peptides and is unable to establish symbiosis. Based on the structural andexpressional similarities between BCR peptides and NCR peptides, we hypothesized that aphid BCR peptides exhibit antimicrobialactivity, similar to some NCR peptides. We herein synthesized BCR peptides and investigated their antimicrobial activities andeffects on the bacterial membrane of Escherichia coli. The peptides BCR1, BCR3, BCR5, and BCR8 exhibited antimicrobialactivities with increased membrane permeability. An sbmA mutant of E. coli, a homolog of bacA of S. meliloti, was moresensitive to BCR peptides than the wild type. Our results suggest that BCR peptides have properties that may be required tocontrol the endosymbiont, similar to NCR peptides in legumes.
Key words: symbiosis, aphid, Buchnera, cysteine-rich peptide, bacteriocyte

Endosymbiosis is often essential for the survival ofhosts and symbionts. A well-studied example is the mutualinterdependence between aphids and Buchnera (30). Aphids
harbor an endosymbiotic γ-proteobacterium, Buchnera
aphidicola, within specialized cells called bacteriocytes (27).
Buchnera provides the host with nutrients, such as essential
amino acids, that aphids cannot synthesize and that are deficientin plant phloem sap, aphids’ sole dietary component (13, 29).
The relationship between aphids and Buchnera is syntrophic
and obligate. Buchnera cells are vertically transmitted throughhost generations by transvariole transfer: they are exocytosed
from the maternal bacteriocyte, temporarily released into theextracellular space, and endocytosed by the posterior syncytial
cytoplasm of the blastula during early embryogenesis (stage
7) (4, 5, 17, 26). This symbiotic relationship is estimated tohave been established 200–250 Myr ago. This long-term
endosymbiotic relationship has shaped the characteristic
streamlined genome, from which Buchnera has lost many genes,
including those involved in the biosynthesis of lipopolysaccharides and phospholipids, gene regulation, and defense
responses, and has, thus, lost the ability to survive outside ofhost bacteriocytes (2, 29).
Seven cysteine-rich peptides (CRPs) that are exclusively
expressed in the bacteriocytes of the pea aphid Acyrthosiphon
pisum have been identified and designated as “BCRs”(Bacteriocyte-specific Cysteine-Rich [BCR] peptides) (31).
* Corresponding author. E-mail: uttan@sci.kagoshima-u.ac.jp;
Tel: +81–99–285–8164; Fax: +81–99–285–8163.
† Present address: Graduate School of Medical and Dental Science,
Kagoshima University, 8–35–1 Sakuragaoka, Kagoshima, Kagoshima
890–8544, Japan

Each BCR peptide consists of a secretion signal peptide andmature peptide (44–84 amino acids) with 6 or 8 cysteine
residues (31). Their expression was initially detected in stage
7 embryos, with Buchnera cells being transported from maternal
bacteriocytes to the embryonic syncytium, and bacteriocytespecific expression is then maintained throughout the rest of
the aphid’s life. Although this expression pattern suggests theimportance of BCRs in the symbiosis with Buchnera, their
physiological activities and functions in symbiosis currently
remain unknown.
CRPs in symbiosis organs are found in other symbioses, suchas that between legumes and nitrogen-fixing α-Proteobacteria
called rhizobia (22), actinorhizal plants and nitrogen-fixing
Frankia (6), and bean bugs and β-Proteobacteria of the genus
Burkholderia (10). In the legume symbiosis, the host plant
forms a specific organ, the root nodule, in which rhizobia
live. Rhizobia penetrate the nodule cells and differentiate into
bacteroids, the symbiotic form. By metabolic adaptation,
bacteroids gain the ability to fix nitrogen (22, 33). In nodules
formed on the roots of legumes, such as Medicago, Pisum, andTrifolium, bacteroids show cell elongation, genome amplification, cell membrane modifications, and the loss of reproductive
activity (23). This terminal differentiation is mediated by
nodule-specific cysteine-rich (NCR) peptides that are producedby the host plants (34, 35).
Medicago truncatula produces more than 600 NCR peptides
in infected nodule cells only (28). NCR peptides are structurally
similar to defensins, i.e., they have signal peptides and mature
peptides that conserve 4 or 6 cysteine residues (22). Some NCR
peptides exhibit antimicrobial activity (34). Synthesized NCR
peptides have the ability to induce cell elongation, polyploidization, and cell membrane modifications in Sinorhizobium

156

meliloti cultured in vitro (34). Signal peptides are cleaved bysignal peptidase, and mature NCR peptides are delivered tothe microsymbionts inside host plant cells. The DNF1 gene ofM. truncatula encodes a subunit of a nodule-specific signal
peptidase; dnf1 mutants cannot establish effective symbiosis(32, 35). In dnf1 mutant nodules, rhizobia remain undifferentiated, and NCR peptides localize within the endoplasmic
reticulum, and, thus, are not delivered to bacteroids. Thesefindings strongly support the view that NCR peptides are
essential for effective symbiosis (34, 35).
To survive exposure to NCR peptides, S. meliloti requires
BacA (14). The S. meliloti bacA mutant is hypersensitive to
NCR peptides: when S. meliloti bacA mutant cells are released
into nodule cells, they are rapidly killed (14). However, they
may survive in the nodule cells of the dnf1 mutant because
NCR peptides are not transported to these cells. These findings
show that BacA is essential for the chronic infection of nodules
as well as bacteroid development (12, 14).
We investigated whether the BCR peptides of A. pisum
exhibit antimicrobial activity and affect cell membrane
permeability, similar to the NCR peptides of legume plants.
We treated E. coli, a model γ-proteobacterium closely related
to Buchnera, with chemically synthesized BCRs. We foundthat 4 out of the 6 BCR peptides assayed exhibited antimicrobialactivities and induced cell elongation and higher intensities of
4ʹ,6-diamidino-2-phenylindole (DAPI) and propidium iodide(PI) fluorescence. The E. coli sbmA mutant, a homolog of
bacA of S. meliloti, was more sensitive to BCR peptides than
the wild type. Similarities in the in vitro activities of BCR
peptides to those of NCR peptides (14, 34) suggest that BCR
peptides are involved in the symbiosis with Buchnera in thepea aphid in a similar manner to NCR peptides in legume plants.
Materials and Methods
Bacterial strains and media
E. coli wild-type strains MG1655 and BW25113 and the sbmAdisrupted mutant JW0368 derived from BW25113 (1) were provided
by the National BioResource Project (https://shigen.nig.ac.jp/ecoli/
strain) and maintained on Luria-Bertani (LB) medium. S. meliloti
1021 and its bacA mutant (8) were maintained on TY medium (3). In
bioassays of the activities of BCR and NCR peptides, all strains were
cultured in M9 liquid medium (25) supplemented with 0.2% glucose.
In estimations of colony-forming units (cfus), E. coli strains wereplated on LB agar plates and Sinorhizobium strains on TY agar plates.
Refolding of BCR peptides
BCR1, BCR2, BCR4, BCR5, and BCR8 peptides were chemicallysynthesized through a custom peptide synthesis service by Medical& Biological Laboratories (Nagoya, Japan) and BCR3 was synthesized
by Biomatik Corporation (Cambridge, Canada). They were refoldedwith a Refolding CA Kit (Takara Bio, Kusatsu, Japan) according to
the manufacturer’s instructions. In brief, peptides were unfoldedusing guanidine hydrochloride with dithiothreitol, and were refoldedin cycloamylose/Tween 40 with d,l-cystine. They were then passed
through an Oasis HLB 3 cc column (Nihon Waters, Tokyo, Japan)
and eluted with 1 mL of elution buffer (4 vol. acetonitrile : 1 vol.
methanol : 5 vol. Milli-Q water containing 0.1% trifluoroacetic acid).
Eluates were dried and then dissolved in Milli-Q water to a final
concentration of 2 mg mL–1. The refolded peptide was verified by
HPLC, and the formation of disulfide bonds was confirmed by mass
spectrometry. BCR6 was not assayed in the present study because
BCR6 is too long (84 aa) to synthesize chemically. The NCR247
peptide was synthesized and used without refolding.

Uchi et al.
Treatment of bacterial strains with BCR or NCR peptides
All bacterial strains were cultured in liquid M9 medium. When
the OD600 of the culture reached 0.3, cells were harvested andwashed with 10 mM Tris·HCl buffer (pH 7.5) three times. Cells
were then suspended in 10 mM Tris·HCl buffer (pH 7.5) toOD600=0.1. Each test peptide was added to the bacterial suspension
at an appropriate concentration and the suspension was incubated at
30°C for 3 h. In the analysis of cell morphology and membrane
permeability, BCR peptides were added to a final concentration of
5 μM. As a control treatment, bovine serum albumin (BSA) wasused at the same concentration as BCR peptides.
Detection of antimicrobial activities of BCR and NCR peptides
To estimate antimicrobial activities, we diluted the bacterial suspension treated with each peptide and spread it on LB (E. coli) or TY(S. meliloti) agar plates. The relative number of cfus was assessed inrelation to the number that appeared on control (BSA-treated) plates
as 100%. To investigate changes in morphology and membrane
permeability, we analyzed the bacterial suspension according toprevious studies (15, 34) that analyzed the activities of NCR peptides.
Bacterial cells are detectable by staining with the fluorescent DNA
dye DAPI. PI, a fluorescent dye that stains nucleic acids, is excluded
from living cells, but enters dead cells or cells with the loss ofmembrane integrity. In brief, we added DAPI and PI together to thesuspension to a final concentration of 10 μg mL–1 each and analyzed
cells using a cell sorter (SH800, Sony, Tokyo, Japan) and confocal
microscopy (A1, Nikon Instech, Tokyo, Japan). The cfus of E. colisuspensions and morphology and membrane permeability of E. coli
cells were not affected by BSA under the experimental conditions
employed in the present study (data not shown).

Results
Antimicrobial activities of synthetic BCR peptides
BCR1, BCR3, BCR5, and BCR8 at 5 μM exhibited strong
antimicrobial activities against E. coli (MG1655) cells; thelatter three prevented colony formation (Fig. 1). When ca.
5×107 cells were treated with 5 μM of BCR1, ca. 1×102 cells

Fig. 1. Antimicrobial activities of BCR peptides. E. coli MG1655 wastreated with 5 μM BCR peptides for 3 h and colony-forming units were
estimated relative to the BSA control. Each value is the mean±SE ofthree independent experiments. Asterisks indicate a significant difference
between BCR4 and the control (P<0.05 by the Student’s t-test). N.C., nocolony appeared.

Antimicrobial Peptides of the Pea Aphid

survived (data not shown). No colony formed from the bacterial
suspension treated with BCR3, BCR5, or BCR8. BCR4
exhibited mild antimicrobial activity, whereas BCR2 showedno significant antimicrobial activity (Fig. 1).
Effects of BCR peptides on E. coli cells
To reveal the effects of BCR peptides on cell morphology,
we used the cell sorter to measure the forward scatter parameter(FSC), which indicates cell size, and the side scatter parameter(SSC), which indicates the complexity of granularity and internal
complexity of cells, including cell formation. Histograms of
FSC and SSC measured by the cell sorter are shown in Fig.
2A. BCR2, BCR3, BCR5, and BCR8 increased FSC (Fig. 2A)
and SSC (Fig. S1). BCR1 and BCR4 slightly increased FSC(Fig. 2A), but not SSC (Fig. S1).
Most control cells stained with DAPI fluoresced at intensitiesof 20,000–30,000 (Fig. 2B). BCR1-treated and BCR3-treatedcells produced a wider range of the signal than control cells,

157

and BCR1-treated cells increased the frequency at higher
intensities (Fig. 2B). Cells treated with BCR2, BCR5, andBCR8 fluoresced at higher intensities (Fig. 2B). BCR4-treatedcells produced slightly higher intensities (Fig. 2B). BCR1,
BCR2, BCR3, BCR5, and BCR8 clearly changed the profile
of PI staining, shifting it to higher intensities and a wider
distribution (Fig. 2C). BCR1 widened the distribution, shifting
the main frequency to a lower intensity and slightly increasing
the higher intensities (Fig. 2C). BCR4 slightly shifted the
distribution to a higher intensity (Fig. 2C). In summary, cell
sorter analyses revealed that some of the BCRs affected cellmorphology and membrane permeability, which increased
the intensities of DAPI and PI staining.
BCR-treated E. coli cells were also inspected by fluorescencemicroscopy. BCR1 and BCR3 significantly elongated cells
and increased the intensities of DAPI and PI fluorescence(Fig. 2D). BCR3 and BCR8 promoted cell aggregation (Fig.
2A, 2D, and S1). BCR4 had no significant effect on either

Fig. 2. Effects of BCR peptides on E. coli cells. E. coli MG1655 cells were treated for 3 h with 5 μM of the BCR peptide or BSA as the control.
Cells were stained with DAPI and PI and analyzed by a cell sorter. (A) Forward scatter (FSC), (B) DAPI, (C) PI. (D) Fluorescent microscopy of
MG1655 cells treated with BCR peptides and stained with DAPI and PI. Images are representative micrographs of cells treated with each peptide.
Scale bars, 5 μm. (E) Ratio of PI-positive cells to DAPI-stained cells. At least 4,000 cells were counted in each treatment. Each value is the mean±SE.
Means denoted by the same letter do not differ significantly (P<0.05, the Student’s t-test).

158

morphology or fluorescence. BCR1, BCR3, and BCR8 significantly increased the fraction of PI-positive cells (Fig. 2E).
Sensitivity of the sbmA mutant to BCR peptides and NCR247
To reveal whether the SbmA protein is related to the antimicrobial activity of BCR peptides, BCR1, BCR3, and BCR8
were used because these BCRs exhibited strong antimicrobialactivity. We treated two strains of E. coli, BW25113 (wildtype) and JW0368 (sbmA mutant), with the BCR peptides atlower concentrations and calculated cfu values. Since nocolonies formed when BW25113 and JW0368 were treatedwith 5 μM BCR1, BCR3, or BCR8 (Fig. S2), we used a
sub-lethal concentration, 3 μM (Fig. 3A), in subsequent
assays. BCR1 and BCR3 reduced the cfus of JW0368 to lessUchi et al.

than those of BW25113 (Fig. 3A). BCR1, BCR3, and BCR8
also caused the elongation of BW25113 and JW0368 cells(Fig. S3).
S. meliloti ΔbacA was significantly more sensitive to 20 μM
NCR247 than wild-type S. meliloti 1021 (Fig. S4), as previously
reported (14). JW0368, an E. coli sbmA mutant, was moresensitive to NCR247 at 5 μM, but not at 20 μM, than BW25113(Fig. S4). Both E. coli strains were more resistant to NCR247
than S. meliloti (Fig. S4). No S. meliloti colonies formed at
5 μM BCR1, BCR3, or BCR8 (Fig. S5). Strain 1021 wasresistant to 3 μM BCR1, BCR3, and BCR8, whereas the ΔbacA
mutant was sensitive to them (Fig. 3B).
The effects of BCR1 and BCR8 on wild-type S. meliloti
cells was analyzed using the cell sorter. BCR1 and BCR8
markedly increased FSC (Fig. 4A), SSC (Fig. S6), DAPI, andPI fluorescence (Fig. 4B). Thus, BCR1 and BCR8 exerted
similar effects on S. meliloti as those on E. coli. BCR4 showedsimilar, but weaker effects.
The observed effects of BCR/NCR peptides on both E. coliand S. meliloti are summarized in Supplementary Tables S1and S2.
Discussion

Fig. 3. Sensitivity of the E. coli sbmA mutant and S. meliloti ΔbacA
mutant to BCR peptides. After a treatment with 3 μM BCRs for 3 h, thecolony-forming units of each strain were estimated relative to the BSA
control. The dataset shown is representative of three independent experiments. (A) E. coli BW25113 (wild type) and JW0368 (sbmA mutant).
(B) S. meliloti 1021 and ΔbacA mutant. Each value is the mean±SE ofthree independent experiments. Asterisks indicate a significant difference
between the wild type and mutant (P<0.01, the Student’s t-test).

We investigated the antimicrobial activities of synthesized
BCR peptides found in the bacteriocytes of pea aphids. Seven
genes for BCR peptides (BCR1–6, 8) have been identified in
the genome of A. pisum (31). The mRNA expression of BCRs
in the embryo is initiated around the developmental stage
coincident with the infection of Buchnera into the embryo
from maternal bacteriocytes, and mRNA expression is maintained exclusively in bacteriocytes throughout the life of theaphid (31). Although expression patterns strongly suggest

Fig. 4. Effects of BCR peptides on S. meliloti. S. meliloti 1021 cells were treated for 3 h with 5 μM BCR peptides or 5 μM BSA. Cells stained withDAPI and PI were analyzed by flow cytometry. (A) Forward scatter (FSC), (B) DAPI and PI. Data are representative of at least three independent
experiments.

159

Antimicrobial Peptides of the Pea Aphid

that BCR peptides control endosymbionts, their functions andactivities have not yet been investigated. We synthesized 6
out of 7 A. pisum BCR peptides and examined their effects onbacteria. All peptides exhibited antimicrobial activity or
permeabilized the membrane of E. coli cells: BCR1, BCR3,
BCR5, and BCR8 exhibited strong antimicrobial activity andpermeabilized the cell membrane, BCR2 only increased permeabilization, and BCR4 only showed mild antimicrobialactivity (Fig. 1 and 2). Thus, at least four aphid BCR peptides
were identified as antimicrobial peptides (AMPs). There wasno obvious correlation detectable among the amino acidsequences, isoelectric points, and antimicrobial activities of
BCR peptides. Since the sequence database search of BCR
peptides returned no significant hits outside of aphid species(31), these BCR peptides constitute a novel class of AMPs
specific to the aphid lineage.
We found that some BCRs exerted antimicrobial effects,
whereas other did not. We also noted that each BCR peptide
exerted antimicrobial effects at a different level and theireffects on bacterial morphology and membrane permeability
varied. In M. truncatula, the genes for NCR peptides are
expressed exclusively in the root nodules, whereas expression
patterns differ among genes (22). Each NCR peptide localizes
to a different zone of the nodule; e.g., NCR035 localizes tothe interzone and nitrogen-fixing zone, while NCR001 only
localizes to the nitrogen-fixing zone (34). NCR035 and NCR247
exhibit strong antimicrobial activities against S. meliloti,
while some NCR peptides, such as NCR057 or NCR224, donot. The genes for all six BCR peptides used in the present
study are exclusively expressed in the bacteriocyte of the pea
aphid (31). Among them, BCR2 and BCR4, similar to NCR057
and NCR224, did not exhibit strong antimicrobial activities;
BCR2 affected cell morphology and membrane permeability
without antimicrobial activity. These results suggest that each
BCR plays a different role in symbiosis, similar to NCR
peptides. Although the location and target of the peptides
within the bacteriocyte have not yet been reported, the pea
aphid also has a diverse line-up of CRPs that may function ina different context in symbiosis, as observed in M. truncatula.
Legumes, such as M. truncatula, of the Inverted-RepeatLacking Clade (IRLC) use several hundred NCR peptides tocontrol their microsymbionts. Aeschynomene spp. legumes,of the more ancient dalbergoid lineage, are expected to haveseveral tens to hundreds of NCR-like peptides (7). On theother hand, only seven genes for BCR peptides have been
identified on the genome of A. pisum. Although the reason forthe marked difference in the number of CRPs between aphid–
Buchnera and legume–rhizobia symbioses remains unclear,different systems of symbiosis may be responsible: Rhizobia
are soil bacteria that may survive independently of their hostplant; therefore, during symbiosis, the host plant may need totightly control them by using NCR peptides with diverse
functions. In contrast, Buchnera has lost the ability to survive
outside of the pea aphid and, thus, may be controlled by BCR
peptides with very restricted functions. Further studies are
required to understand the diverse evolutionary processes of
CRPs among symbiotic systems.
BacA, a membrane protein of S. meliloti, is essential forsymbiosis with M. sativa (12). BacA of S. meliloti is involvedin the modification of lipids with fatty acids (9); however, themolecular mechanisms employed by the BacA protein forresistance to AMPs remain unknown. The bacA mutant of S.
meliloti is sensitive to NCR peptides and is unable to differentiate into bacteroids in host nodule cells, resulting in theabortion of symbiosis (14). SbmA of E. coli is a homolog of
BacA of S. meliloti and may complement the symbiosis defectof the S. meliloti bacA mutant (16). Although differenceswere small, the significantly greater sensitivity of the E. colisbmA mutant and S. meliloti bacA mutant to BCR1, BCR3,
and NCR247 than their parent strains (Fig. 3 and S4) suggests
the involvement of bacterial SbmA and BacA proteins insensitivity to these cysteine-rich AMPs and the similar functionof these BCR peptides to NCR peptides. BacA and SbmA areboth transporters that import a number of structurally diverse
peptides into the cell (11, 18, 20, 21, 36). In S. meliloti, BacA
is essential for bacteroid differentiation and survival in thehost plant cell (12, 14). However, we did not find any homologsof bacA/sbmA in the Buchnera genome. BCR peptides may
function via a BacA/SbmA-independent mechanism andaffect the membrane permeability and survival of Buchnera.
AMPs are common peptides that function in the innate
immunity of eukaryotes (37). Besides BCR peptides and NCR
peptides, AMPs that are expressed and/or function in symbioticorgans have been reported in other symbiotic relationships:e.g., between bean bugs and Burkholderia (10) and between
actinorhizal plants and Frankia (6). The weevil antimicrobial
coleoptericin-A peptide regulates the growth of its symbiont
by inhibiting cell division (19). In the symbiosis between theactinorhizal plant Alnus and Frankia, Alnus provides defensinlike peptide Ag5 to Frankia. The present study revealed thatsome BCR peptides exhibited antibiotic activity and increased
the permeabilities of E. coli and S. meliloti cells. A possiblefunction of antimicrobial BCR peptides is to control thegrowth of Buchnera in bacteriocytes, similar to coleoptericin-A,
Ag5, and NCR, thereby controlling the size of the symbiont
population within the host. These antimicrobial BCRs may
also interact with secondary symbionts or invading microbes
in the aphids. Another possible function of BCRs is topromote metabolite exchange (24). In vitro, Ag5 exhibits
antimicrobial activity against Frankia and, at sublethal
concentrations, induces the permeabilization of the vesicle
membrane, resulting in the release of amino acids, particularly
glutamine and glutamate, from Frankia cells (6, 24), which
contributes to metabolic exchange between Frankia and nodule
cells. Since the pea aphid has lost many transporter genes in
its genome (24, 29), BCR peptides that permeabilize the cell
membrane may be used in the exchange of metabolites
between Buchnera and host cells. Animals and plants may
both use AMPs not only as a defense against microbial attack,but also for symbiosis with microbes, representing a parallel
evolution in symbiosis.
Acknowledgements
We would like to thank the National Institute of Genetics forproviding us with the E. coli strains. This study was supported by theJapan Society for the Promotion of Science KAKENHI Grant
Number 15K14899 to TU, 25291083 and 17H03717 to SS, and the
National Institute for Basic Biology (NIBB) Collaborative Research
Program (12-273, 13-325, 14-326, 15-321 and 16-317). We thank
Mr. Takahiro Bino (NIBB) for his technical support in cell sorting.

160
SS thanks Dr. David L. Stern and Dr. Aishwarya Korgaonkar(Howard Hughes Medical Institute, Janelia Research Campus) for
discussions and Dr. Tatsuhiko Kondoh (Nagoya University) for his
support with biochemical experiments.
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Microbes Environ. 35(3), 2020

https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME20049

MOST VALUABLE PAPER OF THE YEAR 2020

Mechanisms of Rice Endophytic Bradyrhizobial Cell Differentiation and Its Rolein Nitrogen Fixation
Teerana Greetatorn1, Shun Hashimoto3, Taro Maeda4, Mitsutaka Fukudome3, Pongdet Piromyou1,
Kamonluck Teamtisong2, Panlada Tittabutr1, Nantakorn Boonkerd1, Masayoshi Kawaguchi4, Toshiki Uchiumi3†,
and Neung Teaumroong1*†
School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima 30000,
Thailand; 2The Center for Scientific and Technological Equipment, Suranaree University of Technology, Nakhon Ratchasima
30000, Thailand; 3Graduate School of Science and Engineering, Kagoshima University, 890–0065 Kagoshima, Japan; and4
National Institute for Basic Biology, Nishigonaka 38, Myodaiji, Okazaki 444–8585 Aichi, Japan
1(Received April 16, 2020—Accepted June 23, 2020—Published online July 29, 2020)

Bradyrhizobium sp. strain SUTN9-2 is a symbiotic and endophytic diazotrophic bacterium found in legume and rice
plants and has the potential to promote growth. The present results revealed that SUTN9-2 underwent cell enlargement,
increased its DNA content, and efficiently performed nitrogen fixation in response to rice extract. Some factors in rice
extract induced the expression of cell cycle and nitrogen fixation genes. According to differentially expressed genes (DEGs)
from the transcriptomic analysis, SUTN9-2 was affected by rice extract and the deletion of the bclA gene. The up-regulated
DEGs encoding a class of oxidoreductases, which act with oxygen atoms and may have a role in controlling oxygen at an
appropriate level for nitrogenase activity, followed by GroESL chaperonins are required for the function of nitrogenase.
These results indicate that following its exposure to rice extract, nitrogen fixation by SUTN9-2 is induced by the collective
effects of GroESL and oxidoreductases. The expression of the sensitivity to antimicrobial peptides transporter (sapDF) was
also up-regulated, resulting in cell differentiation, even when bclA (sapDF) was mutated. This result implies similarities in
the production of defensin-like antimicrobial peptides (DEFs) by rice and nodule-specific cysteine-rich (NCR) peptides inlegume plants, which affect bacterial cell differentiation.
Key words: rice, endophyte, Bradyrhizobium, cell differentiation, nitrogen fixation

Bradyrhizobium spp. from symbiotic and endophytic rela‐tionships with legumes and non-legumes, such as rice,namely, Oryza breviligulata (Chaintreuil et al., 2000) andOryza sativa L. ssp. indica (Teamtisong et al., 2014) andjaponica (Piromyou et al., 2015a, 2015b). Bradyrhizobium
sp. strain SUTN9-2 is capable of forming symbiotic andendophytic relationships with legume and rice plants(Piromyou et al., 2017; Greetatorn et al., 2019). Biologicalnitrogen fixation (BNF) by endophytic bradyrhizobia in rice
may be caused by the activity of nitrogenase enzyme,
encoded by the gene nifH (the nitrogenase structural compo‐nent) (Teamtisong et al., 2014; Okubo et al., 2016). Anothergene, nifV, is involved in the biosynthesis of homocitratesynthase, which activates the nitrogenase Fe protein in freeliving diazotrophs (Howard and Rees, 1994) andBradyrhizobium sp. (Pagan et al., 1975; Okubo et al., 2016).
The nifV gene was also found in Bradyrhizobium sp.
SUTN9-2 (Noisangiam et al., 2012; Hashimoto et al.,2019). This gene is mostly absent in Rhizobium sp. that effi‐
ciently perform nitrogen fixation only in symbiosis with* Corresponding author. E-mail: neung@sut.ac.th;Tel: +66–4422–4153; Fax: +66–4422–4150.
† These authors contributed equally to this work.
Citation: Greetatorn, T., Hashimoto, S., Maeda, T., Fukudome, M.,
Piromyou, P., Teamtisong, K., et al. (2020) Mechanisms of Rice
Endophytic Bradyrhizobial Cell Differentiation and Its Role in
Nitrogen Fixation. Microbes Environ 35: ME20049.
https://doi.org/10.1264/jsme2.ME20049

legumes (Hakoyama et al., 2009). This finding indicates its
potential as a candidate for use as a biofertilizer or bioinocu‐
lant. However, there is currently no information on the riceendophytic molecular mechanisms that play important roles
in plant colonization and growth promotion.
Elongated SUTN9-2 cells were recently observed in ricetissues at 7 days post-inoculation (dpi) by scanning electron
microscopy (SEM). The elongation of these cells occurred
between 3 (≅1–2 micrometer [μm]) and 7 (≅3 μm) dpi(Piromyou et al., 2017). Based on this finding, we hypothe‐
size that interactions between rice plants and bacterial fac‐
tors may contribute to cell size enlargements and increases
in nitrogen fixation efficiency by SUTN9-2 in rice plants,
similar to bacteroid differentiation in legume plants. The
responses of endophytic Burkholderia kururiensis M130 torice macerate have been investigated using a transcriptomic
analysis. The findings obtained revealed 27.7% of differen‐
tially expressed genes (DEGs) of its open reading frames in
the presence of rice macerate. These genes were involved inmembrane transporter and secretion systems, motility, che‐motaxis, and adhesion, indicating the importance of theexchange of molecules for bacterial endophytic growth andadaptation to rice plants (Coutinho et al., 2015).
The terminal bacteroid differentiation (TBD) of
Rhizobium and Bradyrhizobium spp. has been extensively
examined, particularly in an Inverted Repeat-Lacking Clade(IRLC) producing elongated polyploid bacterial cells thatswitch their cell cycle towards endoreduplication (Mergaert
et al., 2006; Kondorosi et al., 2013; Alunni and Gourion,
Article ME20049

Greetatorn et al.

