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Microbes andEnvironments&

September 2022

Microbes and Environments
DIGEST 2022
CONTENTS
MOST VALUABLE PAPAR OF THE YEAR 2020
Mechanisms of Rice Endophytic Bradyrhizobial Cell Differentiation and Its Role in Nitrogen Fixation
Teerana Greetatorm, Shun Hashimoto, Taro Maeda, Mitsutaka Fukudome, Pongdet Piromyou,
Kamonluck Teamtisong, Panlada Tittabutr, Nantakorn Boonkerd, Masayoshi Kawaguchi,
Toshiki Uchiumi, and Neung Teaumroong35(3), ME20049, 2020

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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

2

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

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Article ME20049

3

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)
4with 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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Article ME20049

Greetatorn et al.
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)
6

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)
8

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 also
12that 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. 36(2), 2021

https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME21007

MOST VALUABLE PAPER OF THE YEAR 2021

Bioelectrical Methane Production with an Ammonium Oxidative Reaction under
the No Organic Substance Condition
Ha T.T Dinh1,2, Hiromi Kambara1, Yoshiki Harada1, Shuji Matsushita1,3, Yoshiteru Aoi4, Tomonori Kindaichi1,
Noriatsu Ozaki1, and Akiyoshi Ohashi1*
Department of Civil and Environmental Engineering, Graduate School of Advanced Science and Engineering, Hiroshima
University, 1–4–1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739–8527, Japan; 2Faculty of Environment, Ho Chi Minh City
University of Natural Resources and Environment, 236 Le Van Sy, 1 Ward, Tan Binh District, Ho Chi Minh City, Vietnam;
3Agricultural Technology Research Center, Hiroshima Prefectural Technology Research Institute, 6869, Hara, Hachihonmatsu,
Higashi-Hiroshima, Hiroshima 739–0151, Japan; and 4Program of Biotechnology, Graduate School of Integrated Sciences for
Life, Hiroshima University, 1–3–1, Kagamiyama, Higashi-Hiroshima, Hiroshima 739–8530, Japan
1(Received January 25, 2021—Accepted May 1, 2021—Published online June 17, 2021)

The present study investigated bioelectrical methane production from CO2 without organic substances. Even though
microbial methane production has been reported at relatively high electric voltages, the amount of voltage required and theorganisms contributing to the process currently remain unknown. Methane production using a biocathode was investigatedin a microbial electrolysis cell coupled with an NH4+ oxidative reaction at an anode coated with platinum powder under awide range of applied voltages and anaerobic conditions. A microbial community analysis revealed that methane production
simultaneously occurred with biological denitrification at the biocathode. During denitrification, NO3– was produced bychemical NH4+ oxidation at the anode and was provided to the biocathode chamber. H2 was produced at the biocathode by
the hydrogen-producing bacteria Petrimonas through the acceptance of electrons and protons. The H2 produced was
biologically consumed by hydrogenotrophic methanogens of Methanobacterium and Methanobrevibacter with CO2 uptake
and by hydrogenotrophic denitrifiers of Azonexus. This microbial community suggests that methane is indirectly produced
without the use of electrons by methanogens. Furthermore, bioelectrical methane production occurred under experimental
conditions even at a very low voltage of 0.05 V coupled with NH4+ oxidation, which was thermodynamically feasible.
Key words: ammonia oxidation, bio-electricity, denitrification, methane production, microbial community