2016). TBD is assessed by host plant factors, including
defensin-like antimicrobial peptides (DEFs) consisting of
nodule-specific cysteine-rich (NCR) peptides, which areproduced in large families of IRLC and Delbergioid legume
clades, together with the BacA transporter protein in
microbes (Mergaert et al., 2006; Alunni and Gourion,2016). NCR peptides and the BacA transporter protein havebeen shown to mediate the polyploidy of Sinorhizobium
meliloti in Medicago nodules by altering the processes
involved in sequential changes in the expression of cell
cycle genes and cell size enlargements (De Nisco et al.,2014; Penterman et al., 2014). The BacA-like transporter ofBradyrhizobium sp. strain ORS285 also provides protection
against the antimicrobial activity of NCR peptides in
Aeschynomene spp. nodules. The BacA-like transporter has
been identified in ORS285, carrying three genes(BRAO285v1_1320006,
BRAO285v1_250005,
andBRAO285v1_950010). These genes are characterized by thepresence of the transmembrane domain pfam06472(ABC_membrane_2) or pfam05992 (SbmA_BacA). How‐ever, only BRAO285v1_1320006 plays a key role in thesymbiotic phenotype in host plants, providing protection
against the antimicrobial activity of NCR peptides in
Aeschynomene spp. nodules. The mutant produced small
nodules, undifferentiated bacteroids, reduced nitrogen fixa‐
tion activity in Aeschynomene indica and Aeschynomene
afraspera, and some dead cells were observed in A. indica
nodules. The other two genes formed normal symbiosis
nodules, differentiated bacteroids, and nitrogen fixation,
indicating that these genes are not important for ORS285 in
Aeschynomene spp. symbiosis (Guefrachi et al., 2015).
These findings suggested that BacA-like proteins in
ORS285 were encoded by BRAO285v1_1320006. How‐ever, Bradyrhizobium BacA-like (BclA), named according
to BacA or the Escherichia coli homolog SbmA, differsfrom Bradyrhizobium proteins due to the presence of a Cterminal cytosolic ATPase domain typical for canonical
ABC transporters (Guefrachi et al., 2015). Furthermore, acorrelation was observed between cell differentiation andnitrogen fixation activity in alfalfa (Medicago sativa) nod‐ules (Vasse et al., 1990). The small bacteroid size (1–2.5 μm) with a low nucleic acid content also exhibited weakacetylene reduction activity. On the other hand, the enlarged
bacteroid size (5–7 μm) had a high nucleic acid content andvery strong acetylene reduction activity (Paau and Cowles,
1978). This finding indicated a correlation between cell size
and nitrogen fixation activity. Thus, the effects of DEFs
from rice plants and the BclA of SUTN9-2 on cell size
enlargement and nitrogen fixation efficiency, occurring
during the interaction between SUTN9-2 and rice extract,
were analyzed. The results obtained provide a better
understanding of the mechanisms and factors involved incell differentiation and nitrogen fixation in this model.
Materials and Methods
Plants and the bacterial strain
The rice plants O. sativa L. ssp. indica cv. Pathum Thani 1 andO. sativa L. ssp. japonica cv. Nipponbare were used in the present
study. Bradyrhizobium sp. strain SUTN9-2 WT (LAXE00000000)

was isolated from the root and stem nodules of A. americana,grown in rice field areas in Thailand (Noisangiam et al., 2012).
SUTN9-2 DsRed-tagged (Piromyou et al., 2015b), ΔnifV, andΔbclA were also used in the present study. SUTN9-2 WT andmutants were cultured at 30±2°C in yeast extract-mannitol (YEM)
broth medium (Somasegaran and Hoben, 2012) for further analy‐
ses. The medium was supplemented with 200 μg mL–1 of strepto‐
mycin and spectinomycin for SUTN9-2 DsRed-tagged, 20 μg mL–1
of cefotaxime for ΔnifV, and 200 μg mL–1 of streptomycin for ΔbclA.
Rice growth and rice extract preparation
Rice seeds (O. sativa L. ssp. indica cv. Pathum Thani 1 and O.
sativa L. ssp. japonica cv. Nipponbare) were dehulled and surface
sterilized with 70% ethanol for 3 min, twice with 10% hydrogen
peroxide for 10 min, with 3% sodium hypochlorite for 1 h, andthen washed 3 times with sterilized water (Greetatorn et al., 2019).
To obtain a small emerging root, surface-disinfected rice seeds
were germinated on 0.85% agar with YEM medium at 37°C for 1
day in the dark. Three germinated seeds (no contamination on
YEM) were transplanted into glass test tubes (22×200 mm)
containing a sterilized aluminum net and N-free rice nutrient solu‐
tion ([mmol L–1]: NaH2PO4·2H2O, 0.6; K2SO4, 0.3; CaCl2·2H2O,
0.3; MgCl2·6H2O, 0.6; EDTA-Fe, 0.045; H3BO3, 0.05;
MnSO4·5H2O, 0.009; CuSO4·5H2O, 0.0003; ZnSO4·7H2O, 0.0007;
and Na2MoO4·2H2O, 0.0001) and pH adjusted to 6.8 (Mae andOhira, 1981). Rice plants were grown for 14 days under controlledenvironmental conditions at 28±2°C and 70% relative humidity on
a 16:8-h day:night cycle (full light, 639 microeinsteins [μE] m–2 S–1).
Whole rice samples (root+shoot; 0.5 g plant–1) were harvested at
14 dpi, sterilized and macerated separately with a sterilized mortar
and pestle in sterilized buffered nodulation medium B (BNM-B)
minimal medium, and kept at 4°C for no more than 3 days forfurther analyses. BNM-B is a synthetic plant growth medium(Ehrhardt et al., 1992) supplemented with succinate, glutamate,
and a cocktail of vitamins (Renier et al., 2011). The rice extract
was used by supplementing BNM-B medium (20–25 g of rice
extract 400 mL–1 in BNM-B). The homogenate was passed throughthree layers comprising a miracloth (22–25 μm), membrane filter(8 μm), and syringe filter (0.2 μm) to discard plant debris and therice extract obtained was further incubated with 108 SUTN9-2 cells
in a 10-mL test tube with a tight cap and stable stage.
Construction of SUTN9-2 bclA and nifV mutants
The bclA deletion mutant (SUTN9-2ΔbclA) was constructed andstandard molecular techniques were used in the present study. The
700-bp upstream and downstream fragments of the bclA gene were
amplified by PCR and the primers used are listed in Table S1. The
two regions were merged by overlap extension PCR and thendigested by EcoRI/XbaI and cloned into the plasmid pNPTS129.
This plasmid cannot replicate in Bradyrhizobium strains and car‐ries sacB and the kanamycin resistance gene, which confer sensi‐
tivity to sucrose, thereby inducing bacterial death (Tsai and Alley,2000). The spectinomycin and streptomycin cartridge from
pHP45Ω was digested by BamHI and introduced into the upstream
and downstream regions previously cloned in the pNPTS129 plas‐
mid. The plasmid was then transferred into SUTN9-2 by triparental
conjugation with the helper plasmid pRK2013 (Tamura et al.,2011). Single recombinant clones were obtained by antibiotic
selection, followed by double recombinant clones by growth on
sucrose with spectinomycin and streptomycin, but not kanamycin.
Candidate clones were checked for the loss of kanamycin resist‐ance from the pNPTS129 plasmid, and the deletion of the bclA
gene was verified by PCR. The mutation of the nifV gene was con‐structed as described by Hashimoto et al. (2019).
Confocal laser scanning microscopy
SUTN9-2 DsRed-tagged was treated with BNM-B and BNM-B
supplemented with rice extract (O. sativa L. ssp. Indica cv. Pathum
Thani) for 7, 14, 21, and 28 days. SUTN9-2 WT, ΔnifV, and ΔbclA

2 / 14

Article ME20049

Bradyrhizobial cell differentiation in rice
were treated with rice extract BNM-B and BNM-B supplemented
with rice extract from indica and japonica for 7, 14, 21, and 28
days, and were then collected and stained with 30 μg mL–1 DAPI
and 15 μg mL–1 FM4-64. Cell size, DNA content, and the cell
membrane were observed in all treated cells under a confocal laser
scanning microscope (Nikon Model Ni-E; Nikon Instech). The
mean DNA content area was calculated from each cell (μm2) using
an ImageJ analysis (Collins, 2007), and the average enlarged cellsize in each treatment was calculated from 20 cells in each replica‐tion for 3 replications.
Flow cytometry analysis
SUTN9-2 WT and DsRed-tagged cells were treated with BNMB and BNM-B supplemented with rice extract (O. sativa L. ssp.
Indica cv. Pathum Thani 1) for 7, 14, and 28 days. SUTN9-2 WTtreated cells were fixed at 4°C overnight in 70% ethanol, the fixedsample was digested with RNase (DNase free), and then stainedwith 50 μg mL–1 propidium iodide (PI) to analyze the DNA content(Deitch et al., 1982). The size of SUTN9-2 DsRed-tagged cells
treated as described above was assessed. The analysis of cell sizeand DNA content was conducted using a flow cytometer (BD
FACSCalibur, BD Bioscience) with Cyflowgic software.
Acetylene reduction assay (ARA)
The effects of rice extract on the nitrogen fixation efficiency of
SUTN9-2 WT (DsRed-tagged), ΔnifV, and ΔbclA cells treated withrice extract were investigated using ARA (Chaintreuil et al., 2000).
The reactions were performed in a 10-mL test tube containing
2 mL each of BNM-B medium and BNM-B medium supplemented
with rice extract (O. sativa L. ssp. indica cv. Pathum Thani 1 andjaponica cv. Nipponbare) as described previously. The reaction
was incubated at 28±2°C for 7, 14, 21, and 28 days. Acetylene wasinjected to reach a final concentration of 10% (v/v) of the gas
phase in the headspace (Somasegaran and Hoben, 2012). After the
incubation, gas from the vessel was injected into a gas chromato‐graph (6′×1/8″ S.S. Hayesep T column; Valco Instruments). Ethyl‐ene gas production was analyzed, as described by Renier et al.
(2011). Total concentrations in the cell suspension were evaluatedusing a plate count of colony-forming units (CFU) on YEM
medium. The medium was supplemented with 200 μg mL–1 ofstreptomycin and spectinomycin for SUTN9-2 DsRed-tagged cells,
20 μg mL–1 of cefotaxime for ΔnifV, and 200 μg mL–1 of strepto‐
mycin for ΔbclA.
Rice cultivation and growth promotion
The rice seeds of O. sativa L. ssp. indica cv. Pathum Thani 1
were surface sterilized and germinated as described above. Germi‐
nated seeds were soaked overnight in YEM broth containing
SUTN9-2 WT, ΔnifV, and ΔbclA (108 CFU mL–1), and three germi‐
nated seeds were then transplanted into glass test tubes(22×200 mm) containing a sterilized aluminum net with N-freerice nutrient solution and N-free supplemented with 1 mmol L–1
ammonium nitrate under controlled environmental conditions, as
previously described. Rice plants were grown and harvested at 7,
14, 21, and 28 dpi, whole rice samples were then dried in an ovenat 65°C for 72 h, and dry weights were measured.
RNA preparation for qRT-PCR
RNA was isolated from three independent cultures of SUTN9-2
WT, ΔbclA, and ΔnifV incubated in BNM-B medium or BNM-B
medium supplemented with rice extract (indica). The cultures wereincubated in a 10-mL test tube with a tight cap at 28±2°C for 21
days. RNA was isolated from 108 cells using Plant RNA extraction
kits (Qiagen). RNAs from rice plant samples (shoot+root) were
harvested at 21 dpi, sterilized, and macerated (Greetatorn et al.,2019). Total RNA extraction was performed according to the man‐ufacturer’s procedure. RNAs were treated with DNaseI (Qiagen) at
28±2°C for 30 min to prevent the contamination of genomic DNA.
The purity of RNA was assessed by PCR on total RNA (250 ng)

with GoTaq polymerase (Promega) using dnaK_SUNT9-2 primers
and EF-1∝_rice plants (Table S1). The quality and concentrationof RNA were assessed by Nanodrop (Thermo Scientific) and agar‐
ose gel electrophoresis.
qRT-PCR and analysis
Transcription levels were measured by qRT–PCR using Applied
Biosystem, QuantStudio Design (Waltham). Primers for the ampli‐
fication of genes involved in the cell cycle (dnaK, gcrA, ctrA,
dnaA, and GcrM), nitrogen fixation (nifH and nifV), and rice
hemoglobin (EF-1∝, hb1, and hb5) are listed in Table S1. PCR
amplification was performed under the following cycling condi‐
tions: an initial denaturation step at 95°C for two min, 35 cycles at
95°C for 2 min and at the annealing temperature of all genes(50°C) for 30 s, followed by a final 5-min extension at 72°C. Rela‐
tive gene expression was analyzed by the comparative Ct method(-ΔΔCT) normalized to the endogenous housekeeping gene, dnaKfor bacterial SUTN9-2 and EF-1∝ for rice plants. Three biologicalreplicates were pooled and analyzed.
RNA preparation for the RNAseq analysis
RNA was isolated from three independent cultures of SUTN9-2
WT and ΔbclA incubated in BNM-B medium and BNM-B medium
supplemented with rice extract (indica). Cultures were incubated ina 50-mL test tube with a tight cap at 28±2°C for 21 days. RNA was
isolated from 3×108 cells using Plant RNA extraction kits (Qia‐gen). Total RNA was extracted according to the manufacturer’s
procedure. The purity of RNA was assessed as described above.
RNA sequencing and analysis
To identify the bacterial genes that respond to rice extract, weused a comparative RNAseq analysis of SUTN9-2. RNA-Seq
libraries were constructed from the RNA sample incubated with orwithout rice extract (20–25 g of rice extract 400 mL–1 in BNM-B)
for SUTN9-2 WT and the ΔbclA mutant. Three biological repli‐
cates were prepared for each treatment. RNA samples wereextracted following the same protocol for qRT-PCR. Eukaryotic
rRNA from the samples was removed using the Ribo-Zero Mag‐
netic Kit (Illumina) and stranded RNA-Seq libraries were con‐structed with the TruSeq Stranded mRNA kit (Illumina). Pairedend sequencing (150 bp) of the libraries was performed by the
NovaSeq platform (Illumina). The libraries were constructed andsequenced at Novogene.
In the gene expression analysis, we adapted the mapping of thetranscriptomic data obtained to the previously reported SUTN9-2
genome. A total of 865M reads (R1+R2) were obtained from the
12 libraries (Table S3). Adapter sequences and low-quality sequen‐
ces were removed from raw single-end reads using fastp (v0.20.0)(fastp: an ultra-fast all-in-one FASTQ preprocessor [Chen et al.,2018]). The trimmed paired-end reads were mapped to the
SUTN9-2 genome (INSDC ID ASM312264v1) and calculated the
TPM-normalized mapped read numbers by RSEM (v1.3.1)(RSEM: accurate transcript quantification from RNA-Seq datawith or without a reference genome, [Li and Dewey, 2011]) withthe --bowtie2 option (bowtie2 version 2.3.5.1) (Fast gapped-readalignment with Bowtie 2, [Langmead and Salzberg, 2012]). The
mapped reads were counted based on the gene models of SUTN9-2(LAXE01000000) predicted by Piromyou et al. (2015b). Differen‐
tially expressed genes (DEGs) were detected based on the false
discovery rate (FDR) (<0.1) and log fold change (logFC) (>0.1 or<–0.1) from the RSEM analysis. In the enrichment analysis, thepublic gene models from LAXE01000000 were annotated withinterproscan (v5.36-75) with --goterms option (for gene ontology
annotation) and with KofamKOALA (downloaded on 13th Aug.
2019) (KofamKOALA: KEGG Ortholog assignment based on the
profile HMM and adaptive score threshold [Aramaki et al., 2020]
for KEGG Orthologs annotation). Enrichment analyses were per‐formed by goseq (v1.38.0) on R (v3.6.1) (gene ontology analysis
for RNA-seq: accounting for selection bias, [Young et al., 2010]).

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qRT-PCR validation
To validate the results of the transcriptome analysis, significant
DEGs were selected to compare their expression in SUTN9-2 WT
in BNM-B with rice extract and SUTN9-2 WT in BNM-B alone,including biphenyl-2,3-diol 1,2-dioxygenase (hpaD), 3-(3hydroxyphenyl) propanoate hydroxylase (mhpA), chaperonin
GroEL (groEL), chaperonin GroES (groES), ABC transporter
ATP-binding protein (sapDF), ABC transporter substrate-binding
protein (sapA), and RND family efflux transporter (cusF). Primers
for the amplification of these genes are listed in Table S1. RNA
preparation, qRT-PCR, and analyses were performed as describedabove.
SbmA_BacA domain proteins and phylogenetic analysis
The identified protein sequences of the SbmA_BacA domain,
including the BacA, Bradyrhizobium homologous, ExsX, andMycobacterium BacA clades, were obtained from Guefrachi et al.
(2015). The BacA-related protein sequences of SUTN9-2 (BclA;
PWE81210.1, SapA; PWE77331.1, and SapDF; PWE82048.1)
were obtained from the National Center for Biotechnology Infor‐
mation (NCBI) database. Protein sequences were aligned using theClustalW program. The phylogenetic tree was constructed using
the neighbor-joining method with confidence levels for 500 repli‐
cates using the MEGAX package (Saitou and Nei, 1987; Kumar etal., 2008).
Statistical analysis
The statistical analysis of data sets was performed with SPSS
software (SPSS 16.0 for Windows; SPSS) on data from three inde‐
pendent samples (each with three technical replicates). Experimen‐
tal data were statistically analyzed according to Steel and Torrie(1980), and means were compared by Duncan’s multiple range test(P≤0.05) (Duncan, 1955).

Results
Cell size enlargement by SUTN9-2 in rice (indica) plants
To confirm whether SUTN9-2 cell size enlargement
occurs within rice plants, the red fluorescent-tagged
SUTN9-2 strain (DsRed) was extracted from rice plant tis‐sues (indica) at 21 and 28 dpi and visualized using confocal
laser scanning microscopy. The cells of SUTN9-2 were lon‐ger, with average sizes of 5.6 and 5.8 μm at 21 and 28 dpi,
respectively, than free-living SUTN9-2 cells (2.8 μm) (Fig.
S1). These results revealed that SUTN9-2 cell size increased
within rice plants. However, small numbers of SUTN9-2
cells were extracted from rice plant tissues. Therefore, rice
extract was prepared and subsequent experiments were per‐formed by incubating SUTN9-2 cells with the extract toinvestigate the influence of rice and its derived molecules on
the differentiation of SUTN9-2 cells.
SUTN9-2 increases its cell size and DNA content inresponse to the rice extract (indica) treatment
Increases in cell size and DNA content in SUTN9-2 cells
in response to the rice extract (indica) treatment were exam‐
ined using a flow cytometer. The sizes of treated SUTN9-2
DsRed-tagged cells in BNM-B minimal medium and BNMB supplemented with rice extract were analyzed. The for‐ward scatter (FS) of a treated cell is related to its size.
BNM-B with rice extract-treated cells had a higher FS than
BNM-B-treated cells at 7, 14, and 28 days (Fig. 1A). Fur‐thermore, the DNA content of treated SUTN9-2 WT cells

was assessed by the fluorescent staining of nuclei using PI.
The DNA content of BNM-B with rice extract-treated
SUTN9-2 cells was higher than that in BNM-B-treated cells.
The DNA content of BNM-B-treated cells was 1 to 2
genome complement (1C to 2C), similar to the ploidy levelof free-growing cells (1C to 2C) (Czernic et al., 2015),
whereas BNM-B with rice extract-treated cells reached 2C
to 7C ploidy levels, similar to the ploidy level of bacteroid
cells (7C to 16C) (Czernic et al., 2015; Guefrachi et al.,2015) (Fig. 1A). These results confirmed that differentiated
SUTN9-2 cells affected rice plants.
SUTN9-2 cells increase their size and nitrogen fixation
activity in response to rice extract (indica)
To investigate whether an enlarged cell size increases
nitrogen fixation activity in response to rice extract,
SUTN9-2 DsRed-tagged cells treated with deionized water(DI), BNM-B, and BNM-B with rice extract (indica) wereanalyzed. Cell size was larger in the BNM-B with rice
extract group than in the BNM-B alone and DI groups. The
average sizes of BNM-B with rice extract-treated cells at 7,
14, 21, and 28 days were 2.76, 3.18, 3.38, and 3.79 μm,
respectively (Fig. 1B), and average nitrogenase activities
were 0.01, 0.02, 0.09, and 0.30 nmol C2H4 log10–1 CFU,
respectively (Fig. 1C). The average sizes of BNM-B alonetreated cells at 7, 14, 21, and 28 days were with 2.52, 2.74,
2.42, and 2.64 μm, respectively (Fig. 1B). Average nitroge‐nase activities in this group were 0.01 nmol C2H4 log10–1
CFU at 7, 14, and 21 days and 0.02 nmol C2H4 log10–1 CFU
at 28 days (Fig. 1C). The average sizes of DI-treated cells at
7, 14, 21, and 28 days were 1.86, 2.04, 1.36, and 1.37 μm,
respectively (Fig. 1B), and these cells only exhibited nitro‐
genase activity of 0.01 nmol C2H4 log10–1 CFU at 14 days
(Fig. 1C). These results demonstrated the influence of rice
extract on the cell size and nitrogen fixation efficiency of
SUTN9-2 cells. Time-dependent increases were observed inelongated cells in the BNM-B with rice extract group, but
not in the BNM-B alone or DI group (Fig. 1B and C).
SUTN9-2 WT and mutants show increases in cell size, DNAcontent, and nitrogen fixation activity in the presence of rice
extract (indica and japonica)
To clarify the effects of rice species variations and bclAand nifV genes on cell differentiation and nitrogen fixation,
SUTN9-2 WT, ΔbclA, and ΔnifV were treated with rice
extract from 2 different species of rice (indica andjaponica). Cell size, DNA content, and nitrogenase activity
were increased in the BNM-B with rice extract (indica)group at 28 days with average cell sizes of 3.28, 3.22, and3.03 μm (Fig. 1D, E, and F), mean DNA content areas of
0.43, 0.33, and 0.32 μm2 (Fig. S2 and S3), and nitrogenase
activities of 0.28, 0.07, and 0.00 nmol C2H4 log10–1 CFU in
SUTN9-2 WT, ΔbclA, and ΔnifV, respectively (Fig. 1D, E,
and F). The BNM-B with rice extract (japonica) group
showed average cell sizes of 2.66, 2.56, and 2.60 μm (Fig.
1D, E, and F), mean DNA content areas of 0.32, 0.33, and0.28 μm2 (Fig. S2 and S3), and nitrogenase activities of
0.06, 0.03, and 0.00 nmol C2H4 log10–1 CFU for SUTN9-2
WT, ΔbclA, and ΔnifV, respectively (Fig. 1D, E, and F).
Average cell sizes at 28 days in BNM-B-treated cells were

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Bradyrhizobial cell differentiation in riceFig. 1. Cell size, DNA content, and nitrogenase activity of SUTN9-2 in response to rice extract (indica). Cell size, enlarged DsRed-tagged cells,
and propidium iodide (PI)-stained DNA content were analyzed by flow cytometry (1 represents free growing, and 2 represents cells treated withrice extract) (A). Cell size and enlarged DsRed-tagged cells by confocal laser scanning microscope (B). Observation of cell size by a confocal
laser scanning microscope and nitrogenase activity by the acetylene reduction assay of SUTN9-2 WT cells treated with DI, BNM-B, and BNM-B+rice extract (indica) (C), and SUTN9-2 WT (D), ΔbclA (E), and ΔnifV (F) with BNM-B, BNM-B+rice extract (japonica), and BNM-B+rice
extract (indica) at 7, 14, 21, and 28 days. Significance at P≤0.05 is indicated by the mean±standard deviation (n=3).

1.73, 1.73, and 1.63 μm (Fig. 1D, E, and F), mean DNA
content areas of 0.11, 0.15, and 0.15 μm2 (Fig. S2 and S3),and nitrogenase activities of 0.05, 0.01, and 0.00, nmol C2H4
log10–1 CFU in SUTN9-2 WT, ΔbclA, and ΔnifV, respec‐tively (Fig. 1D, E, and F). Similar results were obtained at 7,
14, and 21 days (Fig. 1D, E, and F). The effects of rice
extract and ΔbclA and ΔnifV on cell sizes were observed
under a transmission electron microscope (TEM). The cells
of SUTN9-2 WT, ΔbclA, and ΔnifV elongated in response to
the rice extract (indica) treatment (Fig. S4).
These results demonstrated that rice extract (indica)exerted stronger effects on cell elongation and nitrogen fixa‐
tion activity than rice extract (japonica) in WT and mutant
SUTN9-2 cells. Increases in cell size and nitrogenase activ‐
ity were smaller in ΔbclA than in the WT strain. Further‐more, nitrogenase activity was not detected in ΔnifV andtheir cell size was smaller than those of WT and ΔbclA
following the treatment with both rice extracts. These results
also revealed the effects of bclA and nifV genes on cell size
enlargement and nitrogen fixation activity in response todifferent rice varieties.

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Effects of WT and mutant SUTN9-2 on rice (indica) plant
growth
SUTN9-2 has been reported to promote rice (indica)
growth at the early seedling stage (Greetatorn et al., 2019).
Consistent results were obtained in the present study;
SUTN9-2 WT, ΔbclA, and ΔnifV increased rice dry weight
more than the non-inoculated control, particularly at 7 and14 dpi, in N-free medium and ammonium nitratesupplemented medium (Fig. S5). The significant difference
observed at 7 and 14 dpi between rice plants inoculated with
SUTN9-2 WT, ΔbclA, and ΔnifV and the non-inoculated
control was not detected at 21 or 28 dpi (Fig. S5). However,rice dry weight did not show a significant difference
between rice plants inoculated with SUTN9-2 WT, ΔbclA,and ΔnifV at 7, 14, 21, and 28 dpi (Fig. S5). These results
clearly demonstrated that SUTN9-2 promoted rice plant
growth at the early stage at 7 and 14 dpi under the presenceor absence of supplementation with a nitrogen source. How‐ever, the effects of bclA and nifV genes on rice plant growth
remain unclear.
Expression of cell cycle genes in WT and mutant SUTN9-2
in response to rice extract (indica)
SUTN9-2 cell differentiation-related genes, including
gcrA, ctrA, ccrM, and dnaA, were selected based on theirrole in the master cell cycle. Similar results were obtained
for the expression of all genes between SUTN9-2 WT and
ΔbclA. However, ΔbclA showed the down-regulated expres‐sion of the genes listed above. The expression levels of gcrA
and ctrA were lower in the BNM-B with rice extract(indica) group than in the BNM-B alone group, with 0.01and 0.09-fold differences in WT and 0.008- and 0.01-folddifferences in ΔbclA, respectively (Fig. 2A). However, nosignificant difference was noted in gcrA expression between
the BNM-B alone and BNM-B with rice extract groups. In
contrast, the expression levels of ccrM and dnaA werehigher in the BNM-B with rice extract group than in the
BNM-B alone group, with 0.20- and 0.22-fold differences
for WT and 0.10- and 0.10-fold differences for ΔbclA,respectively (Fig. 2A). In spite of this result, the expressionof all genes in ΔnifV was several-fold lower in the BNM-B
with rice extract group than in the BNM-B alone group (Fig.
2A). These results suggested that cell size enlargements may
also be affected by the nifV gene, supporting the observationof cell enlargement under the microscope. They also indi‐cated that the bclA gene was disrupted, whereas other bclAassociated genes were still active and required for cellenlargement, showing a similar pattern of gene expression
to WT. Collectively, these results demonstrated that somefactors from rice extract may affect master cell-cycle regula‐tors.
Nitrogen fixation and BclA transporter gene expression in
WT and mutant SUTN9-2 in response to rice extract (indica)
To examine the effects of rice extract on BclA transporterand nitrogen fixation-related gene expression in SUTN9-2,
their relative expression levels were measured in SUTN9-2
WT, ΔbclA, and ΔnifV. The expression levels of the nifH,
nifV, and bclA genes were significantly increased in WT(0.12-, 0.12-, and 0.11-fold, respectively) and ΔbclA (nifH;
0.14-, and nifV; 0.14-fold) in the presence of rice extract(Fig. 2B). In contrast, the expression levels of these genes
were significantly decreased in ΔnifV (nifH; 0.06-, bclA;
0.008-fold) in the presence of rice extract (Fig. 2B). How‐ever, the expression of the bclA and nifV genes was notdetected in SUTN9-2 mutants lacking bclA and nifV, respec‐tively (Fig. 2B). The results demonstrated that rice extractaffected BclA transporter and nitrogen fixation-related gene
expression, indicating the effect of ΔnifV on nifH and bclA
gene expression.
Expression of rice (indica) hemoglobin genes in response to
WT and mutant SUTN9-2
To assess the relationship between hemoglobin gene
expression and nitrogen fixation efficiency by SUTN9-2 inrice plants, hb1 and hb5 gene expression levels wereassessed in rice plants inoculated with SUTN9-2 WT,
ΔbclA, and ΔnifV. The results obtained showed that hb1 andhb5 expression was more strongly induced in rice inoculatedwith SUTN9-2 WT (0.2- and 0.03-fold), ΔbclA (0.02- and0.007-fold), and ΔnifV (0.06- and 0.01-fold) than with the
non-inoculated control (Fig. 2C). The expression of thesegenes was suppressed in ΔbclA- and ΔnifV-inoculated rice,with the expression level of the hb5 gene being lower thanthat of the hb1 gene (Fig. 2C). These results indicated thatthe activity of hemoglobin in rice plants may be induced in
response to the SUTN9-2 inoculation and may function as
an oxygen scavenger or oxygen stock to facilitate nitrogen
fixation in the endophytic state of SUTN9-2.
SUTN9-2 transcriptome in response to rice extract (indica)
To analyze the RNAseq transcriptome, total RNA was
purified from SUTN9-2 incubated in BNM-B medium in thepresence or absence of rice extract. The main reason for per‐forming the transcriptome analysis was to elucidate themechanisms and factors involved in cell differentiation andnitrogen fixation by endophytic SUTN9-2 in rice plants.
The results obtained showed that the expression of a large
number of genes was significantly altered (FDR value ≤0.1)
in response to rice extract, with the differential expressionof 365 genes being significant, representing 63.8% of DEGs
(Fig. 3A). The percentage of DEGs was higher (42.1%) in
BNM-B with rice extract-treated SUTN9-2 WT cells than in
BNM-B-treated cells (Fig. 3A). The effects of ΔbclA in rice
extract were also demonstrated because 42.1% of genes
were differentially regulated in treated WT SUTN9-2 cells
in the BNM-B with rice extract group compared to ΔbclA in
the BNM-B with rice extract group (Fig. 3A). These results
revealed the influence of rice extract and the bclA gene on
the expression of genes in SUTN9-2.
The highest differentially up-regulated genes in SUTN9-2
WT in response to rice extract were biphenyl-2,3-diol 1,2dioxygenase (PWE78131.1; 2.33 logFC) followed by 3-(3hydroxyphenyl) propanoate hydroxylase (PWE78129.1;
1.85 logFC) (Fig. 3D and E). Both of these genes belong to
the class of oxidoreductase catalytic enzymes involved in
the degradation of plant-related compounds and interact
with oxygen atoms, which are incorporated into the sub‐strate. The third and fifth highest differentially up-regulated

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Bradyrhizobial cell differentiation in riceFig. 2. Relative expression of genes involved in the master cell cycle (A), nitrogen fixation and BclA transporter (B) of SUTN9-2 ΔnifV, ΔbclA,and WT in response to rice extract (indica) at 21 days. Relative expression of the rice hemoglobin gene (hb1 and hb5) in response to SUTN9-2
ΔnifV, ΔbclA, and WT at 21 dpi (C). Significance at P≤0.05 was indicated by the mean standard deviation (n=3).

genes were the molecular chaperones sGroEL(PWE76243.1; 1.42 logFC) and GroES (PWE81524.1; 1.34
logFC), which are required for the proper folding of many
proteins and the function of the nitrogen fixation regulatory
protein NifA. The results from the RNAseq analysis indi‐cated that the differential expression of genes involved innitrogen fixation and nitrogen metabolism in response torice extract was not significant (Fig. 3E), whereas qRT-PCR
results showed that nitrogen fixation genes (nifH and nifV)

were up-regulated (Fig. 2B) because their expression was
detected in rice plants (Piromyou et al., 2017; Greetatorn etal., 2019). However, a key difference between the two
experiments needs to be considered. The experimental setup for the transcriptome analysis was performed on a bigger
scale than that for qRT-PCR in order to obtain a sufficiently
large number of SUTN9-2 cells in rice extract for RNA
purification and the transcriptome analysis. This may havebeen affected by the different oxygen levels in the two

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Fig. 3. Differentially expressed genes (DEGs) of SUTN9-2 WT versus ΔbclA in response to BNM-B (WB and bB) and BNM-B+rice extract(indica) (WRB and bRB). Venn diagram of all DEGs of WB_vs_WRB, WB_vs_bB, WRB_vs_bRB, and bB_vs_bRB (A), showing up-regulated
genes (B), and down-regulated genes (C). MA plot of up- and down-regulated DEGs of WB_vs_WRB and genes of interest were labeled (D).
Expression pattern of DEGs of WB, bB, WRB, and bRB. FDR (≤0.1) and logFC of DEGs of WB_vs_WRB, WB_vs_bB, WRB_vs_bRB, andbB_vs_bRB. The color scale bars are for normalized expression (E).

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Bradyrhizobial cell differentiation in rice

experimental set-ups, which perturbed nif gene expression
in the transcriptome experiment.
DEGs in response to rice extract were also found to code
for a protein involved in the cationic peptide transport sys‐
tem ATP-binding process. These DEGs include genes
involved in the ABC transporter ATP-binding protein(PWE82048.1; 0.84 logFC) and ABC transporter substratebinding protein (PWE77331.1; 7.5 logFC). The gene coding
for efflux pumps was also found to be differentially upregulated in the presence of rice extract (Fig. 3D and E).
This gene belongs to the resistance nodulation and cell divi‐
sion family (RND) efflux system (PWE80966.1; 1.15
logFC), which may be involved in bacterial defenses against
toxic plant metabolites. These results suggested the presenceof cationic antimicrobial peptide (CAMP) in rice plants,
which is toxic and affects bacterial cell differentiation. The
significantly down-regulated DEGs of SUTN9-2 WT in the
response to rice extract were related to flagella(PWE80412.1, PWE80420.1, and PWE80424.1) (Fig. 3E).
In contrast, these genes were up-regulated in SUTN9-2
ΔbclA (PWE80424.1) in response to rice extract (Fig. 3D),
indicating the influence of rice extract and ΔbclA on cell
motility.
qRT-PCR validation
qRT-PCR was performed for DEGs, including hpaD,
mhpA, groEL, groES, sapDF, sapA, and cusF in SUTN9-2
WT that significantly differed between the BNM-B withrice extract (indica) group and BNM-B alone group. The
results obtained showed that the majority of genes were upregulated in response to rice extract, which were similar to
those from the transcriptome analysis. The expression of
mhpA appeared to be more strongly up-regulated in
response to rice extract (0.09-fold), followed by sapDF(0.07-fold), cusF (0.07-fold), groEL (0.06-fold), hpaD
(0.05-foid), sapA (0.04-fold), and groES (0.01-fold), respec‐tively (Fig. 4).
Discussion
The present results indicate that SUTN9-2 undergoes
major changes in cell size and nitrogen fixation efficiency inresponse to rice extract (Fig. 1). The significant increase
observed in the elongated cell size of SUTN9-2 was associ‐ated with more efficient nitrogen fixation. Similarly, a TBD
process occurs before effective nitrogen fixation is estab‐lished. These bacteroids are enlarged and polyploid andhave lost their capacity to produce progeny (Mergaert et al.,2006; Alunni and Gourion, 2016). The small bacteroid size
(1–2.5 μm) in alfalfa (Medicago sativa) nodules had a low
nucleic acid content and did not exhibit acetylene reduction
activity (4.9 μmol C2H2 reduced [1010]–1 bacteroids). The
enlarged bacteroid size (5–7 μm) had a high nucleic acidcontent and was very active for acetylene reduction(83.3 μmol C2H2 reduced [1010]–1 bacteroids) (Paau andCowles, 1978). The bacteroids of bradyrhizobia in A.
afraspera and A. indica exhibit differentiation along with ahigh DNA content, such that the mean DNA content of freegrowing Bradyrhizobium sp. ORS285 was 1 to 2 genome
complement (1C to 2C) and that of the bacteroid was 7C to
16C ploidy levels (Czernic et al., 2015; Guefrachi et al.,2015).
Cell differentiation by Bradyrhizobium bacteroids occur‐
red in the interaction with defensin-like antimicrobial pepti‐des (DEFs), which are found in the IRLC and Dalbergioids
legume families with the action of BclA transporters(Guefrachi et al., 2015). Based on a phylogenetic tree of
SbmA_bacA
domainproteins,
three
genes
of
Bradyrhizobium sp. strain ORS285 were revealed. Two
genes (BRAO285v1_250005 and BRAO285v1_950010) arein the Bradyrhizobium homologous clade and one(BRAO285v1_1320006) in the BclA clade (Guefrachi et al.,2015).
However,only
themutantingene
BRAO285v1_1320006 had abnormal (undifferentiated) bac‐teroid cells, and exhibited markedly reduced nitrogen fixa‐
tion activity in the host plants of Aeschynomene spp.
(Guefrachi et al., 2015). Similarly, SUTN9-2 ΔbclA in thepresent study belonged to the BclA clade. This gene wasdisrupted according to high homology with the BclA of
ORS285 (Fig. S6). Based on the KO (KEGG [Kyoto Ency‐
clopedia of Genes and Genomes] Orthology) annotation in
the reconstruction pathway, the ABC transporter involving
the CAMP resistance of SUTN9-2 ΔbclA was perturbed by
the deletion of the ABC transporter ATP-binding domain(bclA; PWE81210.1) belonging to SapDF (Lopez-Solanilla
et al., 1998). However, the transcriptomic analysis showedthat the ABC transporter substrate-binding protein
consisting of SapA (PWE77331.1) and ABC transporter

qRT-PCR analysis data for significant DEGs in SUTN9-2 WT between BNM-B alone and BNM-B with rice extract (indica). Significanceat P≤0.05 is indicated by the mean±standard deviation (n=3).

Fig. 4.

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ATP-binding protein consisting of SapDF (PWE82048.1)
were significantly and differentially up-regulated in
response to rice extract (Fig. 3D and E, Table S2). A phylo‐
genetic analysis based on the sequences of SbmA_BacA
domain proteins showed that SapA (PWE77331.1) and
SapDF (PWE82048.1) identified in SUTN9-2 were distantfrom the BclA and Bradyrhizobium homologous clades
identified in Bradyrhizobium spp. However, both of thesegenes showed 97% similarity to these clades (Fig. S6)(Guefrachi et al., 2015). These results indicate that SapA
(PWE77331.1) and SapDF (PWE82048.1) play an impor‐tant role together with SUTN9-2 ΔbclA (PWE81210.1; data
not shown) for cell differentiation, nitrogen fixation in sym‐biosis, and responses to rice extract because the cell differ‐entiation of SUTN9-2 ΔbclA was still maintained in both
systems of bacteroids in legume plants (data not shown) andbacteria in response to rice extract (Fig. S2 and S4). How‐ever, decreases were also observed in the elongated cell
sizes and nitrogen fixation activities of ΔbclA and ΔnifV.
These results indicated the effects of bclA and nifV genes on
the cell size and nitrogen fixation activity of SUTN9-2 cells
in response to rice extract. The sap mutant pathogenic bac‐terium Erwinia chrysanthemi was sensitive to killing by
antimicrobial peptides (AMPs) (wheat α-thionin andsnaking-1) from potato tubers (Lopez-Solanilla et al., 1998).
The Sap transporter consists of SapABCDFZ, which shareshomology to the ‘ATP-binding cassette’ (ABC) family oftransporters that show diverse substrate binding and uptake(Hiles et al., 1987; Abouhamad et al., 1991; Parra-Lopez etal., 1993). SapA is predicted to function as a periplasmic
solute-binding protein; SapB and SapC as inner membrane
permease proteins; SapD and SapF as ATPase subunits;
whereas the function of SapZ remains unknown (Mason etal., 2006). Moreover, previous studies identified several
genes encoding cysteine-rich peptides (CRPs), alsosuggesting several uncharacterized AMPs in rice plants(Silverstein et al., 2007), and genes encoding DEFs werealso detected in rice plants (Tantong et al., 2016; Li et al.,2017). These results implied the production of DEFs in rice
plants interacting with the Sap transporter of SUTN9-2,
which affected enlarged cell sizes and high DNA contents in
SUTN9-2 in response to rice extract (Fig. 5), similar to thecell differentiation and polyploidy of bacteroids in legume
plants affected by NCR peptides.
Moreover, the gene coding for RND transporter efflux
pumps (PWE80966.1) was differentially up-regulated in
SUTN9-2 WT exposed to rice extract (Fig. 3D and E).
Efflux pumps are transport proteins involved in the extru‐
sion of toxic substrates into the external environment(Coutinho et al., 2015). The RND efflux system has beenrecognized to play an important role in the successful colo‐
nization of the apple tree by the phytopathogen Erwinia
amylovora (Burse et al., 2004). RND efflux systems arestrongly up-regulated in B. kururiensis M130 in the pres‐Working model of cell differentiation and nitrogen fixation by SUTN9-2 exposed to rice extract. The currently implied AMPs from rice
plants are predictably recognized by BclA ABC transporters belonging to Sap ABC transporter family-like AMP recognition receptors. AMPs aretargeted to the cell membrane of bradyrhizobia, localizing the BclA or Sap transporter, promoting the import of AMPs and providing protection
against the antimicrobial activity of these peptides. AMPs affect master cell cycle regulators by reducing GcrA activity and the CtrA
autoregulation pathway, but promoting DnaA and CcrM for the initiation of DNA replication, resulting in an increase in DNA content andenlarged cell size. A defect in flagellar activity during cell differentiation was observed. This was followed by increased nitrogen fixation activity,
which correlated with an enlarged cell size. Excess oxygen perturbs the nitrogen regulatory protein NifA and reduces nitrogen fixation activity.
Oxidoreductase may play a role in controlling oxygen to an appropriate level for nitrogenase activity. GroESL co-regulated with the nitrogen
regulatory gene nifA and rpoN RNA polymerase for the formation of a functional nitrogenase. The successful colonization of bradyrhizobia in rice
plants is triggered by the RND efflux system and induced rice nsHbs then regulate low oxygen partial pressure and facilitate nitrogen-fixing
endophytic cells.

Fig. 5.

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Bradyrhizobial cell differentiation in riceence of rice extract (Coutinho et al., 2015). This type of
efflux system may be important in bacterial survival
defenses against toxic-plant metabolites, including DEFs
(Fig. 5).
Flagellar biosynthesis is mostly down-regulated in symbi‐
otic bacteroid cells. The transcriptome analysis of
Mesorhizobium loti revealed that genes for flagellar forma‐tion were strongly repressed under the symbiotic condition
because rhizobia under this condition do not need to be
motile (Uchiumi et al., 2004; Tatsukami et al., 2013). In
addition, the relative expression level of the SUTN9-2 flag‐ella biosynthetic protein (fliP) in response to rice root exu‐
date was lower than that without rice root exudate. The
expression level of fliP also slightly decreased with anincrease in plant age (Piromyou et al., 2015b). Similarly, thedifferentially down-regulated expressed genes of SUTN9-2
WT were related to flagellar biosynthesis (PWE80412.1,
PWE80420.1, and PWE80424.1) in response to rice extract.
In contrast, these genes were up-regulated in ΔbclA(PWE80424.1) in response to rice extract (Fig. 3D). This
up-regulation has also been observed in transcriptome
experiments with B. kururiensis in response to rice extract
and allows bacteria to escape host defense responses(Coutinho et al., 2015). Therefore, the down-regulation offlagellar biosynthesis appears to be supporting the cell dif‐ferentiation of SUTN9-2 in response to rice extract, similar
to bacteroids in the symbiotic condition. In contrast, ΔbclA
lacking some parts of the transporter against plant AMPs
up-regulate genes for flagellar biosynthesis, thereby
allowing bacteria to move faster in the plant environment inorder to escape host defense responses (Fig. 5).
Rice extract (O. sativa L. ssp. indica cv. Pathum Thani 1)
affected cell elongation and nitrogen fixation activity in
SUTN9-2 more strongly than rice extract (O. sativa L.
japonica cv. Nipponbare) in all treated SUTN9-2 WT,
ΔbclA, and ΔnifV (Fig. 1D, E, F, and S2). Elongated cellsize was smaller in ΔnifV than in WT and ΔbclA SUTN9-2,
and also in ΔbclA than in WT SUTN9-2 (Fig. 1D, E, andS2). When SUTN9-2 WT was inoculated into the Thai ricecultivar Pathum Thani 1 (indica) and Japanese rice cultivar
Nipponbare (japonica), the number of SUTN9-2 WT cells at
30 dpi was higher in Thai rice root tissues (indica) than inJapanese rice (japonica), with 103 and 101 CFU g–1 root
fresh weight, respectively (Piromyou et al., 2015b). The
population density of SUTN 9-2 was larger in Thai rice(indica) than in Japanese rice (japonica). In addition, Thai
bradyrhizobial strain SUTN9-2 was suggested to promote
the total dry weight of rice (indica) more effectively thanJapanese bradyrhizobial strains (Piromyou et al., 2015a). On
the other hand, rice (indica) responded positively only toputative Thai rice endophytic bradyrhizobia, while this phe‐nomenon was not observed in Japanese rice (japonica)(Piromyou et al., 2015a, 2015b). In addition, the type III
secretion system (T3SS) of SUTN9-2 is involved in bradyr‐hizobial infections in rice plants. The density of the
SUTN9-2 T3SS mutant in Thai rice (indica) was signifi‐
cantly lower than that of SUTN9-2 WT, and this property
was not detected in Japanese rice (japonica), indicating
SUTN9-2 had the ability to overcome native host rice
defense responses through the function of T3SS and alsothat rice developed a system to protect itself from nonnative soil bacteria (Piromyou et al., 2015b). These results
imply that the rice cultivar and bacterial strain are importantfactors that control the compatibility of the rice-bacterium
relationship, which may contribute to bradyrhizobia-hostevolution (Piromyou et al., 2015b). These results support
the existence of a preferable host for SUTN9-2 in rice spe‐cies, which may also contribute to greater increases in thecell size enlargement and nitrogen fixation activity of
SUTN9-2 in response to rice extract from indica than to thatfrom japonica.
The attenuated pattern of ctrA expression in NCR-treatedcells during the cell cycle may be caused by reduced GcrA
activity or by defects in the CtrA autoregulation pathway.
DnaA appears to be necessary for repeated initiation roundsof DNA replication during endoreduplication in vivo
(Collier, 2012). CcrM DNA methyltransferase is present andactive for a short time and essential for methylation at thestart of DNA replication (Collier, 2012). In the present
study, the results obtained suggested that AMPs in rice
extract affect master cell-cycle regulators by suppressing theexpression of ctrA and promoting dnaA and ccrM expres‐sion, resulting in an increase in cell size and DNA content(Fig. 2A, 5, and S3). In addition, the nifV gene may affectcell size enlargement following exposure to rice extract. The
expression of genes involved in the nitrogen fixation activ‐
ity (nifH and nifV) of SUTN9-2 has been detected in theendophytic relationship with rice plants (Piromyou et al.,2017; Greetatorn et al., 2019). Furthermore, the expressionof nifH, nifV, and bclA in WT and ΔbclA SUTN9-2
increased in response to rice extract. In contrast, the expres‐sion of these genes in SUTN9-2 ΔnifV decreased in
response to rice extract (Fig. 2B). These results suggest sim‐
ilarities in the expression model between these genes andthe genes involved in the master cell cycle following expo‐
sure to rice extract. Furthermore, the effects of the interac‐tion between the rice extract and BclA transporter on celldifferentiation and nitrogen fixation were implied based on
the expression of cell cycle and nif genes in response to rice
extract (Fig. 5). Non-symbiotic hemoglobins (nsHbs) havebeen detected in several monocot plants. Rice (O. sativa)
contains five copies of the nsHb gene, namely, hb1-hb5(Lira-Ruan et al., 2002). hb1, hb2, and hb5 are expressed inrice embryonic organs and vegetative organs, and theirappear to function as oxygen carriers or in some aspects ofoxygen metabolism (Garrocho-Villegas et al., 2008; LiraRuan et al., 2011). Hormone and stress response promoters
exist upstream of the rice hb5 gene, which was transcribedin rice organs. The amino acid sequence and protein model
structure of Hb5 differ from those of rice Hbs 1 to 4
(Garrocho-Villegas et al., 2008), suggesting different
expression levels between hb1 and hb5 (Fig. 2C). However,they are present at very low levels inside host rice cells. In
addition, the physiological functions of rice nsHbs are not
involved in oxygen transport, but more closely resemble
known oxygen sensors (Goodman and Hargrove, 2001).
This finding revealed that nsHbs in rice plants may function
as a regulator to maintain low oxygen partial pressure fornitrogenase activity to facilitate nitrogen-fixing endophytic
cells (Fig. 5).

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The RNAseq experiment provided a global view of thegene expression profile in response to rice extract. RNAseq
results indicated that SUTN9-2 endophytic cells wereaffected by rice extract and the ΔbclA mutation, which isconsistent with the results for significant DEGs (Fig. 3A).
The expression of genes involved in the cell cycle and nitro‐gen fixation was up-regulated when SUTN9-2 was exposed
to rice extract. These results are in contrast to those obtained
from the transcriptome analysis of SUTN9-2 in the presenceof rice extract. These genes did not show significant DEGs
in the response to rice extract. Bacteria capable of utilizing
biphenyl and phenylpropanoid compounds as carbon andenergy sources are widely distributed in natural environ‐ments, and may originate from the putrefaction of proteinsin soil or as breakdown products of several constituents ofplants, such as lignin, various oils, and resins (Ferrandez etal., 1997; Díaz et al., 1998; Wesche et al., 2005). The high‐est up-regulated DEGs involved in biphenyl-2,3-diol 1,2dioxygenase (PWE78131.1) and 3-(3-hydroxyphenyl)
propanoate hydroxylase (PWE78129.1) were observed.
These genes belong to the class of oxidoreductases acting on
donors with oxygen and the incorporation of atoms of oxy‐
gen into the substrate (Díaz et al., 1998; Wesche et al.,2005), which may have a role in controlling oxygen to an
appropriate level for nitrogenase activity (Fig. 3D and E).
This result suggests excess oxygen in the experimental setup for the transcriptome analysis (50-mL tube), which
affects nitrogenase activity. A larger scale experiment(50-mL tube) was performed to obtain a large amount of
SUTN9-2 cells in rice extract for the transcriptome analysis,
more than the experimental set-up for qRT-PCR (10-mLtube). The nitrogenase enzyme complex is highly sensitive
to molecular oxygen, which irreversibly inactivates the
enzyme. The inhibition of nif gene expression by molecularoxygen at the nitrogen regulatory protein NifA posttranscriptional stage was detected in B. japonicum (Fischerand Hennecke, 1987; Kullik et al., 1989). Therefore, thesuppression of nitrogen fixation genes observed in the tran‐
scriptome analysis may be due to the effects of excess oxy‐
gen on nitrogenase sensitivity (Fig. 5).
The molecular chaperones GroEL (PWE76243.1) and
GroES (PWE81524.1) were identified as significant differ‐entially up-regulated genes in response to rice extract (Fig.
3D and E). GroESL chaperonins are required for the forma‐tion of a functional nitrogenase in B. japonicum, which isco-regulated together with the symbiotic nitrogen regulatory
gene nifA and transcribed by σ54 RNA polymerase (Fischer
et al., 1993, 1999). However, the requirement of chapero‐
nins for nitrogen fixation does not occur at the level of
RegSR-NifA-σ54- or FixLJ-FixK-dependent gene regulation(Fischer et al., 1999). This finding indicated that the nitro‐gen fixation of SUTN9-2 in response to rice extract may beaffected by GroESL chaperonins with or without Nifdependent gene regulation. Collectively, these studies imply
that the nitrogen fixation of SUTN9-2 was induced in
response to rice extract because the differentially up-regulated
genes of GroESL chaperonins were detected (Fig. 5).
Based on the results and transcriptomic findings reported
herein, a proposed model for the cell differentiation andnitrogen fixation activity of SUTN9-2 in response to rice

extract was shown (Fig. 5). Rice plants are predicted to pro‐
duce AMPs that are recognized during interactions by AMP
recognition receptors (Sap ABC transporter family), thereby
promoting the import of AMPs and protecting SUTN9-2
cells against the antimicrobial activity of these peptides.
Following the recognition and transduction of these AMPs,
several DEGs are induced in SUTN9-2. These AMPs pre‐dictably modulate master cell cycle regulators, thereby
causing cell differentiation. These interactions induced sev‐
eral processes, including oxidoreductase, GroESL chapero‐nin, the RND efflux system, and flagellar biosynthesis,
which may promote cell size enlargement, nitrogen fixation,
and, ultimately, rice growth (Fig. 5). These results imply
similarities in the mechanisms and factors involved in celldifferentiation and nitrogen fixation between endophytic
cells in rice plants and symbiotic cells in legume plants,
which are based on similar mechanisms from both the bacte‐rial side (BclA-like transporters) and plant side (AMPs). It
is important to understand the mechanisms underlying theregulation of the factors, molecules, and signals of the plant
as well as the bacterial cells involved in inducing cell differ‐entiation and nitrogen fixation in endophytic cells requiredfor in-planta survival and plant growth promotion. The spe‐cialized legume plant genes involved in symbiotic interac‐tions may have arisen from a pre-existing non-symbiotic
plant gene, such as rice plants, suggesting convergent
coevolution in these distant plant species.
Acknowledgements
This work was supported by Suranaree University of Technol‐ogy, Kagoshima University, the National Institute for Basic Biol‐ogy, the Office of the Higher Education Commission under NRU
project of Thailand, the National Research Council of Thailand,
the Japan Society for the Promotion of Science, the Thailand Sci‐
ence Research and Innovation, and the Royal Golden Jubilee Ph.D.
program.
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Microbes Environ. Vol. 33, No. 2, 162-171, 2018https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME17203

MOST CITED ORIGINAL PAPER IN THIS
THREE YEARS [2018-2020] (32 citations)

Taxon Richness of “Megaviridae” Exceeds those of Bacteria and Archaea in the Ocean
Tomoko Mihara1, Hitoshi Koyano2, Pascal Hingamp3, Nigel Grimsley4, Susumu Goto5, and Hiroyuki Ogata1,*
1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611–0011, Japan; 2School of Life Science andTechnology, Laboratory of Genome Informatics, Tokyo Institute of Technology, 2–12–1 Ookayama, Meguro-ku, Tokyo 152–8550,
Japan; 3Aix Marseille Université, Université de Toulon, CNRS, IRD, MIO UM 110, 13288, Marseille, France; 4Integrative Marine
Biology Laboratory (BIOM), CNRS UMR7232, Sorbonne Universities, 66650, Banyuls-sur-Mer, France; and 5Database Center forLife Science, Joint-Support Center for Data Science Research, Research Organization of Information and Systems, Wakashiba,
Kashiwa, Chiba 277–0871, Japan(Received December 28, 2017—Accepted February 25, 2018—Published online May 25, 2018)