Methane is the prime component of natural gas and is
widely utilized as an energy source worldwide. It is mainly
produced by biological and physical actions that collectively
contribute to 20–80% of natural gas reserves (Rice andClaypool, 1981). Methane is physically produced throughthe thermal decomposition of organic matter in association
with the formation of coal, gas, and oil (Schoell, 1988).
Conversely, biological methane formation is primarily per‐formed by methanogenic microbes (methanogens) in anae‐
robic environments (Whiticar et al., 1986; Whiticar, 1999).
Only methanogenic archaea are known to act as methano‐
gens and use substrates produced from organic matter
during fermentation, such as acetate, formate, and hydrogengas. This methane fermentation occurs in nature, but has
also been applied as an eco-friendly wastewater treatment
technology (Onodera, 2013; Townsend-Small et al., 2016).
Artificially produced biogas may be utilized as an energy
source after purification.
It is possible to generate electricity from organic substan‐ces. Microbial fuel cell (MFC) technology and its applica‐* Corresponding author. E-mail: ecoakiyo@hiroshima-u.ac.jp;
Tel: +81–82–424–7823; Fax: +81–82–424–7823.
Citation: Dinh, T. T H., Kambara, H., Harada, Y., Matsushita, S., Aoi,
Y., Kindaichi, T., et al. (2021) Bioelectrical Methane Production withan Ammonium Oxidative Reaction under the No Organic SubstanceCondition. Microbes Environ 36: ME21007.
https://doi.org/10.1264/jsme2.ME21007

16

tion to wastewater treatment have been extensively
examined (Logan et al., 2006; Sarmin et al., 2019; Wang etal., 2020). Conversely, in microbial electrosynthesis systems(MESs), methane is produced by providing electricity(Rabaey and Rozendal, 2010; Eerten‐Jansen et al., 2012).
High methane production is expected when MESs areapplied to wastewater treatment because of the combinationof methane fermentation using organic substances and theconversion of CO2 to methane by microbes through electric‐ity (Clauwaert et al., 2008; Clauwaert and Verstraete, 2009;
Zhao et al., 2016; Park et al., 2018; Peng et al., 2019). Ding
et al. (2016) identified 0.8 V as the optimal applied voltage
for appropriate wastewater treatment and maximum meth‐ane production using an MES.
In the MES, bioelectrical methane production is per‐formed without organic substrates (Cheng et al., 2009;
Villano et al., 2010; Zhen et al., 2015). Cheng et al. (2009)
reported that carbon dioxide was reduced to methane at abiocathode potential of <–0.7 V (vs. Ag/AgCl). At –1.0 V(vs. Ag/AgCl), the electron capture efficiency of methane
production was 96%. Two mechanisms have been proposed
for biological methane production using a biocathode. At
high applied voltages, methane may be produced by hydro‐
genotrophic methanogens using abiotic H2 formed in wateroxidation (Wagner et al., 2009; Eerten‐Jansen et al., 2012).
In this case, H2 is an important intermediate for methane
production. The second mechanism is direct electrotrophic
methane production. Cheng et al. (2009) reported that some
Article ME21007

Dinh et al.