Since the discovery of the giant mimivirus, evolutionarily related viruses have been isolated or identified from various
environments. Phylogenetic analyses of this group of viruses, tentatively referred to as the family “Megaviridae”, suggest thatit has an ancient origin that may predate the emergence of major eukaryotic lineages. Environmental genomics has sincerevealed that Megaviridae represents one of the most abundant and diverse groups of viruses in the ocean. In the present study,
we compared the taxon richness and phylogenetic diversity of Megaviridae, Bacteria, and Archaea using DNA-dependent
RNA polymerase as a common marker gene. By leveraging existing microbial metagenomic data, we found higher richness
and phylogenetic diversity in this single viral family than in the two prokaryotic domains. We also obtained results showingthat the evolutionary rate alone cannot account for the observed high diversity of Megaviridae lineages. These results suggestthat the Megaviridae family has a deep co-evolutionary history with diverse marine protists since the early “Big-Bang” radiationof the eukaryotic tree of life.
Key words: Mimiviridae, Megaviridae, species richness, RNA polymerase, ocean metagenome

Acanthamoeba polyphaga mimivirus (APMV), initiallymistaken as a Gram-positive bacterium when it was isolated
via amoeba co-culture in the 1990s (4), was recognized in2003 as a bone fide virus, indeed a ‘giant virus’ with a large
750-nm virion including a fibril-containing rigid surface layer(40). APMV possesses a 1.2-Mb linear dsDNA genome coding
for more than 1,000 genes (43, 65), which is more than those
encoded on the genomes of some small prokaryotes. It isclassified as a member of nucleocytoplasmic large DNA
viruses (NCLDVs), the proposed order “Megavirales” (17),together with various giant viruses discovered after APMV
(6, 45, 46, 63, 78). The unexpected dimensions and complexityof APMV and other exotic giant viruses triggered the reassessment of differences between cellular and viral life forms(66), fueled debates on the origin of viruses (14, 54, 56), andrevived interest in re-defining the concept of viruses (13, 15, 27).
Phylogenetic studies have indicated multiple origins of APMV
genes; some APMV genes appear to be of viral origin, whereas
others appear to originate from cellular organisms (23, 24, 55,71) or unknown sources (i.e., ORFans). Despite the apparent
mosaicity of its complex genome, one coherent finding thatemerges from these studies is that the origin of APMV lineage
is old, being as ancient as the emergence of the Eukarya
domain in the Tree of Life (65). In particular, the ancientorigin of APMV and related giant viruses has been supportedby phylogenies of replication- and transcription-related genes(1, 72, 77, 89). The antiquity of giant viruses further inspired
hypotheses of a putative “Fourth Domain of Life”, althoughthese are still highly controversial (7, 16, 44, 57, 59, 83, 90).
Since the discovery of APMV, numerous APMV relatives* Corresponding author. E-mail: ogata@kuicr.kyoto-u.ac.jp;
Tel: +81–774–38–3274; Fax: +81–774–38–3269.

have been isolated using amoeba co-culture from different
environments including marine sediment, river, soil, contact
lens liquid, and sewage water (2, 31, 41, 88). These viruses
are subdivided into lineage A, B and C mimiviruses (88). Theseamoebal mimiviruses, together with additional giant viruses
infecting microzooplankton, such as Cafeteria roenbergensis
virus (CroV) (25) and Bodo saltans virus (19) as well as
Klosneuviruses recently identified in metagenomes (71),
constitute the family Mimiviridae officially approved by theInternational Committee on Taxonomy of Viruses (ICTV).
Shortly after the discovery of APMV, algal viruses isolated in
the sea, such as Chrysochromulina ericina virus (CeV) andPyramimonas orientalis virus, were found to form a strongly
supported monophyletic group with APMV based on DNA
polymerase phylogenies (28, 51, 52). Since then, the monophyletic group has grown with the inclusion of Phaeocystis
globosa virus (PgV) (69), Aureococcus anophagefferens
virus (AaV) (53), Haptolina ericina virus (HeV RF02), andPrymnesium kappa viruses (PkV RF01 and PkV RF02) (34)
as well as metagenome-assembled Organic lake phycodnaviruses (OLPV1 and OLPV2) (86) and Yellowstone lake
mimivirus (YSLGV) (91). Some of these viruses are officially,
but inappropriately, classified in the Phycodnaviridae family.
Arslan et al. proposed to reassign the family “Megaviridae”
to the monophyletic group that combines the above mentioned
mimiviruses, zooplankton giant viruses, and algal giant viruses(2). Gallot-Lavallée et al. recently proposed to classify mimiviruses and microzooplankton giant viruses of the Megaviridae
family into the subfamily “Megamimivirinae” and the algal
viruses into the subfamily “Mesomimivirinae” (29). The tentative Megaviridae family is the focus of the present study
and it is this proposed Megaviridae nomenclature that we use
henceforth.

Taxon Richness of Megaviridae

Megaviridae constitutes approximately 36% of giant
viruses in epipelagic oceans, with their abundance being in
the order of 103 to 105 genomes mL–1 sea water (32). A recent
metatranscriptomic study also demonstrated that members of
Megaviridae are active everywhere in sunlit oceans and infecteukaryotic communities of various size ranges from piconanoplankton (0.8–5 μm) up to mesoplankton (180–2,000 μm) (9).
The hosts of isolated Megaviridae are still limited to a handfulof eukaryotic lineages, but already encompass an extremely
wide range of unicellular eukaryotes, including Amoebozoa,
Stramenopiles (Cafeteriaceae and Pelagophyceae), Euglenozoa(Kinetoplastida), Haptophyceae (Phaeocystales and Prymnesiales),and Viridiplantae (Chlorophyta). Sequence similarity searches
between metagenomic sequences and known Megaviridae
genomes also indicated the existence of many uncultured
Megaviridae lineages in marine environments (32). Taken
together with the inferred antiquity of Megaviridae, thesefindings suggest that the host range of Megaviridae is markedly
wider than currently recognized, and species richness inside
the family Megaviridae may consequently be comparablewith that of protists, which undoubtedly represent the majorpart of eukaryotic species’ diversity.
DNA-dependent RNA polymerases (RNAPs) of cellularorganisms are multisubunit protein complexes, the structuresof which have been elucidated for all three domains of life
(Bacteria, Archaea, and Eukarya) (82). The number of subunits constituting the machinery differs across the domains oflife (50, 82). Among them, the two largest subunits (hereafterreferred to as Rpb1 and Rpb2) are both highly conserved andmostly encoded as single copy genes in the three domains oflife, although eukaryotes commonly possess distant paralogs(33). Rpb1 and Rpb2 of eukaryotes correspond to the RNA
polymerase β’ and β subunits of bacteria, and to the RpoA
and RpoB of archaea, respectively (39, 80, 81). Archaeal
RpoA is composed of two subunits encoded by two small
genes. Rpb1 and Rpb2 have been selected as two of the 102
genes suitable for the assessment of phylogenetic relationships among prokaryotes (i.e., 102 Nearly Universal Trees)(38). Moreover, Rpb1 and Rpb2 are conserved in all known
members of Megaviridae (50). Transcriptomic and proteomic
studies have indicated that Rpb1 and Rpb2 are expressed
during infection and packed into mimivirus capsids (12, 42,
43, 67). Bacteriophages, such as T7 and SP6, encode singlesubunit RNAPs, which are phylogenetically unrelated tomultisubunit RNAPs (10, 74). Therefore, Rpb1 and Rpb2
possess the required characteristics to be used as phylogenetic markers for both Megaviridae and cellular organisms(65, 72). In the present study, we investigate the taxon richness (or lineage richness) and phylogenetic diversity (PD) of
Bacteria, Archaea, and Megaviridae based on Rpb1 and Rpb2
sequences found in marine microbial metagenomes derived
from prokaryotic size fractions.
Materials and Methods
Sequence data
We used the UniProt database (Release 2016_03) (79) and the
National Center for Biotechnology Information (NCBI) Reference
Sequence (RefSeq) (61) Viral Section (Release 75) database tocollect Rpb1 and Rpb2 protein sequences from cellular organisms

163
and NCLDVs. We additionally used the GenomeNet/Virus-Host
Database (49) to retrieve the nucleotide sequences of Rpb1 and Rpb2.
Marine metagenomic sequence data were obtained from CAMERA(75) and the Tara Oceans project (32, 76) (Table S1). In addition, we
obtained metagenomic data for other non-marine environmentsfrom CAMERA and KEGG/MGENES (35). Collectively, we usedmetagenomic data derived from 58 projects (Table S1). Based on
metagenomic data, we initially prepared files for amino acidsequences for open reading frames (ORFs) that were longer than orequal to 150 codons. The total number of ORFs was 149,645,996:
marine metagenomes (101,856,227 ORFs, 68%), other aquatic
environmental metagenomes (8,385,210 ORFs, 5.6%), mammalassociated microbial metagenomes (38,341,510 ORFs, 26%), andother metagenomes (1,063,049 ORFs, 0.7%). Most of the analyses
presented in the present study focused on data from marine metagenomes, mainly derived from two large scale oceanic microbiome
projects: Tara Oceans and the Global Ocean Sampling project (68).
Data from other environments were used to confirm marine data.
Non-synonymous and synonymous substitution rate ratio
In order to estimate the level of functional constraint on Rpb1/
Rpb2 coding sequences, we computed the numbers of nonsynonymous (Ka) and synonymous (Ks) substitutions per site andtheir ratio (ω=Ka/Ks) using a maximum likelihood method implemented in the codeml program in the PAML package (85). We used
the Mann-Whitney U test to assess the significance of differences inω values between Megaviridae and bacterial sequences.
Reference sequence alignments and phylogenetic trees
We identified Rpb1 and Rpb2 homologs in the UniProt and
RefSeq databases using HMMER/HMMSEARCH (version 3.1;
E-value<1×10–5) (20) based on profile hidden Markov models that
we built from alignments of Rpb1 (COG0086) and Rpb2 homologs(COG0085) (30). We used CD-HIT version 4.6 to reduce the redundancy of the collected known Rpb1/2 sequences (47). The resulting
non-redundant sequences were aligned using MAFFT v7.215 (36)with default parameters and alignment columns containing gaps
were trimmed using trimAl v1.2rev59 (8). We referred to theresulting reference sequence alignments for Rpb1 and Rpb2 as
RAln-Rpb1 and RAln-Rpb2, respectively. We also generated reference sequence alignments solely composed of sequences from
Megaviridae, Bacteria, and Archaea, and referred to the alignments
as RAln-MBA-Rpb1 and RAln-MBA-Rpb2. Maximum likelihood
phylogenetic trees were constructed with the use of FastTree version
2.1.7 (64). The resulting reference trees for Rpb1 and Rpb2 werereferred to as RTree-Rpb1 and RTree-Rpb2, respectively. The
significance of the branches in the trees was assessed using the
Shimodaira-Hasegawa test (73) implemented in FastTree. Referencealignments and trees are available at the GenomeNet ftp site (ftp://
ftp.genome.jp/pub/db/community/RNAP_ref_tree).
Identification of RNAP homologs in metagenomes
In order to identify Rpb1 and Rpb2 homologs in metagenomic
sequence data, we used HMMER/HMMSEARCH (version 3.1) (20)with the default parameters and built 10 HMMs for Rpb1 and 10
HMMs for Rpb2, each of which represents a group of phylogenetically related sequences in our reference phylogenetic trees. Specifically,
these HMMs represent Megaviridae, other NCLDV groups 1 and 2(group 1: Asfarviridae, Poxviridae; 2: Ascoviridae, Iridoviridae,
Pandoravirus, Pithovirus), Bacteria, Archaea, Eukaryotes I to IV (I:RNAP I; II: RNAP II; III: RNAP III; IV: RNAP IV/V), and RNA
polymerases of plastids. We screened metagenomic data for the
Rpb1 and Rpb2 homologs (≥150 amino acid residues) using these
profile HMMs with HMMSEARCH (E-value<1×10–5).
Taxonomic classification
Phylogenetic placement is a bioinformatics technique that is used
to identify the most likely phylogenetic position for a given query
sequence on a reference phylogenetic tree. Pplacer is one of the164

Mihara et al.

phylogenetic placement tools that efficiently analyze large numbersof sequences, including short metagenomic sequences, within linear
computation time (48). Metagenomic Rpb1/Rpb2 sequence fragments
were aligned on the reference alignments (i.e., RAln-Rpb1 andRAln-Rpb2) using HMMALIGN and placed on the reference phylogenetic trees (RTree-Rpb1 and RTree-Rpb2) using Pplacer with theuse of the maximum likelihood mode. These Rpb1 and Rpb2 fragments were taxonomically classified into the above-mentioned 10
phylogenetic groups based on their phylogenetic placement.
Taxon richness and PD
Metagenomic Rpb1/Rpb2 fragments that were taxonomicallyassigned to Megaviridae, Bacteria, or Archaea were re-aligned on
the RAln-MBA-Rpb1 or RAln-MBA-Rpb2 reference sequencealignments using HMMALIGN. Since metagenomic sequenceswere often shorter than full-length sequences in the reference alignments, we examined taxon richness (i.e., the number of sequence
clusters) (3) and PD (22) along the alignment using a 100-residue
sliding window on the alignments (with a step size of 10 residues).
Metagenomic sequences exhibiting gaps at >10% of the sites in thealignment window were discarded.
Taxon richness was computed based on sequence clustering by
the ucluster_fast command of USEARCH v7.0 (21) with three cutoff values for amino acid sequence identities (i.e., 70%, 80%, and90%). The significance of differences between richness curves wasassessed using a Log-rank test (70).
PD was calculated using Phylogenetic Diversity Analyzer (PDA)
version 1.0 (11), based on FastTree phylogenetic trees of metagenomic sequences that were aligned inside the sliding window. In
order for PD scores to be comparable between Megaviridae,
Bacteria, and Archaea, we constructed a phylogenetic tree with1,000 randomly selected sequences for each organism group andcalculated the PD score.
RNAP paralogs in Megaviridae
Some of the sequenced viruses of Megaviridae, such as PgV (69),
OLPV1, and OLPV2, encode two distantly related Rpb2 in theirgenomes. In order to eliminate the effect of the presence of theseparalogs on the richness assessment of Megaviridae Rpb2, we classified Megaviridae Rpb2 metagenomic sequences into two paralogous groups based on phylogenetic reconstructions and performed
additional rarefaction analyses for each of the paralogous groups.

Results
Functional constraints on Megaviridae Rpb2 and Rpb1 arehigher than those on bacterial homologs
The functional constraint on a protein sequence may be
estimated by the ratio (ω) of non-synonymous (Ka) andsynonymous (Ks) substitution rates. A small ω value indicates
an elevated level of functional constraint (i.e., slow pace ofamino acid sequence evolution), while a large ω value, which
is typically smaller than 1 for a functional protein coding
sequence, indicates a low level of functional constraint (i.e.,fast amino acid sequence evolution). We computed ω values
for Megaviridae Rpb2/Rpb1 by comparing close homologs.
We also computed ω values for bacterial Rpb2/Rpb1 bycomparing genes of Escherichia coli K-12 MG1655 withthose of other closely related bacteria (Fig. 1). The average ωvalue for Megaviridae Rpb2 was 0.0205, while it was 0.0582
for bacterial Rpb2. The average ω value for Megaviridae
Rpb1 was 0.0105, while it was 0.0811 for bacterial Rpb1.
These results suggest that functional constraints on Megaviridae
Rpb2/Rpb1 were higher than those on bacterial homologs(P=0.00003 for Rpb2, P=0.003 for Rpb1); however, theFig. 1. Functional constraints on Megaviridae Rpb2 and Rpb1. Nonsynonymous (Ka) and synonymous (Ks) substitution rate ratios (ω=Ka/Ks) are plotted for Megaviridae and bacterial Rpb2 (A) and Rpb1 (B).
We selected pairs of orthologs from Megaviridae (shown in red dots)based on the following criteria: Ka>0.01, Ks<5.0, and the percent standard
error of ω being below 25%. These closely related pairs of viral genes
were all from amoeba infecting mimiviruses. We selected pairs of orthologs
between genes from Escherichia coli K-12 MG1655 and genes fromother bacteria (shown in blue dots) based on the following criteria:
Ka>0.01, Ks<10.0, and the percent standard error of ω being below
25%. ω values were significantly lower for Megaviridae than for bacterial
homologs, indicating a higher level of evolutionary constraint on
Megaviridae homologs.

sample sizes for Megaviridae were small (n=7 for Rpb2, n=3
for Rpb1).
Reference trees and taxonomic classification of metagenomic
sequences
We identified 59,938 Rpb2 and 40,534 Rpb1 homologs in
the UniProt/RefSeq sequence databases using profile HMMs
derived from COG0085 (Rpb2) and COG0086 (Rpb1). Viral
Rpb2/Rpb1 sequences identified by this search all originatedfrom NCLDVs. Among these sequences, 511 Rpb2 and 575
Rpb1 sequences were selected as reference sequences after
reducing redundancy by clustering and discarding unusuallylong and short sequences. Based on the reference sequences,
we built reference phylogenetic trees for Rpb2 (Fig. 2A) and
Rpb1 (Fig. 2B). The reference trees were generally consistent
with the classification of prokaryotes and viruses as well as
eukaryotic paralogs.
Using profile HMMs built from these reference sequences,248,101 and 252,609 sequences were obtained from metagenomes as candidates of environmental Rpb2 and Rpb1,
respectively. These environmental sequences were phylogenetically classified using the reference trees described above,and specific phylogenetic groups were successfully assigned
to 195,195 Rpb2 and 214,521 Rpb1 sequences (Table 1). The
taxonomic assignments were dominated by Bacteria (80% for
Rpb2, 81% for Rpb1), Archaea (5.7% for Rpb2, 6.6% forRpb1), and Megaviridae (10.2% for Rpb2, 6.1% for Rpb1) as
expected from the microbial size fractions (enriched withprokaryotic size organisms and viruses, Table S1) targeted bymost of the analyzed metagenomes. Most of the sequencesthat were taxonomically assigned to Megaviridae were foundin marine metagenomes (Rpb2: 18,633 [93.3%]; Rpb1: 12,225[93.0%]), which is consistent with the previous finding of thehigh abundance of Megaviridae in the sea (32). The detectionof eukaryotic sequences was limited (1,824 for Rpb2 and
1,276 for Rpb1 for RNA polymerase II from marine metagenomes) and likely biased towards picoeukaryotes due to the165

Taxon Richness of Megaviridae

Fig. 2. Maximum likelihood phylogenetic trees of Rpb2 and Rpb1. The Rpb2 tree (RTree-Rpb2) was constructed using 511 representative
sequences (A), and the Rpb1 tree (RTree-Rpb1) with 575 representative sequences (B). Branches are colored as follows: Eukaryotes I-IV (orange),
Bacteria (blue), Archaea (green), Megaviridae (red), plastid (purple), and other NCLDVs (pink).
Table 1.

Number of taxonomically assigned metagenome sequences.
Environment

MarineOther aquatic

Mammal associated

Other

Total

Operational clade name

Rpb2

Rpb1

Rpb2

Rpb1

Rpb2

Rpb1

Rpb2

Rpb1

Rpb2

Rpb1

Eukaryote I
Eukaryote II
Eukaryote III
Eukaryote IV/V
Bacteria
Archaea
Megaviridae
Chloroplast
NCLDVs 1
NCLDVs 2
Total

690
1,824
729
82
111,124
10,177
18,633
2,540
119
1,135
147,053

741
1,276
854
54
125,874
12,826
12,225
7,666
80
1,484
163,080

16
77
17
7
6,387
640
1,330
27
20
126
8,647

14
28
10
5
6,740
784
841
455
6
110
8,993

82
78
68
0
38,192
56
10
91
19
4
38,600

109
108
101
3
39,798
102
79
1,159
9
2
41,470

0
4
0
0
588
300
0
2
0
1
895

2
2
1
0
625
341
0
5
0
2
978

788
1,983
814
89
156,291
11,173
19,973
2,660
158
1,266
195,195

866
1,414
966
62
173,037
14,053
13,145
9,285
95
1,598
214,521

filter size range. Therefore, we excluded eukaryotic sequencesin subsequent analyses and focused on Megaviridae, Bacteria,and Archaea sequences identified in marine metagenomic
data unless otherwise specified.
Taxon richness of Megaviridae RNAP is greater than thoseof Bacteria and Archaea
The average lengths of the Rpb2 and Rpb1 reference
sequences were as follows: Megaviridae Rpb2 (1,239 aa) and
Rpb1 (1,392 aa); bacterial Rpb2 (1,282 aa) and Rpb1 (1,346 aa);archaeal Rpb2 (1,132 aa) and Rpb1 (1,373 aa). In contrast,
most of the metagenomic Rpb2/Rpb1 sequences were foundto be partial: Megaviridae Rpb2 (314 aa) and Rpb1 (314 aa);bacterial Rpb2 (292 aa) and Rpb1 (285 aa); archaeal Rpb2
(313 aa) and Rpb1 (303 aa). These sequences were aligned onreference Rpb2 and Rpb1 alignments (RAln-MBA-Rpb2 andRAln-MBA-Rpb1) composed of complete sequences from
Megaviridae, Bacteria, and Archaea (Fig. S1). We assessedtaxon richness by generating operational taxonomic units(OTUs) from sequences aligned inside a 100-aa window

along the reference alignments. In order to generate OTUs,
clustering was performed with three amino acid sequence
identity thresholds (i.e., 90%, 80%, and 70% identities). Rpb2
and Rpb1 of Megaviridae showed a higher number of OTUs
than those of Bacteria or Archaea at all resampling levels at
each of the three arbitrarily selected sequence rich regions(Fig. 3 and Fig. S2A and S2B). Similar results were obtained
when metagenomic sequences from other environments, suchas freshwater and the human gastrointestinal tract, were
included (Fig. S2C and S2D), and confirmed along the entire
length of the reference alignments; Megaviridae exhibited alarger number of OTUs than the two cellular domains whatever the regions of Rpb2 and Rpb1 considered (Fig. 4A and
B). Log-rank tests indicated that differences in the number of
OTUs between Megaviridae and the two cellular domains
were significant (Fig. 4C and D).
Effects of the existence of Megaviridae Rpb2 paralogs
Rpb2 and Rpb1 were encoded as a single copy in most of
the sequenced bacterial and archaeal genomes; only 1.96%

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Fig. 3. Rarefaction curves of richness for metagenomic Rpb2/Rpb1 sequences. The X-axis indicates the numbers of resampled sequences for eachorganism group and the Y-axis indicates the average number of OTUs over 10 resamplings. Sequence clustering was performed using an 80% aminoacid sequence identity cut-off. Regarding each Rpb2 (A, C) and Rpb1 (B), three positions of the reference alignment were selected for comparisonsof taxon richness between Megaviridae (red), Bacteria (blue), and Archaea (green). In (C), the taxon richness of two paralogous groups of
Megaviridae Rpb2 (pink/orange) were assessed separately.

(Rpb2) and 2.97% (Rpb1) of bacterial, and 1.00% (Rpb2) and
1.00% (Rpb1) of archaeal genomes presented paralogs.
However, during the reconstruction of RNAP reference trees,
we noted that some Megaviridae, such as PgV and OLPV1/2,
encoded two copies of Rpb2 genes. The existence of theseparalogs may contribute to increasing the richness of thehomologous group of sequences, hence inducing bias intaxon richness interpretations. In order to investigate theevolutionary relationships of these paralogs, we reconstructed
Rpb2 trees, including metagenomic sequences, based on thesame three sequence rich sub-alignment regions. The resultsof these analyses revealed that the Rpb2 paralogs were only
distantly related in the reconstructed phylogenetic trees (Fig.
S3). A set of Rpb2 from PgV, OLPV1, and OLPV2 grouped
together, whereas another set of Rpb2 from the same viruses
formed another group. This tree topology strongly suggested
a single duplication event of Rpb2 in the ancestor of theseviruses. Therefore, the existence of Megaviridae Rpb2 paralogs
may lead to an approximately two-fold increase in apparent
richness. In order to obtain a more reasonable estimate for thetaxon richness of Megaviridae based on Rpb2 sequences, weclassified metagenomic Rpb2 homologs into two groups by
taking putative ancient duplication into account (Fig. S3A,
S3B, and S3C). Richness estimates and rarefaction curves forindividually analyzed paralogous groups still indicated alarger number of OTUs for Megaviridae than for Bacteria andArchaea at any given number of resampled sequences (Fig.
3C). Paralogs were not found for Rpb1, except for a pair of
Rpb1 sequences in AaV. Sequence identity between the AaV
Rpb1 sequences was 33%. These sequences were found to beclosely located in phylogenetic trees when metagenomic
Rpb1 sequences were included (Fig. S3D). Therefore, we
considered the influence of the paralogous Rpb1 groups on
the taxon richness estimate to be negligible.

Taxon Richness of Megaviridae

167

Fig. 4. Richness for 1,000 metagenomic Rpb2/Rpb1 sequences along the length of reference alignments. The numbers of OTUs after the resamplingof 1,000 metagenomic sequences were plotted at each sequence region of Rpb2 (A) and Rpb1 (B). The significance (p-value) of differences between
Megaviridae and prokaryotes was assessed using the Log-rank test at each sequence region of Rpb2 (C) and Rpb1 (D).

Comparison of PD between Megaviridae and Bacteria/Archaea
PD is a measure of the diversity of phylogenetically relatedsequences, defined as the sum of all branch lengths in thephylogenetic tree (60). We calculated PD scores for Rpb2 andRpb1 sequences obtained from marine metagenomes using asliding window on the Rpb alignment RAln-MBA-Rpb2 andRAln-MBA-Rpb1 (Fig. 5). Megaviridae showed higher PD
scores than Bacteria and Archaea along the entire length of
the alignments.
The phylogenetic distribution of Megaviridae Rpb2
sequences on the reference tree indicated that a larger numberof metagenomic sequences mapped to Mesomimivirinae
subfamily branches (95.1%) than to Megamimivirinae subfamilybranches (4.6%) (Fig. 6). Among the Megamimivirinae
branches, a larger number of environmental sequences (275
sequences) mapped to the branch leading to CroV than to thebranches leading to Klosneuviruses (52 sequences) or amoebal
mimiviruses (37 sequences). A notable feature of the Rpb2
phylogenetic distribution was that the deeper the branches(i.e., the closer to the root), the higher the number of environmental sequences they got assigned: e.g. 5,414 sequences
mapped to the root of one of the Mesomimivirinae Rpb2,
whereas only 234, 259, 1,247, and 438 sequences mapped to
the leaves representing OLPV1, OLPV2, PgV, and CeV reference genomes, respectively.
Discussion
In the present study, we extracted environmental Rpb1 and
Rpb2 sequence fragments from a large set of microbial

Fig. 5. Phylogenetic diversity of metagenomic Rpb2/Rpb1 sequences
along the length of reference alignments. Phylogenetic diversity (PD)scores were computed with phylogenetic trees constructed using 1,000
metagenomic sequences at each sequence region of Rpb2 (A) and Rpb1
(B).

metagenomes (58 projects) and classified them into taxonomic
groups using a phylogenetic placement method. Taxonomic
assignments revealed a large representation of bacterial

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Fig. 6. Numbers of metagenomic sequences assigned to branches of Megaviridae Rbp2. This phylogenetic tree is part of the full phylogenetic tree
in Fig. 2A. The numbers in red squares are the numbers of sequences in the Megamimivirinae subfamily and those in purple are the numbers of the
Mesomimivirinae subfamily. The dashed branch lines near the root of the tree represent the status of AaV sequence not forming a monophyletic
group with the other members of Megaviridae in the full reference tree in Fig. 2A.