methanogens must use electrons with CO2 to directly pro‐
duce methane, without hydrogen as an intermediary. Previ‐
ous studies on extracellular electron transfer demonstrated
that applied voltage may not be effective at promoting meth‐ane production, suggesting a pathway without H2 (Rotaru etal., 2013; Lohner et al., 2014; Holmes et al., 2017; Lee etal., 2017). However, there are insufficient experimental data
to prove direct electrotrophic methane production. It cur‐rently remains unclear whether bioelectrical methane pro‐duction occurs via direct and/or indirect reaction(s) in MESs.
The CO2 reduction potential to methane E0’cat at the bio‐
cathode is –0.24 V (vs. SHE) under the standard conditionat pH=7. When coupled with H2O oxidation (E0’an=0.81 V
vs SHE) at the anode, methane production in an MES occurs
thermodynamically by applying more than 1.05 V under the
standard condition. If the oxidation of inorganic compoundswith a lower potential (such as NH4+ oxidation to NO3– andN2: E0’an=0.36 V and –0.29 V vs. SHE, respectively) occurs
instead of H2O oxidation, methane may be produced at alower applied voltage. However, MES studies have not pro‐vided sufficient information on the oxidation reaction at theanode, with experiments being conducted at relatively highvoltages.
In the present study, we designed an MES experiment inwhich an organic substrate was not supplied, and NH4+ wasadded to the anode chamber to investigate whether methane
production is possible even at very low applied voltages.
Although the reaction of electrotrophic methane production
with NH4+ oxidation to N2 thermodynamically proceeded
even without a supply of electricity, this is the first study toreport coupling to the NH4+ oxidative reaction. In addition,
the microbial community was analyzed to identify theorganisms involved in bioelectrical methane production.
Materials and Methods
MES set-up
The MES used in the present study consisted of two glass cham‐bers, each with an effective volume of 70 mL, which were con‐nected by a 10-cm salt bridge containing 2% (w/w) agar (KF-30;
Fujirika) and 20% (w/w) KCl (Fig. S1). The top of each chamber
was connected to a 10-mL loss-of-resistance glass syringe torelease the pressure generated in the chamber by the gas produced
and also facilitate gas collection. A 9-cm2 electrode of carbon cloth(Toyobo) was installed in both chambers. The biocathode andanode electrodes were connected to a DC power supply (Array
3600 Series; T&C Technology) using a platinum wire. A 100-Ω
resistor was inserted between the power supply and biocathodeelectrode to estimate the electric current by measuring voltage
using a digital multimeter (FlePow; Levin Japan). Even if theexternal resister was inserted, the effect on the actual applied volt‐age was negligible when the internal resistance of the MES was
high. A small amount of anaerobic sludge taken from a laboratoryscale upflow anaerobic sludge blanket (UASB) reactor was inocu‐
lated on the surface of the cathode electrode. Platinum powder(10% by weight of platinum on carbon powder; E-TEK, C-1 10%
Pt on Vulcan XC-72) was coated on the surface of the anode, as
described in previous studies (Müller and Spitzer, 1905; Nutt andKapur, 1968; De Vooys et al., 2001; Bunce and Bejan, 2011; Li etal., 2017).

MES operation
The MES was operated in the batch-processing mode at 30°C ina thermostatic chamber. The anodic and biocathodic chambers
were filled with the same medium without organic substances anddeoxidized through a nitrogen purge. The medium was composedof NaHCO3 (200 mg L–1), NH4Cl (190 mg L–1), NaH2PO4(17 mg L–1), and Na2HPO4 (124 mg L–1), as well as trace elements,
including FeSO4·7H2O (7 mg L–1), CoCl2·6H2O (1.7 mg L–1),
ZnSO4·7H2O (1.5 mg L–1), HBO3 (0.6 mg L–1), MnCl2·4H2O(4.2 mg·L–1), NiCl2·4H2O (0.4 mg L–1), CuCl2·2H2O (0.27 mg L–1),
and Na2MoO2·2H2O (0.25 mg L–1), at a pH of 7.5. The medium
was completely replaced at intervals of 3, 5, 6, and 13 d, with thebatch experiment being repeated 20 times over 110 d of operation.
Each batch duration time was determined according to gas produc‐tion for gas sampling. Each batch experiment was performed at aconstant applied voltage in the range of 0.05–3.0 V to investigate
whether methane production is possible even at low voltages. Aftersetting up the MES, a voltage was immediately supplied toenhance microbial activity at the biocathode, and the anode wasunsterilized.
Sampling and analyses
The volume of gas production in the respective chambers wasmeasured using an airtight syringe. CH4, N2, CO2, and H2 concen‐
trations were then measured using a gas chromatograph equippedwith a thermal conductivity detector (GC-TCD; Shimadzu
GC-8A). NH4+, NO3–, and NO2– concentrations in the medium were
measured by ion chromatography (Shimadzu HPLC-20A) at thestart and end of each batch operation. Dissolved CH4 and N2 con‐centrations were estimated using Henry’s law.
Microbial community
The sludge sample at the biocathode was collected on day 110 of
the last MES operation and washed with phosphate buffer. DNA
was extracted using the FastDNA® SPIN Kit for Soil (MP Biomed‐
icals), according to the manufacturer’s instructions. PCR amplifi‐
cation of the 16S rRNA gene was performed using the primer
sets 341’F (5′-CCTAHGGGRBGCAGCAG-3′) and 805R (5′GACTACHVGGGTATCTAATCC-3′) with KAFA HiFi Hotstart
ReadyMix (Kapa Biosystems). PCR conditions were as follows:
the initial denaturation of DNA at 95°C for 3 min, followed by 25
cycles at 95°C for 30 s, at 55°C for 30 s, and at 72°C for 30 s, witha final extension at 72°C for 5 min. The PCR product was purified
and sequenced by the emulsion method using Illumina/Miseq (Illu‐
mina) at Hokkaido System Science. The sequences obtained wereanalyzed using QIIME (v1.8.0) (Caporaso et al., 2010). Opera‐
tional taxonomic units (OTUs) were grouped based on a thresholdvalue of 97% identity for DNA using the UCLUST algorithm(Edgar, 2010). These OTUs were classified using the Greengenes
database (McDonald et al., 2012; Werner et al., 2012).
Sequence data were deposited in the DDBJ database under
DDBJ/EMBL/GenBank accession number DRA011341.