Rpb1/2 sequences (~80%) and fewer archaeal and Megaviridae
sequences (5–10%). Megaviridae sequences were preferentiallydetected from metagenomes originating from aquatic
environments. This is consistent with previous findings, althoughmembers of Megaviridae have been isolated from various
environments including oceans, lakes, rivers, air conditioning
cooling systems, drainage, and soil (41). The over-representationof bacterial Rpb1/2 sequences in metagenomes is expected
given their known dominance in various environments (26,32). When the same sequence similarity thresholds wereapplied for taxon delineation for cellular organisms and viruses,
Megaviridae showed significantly higher taxon richness thanBacteria and Archaea. As a more general measure that does
not require sequence identity thresholds, we also examined
PD. The PD indices of Megaviridae were also systematicallyhigher than those of Bacteria and Archaea.
A possible reason for why apparent taxon richness in the
Megaviridae is so vast could be a fast evolutionary rate in
Megaviridae. However, our results indicated that functional
constraints are higher for Megaviridae Rpb1/2 than forbacterial homologs. This result suggests that the rate ofsequence evolution is lower for Megaviridae Rpb1/2 than forbacterial homologs if their mutation rates are similar. BlancMathieu and Ogata (5) previously indicated that the mutation
rate of giant viruses may be as low as prokaryotes based on
Drake’s rule, postulating that “the mutation rate per genome
has evolved towards a nearly invariant value across taxa”, aswell as the finding that giant viruses encode many DNA
repair enzymes. A recent study monitoring more than one
year of experimental evolution consistently demonstrated
that the mutation rates of a giant virus, Lausannevirus, and abacterium remained similar over the length of the experiment(58). Therefore, the average mutation rate of Megaviridae
may be similar to that of prokaryotes.
Even if a high mutation rate potentially contributed toaccelerated evolution, fast evolution is not sufficient to
explain the prominent radiation of evolutionarily deep lineages
because radiation requires niche expansion (62). As a matterof fact, the richness of prokaryotes, which evolve faster thaneukaryotes, is less than that of eukaryotes in marine environments.
Recent studies revealed the presence of ~110,000 OTUs atthe species level for eukaryotic plankton in the global sunlit
ocean (18), but only 36,000–45,000 OTUs for prokaryotes inthe same type of environment (76, 92). The markedly hightaxon richness of Megaviridae revealed by our study parallels
the high richness of eukaryotes, the potential hosts of
Megaviridae. Based on the ancient origin of Megaviridae thathas been inferred to antedate the emergence of major eukaryotic
lineages, our results strongly support the Megaviridae family
having a phylogenetically deep and wide co-evolutionary
history with diverse marine protists. This virus-host co-evolution
may have been at work from the early “Big-Bang” radiation
down to the more recent diversification of the tree of eukaryotes.
In other words, the long history of the diversification of

Taxon Richness of Megaviridae

eukaryotes may have played a key role in the successive
niche expansion of Megaviridae. A similar co-evolutionary
history was also proposed for a family of RNA viruses (37).
Many of the Megaviridae sequences were placed in the
branches leading to Mesomimivirinae (Fig. 6), which are
currently represented by algae-infecting viruses, such as PgV
and CeV. The host range of algal species of this clade spansfrom Haptophyceae (Phaeocystales and Prymnesiales) toPelagophyceae and Chlorophyta, which are deeply separated
from one another in the eukaryotic tree. It is also important tonote that even haptophytes alone constitute a very rich groupof unicellular eukaryotes (18). Among the Megamimivirinae
subfamily, one of the most abundant lineages observed inmarine metagenomes was the microzooplankton infecting
CroV; however, since the deeper branches also received many
sequence assignments, the inference of potential hosts forthese sequences are difficult. Overall, the phylogenetic positions of these marine Megaviridae marker genes point to diverse
protists, including unicellular algae and microflagellates, as
the potential host of these uncultured Megaviridae. Although
the amoebal co-culture method (41) has permitted many new
mimiviruses to be analyzed, further efforts to isolate viruses
from diverse eukaryotes are desirable in order to increase thegenome sampling coverage of this diverse clade.
In the present study, we showed that the taxon richness of
Megaviridae exceeded that of the prokaryotic domains in theocean. Investigations on the as yet uncovered diversity of
Megaviridae will require the development of experimental
alternatives to virus isolation by co-culture method, which isa labor-intensive process depending on the culturability ofeukaryotic hosts. These methods include single cell genomics(87), single virus genomics (84), the development of degenerate
PCR primers, and a co-occurrence network analysis (32).
Acknowledgements
This work was supported by The Canon Foundation (No.
203143100025), JSPS/KAKENHI (Nos. 16KT0020, 17H03850,
and 26430184), Scientific Research on Innovative Areas from the
Ministry of Education, Culture, Science, Sports and Technology(MEXT) of Japan (Nos. 16H06429, 16K21723, and 16H06437), andthe Collaborative Research Program of the Institute for Chemical
Research, Kyoto University (No. 2016-28, 2017-25). PH was supported by the OCEANOMICS “Investissements d’Avenir” programof the French government (No. ANR-11-BTBR-0008). Computational
work was completed at the SuperComputer System, Institute for
Chemical Research, Kyoto University. We thank the Tara Oceans
consortium, staff, and sponsors who supported the Tara Oceans
Expedition (http://www.embl.de/tara-oceans/) for making data
accessible. This is contribution number 72 of the Tara Oceans
Expedition 2009–2013.
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https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME14054

MOST CITED ORIGINAL PAPER IN THISTEN YEARS [2011-2020] (139 citations)
MOST CITED ORIGINAL PAPER IN 2020 (59 citations)Short Communication
Intestinal Colonization by a Lachnospiraceae Bacterium Contributes
to the Development of Diabetes in Obese Mice
Keishi Kameyama1*, and KiKuji itoh2
1Institute for Innovation, Ajinomoto Co., Inc., Kawasaki 210–8681, Japan; and 2Department of Veterinary Public Health, Graduate

School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113–8657, Japan(Received April 4, 2014—Accepted July 17, 2014—Published online October 4, 2014)

The aim of the present study was to identify bacteria that may contribute to the onset of metabolic dysfunctions. We isolated
and identified a candidate bacterium belonging to Lachnospiraceae (strain AJ110941) in the feces of hyperglycemic obese
mice. The colonization of germ-free ob/ob mice by AJ110941 induced significant increases in fasting blood glucose levels aswell as liver and mesenteric adipose tissue weights, and decreases in plasma insulin levels and HOMA-β values. These results
indicated that the specific gut commensal bacterium AJ110941 influenced the development of obesity and diabetes in ob/ob
mice with genetic susceptibility for obesity.
Key words: gut microbiota, diabetes, T-RFLP, gnotobiotic mouse, Lachnospiraceae

The gut microbiota has been shown to influence hostenergy homeostasis, metabolism, and inflammation; thus,
microbial communities within the gut are now recognized as
an important environmental factor in the onset of obesity andtype 2 diabetes (T2D) (5, 7, 12, 14, 23). As a consequence,
interest in the development of methods to target gut microbiota
as a therapy for T2D is increasing (2, 12, 18, 21).
Several studies have conducted metagenomic analyses in
an attempt to identify members of the gut microbial community that contribute most significantly to the progression of
T2D. Qin et al. performed a metagenomic analysis on fecal
samples obtained from 345 Chinese men and women with orwithout T2D (19). Karlsson et al. also conducted a metagenomic analysis to compare microbial communities in fecal
samples collected from 145 European women who had either
T2D, impaired glucose metabolism, or were healthy (13).
Both of these studies reported that butyrate-producing
Roseburia species and Faecalibacterium prauznitzii wererarer in subjects with T2D; however, the identified bacteriathat existed in high population in T2D subjects differed between
these studies (8). Cani et al. proposed that lipopolysaccharide(LPS), a cell wall component of almost all Gram-negative
bacteria, may be a key substance responsible for metabolic
endotoxemia, low-grade systemic inflammation, and insulin
resistance in mouse models (1, 3). In support of this, aprevious study reported that experimental endotoxemia
induced adipose inflammation and insulin resistance inhuman subjects (17).
We herein searched for the specific gut commensal bacterium related to metabolic syndrome using the terminal
restriction fragment length polymorphism (T-RFLP) analysisof fecal samples from a mouse model of T2D. In this study, acomparison of homozygous db/db (diabetic) with heterozygous db/+ (non-diabetic) mice revealed that a specific* Corresponding author. E-mail: keishi_kameyama@ajinomoto.com

fragment of the T-RFLP analysis was present at significantly
higher levels in db/db mice than in db/+ mice. Even thoughob/ob obese mice gain weight rapidly and show the symptoms of insulin resistance, most of the obese mice do notdevelop severe hyperglycemia compared with db/db mice(22). However, one of the ob/ob mice in the present study
showed an abnormally high fasting blood glucose (FBG)
level similar to db/db mice. The specific fragment observed in
db/db mice was also the most prominent in the abnormally
hyperglycemic ob/ob mouse. We hypothesized that the specific bacterium harboring the fragment may have contributed
to the progression of T2D; therefore, we isolated and cultured
the bacterium (strain AJ110941). We then determined thatcolonization by the isolate with the LPS producer E. coli
induced hyperglycemia and the accumulation of adipose
tissue in the gnotobiotic ob/ob mouse model.
All experimental procedures were reviewed and approvedby the Animal Care Committee of Ajinomoto Co., Inc.
Male 5-week-old homozygous BKS.Cg-Dock7m +/+Leprdb/J(db/db, diabetic) mice, heterozygous control (db/+, nondiabetic) mice, and homozygous B6.V-Lepob/J (ob/ob, obese)
mice were obtained from Charles River Japan (Yokohama,Japan). Male 5-week-old germ-free ob/ob mice were obtained
from Sankyo Lab Service (Tokyo, Japan). These mice werehoused in a controlled environment (on a 12-h light/darkcycle with lights turning off at 19:00) with free access tostandard chow CRF-1 (Oriental Yeast, Tokyo, Japan) andwater and kept in specific pathogen-free (SPF) or germfree/gnotobiotic conditions throughout the experimental
period. Fresh feces and blood were collected from 5- and11-week-old mice after fasting for 16 h.
Blood glucose (FBG) levels were determined using
DRI-CHEM 7000V (Fujifilm, Tokyo, Japan). Plasma insulinlevels were determined using an ultrasensitive mouse insulinkit (Morinaga Institute of Biological Science, Yokohama,Japan). Plasma glucagon levels were determined using the428

Glucagon ELISA Kit Wako (Wako Pure Chemical Industries,
Osaka, Japan). Insulin resistance (HOMA-IR) and β-cellfunction (HOMA-β) were both calculated on the basis of thefasting levels of plasma glucose and insulin according to thehomeostasis model assessment (HOMA) method (16).
Fresh fecal samples were collected from mice, added to
99% ethanol, and stored at −30°C. Bacterial DNA wasextracted from the fecal samples using the FastDNA spin kit
for soil (MP Biomedicals, Santa Ana, CA) using the FastPrep
instrument (MP Biomedicals). T-RFLP analyses of the mouse
gut microbiota were performed as previously described (10).
Two universal primers, 27F labeled with 6-carboxyfluorescein(FAM): 5'-FAM-AGAGTTTGATCCTGGCTCAG-3' and
1492R: 5'-GGTTACCTTGTTACGACTT-3' were used in
PCR to amplify the 16S rRNA gene coding region. Purified
PCR products of the 16S rRNA gene were digested with MspI
(Takara Bio, Otsu, Japan).
To isolate and culture the bacterium, fresh feces werecollected and immediately weighed and transferred to ananaerobic chamber. The feces were homogenized with a
50-fold volume (v/w) of an anaerobic diluent, serially diluted,
and then plated on Eggerth-Gagnon (EG) agar plates (11).
The agar plates were incubated at 37°C for 4 d in theanaerobic chamber. All of the colonies were identified bytheir fragment size, and a target colony with a fragment sizeof 282 bp was then selected. The colony was passaged onto
new EG agar plates, and the bacterial purity of the isolate was
ensured by re-streaking and microscopic observations. The
16S rRNA gene sequence of the isolated bacterium wasdetermined as previously described (15). The sequence wasassembled using GENETYX version 7 (GENETYX, Tokyo,
Japan). The identification and phylogenic tree analysis of the
16S rRNA gene sequence were carried out using the
Ribosomal Database Project (RDP) (6).
When the mice were 8 weeks old, germ-free ob/ob mice
were separated into 3 groups for the germ-free/gnotobiotic
experiment: Group-1 (n=4), germ-free; Group-2 (n=4),
colonization by the non-pathogenic Escherichia coli strain
E-17, which had been isolated from SPF mice; Group-3(n=4), colonization by AJ110941 with E. coli E-17. Groups-2
and -3 were then orally inoculated with E. coli E-17 or
AJ110941 with E. coli E-17 (approximately 1×108 cells suspended in anaerobic PBS, respectively). Group-1 was orally
administered PBS only. These mice were maintained under
the germ-free or gnotobiotic conditions for 8 weeks.

Kameyama et al.

We compared differences in gut microbiota patterns
between db/+ and db/db mice. Hyperglycemia was observedin 11-week-old, but not in 5-week-old mice (Fig. S1). The
282 bp fragment was significantly higher in db/db than db/+
mice at both 5 and 11 weeks old (Fig. S2). We then evaluatedthe presence of the 282 bp fragment in another metabolic
syndrome model, the ob/ob mouse. The highest FBG level
observed among all ob/ob mice was in Mouse Number 4 (Fig.
S3). The 282 bp fragment was also more prominent in hyperglycemic Mouse Number 4 than in the normal glycemic
ob/ob mouse at both 5 and 11 weeks old (Fig. S4). Based onthese db/db and ob/ob mice results, we focused on this 282 bp
fragment bacteria.
We isolated a colony harboring the 282 bp fragment andestablished an axenic strain (strain AJ110941). The isolate
was determined to be closely related to the genus Anaerostipes
in the family Lachnospiraceae (Fig. 1).
We then generated gnotobiotic ob/ob mice colonized by
AJ110941 with E. coli. Eight weeks after the inoculation, thececum, liver, adipose tissue, and blood were collected after a
16-h fast. The gut microbiota of the cecal contents from thethree groups was determined by T-RFLP analysis. No PCR
amplicon was detected in Group-1. Only a single fragment(488 bp) that corresponded to E. coli was detected in Group2. Only two fragments (282 bp and 488 bp) were detected inGroup-3. These results clearly indicated that germ-free orgnotobiotic conditions were maintained during the experimental period. The weights of the liver and mesentericadipose tissue significantly increased in Group-3, whereas no
significant difference was observed in body weights between
the three groups. FBG and plasma glucagon levels weresignificantly higher in Group-3, while plasma insulin levels
were significantly lower. On the other hand, no significant
differences were observed in these parameters between
Groups-1 and -2. The homeostasis model assessment wascalculated from FBG and plasma insulin levels as an index of
insulin resistance (HOMA-IR) and pancreatic β cell function(HOMA-β). HOMA-β was significantly lower in Group-3,
whereas no significant differences were noted in HOMA-IR
between the three groups (Table 1). These results suggested
that colonization by AJ110941 may have promoted thedysfunction of pancreatic β-cells.
Therefore, AJ110941 should be regarded as one of theimportant causative gut bacteria for the induction of T2D. A
previous study reported that the relative abundance of theFig. 1. Phylogenetic tree based on the 16S rRNA gene constructed by Weighbor, the weighted neighbor-joining tree building algorithm, showing
AJ110941 and other strains of the family Lachnospiraceae. Clostridium perfringens ATCC13124T was used as an outgroup. The numbers close to
the nodes represent bootstrap values (n=100 replicates). The scale bar represents 0.05 substitutions per nucleotide position.

A Lachnospiraceae Bacterium Involved in Diabetes
Table 1.

429

Phenotypes of germ-free and gnotobiotic ob/ob mice

Body weight without the caecum (g)Cecum (% of body weight)
Liver (% of body weight without the cecum)
Mesenteric adipose tissue (% of body weight without the cecum)
Fasting blood glucose (mg dL−1)
Fasting plasma insulin (μU mL−1)
Fasting plasma glucagon (pg mL−1)
HOMA-IR
HOMA-βGroup-1

Group-2

Group-3

58.10 ± 5.98a
11.63 ± 4.89a
6.91 ± 0.66a
1.64 ± 0.46a
216.5 ± 113.6a
339.7 ± 46.44a
179.9 ± 43.0a
132.5 ± 23.3a
1350.4 ± 468.9a

61.75 ± 4.29a
12.88 ± 1.94a
7.29 ± 1.83a
1.81 ± 0.39a
286.5 ± 54.1ab
299.8 ± 105.0a
324.2 ± 42.4a
187.5 ± 70.6a
638.5 ± 411.9ab

63.48 ± 10.37a
4.47 ± 0.93b9.70 ± 0.46b
2.63 ± 0.30b
433.25 ± 65.4b
159.4 ± 50.9b
529.0 ± 18.3b
184.4 ± 64.1a
141.7 ± 40.7b

Group-1: germ-free, Group-2: colonization by E. coli, Group-3: colonization by AJ110941 with E. coli. Data are expressed asmeans±SD. Values not sharing a common letter are significantly different at p<0.05 by the Tukey–Kramer multiple comparisons test
(n=4 per group) using the JMP 10.0.0 statistical software package (SAS Institute, Cary, NC).

taxonomic family Lachnospiraceae was increased by earlylife subtherapeutic antibiotic treatments in an obese mousemodel (4). Additionally, a metagenomic study indicated thatthe taxonomic family Lachnospiraceae may be associatedwith T2D (19). However, it remains unclear whether bacteria
belonging to the family Lachnospiraceae actually affectobesity and FBG levels in vivo. Therefore, we generated
gnotobiotic ob/ob mice colonized by AJ110941 with E. coli
to reveal a possible causal relationship. In a preliminary
examination, we microscopically observed that AJ110941
did not singly colonize the intestinal tract of ob/ob germ-freemice. E. coli, which is a facultative anaerobe, may be needed
to maintain an oxygen-free environment in the intestinal tract
because AJ110941 needs strict anaerobic conditions for
growth. In our study, a mono-association with the LPS
producer E. coli induced neither hyperglycemia nor theaccumulation of adipose tissues. In contrast, LPS derived
from Gram-negative bacteria in the gastrointestinal tract waspreviously identified as one of the most important factors
inducing the development of T2D (1, 3). Our results indicatedthat the presence of LPS in the intestinal tract was necessary,
but not sufficient for the pathogenesis of diabetes. We speculated that AJ110941 may have assisted with the translocationof LPS into the blood from the intestinal tract. We are
currently investigating the effects of AJ110941 on LPS
translocation in in vitro/vivo models.
Regarding the particular gut bacterium involved in metabolic syndrome, Methanobrevibacter smithii and Bacteroides
thetaiotaomicron were previously shown to enhance hostenergy storage in di-associated mice (20). Furthermore, Fei
and Zhao reported that the strain Enterobacter cloacae B29,
which was isolated from an obese human subject, induced
obesity and insulin resistance accompanied by serum endotoxemia in mono-associated mice (9).
This is the first study to have successfully identified a
specific Lachnospiraceae bacterium involved in metabolic
disorders. Future studies are needed to elucidate the molecular mechanisms underlying the adverse effects of AJ110941
on glucose and lipid metabolism in mouse models. It remains
unclear whether AJ110941 or its closely-related species
inhabits the human intestinal tract. We intend to perform apreliminary epidemiological study on obese and diabetic
subjects to address this question.
The 16S rRNA gene sequence of the isolated bacterium(strain AJ110941) is available in the DDBJ/EMBL/GenBank
databases under the accession number AB861470.

Acknowledgements
We thank Ranko Nishi and Mizuki Kobayashi for their research
assistance.
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MOST CITED REVIEW PAPERS IN THISTEN YEARS [2011-2020] (156 citations)

Microbes Environ. Vol. 26, No. 2, 101–112, 2011

http://wwwsoc.nii.ac.jp/jsme2/ doi:10.1264/jsme2.ME10179

Minireview
The Ecology, Biology and Pathogenesis of Acinetobacter spp.: An Overview
HAMUEL JAMES DOUGHARI1*, PATRICK ALOIS NDAKIDEMI1, IZANNE SUSAN HUMAN1, and SPINNEY BENADE1
1Applied Sciences Faculty, Cape Peninsula University of Technology, P.O. Box 652 Cape Town 8000, South Africa(Received October 9, 2010—Accepted February 16, 2011—Published online March 18, 2011)

Acinetobacter are a major concern because of their rapid development of resistance to a wide range of antimicrobials, and rapid profundity in transformation, surviving desiccation and persistinge in the environment for a very longtime. The organisms are associated with bacteraemia, pulmonary infections, meningitis, diarrhea and notorious nosocomial infections with mortality rates of 20 to 60%. Transmission is via person-to-person contact, water and foodcontamination, and contaminated hospital equipment. The increasing virulence and rapid development of multidrug
resistance by these organisms highlights the need to search for alternatives for chemotherapy. A poor understandingof the organisms and dearth of information about their occurrence especially in developing countries informed theneed for this review paper.
Key words: Acinetobacter, acinetobactins, biofilms, coccobacilli, ecology, taxonomy

Introduction

Recent taxonomy

The name “Acinetobacter” originates from the Greek word
“akinetos” meaning “unable to move”, as these bacteria
are not motile yet they display a twitching kind of motility.
Bacteria of the genus Acinetobacter have gained increasing
attention in recent years first, as a result of their potential
to cause severe nosocomial (Greek nosos disease, andkomeion to take care of) infections (6, 7, 62, 63, 85, 108, 112,117), second, for their profundity in developing multidrug(MDR) and extreme drug resistance (XDR) (5, 56, 86, 91,115) third, for the ability of some strains to produce
verotoxins (VA) (48), and fourth, for the role members of thegenus play in enhanced biological phosphorus removal in
wastewater (16, 43, 80). Recently, Acinetobacter spp. havedemonstrated a hydrocarbon-degrading capability (74, 75,122), that is of interest for soil bioremediation and a specificstrain Acinetobacter baylyi ADP1 has shown remarkablecompetence for natural transformation irrespective of DNA
source, thus making it a potentially important tool for biotechnology (2, 19, 111, 112). Possible suggested applicationsof Acinetobacter spp. are summarized in Table 1.
In addition, since the environment, soil, and animals are
their natural habitats, food and water contamination exposes
humans to infections. The ability of these bacteria to colonize almost any surface and to acquire antibiotic resistancedistinguishes them from other infectious bacteria. Despite
the huge increase in the frequency of infections caused by
MDR Acinetobacter, there is still a lack of awareness of theimportance of these microorganisms (30). This review therefore gives an overview of the biology, ecology and medicalsignificance of the entire genus Acinetobacter in a broad
sense with a view to providing basic general information on
this group of bacteria for a better understanding and the possible adoption of proactive and effective control measuresagainst infections associated with some of the bacteria.

The first strain of Acinetobacter spp. was isolated
from soil and identified as Micrococcus calcoaceticus byBeijerinck in 1911 (2, 13). Acinetobacter group werepreviously insufficiently defined for a very long time andconfusedly classified into more than a dozen different genera(Achromobacter, Alcaligens, Cytophaga, Diplococcus,
Bacterium, Herellea, Lingelsheimia, Mima, Micrococcus,
Moraxella and Neisseria) (2, 15, 27, 90, 97).
The genus Acinetobacter was first created in 1954 by
Brisou and Prevot to separate the non motile from the
motile members of the tribe “Achromobactereae” and wascomposed of non-pigmented Gram-negative saprophytic bacteria comprising both oxidase-negative and oxidase-positive
species. In 1957, Brisou identified a typical species named
Acinetobacter anitratum (15). Baumann et al. (3) using
distinct nutritional properties later characterized the organisms as oxidase-negative and proposed to classify themunder the genus Acinetobacter. In 1971, the subcommittee on
Moraxella and allied bacteria accepted this proposal andthe genus was limited to oxidase-negative strains (71).
Three species were initially included in this genus but
because of difficulties in distinguishing them based on
differences in physiological characteristics, all the species
were named A. calcoaceticus (2). In fact, Bergy’s Manualof Bacteriology placed these bacteria in the family
Neisseriaceae with only A. calcoaceticus as a species andthe two subspecies A. anitratum and Acinetobacter lwofii(59). Several years later, Bouvet and Grimont (12) identified
more than fifteen genomic species, including Acinetobacterbaumannii (formerly A. calcoaceticus var anitratum and A.
glucidolytica non liquefaciens), Acinetobacter haemolyticus,
Acinetobacter junii, Acinetobacter johnsonii and Acinetobacter radioresistens (12, 13, 27).
The species’ names have undergone considerable taxonomic changes over the years as molecular methods haveadvanced understanding of the genetic make-up of this groupof organisms (109). Recent classifications which seem

* Corresponding author. E-mail: jameshamuel@yahoo.com;
Tel: +27–7–3355–0274, Fax: +234–703–559–9712.

102

DOUGHARI et al.
Table 1. Possible applications for Acinetobacter spp. and their products

Bioremediation ofwaste waters andeffluents

Bioremediation of soils andeffluents contaminatedwith heavy metalsi) Phosphate removali) Textile or tannery industrial
effluent containing heavy
metalsii) Lead from digested
sewage sludgeiii) Chromium-contaminatedactivated sludge or
wastewateriv) Silver contaminatedphotographic wastewaterii) Degradation of
petrochemicalsiii) Breakdown oforganic pollutants

Production of biopolymers
and biosurfactanti) For prevention of dental
plaqueBiomass productioni) Protein productionii) For use in paper-makingii) Manganese leachingand other industries
from oresiii) For efficient emulsification iii) Production of immuneof oil waste pollutants
adjuvants

Clinical usesi) Production of
glutaminase-sparaginaseii) Production of L(−)
carnitineiv) For incorporation incosmetics, detergents andshampoos(82, 107)

to have gained wide acceptance among bacterial taxonomists have recognized this group of heterogeneousbacteria as gamma proteobacteria classified in the order
Pseudomonadales and the family Moraxellaceae (13). Thus
the taxonomical classification is given as; Domain—Bacteria,
Phylum—Proteobacteria, Class—Gammaproteobacteria,
Order—Pseudomonadales, Family—Moraxellaceae, Genus—Acinetobacter (DNA G+C content 39–47%) and species(with A. baumannii, A. haemolyticus and A. calcoaceticus
as species of clinical importance). Recent classifications
using cell shape, absence of flagella, G+C content of
DNA and nutritional properties, placed these organisms (A.
baumannii, A. haemolyticus and A. calcoaceticus as well asother Acinetobacters) in the genus Moraxella, now known
as Acinetobacter (2). Based on DNA-DNA hybridization
studies, 32 species of Acinetobacter have now been recognized, with 22 assigned valid names and the rest assigned
numbers and referred to as a ‘genomic group’ (42, 91).
Among the named species, A. baumannii is the main species
associated with clinical infections followed by the non-A.
baumannii species A. haemolyticus, A. junii, A. johnsonii and
A. lwofii (2, 49). Recently, the emergence of other speciesof clinical importance such as A. ursingii and A. schindleri
has been reported (95).
Another difficulty associated with classification is theclose resemblance between species such that phenotypic
differentiation becomes very difficult. For instance, there is a
close relationship between A. baumannii and A. calcoaceticus,
and genomospecies 3 and 13. As a result of the difficulties indistinguishing isolates phenotypically in the former pair, theterm A. baumannii-A. calcoaceticus complex or Abc complex has been used. Furthermore, some authors still reportthese isolates as A. calcoaceticus subspecies anitratum.
This situation led to contributors to the Manual of ClinicalMicrobiology to conclude that the majority of species of thisgroup of bacteria cannot be reliably distinguished based on
phenotypic tests (13, 37, 94, 102).
To avoid confusion therefore, a more reliable classification based on combination of the results of DNA-DNA
hybridization and on phenotypic characteristics was adopted(2). In clinical practice however, these taxonomic complications have led to the under-recognition and misclassificationof the species.