Results
Performance of batch experiments
In MES batch operations, we initially attempted to apply
a relatively high voltage of 2.0 V over 3 d. Once a higher
current of approximately 0.18 mA was observed, it immedi‐
ately decreased to 0.09 mA and gradually declined overtime, as shown in Fig. 1. However, no gas bubbles werevisible in either the biocathode or anode chambers, despite
the sludge inoculation being expected to enable methane
production activity. The 3-d batch experiment was then

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Bioelectrical Methane Production

Fig. 1.

Time courses of applied voltage (a), current (b), and gases (CH4, N2, and CO2) produced (c) in batch experiments.

repeated with changes in bulk liquid, but at lower voltagesof 1.6 V and 1.4 V, resulting in current behaviors that were
the same as those at 2.0 V. Under these conditions, very few
bubbles were observed in the biocathode chamber. In thenext batch operation, bubbles were observed where theapplied voltage was returned to 2.0 V. However, it was notpossible to sample the gas produced because of its low vol‐ume.
Therefore, we changed the batch interval time from 3 to
5 d from day 12 onwards, except for some special batches.
More bubbles were produced in the biocathode chamber andcollected as a gas in the fifth batch operation at 2.0 V. The
gas produced was approximately 2.0 mL on days 12–17(Fig. 1). As expected, methane was detected, but its concen‐
tration was only 10.0%. The main component of the gas was
N2, with a very low concentration of CO2. The current wasmarkedly higher than that in the previous batch experimentat the same voltage. The current also decreased for approxi‐
mately 3 d, but increased thereafter. A significant difference
was observed in current behavior between the small andlarge gas production chambers. In control batch experiments
without the inoculation, methane was not detected in therange of 1.0–2.0 V; however, hydrogen production wasobserved at an applied voltage higher than 1.2 V in the cath‐ode chamber. Methane production is expected to be derived
18

from inorganic carbon in the presence of microbes on the
carbon cloth, the biological activity of which may beenhanced after 17 d of operation; however, no bubbles wereobserved in the anode chamber under any of the conditions
used.
To investigate the effects of voltage on methane produc‐tion, experiments were continuously performed while
decreasing the applied voltage step-by-step down to 0.1 V
until day 61 (Fig. 1). The current slightly decreased with
voltage reductions; however, its pattern of behavior was
similar in each batch period. Gas containing CH4 and N2
was produced at any voltage, except during days 22–27
when gas sampling failed.
Since high N2 concentrations of approximately 75 to 90%
were detected, we reconducted batch experiments underalmost identical conditions over a range of 0.05–3.0 V ondays 55–110 to reveal the source of N2 yield by measuring
ammonium, nitrate, and nitrite. In the last batch operation,
we also attempted methane production at a very low voltage
of 0.05 V. A small amount of gas containing 6.09% CH4 wascollected, even at the lowest voltage, particularly over a pro‐longed period of 13 d. NH4+ and NO3– concentrations
decreased in both the biocathode and anode chambers (Fig.
S2). Total nitrogen ions in the two chambers decreased in allbatches, suggesting that the yield of N2 was derived from

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Effects of voltage on gas production
Although a 100-Ω external resistor was inserted, the volt‐age supplied was nearly equal to the actual applied voltage
between the biocathode and anode because the current ver‐sus supplied voltage was small throughout the experiment,
as shown in Fig. 1. The gas production rate was signifi‐
cantly dependent on the applied voltage, as shown in Fig. 2.
CH4 production slightly increased in proportion to the volt‐age with 0.306 mL at 1.2 V, after which it decreased to
0.128 mL at 3.0 V. These results suggest that a very highvoltage does not always enhance methane production andmay have a negative effect on microbes. The N2 production
rate was similar to that of CH4 with respect to the effects ofvoltage; however, large fluctuations were observed. Thissuggests that microbes also play a role in N2 production.
The retained microbes were expected to grow and increase
with the operational time. However, they were slightly
detached when bulk liquid was replaced as a result ofchanges in batch conditions. Therefore, the number ofmicrobes was unstable, possibly contributing to fluctuations
in gas production.

Fig. 3.

0.4

Gas production rate (mL d-1 )inorganic nitrogen ions. Regarding the nitrogen balance, astrong relationship was observed between the amount ofconsumed NH4+ plus NO3– and produced N2 (Fig. S3).
Cecconet et al. (2019) reported the accumulation of NO2–
and N2O in a biocathodic denitrification process for ground‐water bioremediation. However, these intermediates in deni‐
trification were not detected in this MES experiment. The
lack of accumulation of intermediates may have beencaused by the slow reaction.

N2
N2
CH
CH44

0.3
0.2
0.1
0.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Voltage (V)Fig. 2.

Gas production rates at different applied voltages.

Microbial community
In the 16S rRNA gene sequencing of the biomass sample
on day 110, more than 100,000 reads, including domain bac‐teria and archaea, were obtained, and the number of OTUs
exceeded 1,200. Sequencing results revealed the presence ofbacterial and archaeal communities (Fig. 3). Archaea com‐prised only 3.9% of the total reads.
The major families of bacteria were Porphyromonadaceae,
Rhodocyclaceae, and Geobacterceae, accounting for 26.8,
11.4, and 10.7%, respectively. The three families made up
approximately 45% of all microbes. Of the most dominantfamily Porphyromonadaceae, approximately 50% was theobligately anaerobic genus of Petrimonas, while 29.8%
uncultured genera were detected (Fig. 3). Petrimonas con‐sists of hydrogen-producing bacteria (Lu et al., 2012; Sun etal., 2015; Liu et al., 2016), suggesting that hydrogen is pro‐Microbial community of a biomass sample on day 110, based on the 16S rRNA gene.

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Bioelectrical Methane Production

Discussion
NH4+ oxidation was observed in the anode chamber,
although at insignificant amounts, indicating that NH4+ wasoxidized by donating electrons to the biocathode. Platinum
is commonly accepted as the most promising catalyst in theelectrochemical oxidation of ammonia (e.g., De Vooys etal., 2001; Li et al., 2017). Müller and Spitzer (1905)
reported that the anodic products of electrolyzing ammonia
at a platinum anode were mainly NO3– and N2 (25%–35%).
With over-oxidation, NO2– and NO3– products wereobserved at applied voltages of higher than +0.6 V (vs Ag/
AgCl) (Endo et al., 2005; Bunce and Bejan, 2011). NO3–
was also reportedly formed from NH4+ by catalytic oxida‐
tion with PtOx (Fóti and Comninellis, 2004; Panizza andCerisola, 2009). Since platinum powder was coated on thesurface of the anode with a carbon cloth electrode in thepresent study, similar reactions to the electrolysis of water
occurred; however, neither N2 nor NO2– production wasobserved.
The oxidation of NH4+ at the anode may be represented asfollows:
1/8 NH4 +3/8 H2O → 1/8 NO3 +5/4 H +e
+