Biology, cultural and biochemical characteristics of the
Acinetobacter group
The genus Acinetobacter consists of strictly aerobic, nonmotile, catalase-positive, indole-negative, oxidase-negative,
Gram-negative, non-fermentative encapsulated coccobacillirods (13, 111). Many strains are unable to reduce nitrates tonitrites (9). The bacteria are strictly aerobic and although
they flourish on most laboratory media at temperatures of
20–30°C with the clinical isolates growing at 37 to 42°C, formost strains the optimum temperature is 33–35°C. In the
exponential phase of growth, they are bacilli 0.9 to 1.6 μm in
diameter and 1.5 to 2.5 μm in length, often in pairs or assembled into longer chains of varying length. Acinetobacter spp.
are non-fastidious and can be grown on standard laboratory
media (64). On blood agar (BA), colonies show typical morphology and size: Non-pigmented, white or cream colored,smooth or mucoid (when capsule is present), opaque, 1–2
mm in diameter (after 18–24 h incubation at 37°C) (9), on
eosin methylene blue agar (EMB), colonies are bluish tobluish gray, on Herellea agar (HA) they are pale lavender incolor (9), while on Leeds Acinetobacter Medium (LAM) the
bacteria are pink on a purple background. In aged cultures
the bacteria may be spherical or filamentous. The organisms
can be recovered after enrichment culture from virtually all
samples obtained from soil or surface water (85). The members of the Acinetobacter group are nutritionally versatile
chemoheterotrophs and the range of substrates they use assole carbon and energy sources parallels that of the aerobic
pseudomonads.
The cell wall of Acinetobacter is typical of that of Gramnegative bacteria, however de-staining is difficult due to atendency to retain crystal violet and this can lead to incorrect
identification as Gram-positive cocci (1). The cells of
Acinetobacter vary in size and arrangement. Acinetobacter
generally form smooth and sometimes mucoid colonies onsolid media, ranging in color from white to pale yellow orgrayish white. Some environmental strains have beenreported to produce a diffusible brown pigment (1, 85).
Several clinical isolates show hemolysis on sheep blood agar
plates (85).
Pathogenesis, virulence factors and resistance
Pathogenesis. In the past, Acinetobacter spp. were con-

Ecology of Acinetobacter spp.

sidered saprophytes of little clinical significance (7), but with
the introduction of powerful new antibiotics in clinical practice and agriculture and the use of invasive procedures in
hospital intensive care units (ICUs), drug resistant-related
community and hospital-acquired Acinetobacter infections
have emerged with increasing frequency (49). A. baumannii
is an important emerging nosocomial pathogen worldwide,
followed by A. lwofii and A. haemolyticus. Conditions attributable to Acinetobacter spp. include blood stream infections(BIs), ventilator-associated pneumonia (VAP), bacteremia,
meningitis, urinary tract infections, cholangitis, peritonitis,skin and wound infections, ventriculitis, and infective
endocarditis (10, 56, 117). The bacteria can also colonize theskin and respiratory tract without causing an infection. An
infection results if the host first line of defence is compromised. Studies have however, revealed that colonization
increased with hospital stays (82). In gastroentistenal infections with A. lwoffıi and H. pylori infections for example, thenormal tissue architecture of the gastric epithelium is alteredleading to chronic gastritis (Fig. 1). Infections with A.
lwoffıi induce production of pro-inflammatory cytokines
which increase gastrin levels that in turn promote proliferation of the gastric epithelium. Persistent inflammation
including the activation of antigen-presenting cells (APCs),
release of pro-inflammatory cytokines such as macrophage
chemotactic protein (MCP), macrophage inflammatory protein (MIP), Toll-like receptor (TLR), somatostatin (SOM),
reactive oxygen species (ROS) involved in acid secretion(Fig. 1) and changes in the number of gastric epithelial cells
can lead to gastritis, peptic ulcers, and more rarely, gastriccancer (94). Though colonization occurs more frequently
than infections, studies have also shown that lethal infections
result from pathogenic strains in immunosuppressed animals
with mortality rates of 75 to 100% (96). The bacteria havealso been associated with bacteremia, sepsis in neonatal
intensive care units and pediatric oncology units, as well as
community acquired meningitis and endophthalmitis (25,106, 110). Other conditions include suppuration; abscessesof the brain, lung, and the thyroid, secondary infections ofFig. 1. Schematic model of bacterial colonization of the gastricmucosa, activation of antigen presenting cells (APC), release of proinflammatory cytokines and alteration in the number of gastricepithelial cells involved in acid secretion. MCP, macrophage chemotactic protein; MIP, macrophage inflammatory protein; TLR, Toll-likereceptor; SOM, somatostatin; ROS, reactive oxygen species (92).
(Reproduced with permission, copyright Elsevier).

103

wounds or surgical trauma, and purulent lesions of the eye.
The organisms are ranked 9th after S. aureus, E. coli,
Klebsiella spp. P. aerugenosa, C. albicans, Enterococci,
Serratia and Enterobacter as agents of nosocomial BIs, andaccount for 34% of the mortality and 43% of deaths due tohospital-acquired infections (29). They are the second mostcommonly isolated nonfermenters in human specimens (81),after Pseudomonas aeruginosa and their incidence is on theincrease and mortality rates are quite high (56, 111, 119). A.
baumannii was found to be associated with a series of fatal
cases of community pneumonia (29) and A. haemolyticus,
with endocarditis and verotoxin production, and hence
bloody diarrhea (11, 17, 47).
Pathogenic mechanisms. The pathogenic mechanisms ofAcinetobacter spp. are little understood or studied (84).
Though the infective doses of Acinetobacter in human infections have yet to be determined, intraperitoneal injections inmice with 40 clinical isolates of Acinetobacter showed theLD50 to range from 103 to 106 viable cells per mouse(82). Though A. baumannii is the most studied species,
the precise mechanisms involved in the establishment andprogression of infections by this species are unclear. The
organism is not known to produce either diffusible toxins
or cytolysins, and few virulence factors have been identified (45). However, comparative genomic studies with A.
baumannii and the environmental A. baylyi have identified genes involved in pilus biogenesis, iron uptake andmetabolism, quorum sensing and a type IV secretion system
as making up part of the organism’s ‘virulome’ (106, 111).
Other authors have also reported common virulence factors among the Acinetobacters which are discussed below.
There is a need for microbiologists to further investigate
these virulence mechanisms for possible discovery of more
effective control measures.
Virulence factors. i) Cell surface hydrophobicity andenzymes: Acinetobacter spp. have been demonstrated to
exhibit cell surface hydrophobicity, an important determinant bacterial adhesion. For a successful infection to occur,bacteria must successfully adhere to host cells (24). The
hydrophobicity of a microorganism protects it from being
phagocytosed and appears to play an important role in its
attachment to various polymers. Hydrophobicity also confers
the ability to adhere to plastic surfaces, such as cathetersand prostheses. Through this hydrophobicity, Acinetobacter
spp. coaggregate into flocs in sludge. Non-flocculating A.
johnsonii S35 displays significant coaggregation with threeother bacterial species, Oligotropha carboxidovorans,
Microbacterium esteraromaticum, and Xanthomonas
axonopodis (76). The degree and mechanism of coaggregation were found to be pair-dependent; and cell surfacehydrophobicity was an important factor controlling thecoaggregation of A. johnsonii S35 and its partner strains (76,89). The hydrophobic properties of bacterial strains dependon their surface structure, which in turn determines thedegree of hydrophobicity: the rougher the cell surface, thegreater the hydrophobicity and vice versa. In Acinetobacter,the presence of protein protrusions on the cell surface confershydrophobicity. A recent study using scanning electron
microscopy showed the presence of blister-like protein protrusions on A. johnsonii S35 and A. junii S33, these bacterial

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cells were able to coaggregate efficiently with other bacterial
cells compared to a mutant strain A. johnsonii IAM1517
with smooth cell surfaces (89) which was unable to form
aggregates (89).
Surface hydrophobicity is also mediated by colonization
factors, complimentary cell surface receptors, fimbriae andother cell wall components (14, 60) and cell surface enzymes
that facilitate the adhesion of bacterial cells to host cells.
For example, the urease activity of Acinetobacter promotes
colonization of the mouse stomach (24). Urease also helps
Acinetobacter spp. colonize the hypochlorhydric or achlorhydric human stomach inducing inflammation (100). Polysaccharide slimes on the bacterial cell surface are reported toconfer hydrophobicity (53, 61 93, 97). Other virulenceconferring enzymes secreted by the bacteria include esterases,
certain amino-peptidases, and acid phosphatases (93, 108).
Esterases have strong hydrolyzing activity against shortchain fatty acids, thereby causing damage to lipid tissues.
Hydrolytic enzymes usually confer the bacterium with verystrong hemolytic activity. The most extensively studiedhydrolytic enzymes in Ps. aerugenosa are phospholipases C(PLC)-H, which is encoded by plcS, is acidic and has strong
hemolytic activity, and PLC-N, which is encoded by plcN, isbasic, and has no hemolytic activity (93). Recent studies
revealed that two copies of the phospholipase C (plc) genewith 50% identity to that of Pseudomonas are found in A.
baumannii. It is therefore assumed that these lipases serve asimilar function, although this is yet to be elucidated (111).
Hoštacká and Klokočníková also reported the secretion ofphosphotidylethanolamine and sphingomyelin which are allcytotoxic to leucocytes (53).
ii) Toxic slime polysaccharides: Toxic slime polysaccharides have also been reported among Acinetobacter spp. (53).
They are usually produced during the exponential phase of
growth and are made up of the glucose building blocks Dglucuronic acid, D-mannose, L-ramnose and D-glucose. The
slime polysaccharides are toxic to neutroplils, and inhibit
their migration as well as inhibit phagocytosis, but without
disrupting the host immune system (50, 53). It is importantto understand these structures in order to develop effective
control measures. Currently, the authors are focusing on
determining the hydrophobicity of A. haemolyticus isolates
from water and wastewater samples and the effect of stressand phytochemical extracts on this hydrophobicity.
iii) Verotoxins: Grotiuz et al. (47) first reported the
production of verotoxins in Acinetobacter (from A.
haemolyticus). Verotoxins are associated with bloody diarrheas and produced by many enteric bacteria including E.
coli and Shigella dysenteriae (31). The toxins belong to aparticular protein subfamily, the RNA N-glycosidases which
directly target the cell ribosome machinery, inhibiting protein synthesis. Verotoxins can be classified into 2 antigenic
groups, vtx-1 and vtx-2, which include (especially vtx-2) an
important number of genotypic variants. The mechanism bywhich A. haemolyticus produces this toxin is, however, notwell understood. Lambert et al. (65) speculated that A.
haemolyticus acquires vtx2-producing activity via horizontal
gene transfer in the gut lumen, since it can be rapidlytransformed. In any case, the pathogenicity, basic structure,and chemical components of the toxins are the same as

DOUGHARI et al.

those of verotoxins from E. coli and other bacteria (65). The
emergence of verotoxin-producing A. haemolyticus strains isworrisome given the high transformability of Acinetobacter
spp. This therefore calls for intensive surveillance of theseorganisms especially in the environment for the developmentof proactive control measures. The current work therefore
focuses on isolation and identification of A. haemolyticus
from environmental samples and the determination of theirverotoxin production and antimicrobial resistance profiles aswell as the effect of phytochemical extracts on verotoxin
production by the isolates.
iv) Siderophores: Siderophores are host iron-binding protein structures responsible for iron up take in bacteria.
One possible defense mechanism against bacterial infections
is the reduction of free extracellular iron concentrations via
iron-binding proteins such as lactoferrin or by transfer (14,121). The normal concentration of free iron in the body is10−8 M, and the concentration required for bacteria to survive
in the human body is 10−6 M. Bacteria meet their iron
requirement by binding exogenous iron using siderophores
or hemophores (61, 72, 121). Bacterial siderophores arecalled aerobactins. Acinetobacter siderophores are called
acinetobactins and are chiefly made up of the amine histamine which results from histidin decarboxylation (78). Iron
import into the bacterial cell is however regulated by a ferric
regulator uptake protein serving as a transcription repressor
to induce siderophore synthesis or degradation (111).
v) Outer membrane proteins (OMPs): Outer membraneproteins (OMPs) in some Gram-negative bacteria are known
to have essential roles in pathogenesis and adaptation inhost cells as well as in antibiotic resistance. Several OMPsof the OmpA family have been characterized in various
Acinetobacter strains (28, 45). Vila et al. (115) reported
homology between the genome sequence of OmpA of A.
radioresistens, A. baumannii and A. junii. The OmpA proteins induce apoptosis of epithelial cells (21), stimulating
gastrin and interlukin B gene expression (55). In a recent
study, Vallenet et al. (111) showed that A. baylyi OmpA
has emulsifying activity and that only one gene in each
Acinetobacter strain encodes an OmpA protein. In otherwords, these proteins share more than 89% of their aminoacids and thus have the same chromosomal context. The cellsof Acinetobacter spp. are surrounded by OmpA, a protein tokill host cells (20). During an infection, OmpA binds toeukaryotic cells and gets translocated into the nucleus where
it causes cell death (20, 28, 99).
Resistance to antibiotics and mechanisms of resistance.
The major problem with Acinetobacter spp. is their resistance to antibiotics (66, 67). Savov et al. (101) reported thatthese organisms are most commonly resistant to ampicillin,
cephalothin, carbenicillin, gentamicin, amikacin, chloramphenicol, tetracycline, co-trimoxazole, ciprofloxacin andcefoperazone. Previously ampicillin, second generation
cephalosporins, quinolones, minocyline, colistin, amynoglycosides, impenim, sulbactam and gentamicin were used totreat Acinetobacter infections. Resistance to these antibiotics
has hindered therapeutic management, causing growing concern the world over (32, 47, 87, 91, 115). A. baumannii has
been developing resistance to all antibiotics used in treating
infections. Currently, most A. baumannii strains are resistantEcology of Acinetobacter spp.

105

to aminoglycosides, tetracyclines, cephalosporins, ampicillins,
cefotaximes, chloramphenicols, gentamicins and tobramycins (91). The activity of carbapenems is further jeopardized by the emergence of enzymatic and membrane-based
mechanisms of resistance (85).
Antimicrobial resistance among Acinetobacter is either
intrinsic or acquired via transformation. Several mechanismsof resistance including altered penicillin-binding proteins,
low/decrease permeability of the outer membrane to antibiotics or increase in the active efflux of the antibiotics, target site mutations, and inactivation via modifying enzymes
have been reported (56, 115). Mechanisms of resistance toantibiotics by Acinetobacter spp. vary with species, the typeof antibiotic, and geographical location (56). Thus β-lactam
antibiotics are inactivated by the production of β-lactamases
or alterations of penicillin-binding proteins and decreased
permeability of the outer membrane to β-lactams (92); cephalosporins by chromosomally encoded cephalosporinases
and occasionally, by cell impermeability and aminoglyco-sides via aminoglycoside-modifying enzymes; quinolones byaltering the target enzymes DNA gyrase and topoisomerase
IV through chromosomal mutations, a decrease in permeability and increase in the active efflux of the drug by the microbial cell (67). Several efflux pumps acting against antibiotics
have been described for Acinetobacter spp. grouped as (i)major facilitator superfamilies (MFSs) comprising the Tet(A)-efflux system for tetracycline, Tet (B)-efflux system fortetracycline and minocycline and Caml A-efflux system forchloramphenicol, (ii) resistance-nodulation division (RND)
comprising Ade ABC (ATP binding cassettes)—efflux systems against aminoglycosides, β-lactams, chloramphenicol,
erythromycin, tetracyclines, ethidium bromide, and reduced
susceptibility to fluoroquinolones and (iii) multi drug andtoxic compounds extrusion systems (MATEs) comprising of
Abe M—efflux system against norfloxacin, ofloxacin, ciprofloxacin, gentamicin, 4,6-diamino-2-phenylindole (DAPI),
triclosan, acriflavin, doxocrubicin, rhodamin 6G and ethidium bromide (114). Major mechanisms of resistance to dif-

Table 2. Major mechanisms of resistance identified for the different classes of antibiotics
Antimicrobial class/resistancemechanismβ-Lactamβ-Lactamases

Carbapenemases

OMP loss
Efflux pump
Altered PBP expression
Tetracyclines
Efflux pump
Ribosomal protection
Glycylcyclines
Efflux pump
Aminoglycosides
Enzymatic degradation

16S rDNA methyltransferases
Quinolones
DNA gyrase/topoisomerase mutations
Efflux pumps
Chloramphenicol
Efflux pumps

Trimethoprim/sulfamethoxazole
Efflux pumpDihydropteroate synthase
Dihydrofolate reductase
Macrolides
Efflux pumps
Polymyxins

Class/family

Intrinsic cephalosporinase
Class A/high-prevalence ESBLAClass A/low-prevalence ESBLAClass D OXA enzymes/ESBLM–D
Class D OXA enzymes/ESBLCARBA-D,
MBLs/ESBLCARBA-B,
Class A carbapenemase/ESBLCARBA-A
CarO, HMP-AB, 33–36 kDa protein, 43 kDa protein
AdeABC

Variants

AmpC (ADC1–7)
VEB-1, -2, PER-1, -2, TEM-92, -116,
SHV-12, -5, CTX-M-2, -3
SCO-1
OXA-51-like
OXA-23–27, -37, -40, -58like, VIM, IMP, SIM
GES-11

PBP2 downregulation
MFS, RND

A, TetB, AdeABC
TetM

RND

AdeABC

Acetyltransferases
Nucleotidyltransferases
Phosphotransferases

AacC1/2, AadA, AadB
Ant1
AphA1, AphA6,
ArmA

RND, MATE, BIMP

GyrA/ParC
AdeABC, AdeM, AbeS

RND
MFS
BIMP

AdeABC, AdeIJK
CmlA, CraA
AbeS

RND

AdeABC, AdeIJK
SulI/II
FolA

MATE, BIMP
PmrAB two-component mutation

AbeM, AbeS

MBL—metallo-β-lactamase; OMP—outer membrane protein; HMP—heat modifiable protein; PBP—penicillin-binding protein; MFS—major
facilitator superfamily; RND—resistance–nodulation–cell division; MATE—multidrug and toxic compound extrusion; BIMP—bacterial integral
membrane proteins (46).

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ferent classes of antibiotics are listed in Table 2.
Owing to this escalation of multidrug resistance, deliberate efforts should be made towards investigating othersources of antibiotics with novel mechanisms of action,with a view to developing effective control measures against
the recalcitrant bacteria. Investigations of phytochemicals
should be considered since they form a very rich source ofantibiotic substances (32) with potential activity against
microbial pathogens.
Resistance to environmental and host factors. The emergence of nosocomial or community-acquired infections ofAcinetobacter is a result of high adaptability to adverse
environmental conditions, an ability to persist for several
days in dry and harsh environments such as the hospitalenvironment, the increased use of broad spectrum antibiotics, the vulnerability of individuals or patients, andthe rapid transformation of organisms that results in increased multidrug resistance. To survive and multiply inthe host, many bacteria produce a variety of substancesthat allow them avoid the defense mechanisms of the host(58). Acinetobacter spp. are found as natural inhabitantsof human skin and repeated isolation may suggest thatthey are potential pathogens. Acinetobacter spp. are able tosurvive on moist and dry surfaces (118) and some strains
have been found to be tolerant of soap (9, 56). The abilityof Acinetobacter to persist in dry conditions, on inanimate
objects, and in dust for several days and weeks has been reported. Recent isolates of A. lwoffii compared to the isolates
from the 1970s are relatively resistant to irradiation. This
raises concerns about the persistence of A. lwoffii on
medical devices that are sterilized by gamma irradiation,
especially in intensive care units (ICU) (93).
Acinetobacter species also survive exposure to the commonly used disinfectants like chlorhexidine, gluconate andphenols, particularly those not used in the appropriate concentrations (40). Compared with other genera of Gramnegative bacilli, Acinetobacter is able to survive much betteron fingertips or on dry surfaces when tested under simulated
environmental conditions (118). Apart from being able togrow at a very broad range of temperatures, they are alsoable to resist drying. The presence of more electron dense
cell walls and nucleic acids are thought to be responsible
for the heat (50–75°C) resistance (56). In fact, while Ps.
aerugenosa and E. coli can only survive heat for a maximumof 24 h, A. baumannii can survive for up to 25 days (58).
Survival for 157 days (A. radioresistens), over 30 days (A.
baumannii) and 21 days (A. lwoffii) has been reported (54,57, 88). A. baumannii has also demonstrated resistance tothe killing action of normal human serum (NHS) and thepossession of a lipopolysaccharide was thought to be partly
responsible (42). King et al. (61) also suggested modulationof pathogen interaction with serum by a complement regulator. The complement system is the host innate immune
defense comprising a series of serum proteins that initiates
the death of the bacterium through either lysis or opsonization. One of the mechanisms by which bacterial cells resist
killing by serum compliments is by producing surface proteins that bind human factor H (FH), and thereby inhibit thedeposition of complements on the bacterial surface (41, 63).
The mechanism by which Acinetobacter spp. resist serum

DOUGHARI et al.

compliments is however yet to be discovered.
Transfer of resistance among Acinetobacter spp.
Resistance to antibiotics is transferred among Acinetobacter spp. via plasmids and transposons. While plasmids
are DNA elements that carry antibiotic and heavy metal
resistance conferring genes capable of autonomous replication, transposons are sequences of DNA that can move (ortranspose) themselves to new positions within the genome ofa bacterium (or any other prokaryotic cell). These elements
are often present in resistant bacteria and have been reported
in clinical isolates of Acinetobacter spp. (40). Plasmids andtransposons are easily transferred between bacteria via theprocess of genetic transformation. Transformation occurs
between Acinetobacter spp. Due to the high frequency anddegree of adaptability and transformability among somestrains of Acinetobacter spp., species capable of colonizing
multiple settings can mediate the transfer of novel antibiotic
resistance genes from antibiotic-producing environmental
species to clinical isolates. For example, an intermediategroup including spp. 7, 8, and 9 is capable of adapting tohuman tissues as well as remaining in the environment thus
serving as effective vehicles for conveying resistance genes
between species (93). Gene transfers in Acinetobacter spp.
also occur via conjugation and transduction. Conjugation in
Acinetobacter involves a wide host range and chromosomaltransfer, while transduction involves a large number ofbacteriophages with a restricted host range (93). Owing to
the high transformation ability of Acinetobacters, the role ofgenetic elements in the virulence of this group needs to bethoroughly investigated and adequately understood as in thecase of E. coli. This will no doubt open up more frontiers
for more effective control measures and the application of
the organisms in biotechnology.
Epidemiology and ecology
Several studies have reported the epidemiology of A.
baumannii infections in different parts of the world including
Europe, the United States and South America (64, 104).
Although these organisms are often associated with nosocomial (117) infections, community acquired diarrhea
outbreaks and pneumonia have been reported with somefrequency in tropical regions of the world especiallyduring warm (summer) and humid months (18, 56). An infrequent manifestation of Acinetobacter is nosocomial meningitis and these cases have been reported after neurosurgicalprocedures (18, 56, 58). The morbidity and mortality
rates of Acinetobacter infections are comparable to those ofmethicillin-resistant Staphylococcus aureus (MRSA), andthe organisms have been termed ‘Gram-negative MRSA’,
manifesting similar epidemiological behavior to MRSA. The
impact in terms of morbidity and mortality is probably closer to that of coagulase-negative staphylococci and available
data suggest that the mortality rate ranges from 20% to60% (58). Thus several reports have alerted clinicians to theemergence of a potentially difficult and dangerous organism that is responsible for outbreaks of infection and cancause severe problems (58). Owing to the morphologicalEcology of Acinetobacter spp.

similarity between Acinetobacter and Neisseriaceae (bothbeing Gram-negative diplococci), care should be taken while
examining the Gram stain. Neisseria meningitidis is, however,
far more common as an agent of meningitis. Uncommon
conditions involving Acinetobacter are contiguous osteomyelitis, peritonitis associated with continuous ambulatory
peritoneal dialysis, ophthalmic infection, skin and wound
infections, abscesses, sepsis, endocarditis and burn infections. Despite the increasing significance of Acinetobacter,there are no significant epidemiological reports on the incidence of infections from many parts of the world particularly, developing countries. Epidemiologic investigations
on Acinetobacter spp. of clinical significance other thanA. baumannii as well as on the epidemiology of acinetobacteriosis in developing countries are essential.
Acinetobacters are mostly free living saprophytes foundubiquitously in nature (111). However, different species of
the genus are generally associated with different habitats.
Acinetobacter genomospecies 3 is found in water and soil, on
vegetables, and on human skin; A. johnsonii and A.
haemolyticus are found in water, wastewater, soil, on humanskin, and in human feces; A. lwoffii and A. radioresistens arefound on human skin; and Acinetobacter genomic species 11
is found in water, in soil, and on vegetables as well as thehuman intestinal tract (10, 26).
Human and animal body. Acinetobacter spp. are generally considered part of the normal flora of the skin andmucous membranes or the pharynx, human respiratory
secretions, urine, rectum (116) and other human clinicalsamples (101). They are the only group of Gram-negative
bacteria that may be natural residents of human skin, withcarriage rates of 42.5% in healthy individuals and as highas 75% in hospitalized patients (101). In a study conductedby Seifert et al. (103) Acinetobacter spp. were isolated fromvarious parts of the human body including the forehead,nose, ear, throat, trachea, conjunctiva axilla, hand, groin,
vagina, perineum and toe web. The organisms are also foundin the distal urethra of healthy people (63). Generally
the species most frequently isolated are A. johnsonii, A.
lwoffii, A. radioresistens, A. baumannii, A. calcoaceticus,
A. haemolyticus and Acinetobacter genomospecies 3 and13. Colonization of the intestinal tract by Acinetobacter
spp. however, is controversial. While some authors suggestthat it is an unusual event (46), others report that the gastrointestinal tract is the most important reservoir of resistantstrains (23). The difference is probably due to the epidemiological situation i.e. whether there is an epidemic outbreakor not.
Acinetobacter spp. have been isolated from different
animal sources including birds; fish and rainbow trout (10,49). On several occasions, chicken septicemia (15% deathrate), septicemia in turkeys and calves, mastitis and metrititis
in cows, abortions in cattle, pigs and horses, keratoconjunctivitis in cattle, omphalitis in calves, ear infections in cats,and respiratory infections and Balanoposthitis in horses havebeen reported (38). A. beijerinkii and A. baumannii areamong the species of Acinetobacter implicated in animal
diseases (38). The organisms have also been isolated fromlice collected from homeless people (68).