–

+

–

(1)

The NO3– produced was transferred to the biocathodechamber through the salt bridge by diffusion, resulting in adecrease in the concentration of NO3– in the anode chamber
20(Fig. S3) because of N2 production in the biocathode cham‐ber.
CH4 and N2 production in the biocathode chamber sug‐gest that the reduction reactions of NO3– and CO2, respec‐tively, are represented as follows:
1/8 CO2+H++e– → 1/8 CH4+1/4 H2O (2)
1/5 NO3–+6/5 H++e– → 1/10 N2+3/5 H2O (3)Based on Faraday’s laws of electrolysis, the number ofdonated electrons, Ne [mol], may be calculated from themeasured current using the following equation:
Ne =

∫ Idt
F

(4)

where I is current (A), t is time (s), and F is Faraday’s con‐stant (C mol–1).
Assuming that all yield electrons Ne, calculated as per Eq.
(4), are used for the reduction of only CO2 or NO3–, theamounts of CH4 and N2 produced versus the electron yield
were calculated using Eqs. (2) and (3), respectively. Meas‐
ured CH4 production was markedly less than the calculated
value, while measured N2 production was also smaller than
the theoretical value under this assumption (Fig. S4). There‐
fore, the two reductions were simultaneously performed,
and Ne was distributed in both reductions. The required
electrons for measured CH4 production from CO2 reduction
were estimated using Eq. (2), with the ratio of required elec‐
trons to total measured electrons Ne shown in Fig. 4. The
electron ratio slightly decreased with the applied voltage,
rather than remaining constant. At very low voltages of 0.05
and 0.1 V, approximately 40% of the current was used forCO2 reduction to CH4, while only approximately 5% was
utilized at 3 V. Assuming that the current to electron ratio
was used for CO2 reduction and that the remaining electrons
were used for NO3– reduction to N2, as per Eq. (3), it is pos‐sible to estimate CH4 and N2 production from Ne. Fig. 5
compares measured and estimated CH4 and N2 production,with the curve showing the relationship between the elec‐tron ratio and voltage in Fig. 4 used in the calculation. A
good agreement was observed for both CH4 and N2 produc‐tion, meaning that the electron balance was almost main‐tained in this experiment, and the production of CH4 and N2
100
Ratio of required electrons forCH4 production to total
measured electrons (%)

duced in the biocathode chamber. Most bacteria belonging
to Rhodocyclaceae exhibit denitrification activity (Zhao etal., 2013; Wang et al., 2017). The predominant Azonexus
genus detected, which may grow on molecular hydrogen as
an electron donor (Zhao et al., 2011; Liang et al., 2021),
plays an important role in the denitrification process to pro‐
duce nitrogen gas. Only Geobacter was detected within the
Geobacterceae family. The presence of electrically conduc‐tive pili or flagella on Geobacter species is reportedly linked
to electron transfer in the MFC (Cabezas et al., 2015; Yan etal., 2020). In the present study, Geobacter appeared to be
responsible for electron transfer to yield biogas.
Regarding archaea, all OTUs were Euryarchaeota. The
majority of Euryarchaeota detected were methanogens, withthe dominant family (81.7%) being Methanobacteriaceae, ahydrogen-utilizing methanogen (Fig. 3). Two genera,
Methanobrevibacter and Methanobacterium, were detected
at concentrations of 63.8 and 36.3%, respectively (Fig. 3).
They played a major role in CH4 production in the bioca‐
thode chamber. In addition, Methanosaetaceae, an obligate
acetoclastic methanogen, was detected, albeit at a low con‐centration (7.1%); therefore, acetate may be produced andconverted to CH4. However, its contribution appears to havebeen insignificant.
The biological contributors to denitrification and methane
production were identified; the produced gas containing
CH4 and N2 may be explained by the presence of thesemicrobes. Therefore, we demonstrated the biological pro‐duction of CH4 through the provision of electricity, even atvery low voltages, and in the absence of organic substancesin the MES.