107

Food contamination. Acinetobacter spp. have been associated with food contamination. Several foods including
vegetables have long been known to be an important sourceof contamination with Gram-negative bacteria such as
Escherichia coli and Klebsiella spp. (85). Acinetobacter spp.
have been recovered from vegetables, apples, melons, cabbages, cauliflowers, lettuce, cucumbers, peppers, mushrooms, radishes, carrots as well as tubers such as potatoes
and cereals such as sweet corn (10, 85). Acinetobacter spp.
have also been implicated in the spoilage of bacon, chicken,
meat, fish and eggs even when stored under refrigeration orafter adequate gamma irradiation (84, 108). Hospital food
could also be a potential source of A. baumannii (10, 108).
Many strains isolated from foods have lipolytic activity andsome strains produce diffusible pigments.
Soil and wastewaters. Water and soil also provide a home
to various microorganisms. Acinetobacter genomospecies
3, A. baumannii, A. calcoaceticus acinetobacters, and A.
calcoaceticus, A. johnsonii, A. haemolyticus, and Acinetobacter genomic species 11 have been reported to inhabit soil
and aquatic environments (86). The organisms have also
been isolated from freshwater ecosystems; raw sewage andwastewater treatment plants (10) and activated sludge (16,83, 85). A. baylyi, A. bouvetii, A. grimontii, A. tjernbergiae,
A. towneri, and A. tandoii are commonly found in naturalenvironments but occasionally isolated in activated sludge
and have not been found associated with humans (19, 85).
They are able to store phosphate as polyphosphates and may
have potential applications in the biological removal ofphosphates (2).
Biofilms. Biofilms are composed of microorganisms attached to surfaces and encased in a hydrated polymericmatrix made of polysaccharides, proteins and nucleic acids(100, 113). Biofilms function in a manner similar to tissues,
using a primitive circulatory system to pump fluids andnutrients through channels in the matrix by changing theionic strength of the extracellular milieu, causing periodic
contraction of matrix polymers.
Pilus mediated biofilms (PMBs) are formed by Acinetobacter spp. especially A. baumannii, A. haemolyticus, A.
lwoffii and A. calcoaceticus thus forming thin layers of
microorganisms on glass, medical devises, metals, ceramics
and other inanimate objects (28, 39). The biofilm thus constitutes a colonial niche for the bacteria from where contact
with humans will result in infection. The hydrophobic
surface polysaccharide and pili on the bacterial cell surface
initiates adherence to human epithelial cells thus initiating
the infection process (28).
Life in biofilms provides microbes with protection against
assault from the outside world with barriers against penetration by antimicrobial agents, oxygen and nutrients, alongwith depressed growth rates and an activated adaptive stressresponse (99). It also enables the organisms to resist theimmune host response. Vidal et al. (90) and Lee et al.
(113) reported the readiness of A. baumannii to adhere toboth biological and abiotic surfaces, on which it is able toform biofilms thus ensuring its survival (69, 112).
Unlike in other bacteria where the formation of biofilms
is facilitated by intrinsic factors such as the presence of typeIV pili, flagella, curli and fimbriae, in Acinetobacter spp.,

108

putative chaperon secretion membrane systems (csms) andputative surface adherence protein regions (sapr) have beenreported to be responsible (39, 110). The process generally,
involves reversible attachment, irreversible attachment, maturation and dispersion. A. baumannii-associated infections
are often contracted via biofilms on Foley catheters, venous
catheters, or cerebrospinal shunts.
Hospital environment. A. baumannii is the most important bacterial species associated with nosocomial or hospitalacquired infections. These infections are caused by organisms present in hospitals or other clinical facilities. Most ofthese infections emerge while the patient is in the hospitalbut others are not detected until the patient has been discharged. In the early 70s, nosocomial pathogens were predominantly Gram-negative bacilli especially Ps. aeruginosa,
and Enterobacteriaceae, while the 90s saw the emergence ofGram-positive methicillin-resistant S. aureus (MRSA) andvancomycin-resistant enterococci (VRE) (111, 115). In
recent times however, in addition to MRSA and VRE(mainly in the USA), the introduction of broad spectrum
antibiotics in hospitals has resulted in the emergence of multidrug resistant strictly Gram-negative nosocomial bacterial
pathogens including Ps. aeruginosa, Klebsiella pneumonia
and A. baumannii (33). In a hospital environment, they havebeen isolated from reusable medical equipments such as ventilator tubings, arterial pressure monitoring devices, humidifiers, washbasins, plastic urinals and respirometers (26, 52,
112, 118). The organisms have also been isolated from theskin of healthcare personnel, mattresses, pillows and in all
types of ventilator equipment and moist situations such as
sinks and tap water (8, 56). The presence of MDR and nosocomial Acinetobacter in the hospital environment complicates treatment since such infected patients often need to beisolated (70, 77). This underlies the need to fully study theseorganisms and proffer alternative chemotherapeutic solutions.
Though many Acinetobacter spp. are only found incertain habitats, some are distributed widely in nature. A.
calcoaceticus is found in water and soil and on vegetables(2, 10).
Diagnosis
Infection or colonization with Acinetobacter is usuallydiagnosed by the culture of environmental and clinical samples. The environmental samples include wastewater, activated sludge, sewage, aquaculture freshwater habitats, frozen
shrimps (49) soil (16, 112), vegetables (10), fresh andspoiled meat (34) animal droppings and river water while theclinical samples include blood, cerebrospinal fluid, endoctracheal aspirate, pus (91), sputum, urine, respiratory secretions(115), Catheter tips (24), wound, stool or sterile body fluid,skin, cordon of newborns, nasal swabs, hand swabs of hospital workers and hospital environments (swabs on surfaces ofmachines, wash-hand basins, floors, tables, UV lamps) (22).
Microbiologic cultures can be processed by standard methods on routine media. Antimicrobial susceptibility can be
determined by various means, with the agar-dilution method
being the gold-standard.
A glance at the literature shows just how non fastidious

DOUGHARI et al.

and versatile Acinetobacters are in terms of growth on
media. A wide range of media has been employed incultivating organisms from different sources. For routine
clinical and laboratory investigations, traditional methods
have used agar (34), brain heart infusion agar (108), nutrient
agar, tryptic soy agar (9), Simon’s Citrate agar (29) Violet
red bile agar, Luria Bertani agar (49) Eosin-methylene
blue, MacConkey agar and Holton medium (34). For
environmental screening, especially where Acinetobactermay be in very low numbers, Bauman’s’ Enrichment
Medium has been employed (13, 49) and broths including
MacConkey broth, trypton soy broth, Brain Heart Infusion
broth (49) and Luria broth (29, 63). Highly contaminated samples are inoculated in a liquid mineral medium containing asingle carbon source and ammonium or nitrate salt as the nitrogen source with the final pH of the medium being 5.5 to 5.6(63). Shaking during the incubation is employed in order forthe strictly aerobic acinetobacters to outgrow pseudomonads.
The broths can later be transferred onto Eosin-methylene
blue, MacConkey agar or a selective medium such as Herellea
agar or Leeds Acinetobacter Medium in which antibiotics
have been added to suppress the growth of other bacteria(51, 108).
Biochemical typing methods include the use of colorimetric based GN card ID 32 GN, API 20NE, RapID NF Plus andVitek 2 systems (19) all of which are antibody-based agglutination tests. Serological identification has been attemptedwith the analysis of capsular type and lipopolysaccharide(98) molecules as well as protein profiles for taxonomy andepidemiological investigations.
Because of the widespread nature of Acinetobacter spp.,
typing methods are required for genomic characterization(49). The differences in antimicrobial efficacy against different species, and the need to select effective chemotherapeutic
agents, require the accurate identification of Acinetobacter
spp. to the species level. Thus several molecular diagnostic
methods including the polymerase chain reaction (PCR)(47), PFGE, RAPD-PCR DNA fingerprinting (16, 86), fluorescent in situ hybridization (FISH) (112), 16S rRNA gene
restriction analysis (ARDRA) (79), and 16S rRNA gene
PCR-DGGE fingerprinting for genetic characterization ofAcinetobacter spp. from environmental samples Vanbroekhoven et al. have been employed (112). A recent diagnostic method which was reported to have high specificity
and can discriminate between Acinetobacter species is the
microsphere-based array technique that combines an allelespecific primer extension assay and microsphere hybridization (73). The method was reported to be so efficient that 13
different species of Acinetobacter were discriminated inless than 9 h with 90% accuracy and precision (73). The
use of DNA-DNA hybridization and sequence analysis (19,49) is considered the gold standard, but the method is laborintensive and impractical in most clinical laboratories.
Other methods that have been employed in the epidemiological investigation of outbreaks caused by Acinetobacter
spp. include biotyping, phage typing, cell envelope protein
typing, plasmid typing, ribotyping, restriction fragment
length polymorphisms and arbitrarily primed PCR (AP-PCR)(4). These methods are however too expensive and too technical for use in unequipped laboratories. Exploration of sim-

Ecology of Acinetobacter spp.

ple laboratory culture procedures will enhance isolation ofthese organisms especially in developing countries where
electricity and sophisticated diagnostic procedures, andtrained manpower are lacking.
Factors predisposing individuals to acinetobacterioses
Though it is generally agreed that A. baumannii is themost medically significant Acinetobacter spp., there is an
ongoing debate on the clinical impact of the Acinetobacterswith controversial views on whether these organisms
increase morbidity or mortality. While some researchers areof the opinion that A. baumannii infections are responsible
for an increase in patient mortality, others are of the opinion
that infections occur in critically ill patients and mortality
is as a result of other underlying diseases (28, 35, 36).
Whatever the case may be, virtually every study has concluded that there is a detrimental effect (45). The lack of aconsensus on the degree of mortality may be due in partto the difficulty in distinguishing between colonization andinfection, which is compounded by limited information on
the pathogenesis. There is also extensive methodologicalheterogeneity between studies (prospective versus retrospective), and variation in the definitions of cases versus controls(A. baumannii infection versus other infection, polymicrobial
versus monomicrobial) (48, 70). There are also problems
with the accurate identification of organisms to the species
level as well as assessment of the impact of specific clones,
which may differ in virulence potential (45). In addition, theability of organisms to rapidly develop multidrug resistanceand to persist in harsh environmental conditions calls for theneed to take Acinetobacter infections seriously.
Individuals vulnerable to Acinetobacter infections include
those who have recently undergone major surgery, thosewith malignant diseases or burns, and immunosuppressed
patients such as the elderly, neonates with low birth weights,
and patients with prolonged illnesses (84, 107, 108). Nosocomial pneumonia occur in intensive care units (ICUs) with afrequency of 3 to 5% (even higher in patients with mechanical ventilation) and with crude mortality rates of 30 to 75%
have been reported (107, 108). Bacteremia is very common
in elderly immunocompromised patients. The main source ofbacteremia in these patients is bacterial pneumonia, and themost important predisposing factors are malignant diseases,
trauma, prolonged antibiotic treatment, prolonged intravenous lines, post operations, urinary catheterization, renaltransplants chest tubes, mechanical ventilation, parenteralnutrition and long hospitalization (64, 82). Poor hygienic
conditions, and contaminated food and water are common
sources of infection. In addition, it’s been reported that lower
temperatures and an acidic pH may enhance the ability of
Acinetobacter spp. to invade dead tissues (58).

109

found to be synergistic in vivo. Quinolone and amikacinsynergy was also observed. The treatment of a serious
infection with Acinetobacter should be combination therapy
based on laboratory antimicrobial susceptibility results.
Local antimicrobial prescribing habits should be criticallyguided by the susceptibility results. Suspected hospitaloutbreaks involving multidrug-resistant Acinetobacter
infections should not be regarded with akinesis but betackled ferociously and promptly. The prompt revisionof infection control procedures such as hand-washing, patientisolation, ventilator care and good housekeeping is also pertinent. Chemotherapeutic approaches for most antimicrobialresistant Gram-negative infections, include the use of
carbapenems (imipenem and meropenem), but carbapenemresistant Acinetobacter is increasingly reported (56, 101).
Resistance to the carbapenem class of antibiotics complicates
the treatment of multidrug-resistant Acinetobacter infections. However, colistin and polymyxin B have been used totreat highly resistant Acinetobacter infections; unfortunately
renal toxicity of colistin has made its choice unattractive.
Acinetobacter isolates resistant to colistin and polymyxin B
have also been reported (44). The best approach is combination therapy where studies have demonstrated in vitro
susceptibility of multidrug-resistant Acinetobacter to various
synergistic combinations of antimicrobials including
carbapenems, colistin, rifampin, and ampicillin-sulbactam(67, 101, 121). The clinical utility of these combinations
against pan-resistant Acinetobacter remains to be determined(44, 56, 120).
The costs associated with controlling an outbreak of
Acinetobacter infections can be staggering, and some institutions have even been forced to close entire units in order
to interrupt the transmission of Acinetobacter (64, 104, 109).
Therefore, there is a very compelling need to prevent transmission in the healthcare setting and keep the organism frombecoming endemic in an institution. It is also importantthat contamination of the environment, water or food should
be guarded against especially by MDR strains. Careful personal and hand-hygiene should be observed. The disinfectionof hands with-alcohol based disinfectants and observationof standard hospital practices cannot be overemphasized.
Laboratories should embark on active surveillance to detect
cultures and patients who are colonized with multidrugresistant Acinetobacter as well as a community-based
surveillance to determine carriage rates. Other measures
successful in the control of outbreaks include isolation
precautions for infected or colonized patients, cohorting ofpatients, patients’ relatives and staff, environmental disinfection, antimicrobial control, and unit closure (105, 118).
Investigations for novel antibiotic substances with possible
activity against Acinetobacter spp. from plants and othernatural sources with a view to sourcing alternative treatment
should be seriously considered by both research institutions
and pharmaceutical companies.

Treatment, prevention and control
Treatment of Acinetobacter infections should be individualized according to susceptibility patterns as the
carbapenems, some fluoroquinolones and doxycycline may
retain activity. Impenim with an aminoglycoside andβ-lactam/β-lactamase inhibitor with an aminoglycoside wasConclusion
Acinetobacter previously ignored as low-grade pathogens
have suddenly generated interest. The emergence of multiresistant strains, some of which are pan-resistant to antibiot-

110ics, that suddenly cause an outbreak of infection, and theability of the organism to resist desiccation and its rapidtransformation potential has overwhelmed scientists worldwide. Despite an exponential rise in A. baumannii infections
over the past decade, many questions remain unanswered(45). While knowledge of the virulence and particularly theresistance mechanisms is increasing, the populations at risk
and the pathogenesis of severe infection are still poorly
understood. The association of the organisms with conditions
such as bacteraemia or pulmonary infections, diarrhea andnosocomial infections further highlights a major concern.
Most available reports are on A. baumannii as the mostimportant clinical species, while investigations are concentrated more on clinical samples. Other samples including
environmental and food samples should be investigated forboth A. baumannii and other species with a view to investigating their health implications. Control measures should
not be limited to investigations of the environment (food,water etc.) for microbial characterization and studies ofAcinetobacter virulence and resistance mechanisms alone
but the use of animal models should also be considered. The
dearth of available treatments remains a major concern andalthough further work on the use and efficacy of combination
therapies is warranted, a more urgent priority must be thedevelopment of novel therapeutic agents (45) including
studies on the effect of phytochemicals from plant sources(32).
Acknowledgements
This study was supported by Cape Peninsula University ofTechnology, Cape Town South Africa (research fund RP03).
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Microbes Environ. Vol. 32, No. 4, 300-313, 2017https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME17017

MOST CITED REVIEW PAPER IN 2020 (64 citations)

Minireview
Microbial Ecology along the Gastrointestinal Tract
Ethan T. Hillman1, Hang Lu2, Tianming Yao3, and Cindy H. Nakatsu4*
1Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, Indiana 47907, USA; 2Department of

Animal Science, Purdue University, West Lafayette, Indiana 47907, USA; 3Department of Food Science, Purdue University, West
Lafayette, Indiana 47907, USA; and 4Department of Agronomy, Purdue University, West Lafayette, Indiana 47907, USA(Received January 31, 2017—Accepted August 19, 2017—Published online November 10, 2017)

The ecosystem of the human gastrointestinal (GI) tract traverses a number of environmental, chemical, and physical conditions
because it runs from the oral cavity to the anus. These differences in conditions along with food or other ingested substrates
affect the composition and density of the microbiota as well as their functional roles by selecting those that are the mostsuitable for that environment. Previous studies have mostly focused on Bacteria, with the number of studies conducted on
Archaea, Eukarya, and Viruses being limited despite their important roles in this ecosystem. Furthermore, due to the challenges
associated with collecting samples directly from the inside of humans, many studies are still exploratory, with a primary focus
on the composition of microbiomes. Thus, mechanistic studies to investigate functions are conducted using animal models.
However, differences in physiology and microbiomes need to be clarified in order to aid in the translation of animal model
findings into the context of humans. This review will highlight Bacteria, Archaea, Fungi, and Viruses, discuss differences
along the GI tract of healthy humans, and perform comparisons with three common animal models: rats, mice, and pigs.
Key words: Microbiome, mycobiome, virome, human gastrointestinal (GI) tract, animal models, diet

Researchers have been investigating the ecology of theintestinal microbiota for decades (120, 165) in order to identify,
characterize, and count their numbers. These extensive efforts
are due to the important roles the intestinal microbiota play indigestion, the production of essential vitamins, and protectionof the gastrointestinal (GI) tract from pathogen colonization(141). In the past few decades, molecular techniques targetingthe 16S rRNA gene and other genetic markers have beendeveloped to characterize and analyze bacterial communities.
These methods have been used to reveal the important roles
played by microbes in the GI tract (23, 180, 183, 184, 189,
212). In healthy individuals, the microbiome (microbialcommunity) and host have a mutualistic relationship in which
both partners benefit; however, pathogens may invade andcause disease under certain conditions. The initial aim of
most studies was to elucidate the role of the microbiome indisease. More recently, surveys have been performed onhealthy individuals in order to assess the contribution of the
microbiota to health, particularly in response to dietarychanges/supplementation with probiotics and/or prebiotics.
The human GI tract is a complex system that starts fromthe oral cavity, continues through the stomach and intestines,
and finally ends at the anus (Fig. 1). The density and compositionof the microbiome change along the GI tract, with major
populations being selected by the functions performed at thevarious locations. Bacteria along the GI tract have several
possible functions, many of which are beneficial for healthincluding vitamin production, the absorption of ions (Ca, Mg,
and Fe), protection against pathogens, histological development,enhancement of the immune system, and the fermentation of“non-digestible foods” to short chain fatty acids (SCFA) and* Corresponding author. E-mail: cnakatsu@purdue.edu;
Tel: +1 (765) 496–2997; Fax: +1 (765) 496–2926.

other metabolites (19, 58, 63, 77, 138). The roles of fungi andviruses have not been examined in as much detail; however,they are known to play important roles in microbiota dynamics
and host physiology/immunity related to health and disease(45, 94, 133).
Food passes through the GI tract and the absorption rate ofnutrients is largely dependent on the activities of various
enzymes in the digestive system, such as amylase in saliva,
pepsin in the stomach, and pancreatic enzymes in the small
intestine. These mechanisms have been extensively examined(61, 62), particularly in the stomach. However, many foodcomponents cannot be digested in the upper GI tract and arepassed into the lower intestinal tract, in which they are fermented
by microbes. Functional studies commonly use animal models
in order to obtain a better understanding of the processes in
the GI tract that may lead to better health or decrease disease.
However, information from animal models may not be directlytranslatable to humans. Therefore, researchers need to consider
the limitations of the selected animal model when extrapolating
findings to humans.
Although microbiome studies often include an ecologicalcomponent, most of the research performed to date has
focused on Bacteria and not all of the biota. This represents alogical approach because Bacteria comprise most of the
microbiome. However, even biota representing a small proportion of the microbiome may play important roles in theecosystem (133). Therefore, researchers need to start shifting
their approach to include eukaryotic, prokaryotic, and viral(33, 133) interactions in efforts to elucidate the roles of allcomponents of the microbiome.
In recent years, a number of reviews have summarized
findings from the increasing number of studies being performedin this field (36, 73, 176, 188). While most studies have focusedon disease, the microbiome is also important for maintaining

Microbial Ecology of the GI Tract

301

Fig. 1. Microbiome composition of Bacteria (1, 5, 20, 21, 43, 147, 156, 223), Eukarya (52, 85, 114, 126, 182, 197), and Viruses (45, 134, 151,
215) among the physiological niches of the human gastrointestinal (GI) tract. Phylum level compositional data are presented where available alongwith the most common genera in each GI tract location. The colors on the doughnut plots correspond to the legend in the lower left corner; the GI
tract is colored according to the pH scale shown at the bottom of Fig. 1. (* Malassezia was very abundant in one study and was not detected in anotherstudy. ** The abundance of Helicobacter may vary greatly between individuals. *** Proportions of these and other colon genera vary with age, diet,& geographical location.)

health. We herein highlight differences in the microbiome(Bacteria, Archaea, Fungi, and Viruses) along the GI tract ofhealthy humans, and how it compares to those of typical animalmodels used in research. One finding that is consistent tomost studies is that the microbiome of healthy individuals is
unique; however, there are still some generalities that will be
discussed in this review.
Microbiome diversity
Many factors contribute to the diversity of microbiomes,
and most studies have demonstrated the individuality of
microbiomes among subjects. Previous findings support microbial communities being more similar in subjects that are
genetically related (191), of a similar age (135, 213), or with
common diets (including the influences of ethnicity andgeography) (63). Diseases will also have an impact on microbiome diversity, including autoimmune and neoplastic diseases,
such as inflammatory bowel disease, diabetes, obesity, cardiovascular diseases, allergies, and cancer (37, 121). Treatments
for diseases may also affect a patient’s gut microbiota, andthe consequences of antibiotic use have been intensively
investigated (22, 95).
The host genotype has been shown to influence the devel-

opment of the gut microbiota, and the immune system has
been identified as a contributing factor (188). Crosstalk
between the microbiome and human immune system occurs
in response to a number of environmental factors, such as
diet, xenobiotics, and pathogens. Microbial host interactions
occur in the gut, mainly in the epithelial cell layer, myeloid
cells, and innate lymphoid cells, in which crosstalk and feedback loops contribute to the microbiome composition, hostphysiology, and disease susceptibility. These interactions
contribute not only to the bacterial community along the GI
tract, but also to the other microbiota (Fungi, Archaea, andViruses). Our understanding of the immunology associatedwith Fungi (150) and Archaea is currently limited. Transkingdom
commensal relationships among microbiota (including Viruses)
are considered to form from infancy (29, 30, 106, 200) andseveral co-occurring relationships have been identified (35,75, 76, 85, 214).
Bacteria. A more complete picture of human-associatedbacterial communities obtained using molecular techniques
has revealed that their diversity is greater than initiallyconsidered through cultivation (9, 20, 56, 90, 113). Using
almost full-length 16S rRNA gene sequences, predicted taxa
numbers range from 100–300 (20, 56), while pyrosequencing
suggests there are 1000s of phylotypes (38, 49). Most of the302

gut bacteria identified by 16S rRNA gene sequencing belong
to the five phyla originally identified by cultivation, namely,
Bacteroidetes, Firmicutes, Actinobacteria, Proteobacteria, andVerrucomicrobia (90), and, at lower proportions, Fusobacteria,
Tenericutes, Spirochaetes, Cyanobacteria, and TM7 (189).
At lower levels of the taxonomic classification, microbiome
compositions vary with each individual. Attempts have been
made to identify a single core microbiome of Bacteria in the
GI tract. Although this has not been possible in the lower GI
tract (mainly using fecal samples) based on taxonomy, it
appears there are core microbial functions (152, 189, 191). It
is possible to identify some core microbiota in the oral cavity,esophagus, and stomach (148). Although extensive efforts
have been made to cultivate representative gut microbiota in
an attempt to gain a better understanding of the relationshipbetween taxa and function (156), there are still many undescribedtaxa with unknown functional roles in the gut.
As the price of sequencing decreases, it is becoming morecommon to use a metagenomic approach that provides information on all microbiota and potential functions (3, 70, 167,
189). This provides a means to go beyond Bacteria andobtain information on eukaryotic microbes (mainly fungi)
and viruses. Although Fungi, Archaea, and Viruses in the
microbiome are a part of the ‘rare biosphere’ (organisms thatcomprise <0.1% of the microbiome) (173), they still have asignificant impact on host health.
Fungi
Fungi are considered to comprise approximately 0.03% of
the fecal microbiome (143); making them approximately
3,300-fold less abundant than Bacteria. Fungal diversity inthe human gut is also lower than that of Bacteria (143, 166),
although more taxa are being found as the number of individuals
being studied using next generation sequencing is increasing(44, 126, 166, 182). In 2015, a review of 36 fungal gut microbiome studies revealed that there have been at least 267 distinct fungi identified in the human gut (181), while anotherstudy reported 221 (72). Despite the number of taxa that havebeen reported, most fungi are highly variable among individuals,
with few appearing to be common to all.
Cultivation-based analyses have typically identified Candidaas the most common fungal genus (166), and it is also frequently identified using non-cultivation-based methods, whereas
the other taxa identified have been variable, which may be
because of the analytical method used and/or subject variability.
For example, 66 genera of fungi were found using pyrosequencing when 98 individuals were examined, with the genera
Saccharomyces, Candida, and Cladosporium being the mostprevalent (85). Mucor was common in Spanish individuals(126) and the most common fungi in 16 vegetarians were
Fusarium, Malassezia, Penicillium, and Aspergillus (182).
These studies suggested that some taxa, e.g., Penicillium andAspergillus, are not resident in the gut and enter throughenvironmental sources, such as food and water, in which they
are commonly found. This may account for some of thevariability in taxa reported in various studies and for theincreasing number of fungi being identified as more studies
are being performed, even those based on cultivation (71).
Under certain conditions, some fungi may flourish and become

Hillman et al.

pathogenic including Candida, Aspergillus, Fusarium, andCryptococcus (44, 84, 140, 143). More information on fungalinteractions and diseases is available in a review by Wang etal. (204).
Despite their low abundance, fungi appear to have developed in mammalian guts along with the rest of the body from
infancy (106, 169). Although there is no consensus of a core
mycobiome, Candida, Saccharomyces, and Malassezia havebeen commonly reported (72). Most of the fungal species
detected appear to be either transient or environmental fungithat cannot colonize the gut and are often found in a single
study and/or one host only. A previous study indicated thatthe fungal community is unstable; only 20% of the initially
identified fungi were detected again 4 months later (78).
More studies on the stability of the mycobiome are needed inorder to establish the ecological roles of the components ofthe mycobiome. Many non-bacterial organisms have beenfound in numerous mammalian systems, which indicates thatthey play an important role that has been largely overlooked
and may lead to important discoveries and understanding in
the coming years.
Archaea
The most commonly reported genus of Archaea that has
been found in the GI tract is Methanobrevibacter (51, 55, 66,
85, 109). Other genera that have also been detected are
Methanosphaera (51), Nitrososphaera, Thermogynomonas,
and Thermoplasma (85) and the new candidate species,
Methanomethylophilus alvus (27, 131). Although Archaea
comprise a very small proportion of the microbiota,
Methanobrevibacter species are important contributors tomethanogenesis (66). Differences in Archaea in microbiome
samples may be due to the method used (51) and/or
complex relationships with other microbiota. For example,
Methanobrevibacter and Nitrososphaera were previously
shown to be mutually exclusive and potentially related tocarbohydrate intake (85). More studies are needed in order toclarify the interaction between Archaea and other microbiotagroups, which may contribute to our understanding of their fitness
and function (beyond methanogenesis) in the microbiome.
Viruses
Viruses in the human microbiome have also been understudied and available information is limited (161); the majorityof data are related primarily to disease and do not address thecommensal virome (34, 40). The majority of viral reads instudies that have been performed cannot be assigned to a
known group; this has contributed to the difficulties associatedwith assessing their roles in the GI tract (124, 160). A numberof teams have made extensive efforts in order to advancehuman virome studies (157, 161). In the last ten years, thenumber of identified polyomaviruses has increased from 4 to
13 species (some that cause disease and some that do not)(47), and the accuracy of identification techniques has been
improved to identify taxa at the genus level (199) and use
metagenomic information for viral taxonomy (172). Viral
communities are mainly comprised of bacteria-infecting
phage families (~90%), while eukaryotic viruses (~10%) areMicrobial Ecology of the GI Tractin lower abundance (157, 161). Metagenomic analyses havesuggested that the new bacteriophage, crAssphage associatedwith Bacteroides, is potentially common in humans (53). The
greatest diversity of phages is considered to occur in infantsand decreases with age, in contrast to increases in bacterial
diversity (116, 117, 162). With the availability of methods toenrich viruses in samples (41), and with more metagenomic
sequences and bioinformatics tools to identify viral sequences(53, 139), more information will be obtained on viral diversity and associated physiological factors in humans.
Similar to the microbiota, considerable variability appears
to exist in the viral taxa found among subjects (133). Limited
information is currently available on the functional roles ofmost viruses in the human GI tract. However, some possiblefunctions are: to increase bacterial fitness as sources of genetic
information (e.g., the source of antibiotic resistance genes), toincrease the immunity of bacteria or the human host, and toprotect against pathogens (40, 64, 157). The general consensus
is that the presence of bacteria is beneficial for viruses that are
increasingly trying to evade the immune system. This relationship may also be beneficial to bacteria as viruses may besources of potentially advantageous genes (resistance or tolerance to stress environments). Researchers are now examining
the ecological and evolutionary influences of phages on bacterial ecosystems (102), and the findings obtained may provide
insights into the important roles played by phages in the gut
microbiome.
The GI tract
Many challenges are associated with studying the microbial ecology of the GI tract because it is composed of chemically and physically diverse microhabitats stretching fromthe esophagus to the rectum, providing a surface area of
150–200 m2 for colonization or transient occupation by
microbes (16). The adult GI tract was initially estimated toharbor 1014 bacteria, 10 times more cells than the human body(16, 120); however, a more recent calculation estimates there
to be 1013 bacteria, which is equivalent to the number ofhuman cells (170). Lower bacterial numbers (103 to 104 bacteria mL–1 of intestinal content) are found in the upper end of
the GI tract, stomach, and small intestine, in which pH is low
and the transit time is short (16). The highest biodiversity(richness and evenness) of bacteria (1010–1011 bacteria g–1 ofintestinal content) is in the colon, in which cell turnover rate
is low, redox potential is low, and the transit time is long.
This section highlights the different functions and associatedmicrobiota along the human GI tract starting from the oralcavity, then the esophagus, stomach, and intestines (Fig. 1).
The oral cavity. Activity in the mouth may have a large
impact on the further digestion of food in the lower GI tract.
Food is mechanically ground into small particles, typically
0.1 mm, which increases the surface area. The oral microbiome
is composed of transient and commensal populations that
often form biofilms on soft and hard surfaces in the mouth(8). The most up-to-date information on taxa of the oral
microbiome may be found in the Human Oral Microbiome
Database (HOMD, http://www.homd.org/) (50). Informationin this database is limited to Bacteria and one Archaea.
Cultivation-independent analyses indicate that the most