80
60

for N240
20
0

for CH4
0.0

1.0

2.0
Voltage (V)3.0

Fig. 4. Relationship between the ratio of required electrons for CH4
production to total measured electrons Ne and applied voltage.

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Dinh et al.
0.15
N2
N2
CH4
CH4

0.1

0.05

0

0

0.05

0.1

0.15

Estimated gas production (mmol reactor-1)
Comparison between measured and estimated gas production.
An estimation of produced CH4 and N2 was performed assuming that
all electrons Ne were used for the reduction of both CO2 and NO3–,
while electrons from the ratio in Fig. 4 were used for CH4 production.

Fig. 5.

may theoretically be performed in the biocathode chamberaccording to the reduction reactions of Eqs. (2) and (3).
However, at a high voltage of 3.0 V, the calculated value of
produced N2 was markedly greater than the measured value(Fig. 5), indicating that some electrons were used for otherreductions by chemical and/or microbial reactions. If NO3–
reduction to NH4+ instead of N2, which is the reverse reac‐tion at the anode, is performed at the biocathode at high vol‐tages, the reversible reactions will lead to a waste ofelectrons yielded in the MES. A previous study reported thata high imposing voltage exerted a negative effect on metha‐nogens (Ding et al., 2016) and nitrate-reducing bacteria (Liet al., 2001; Ding et al., 2016), and excessive voltage not
only inhibited microbial activity, but also induced chemical
reactions.
Hydrogen was not detected. However, hydrogen was
expected to be produced in the biocathode chamber becauseof the presence of hydrogenotrophic methanogens, such as
Methanobacterium and Methanobrevibacter, and the hydro‐
genotrophic denitrifiers of Rhodocyclaceae (Azonexus). Fur‐thermore, the hydrogen-producing bacteria Petrimonas were
present. Previous studies on MESs also detected hydrogeno‐
trophic methanogens, such as Methanobrevibacter,
Methanocorpusculum, and Methanoculleus sp. (Sasaki etal., 2011; Van Eerten-Jansen et al., 2013; Jiang et al., 2014;
Siegert et al., 2015). Cheng et al. (2019) reported that
Methanobacterium palustre methanogens directly use elec‐
trons to produce methane without organic substances. How‐ever, this study did not provide sufficient evidence ofelectron utilization. Although Geobacter species are wellknown to have the ability to transfer electrons, it was sur‐prising that the dominant genus identified in this study was
Petrimonas due to the lack of available information on theelectron transfer ability of this genus. However, Petrimonas
may accept electrons to produce hydrogen, which may beprovided to the detected hydrogenotrophic methanogens anddenitrifiers in the absence of an organic substrate in thereactor. During the bio-electrochemical production of hydro‐gen, it is reasonable to assume that a very small amount ofhydrogen is electrochemically formed and biologically con‐sumed. However, this electrochemical pathway may only
negligibly contribute to production because hydrogenproducing Petrimonas was dominant in the microbial com‐munity.
Hydrogenotrophic methanogens and denitrifiers compete
for the shared substrate of H2 produced at the biocathode.
Denitrifiers are dominant in wastewater treatments underanoxic conditions in the presence of nitrate; this phenom‐
enon may be explained by Gibbs free energy. The energy
obtained in the denitrification reaction of Eq. (3) is mark‐edly larger than that in the methane production reaction of
Eq. (2). However, under hydrogenotrophic conditions,methanogens and denitrifiers were both enriched eventhough denitrification dominated throughout the experiment.
At the lowest applied voltage of 0.05 V, approximately 40%of the H2 produced was utilized for methane production by
the methanogens. However, with an increase in the applied
voltage, the utilization ratio decreased (Fig. 4), indicatingthat the applied voltage affected the utilization of H2 bymethanogens and denitrifiers. H2 production and concentra‐
tions are expected to increase at higher voltages. Microbeswith a high affinity for substrates generally consume sub‐strates faster than those with low affinity. The Monod con‐stant Km for H2 uptake was reportedly 1 and 2 μM for
Methanobacterium ruminatium (Lovley and Goodwin,1988) and Methanobrevibacter formicium (Schauer andFerry, 1980), respectively. In contrast, Smith et al. (1994)
reported that the Km of hydrogenotrophic denitrifiers ranged
between 0.3 and 3.32 μM. If methanogens had lower Km
than the denitrifiers at the biocathode, indicating a higher
affinity for H2 and lower maximum H2 uptake rate, the phe‐nomenon of a decreasing current ratio in methane produc‐tion with an increasing applied voltage, as shown in Fig. 4,
may be explained by this difference in Km between metha‐nogens and denitrifiers.
Based on the experimental results obtained, Fig. 6
proposes a scheme for the process of electronic methane
production used in the present study, without organic sub‐stances in the MES. Ammonium is oxidized to nitrate by aPt catalyst at the anode with electron release. The nitrateformed is transferred into the biocathode chamber throughthe salt bridge. At the biocathode, the hydrogen-producing
bacteria Petrimonas biochemically produce H2 by accepting
electrons and protons. The H2 produced is biologicallyconsumedbyhydrogenotrophic
methanogens
ofMethanobacterium and Methanobrevibacter coupled with
CO2 uptake, and by the hydrogenotrophic denitrifiers of
Rhodocyclaceae (Azonexus), with transferred nitrate reduc‐tion resulting in the production of methane and N2, respec‐tively. Consequently, the overall reaction at the anode andbiocathode in the MES is as follows:
1/8 CO2+1/3 NH4+ → 1/8 CH4+1/6 N2+1/3 H++1/4 H2O
ΔG0’=–3.134 kJ mol–1 e– (5)Thermodynamically, this reaction proceeds under the
standard condition even without the provision of external