303

common genus is Streptococcus, while other genera include
Neisseria, Gemella, Granulicatella, and Veillonella, but notin all individuals examined (1, 91, 92, 107). The taxa present
appear to be dependent on interactions between microbes
within the community. For example, using a graph theorybased algorithm of an organism’s nutritional profile, the species
Streptococcus oralis and S. gordonii have low metabolic
complementarity and high metabolic competition, indicating
they are antagonistic to each other (110). In contrast,
Porphyromonas gingivalis was shown to have high metabolic
complementarity, indicating its ability to grow symbioticallywith diverse oral microbiota taxa. This computational method
was tested and confirmed with growth assays, making it aviable means to assess the ability of species to inhabit thesame environment. This has also been shown using an in situ
spectral analysis of microbiota in biofilm plaques. Biofilms
were shown to be composed of a number of taxa with
Corynebacterium at the foundation (209). The other taxa areconsidered to play complementary roles driven by the environmental and chemical gradients formed in biofilms that
control nutrient availability. These findings indicate that,
despite the large number of taxa identified in oral microbiome
studies, the core taxa of all microbiota may be identified in
the future based on spatial locations and functional roles (10).
Similar to Bacteria, large variations have been noted inviruses found in the oral cavity among subjects (151). Most
viruses are bacteriophages (approx. 99% of known sequences).
Viral communities are reproducible across time points withina subject, suggesting that they are stable; however, the humanand bacterial host significantly influence compositions (2,151, 163). In addition to interactions among oral bacteria,
many may associate with phages (57). Depending on the hostrange of the oral virome, this may make phages very common
inhabitants of the oral cavity. Furthermore, in addition tosurvival within bacterial hosts, phages may also survive in theoral mucosa and contribute to host immunity (11). These are
all new avenues of oral virome research that will likely be
investigated in greater depth in the future.
In addition to the bacterial microbiome, two cultivationindependent studies have been conducted on oral fungi.
Approximately 100 fungal species (20 genera) were detected
in one study of the oral mycobiome of healthy individuals(68). Among the fungi detected, Candida species were the mostcommon and abundant, while the other genera consisted of
Cladosporium, Aureobasidium, Saccharomycetales, Aspergillus,
Fusarium, and Cryptococcus. Most of these genera were also
detected in a recent study on three subjects; however,Malassezia, a skin pathogen, accounted for the most sequencereads (52). Most of the other studies conducted on the oral
mycobiome have focused on the role of fungi in disease (69,136). Since the oral microbial community is directly exposedto the environment, the presence of a dynamic and transientcommunity is expected, but warrants further study.
Esophagus. After swallowing, food is transported down
the esophagus by peristalsis to the stomach. Limited information is available on microbes inhabiting the esophagus (5, 91,147), and this may be due to the difficulties associated withobtaining samples because biopsies have typically been used.
However, a less invasive method using an esophageal string
has recently been demonstrated to be a feasible alternative

304

and yields similar findings to non-cultivation-based analysesof biopsies (60). Similar to the oral cavity, the most common
genus found in the esophagus is Streptococcus; however, an
overall comparison of the two communities has indicated thatthe number of taxa significantly differ between the two locations (15, 60). Among the few studies conducted on the viraland fungal microbiota of the esophagus, the focus has beenon association with disease (204) and none of the pathogenic
taxa inhabit healthy individuals.
Stomach. The stomach is the first digestive organ in the
body (89). It holds food and mechanically mixes it with proteolytic enzymes and gastric acids that aid in the breakdown
and subsequent absorption of nutrients. The growth of many
common bacteria is inhibited by these acidic conditions(pH<4), making this a unique community with the lowest
number of microbes, ranging between 101 and 103 CFU g–1.
In addition to digestion, the acidic conditions of the stomach
are considered to have evolved as a means of protection from
pathogens. This hypothesis is supported by the recent findingof a lower pH in the stomachs of scavengers and higher pH in
herbivores, which are less likely to encounter pathogens intheir food (13). Caution is needed when comparing the findingsof various studies throughout the GI tract because gastricjuice has a lower pH than the mucosal layer, resulting indifferences in the microbiota present (89).
Despite the low pH, non-cultivation-based analyses on
stomach biopsies revealed a more diverse microbiota than
expected (5, 20, 115). Regardless of variations among
subjects, there appears to be two major groups of individuals:
those with and without Helicobacter pylori (20). There is athird subset in which H. pylori is present in lower proportions
in some individuals that were negative using conventional
testing. Microbiomes dominated by H. pylori had significantly
greater proportions of the phylum Proteobacteria, of which it
is a member, and lower alpha diversity (5, 20). Other common genera are Streptococcus and Prevotella, both of which
are also found in the oral and esophageal communities;
however, the communities at these locations appear to differ(5). Limited information is available on fungi analyzed inbiopsy samples; although a cultivation study detected Candida
species, this appeared to be associated more with disease(224). The major interaction currently studied in the stomach
microbiota is with Helicobacter because of its association
with gastritis, peptic ulcers, and gastric cancer. However, thistaxon has been suggested to be beneficial for health, leadingsome to question whether the complete eradication of thismicrobe is the best option (67, 89).
In contrast, less information is available on the microbiomeof stomach fluids; it appears to harbor fewer Helicobacter
and an analysis of transcripts indicated that Actinobacteria
are the most active phylum; however, the other major phyla,
Firmicutes, Bacteroidetes, and Proteobacteria, are alsopresent (197). In the same study, it also appeared to harbornovel fungi; 77.5% of the ITS reads were not identified at thephylum level or lower. Candida and Phialemonium were theonly two identifiable fungal genera in all subjects tested,
whereas an additional 66 genera were present in at least oneof the nine subjects examined. Based on the infrequency andnumber of reads in this analysis, most of the taxa identified instomach fluids appear to be transient, and those playing an

Hillman et al.

active role are limited in this location.
Intestines. After mixing in the stomach, chime slowly
passes through the pyloric sphincter and enters the intestines,
in which the major digestion and absorption of nutrients
begin (12). Humans have a small and large intestine. The
small intestine, the main location in which food digestion andabsorption occurs, is further divided into three parts, theduodenum, jejunum, and ileum. The duodenum, in whichfood chime enters from the stomach, is directly associatedwith digestion and is linked to the pancreas and gallbladder.
Bile salts from the gallbladder and enzymes from the pancreas
enter the duodenum and mix with stomach chime in order tostart the digestion process. The epithelium in the jejunum andileum is responsible for glucose absorption into the bloodstream via glucose transporters and sodium ions. The small
intestine is followed by the large intestine (colon), which has
a larger diameter, but shorter length and is divided into four
sections: the ascending colon (cecum), transverse colon,
descending colon, and sigmoid colon (123). Water and minerals
are continuously absorbed along the colon before excretion.
Furthermore, complex foods that cannot be digested by thehost are used as growth substrates for the colonic microbiota(25, 178).
Spatial and temporal variabilities have been noted in the
microbial composition among the different intestinal structures
based on their functional roles and timing of food intake (18,129, 186). Although spatial variability exists along the intestinal
tract, the bacterial microbiome at the phylum level is considered
to remain fairly stable over time (43, 155); however, manyfactors may affect its stability (119). Undigested food andmost of the microbiota are found in the lumen, the central
space surrounded by the mucosal layer of the tubular intestinal
structure. The main absorption of growth substrates occurs
through the epithelial cells of the mucosa, which also prevents
the entry of the microbiota into host cells (174). A number ofimportant host-microbe interactions occur within the mucosa.
Energy from microbially produced metabolites, such as butyrate,
contributes to epithelial metabolism (97). Most of the gut isanaerobic, but there is an oxygen gradient in the mucosa thatprovides a competitive advantage for facultative anaerobes(174). Recent studies have also shown the importance of
metabolites produced by transkingdom microbiota to hostphysiology (185, 187, 188). Microbiota, such as Akkermansia
mucinophila, are commonly found residing in the mucus layer
and feed on mucin (39, 48). Therefore, the effects of hostinteractions with the gut microbiota, particularly those in thelarge intestine, have a prominent impact on overall human health,including energy reabsorption and immune system development.
Due to the difficulties associated with collecting multiple
samples along a healthy human GI tract in order to capture
the spatial heterogeneity of microbes in this environment,
most studies use fecal samples as a surrogate. However, thislimits the availability of regio-specific community information on the GI tract, resulting in portions, such as the small
intestine, remaining poorly characterized. The few studies
conducted on the small intestine have limited subject numbers
because they used biopsy samples (4, 201, 203) or ileotomy
patients (108, 195, 222). The bacterial genera most commonly
found among these studies were Clostridium, Streptococcus,
and Bacteroides. The number of studies that include fungi are305

Microbial Ecology of the GI Tract
Table 1.
StomachSmall intestine
Cecum
Appendix
Colon

Comparison of the anatomy of the intestinal tract in humans and animal models

Human

Mouse

Rat

Pig

Four regions: cardia, fundus,
body, and pylorus
pH 1.5 to 3.5
5.5–6.4 m in length
pH 6.4 to 7.3
Smaller than the colon
No fermentation
pH 5.7
Present
Divided into the ascending,transcending, and descendingcolon
Main fermentation
Thick mucosa
pH 6.7

Three regions: forestomach,body, and pylorus
pH 3.0 to 4.0
350 mm in length
pH 4.7 to 5.2
Larger than the colon
Main fermentation
pH 4.4 to 4.6
AbsentNot divided

Three regions: forestomach,body, and pylorus
pH 3.0 to 4.0
1,485 mm in length
pH 5.0 to 6.1
Larger than the colon
Main fermentation
pH 5.9 to 6.6
AbsentNot divided

No fermentation
Thinner mucosa
pH 4.4 to 5.0

No fermentation
Thinner mucosa
pH 5.5 to 6.2

Four regions: esophagus, cardia,
fundus, and pylorus
pH 1.5 to 2.5
1.2–2.1 m in length
pH 6.1 to 6.7
Smaller than the colon
Some fermentation
pH 6.0 to 6.4
AbsentDivided into the ascending,transcending, and descendingcolon
Main fermentation
Thick mucosa
pH 6.1 to 6.6

Adapted from (59, 96, 128, 130, 137, 196)

even more limited, with the genera Candida and Saccharomyces
being the most frequently detected (108, 114). Caution is alsoneeded when extrapolating these findings to all individuals
because the health of some subjects was compromised whensamples were obtained.
Bacteria in the colon account for approximately 70% of all
bacteria in the human body because it is the main site for thebacterial fermentation of non-digestible food components
such as soluble fiber. The small number of studies that haveexamined microbial communities directly in the colon suggeststhat the bacterial composition is similar to that found in feces(86). However, fecal communities do not represent a single
colonic environment, such as the mucosa (223), but a mixtureof indigenous and transient microbes from the entire GI tract.
In studies in which a global view of the GI tract microbialcommunity is of interest, fecal material represents a good
surrogate and is easily obtained, allowing for multiple samples
to be obtained over short and long time periods from healthy
individuals. The majority of microbiome reviews have extensively covered colonic communities using feces (74, 92, 121,
148, 189); therefore, we will not describe its composition indetail. However, later in this review, the impact of diet on the
microbiome composition will be discussed. Furthermore, theabove sections on fungi and viruses provide information on
the taxa of these groups in the intestines.
Summary of the GI tract. The use of non-cultivationbased methods to investigate the microbiota in the GI tract
has increased our knowledge of their diversity. One groupthat we neglected to mention in this review was Protozoans/
Protists; however, recent reviews are available (79, 145).
Despite representing a smaller biomass than fungi, they also
appear to be important to the ecological structure of the gut
microbiome. The predator-prey relationship they have withother microbiota (145) may, in some cases, lead to disease
prevention (7). Difficulties are associated with elucidating thefunctional roles played by these various taxa at different points
along the GI tract. Therefore, it is still important to obtain
cultivated representatives to investigate their role and ecological significance along the GI tract. This consideration isimportant for all microbiota; however, it represents a larger
issue for low diversity groups, such as fungi, which may not
be numerically abundant, but still play a significant role (17).

Use of animal models
Animal models have been widely adopted in human gut
microbiome research (28, 98, 220) to reduce confounding
experimental factors such as genetics, age, and diet, which may
be more easily controlled in laboratory animals. Additionally,animal models with modified genetic backgrounds are available
for investigating potential mechanisms (137). Ideally, animalmodels with relatively similar genetic information (217), gut
structures, metabolism (142), and diets and behavior patterns(202) to humans need to be selected. Comprehensive comparisons of mice (137) and pigs (217) to humans were recently
conducted in order to aid in translating information fromanimal models to humans. In this section, we will highlightsome of their findings and compare GI tract structures andmicrobial community compositions. Furthermore, someadvantages and limitations associated with the use of animalmodels in human microbiome research will be discussed.
Similarities exist in the anatomy of the GI tract between
humans and most animal models (Table 1). However, differences in anatomical structures and pH at different locations
along the GI tract may contribute to differences in the microbiota found in humans versus animal models (26). The humancolon also has a thicker mucosal layer than those of mice andrats (137), which may have an effect on the diversity of the
microbiota colonizing the colon. Human gut bacteria aredominated by two phyla: Firmicutes and Bacteroidetes (189),
which also dominate the GI tract of commonly used model
animals (112). However, at lower taxonomic levels, somedifferences have been reported in microbiome compositionsin the gut between humans and animal models (Table 2). The
dominant taxa reported have varied as the number ofcomparisons performed has increased (137, 152); therefore,
the findings shown in Table 2 need to be used cautiously.
A pig gut gene catalogue of metabolic function wasrecently developed and compared to catalogues available forhumans and mice (217). They found that 96% of the KEGG
orthologs in humans were also present in pigs, whereas theoverlap at the gene level was markedly lower (9.46%).
However, there was a greater overlap between humans andpigs than between humans and mice. Microbial activity also
differs along the GI tract, with the most relevant being fermentation occurring in the ceca of most animal models, but306

Hillman et al.
Table 2.
Bacteria

Archaea
Viruses

Eukarya

Major taxa of the gut microbiota in humans and animal models

Human

Mouse

Rat

Pig

Firmicutes
Bacteroidetes
Actinobacteria
Proteobacteria
Methanobrevibacter
Nitrososphaera
Herpesviridae
Papillomaviridae
Polyomaviridae
Adenoviridae
CandidaMalassezia
Saccharomyces
Cladosporium

Firmicutes
Bacteroidetes

Firmicutes
Bacteroidetes

Firmicutes
Bacteroidetes

Methanobrevibacter

Methanobrevibacter

Variable

Variable

Ascomycota
Basidiomycota
Chytridiomycota
Zygomycota

Ascomycota
Basidiomycota
Chytridiomycota
Zygomycota

Methanomicrobia,
Methanosphaera
Picornaviridae
Astroviridae
Coronaviridae
Caliciviridae
Kazachstania
CandidaGalactomyces
Issatchenkia

Adapted from (85, 103, 105, 112, 125, 137, 153, 154, 171, 179, 193, 194, 215, 216, 221)

not in humans (137). Strengths and weaknesses are associatedwith the major animal models being used, and these need tobe taken into consideration when conducting translational
research.
Rats. The use of rats as lab animals dates back to the1850s. They were considered to be a good candidate forhuman microbiome research because the rat contains thesame four dominant bacteria phyla in the GI tract (31), withFirmicutes (74%) and Bacteroidetes (23%) representing thelargest proportions (221). The advantages of using rats inhuman microbiome research include quick reproduction, afully sequenced genome, and easy handling and maintenancedue to their relatively small size. The limitation of this modelis that the diet used in rats differs from that for humans, andtheir behavior and living environment are also different,
which will affect the gut microbiota. The diet used in rat
studies is normal chow that is rich in fiber (205), and diet may
rapidly alter gut microbiota diversity (46). Although moststudies emphasize the impact of diet on the microbiota in thececum and/or colon (feces), the oral cavity of rats has beenused to clarify the impact of diet on the microbiome (93).
Mice. Many of the strengths and weaknesses associatedwith using rats are also applicable to mice. Similar to humans,
the microbiota in the GI tract of mice is dominated by
Firmicutes (74%) and Bacteroidetes (23%) at the phylum
level (217). However, there are differences at the genus level,
and this has led to the use of “humanized” mice. This isachieved by inoculating human gut microbiota into germ-free(GF) mice (192) or mice treated with antibiotics to eliminate
their gut microbiome (83). The microbiome of these mice
after fecal transplants may have a composition at the phyla
level that is 100% similar to humans and 88% at the genus
level (137). A recent study (175) used humanized mice to test
microbiome diversity after feeding with poorly accessible
carbohydrates, and found a similar reduction in OTU numbers to a human study (219). However, there are also some
limitations to using these animals, including the diet andenvironmental living conditions. Furthermore, gnotobiotic
mice may not reflect the human-microbe relationship due totheir weaker immune system (6).
Approximately 10 years ago, Scupham (168) showed that
all four major fungal phyla, Ascomycota, Basidiomycota,
Chytridiomycota, and Zygomycota, were present in the murine

gut. Additionally, many genera were identified, including
Acremonium, Monilinia, Fusarium, Cryptococcus, Filobasidium,
Scleroderma, Catenomyces, Spizellomyces, Neocallimastix,
Powellomyces, Entophlyctis, Mortierella, and Smittium. When
comparing these studies to the human gut, it is important tonote that this study indicated a more diverse fungal community
than those found in humans; the eukaryotic diversity of thehuman gut is low (143).
Pigs. Pigs have been used as surrogates for human microbiome research due to their highly similar genetics, physiological structures, behavior, metabolism, and immune functions to those of humans (81, 202). The greater similarities in
the omnivorous diet and GI tract structure between pigs andhumans are more advantageous than the murine model. The
microbiome of pigs is dominated by two phyla: Firmicutes
and Bacteroidetes (104); however, there are some notabledifferences at the genus level. The genus Prevotella wasfound to be common in two pig metagenomic studies (104,118). Since the number of pigs used in most studies is lessthan humans, the pig core microbiome at the genus level may
change as more pigs are studied. Another contributing factor
to shaping the microbiome composition is diet. Most studies
have found that the number of Bifidobacteria in pigs, eventhose on high fiber diets, is lower than that in humans (132,218), while that of Lactobacillus is higher (149). In nutrition
studies, humans and pigs are both dependent on the quality of
the nutrient load; however, the pig cecum has a larger capacity
to ferment indigestible compounds than the human cecum(54). The microbiota composition in pigs may differ from thatin humans due in part to differences in diet (81). Similar tomice, humanized GF pigs have been developed and themicrobiome after human fecal transplantation more closely
resembles that of the donor than conventional pigs (144).
However, the same disadvantages associated with using GF
mice are also true for GF pigs.
The genome of pigs may be mutated to study human diseases;
this is typically performed using miniature pigs such as thosefrom the Ossabaw and Gottingen islands (146). Genetic
mutations for metabolic syndrome and insulin resistance havesuccessfully been performed using Ossabaw pigs to study
human diseases such as type 2 diabetes (14, 177) and obesity(101). The ratio of Firmicutes to Bacteroidetes is higher inobese Ossabaw pigs than in lean pigs (146), similar to someMicrobial Ecology of the GI Tractobese humans (111, 190). This finding suggests that Ossabaw
pigs are a good model for researching the role of the microbiota
in human obesity. However, disadvantages are associatedwith using miniature pigs, mainly the higher cost for maintenance and longer reproductive period than rodents (146).
Although more extensive efforts have been made to investigate fungi in pigs than in other animal models, many ofthese studies were cultivation-based or for use as probiotics.
Fungi in pigs have been recently studied using a non-cultivation
approach and up to 17 species of yeast (belonging to thegenera Kazachstania, Galactomyces, Candida, Issatchenkia,
Pichia, Rhodotorula, and Trichosporon) were common in thegut (194). The number of studies on viruses is limited, but thecomposition appears to be highly variable among samples(164, 171) and affected by disease (24). These groups need to
be examined in more detail in order to establish whether pigs
are good models for use in understanding fungi and viruses inhumans.
Animal model summary. The convenience and cost ofusing animal models for human research are appealing.
However, researchers need be very careful when selecting
animal models appropriate for their objectives, particularly
when the objective is to directly extrapolate findings fromanimals to humans, due to the significant differences in GI
tract physiology and microbiome composition (65, 137, 217).
Diet in healthMany studies have found that diet is one of the main factors
shaping the composition of gut microbial populations. Dietary
approaches, such as the ingestion of non-digestible carbohydrates (prebiotics) and fermented food products containing
live cultures (probiotics), have been suggested to confer healthbenefits by enhancing the growth of beneficial intestinal bacteria(100, 158). As described earlier, the microbiota may break
down food components, such as non-digestible carbohydrates,
which are indigestible by the host in order to aid in maximizing
available nutrients (9) and produce metabolites that contributeto host health. Probiotics have been used as a means toreplenish the gut with “beneficial” microbiota after antibiotic
treatments or to treat diseases (82, 159). This section will
highlight some studies that demonstrated the health benefitsof prebiotics and probiotics and possible roles played by the
microbiota.
Dietary prebiotics and probiotics. Non-digestible andfermentable food components are often consumed as prebiotics
to selectively stimulate the growth and/or activity of endogenous colonic bacteria that may be beneficial to host health.
The increased consumption of prebiotics often correlates withenhancements in certain bacterial genera (a common example
is Bifidobacterium sp.); however, the reason they are beneficial remains unclear (208). Challenges are associated withelucidating the role being played by specific bacterial phylotypes because many of their processes are interactive (207).
For example, SCFA produced by bacterial fermentation may
lower intestinal pH, thereby increasing the solubility ofessential minerals, such as calcium, iron, and magnesium,and consequently enhancing their absorption and improving
health. Metabolites produced by microbes may also play animportant role in cellular differentiation and proliferation in307

the colonic mucosa by inducing apoptosis and may confer
protection against colitis and colorectal cancer by modulating
oncogene expression. These functions do not appear to be
performed by a single species; a number of different species
may be acting independently or in combination. Research isleading to an understanding of microbial community structure
and composition dynamics with respect to diet aids in establishing testable hypotheses for future research in health andbeneficial microbes (32). Most research has been performedon the influence of beneficial intestinal bacteria such as
Bifidobacterium spp. and Lactobacillus spp. on host healthmonitored using a cultivation approach. Cultivation-independent
approaches have now become more popular, leading to theidentification of new beneficial microbiota taxa and theirpotential functional roles in the gut as they relate to diet.
Dietary fibers and oligosaccharides are carbohydrate ingredients that vary in composition and structure, but are considered
to be non-digestible because of the lack of appropriate intestinal enzymes to hydrolyze them or structural hindrances thatprevent enzyme access in the gut. Although bacteria in thelower gut may ferment these carbohydrates, the rate anddegree of fermentation vary with the polysaccharide (80).
The range of fermentation in the colon for various fibers is
broad, from approximately 5% for cellulose to nearly 100%for pectin (42). The resulting SCFA, including butyrate andpropionate, are considered to reduce pH and solubilize minerals,
thereby improving their absorption and subsequent utilization.
Inulin, a long chain fructooligosaccharide (FOS) often
obtained from chicory root, and FOS from other sources are
the fibers that have been studied in the most detail (206).
Several novel fibers have been tested in an in vitro large
intestine model for their effects on the microbial stimulationand production of SCFA (122). All these novel fibers stimulated the growth of beneficial Bifidobacteria and some
Lactobacillus species along with increases in SCFA production. Only a few studies have examined the effects of fibers
and resistant starches on the human microbiome (87, 127,
198, 210, 211). A soluble corn fiber product has been demonstrated to increase Ca absorption in a number of different
studies (210, 211). More benefits to human health may be
attributed to the consumption of prebiotics and fermentationby the gut microbiome.
The number of studies that include diet effects on Archaea,
Fungi, and/or Viruses are limited; however, some examples
are included herein. Examinations of Archaea, Fungi, andBacteria correlations in response to diet revealed a syntrophic
model involving Candida, Prevotella, Ruminococcus, andMethanobrevibacter (85). Candida was considered to break
down carbohydrates into metabolites used by Prevotella andRuminococcus that produce CO2 for Methanobrevibacter(85). However, shifts in carbon sources or breaking down
starches via amylases from the human mouth may alter thisrelationship because Prevotella may no longer be dependent
on Candida. This is a good example of how Archaea, which
represent a very small portion of the microbiome, are a key
contributor to methanogenesis and waste decomposition. The
absence of Archaea may have severe effects on the surrounding
community as hydrogen, glucose metabolites, and other carbon
sources accumulate. Other organisms will eventually fill thisniche, but may diminish or accumulate new metabolites that

308

Hillman et al.

ultimately shift the surrounding community based on theirfitness for using these substrates.
A recent study investigated rapid changes in the microbiome
composition when diets were either high in animal-based orplant-based fat and protein (46). The fungus Candida wasfound to increase in subjects placed on a plant-based diet,whereas Penicillium increased on animal-based diets. The
most commonly found fungi in vegetarians were Fusarium,
Malassezia, Penicillium, Aspergillus, and Candida (182).
Caution is needed when interpreting findings because someof these fungi may be found on food prior to ingestion (46,78, 182)
Phages assembled in the gut may also be modified by diet.
A recent study examined changes in the fecal viral community over an 8-d period in six subjects supplied different diets(134). Shotgun sequencing of virus-like particles revealed
that interpersonal differences in the virome were the largest
source of variations in this study. However, the virome ofsubjects whose diets were changed differed more than inthose who maintained their normal diet. Although this is only
one study with a few human subjects, studies using a mousemodel and different dietary fats support these findings (88,99). Collectively, these findings indicate that diet plays a keyrole in shaping the gut virome, and further research is neededin order to investigate interactions between diet and the virome.

5.
6.
7.

8.
9.
10.
11.
12.
13.
14.
15.
16.

Summary
Advances have been made in the last decade in our understanding of the role of the GI tract microbiome in humanhealth. This review has highlighted changes and differencesin the microbiome along the GI tract that are due to changes
in physical, chemical, and biological interactions. Althoughextensive research has been conducted on Bacteria in fecal
samples, the main kingdom inhabiting the gut, our knowledge
is still insufficient, particularly in other regions of the GI tract.
Furthermore, other groups (Archaea, Fungi, and Viruses) have
not yet been investigated in adequate detail, demonstrating areal void in knowledge. This highlights that the basic ecologyof microbiomes is important for gaining a greater understanding
to improve human health and decrease disease. In order toachieve this goal, it is important to include all microbiota instudies and remain cognizant of the limitations associatedwith understanding the entire GI tract of humans despite
challenges in sampling and cultivation. Furthermore, the useof appropriate animal models in mechanistic studies requires
careful consideration.
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Chih-Yu Chiu, Academia Sinica, Taipei, Taiwan
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Peter Dunﬁeld, University of Calgary, Calgary, Canada
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Manabu Fukui, Hokkaido University, Sapporo, Japan
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Shir-Ly Huang, National Central University, Jhongli City, Taiwan
Yuichi Hongoh, Tokyo Institute of Technology, Tokyo, Japan
Hiroyuki Imachi, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
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Elena P. Ivanova, Swinburne University of Technology, Melbourne, Australia
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Yasuhiro Kasahara, Hokkaido University, Sapporo, Japan
Takafumi Kataoka, Fukui Prefectural University, Obama, Japan
Junichi Kato, Hiroshima University, Higashi-Hiroshima, Japan
Yoshitomo Kikuchi, National Institute of Advanced Industrial Science and Technology (AIST), Sapporo, Japan
Hiroyuki Kimura, Shizuoka University, Shizuoka, Japan
Tomonori Kindaichi, Hiroshima University, Higashi-Hiroshima, Japan
Staﬀan Kjelleberg, The University of New South Wales, Sydney, Australia
Ken-ichi Kucho, Kagoshima University, Kagoshima, JapanKen Kurokawa, National Institute of Genetics, Mishima, Japan
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Lian-Hui Zhang, Institute of Molecular and Cell Biology, Proteos, Singapore
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Shusei Sato, Tohoku University, Sendai, Japan
Masaru Usui, Rakuno Gakuen University, Ebetsu, Japan
Torahiko Okubo, Hokkaido University, Sapporo, Japan
Takayuki Wada, Osaka City University, Osaka, Japan
Masafumi Shimizu, Gifu University, Gifu, Japan
Tomoyasu Nishizawa, Ibaraki University, Ami, Japan
Rie Yatsunami, Tokyo Institute of Technology, Yokohama, Japan
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