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Bioelectrical Methane Productione-

N2
Geobacteraceae

Porphyromonadaceae

H2O

H2

e-

H+

Methanobacteriaceae

Rhodocyclaceae

e-

H2

NO3-

NO3H+

NO3H+

e-

NH4+

CO2
CH4
H2O

Biocathode

Pt

Anode

Scheme of electronic methane and nitrogen production in
MES without organic substances.

Fig. 6.

energy, such as electricity, because of the negative Gibbs
free energy ΔG0’ value. The actual condition, for example,
at an applied voltage of 0.1 V was as follows: pCH4=0.36
atm, pN2=0.65 atm, pCO2=0.03 atm, [H+]=15.1×10–5 M, and[NH4+]=5.38×10–3 M. In this case, the actual Gibbs freeenergy ΔG (=ΔG0’+RT ln[K]) was estimated to have a valueof –5.18 kJ mol–1 e–, suggesting that the production of meth‐ane and N2 is expected. Therefore, the present study
revealed that even in an inorganic environment, biologicalmethane production coupled with denitrification is possible
in combination with catalytic ammonium oxidation, even atvery low applied voltages <0.1 V, through the three key
players of hydrogenotrophic methanogens, denitrifiers, andhydrogen-producing bacteria.
Acknowledgements
This research was supported by the Japan Society for the Promo‐tion of Science (JSPS) as a Grant-in-Aid for scientific research(grant numbers 17H01300). We would like to thank Editage(www.editage.jp) for English language editing.
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Microbes Environ. Vol. 34, No. 3, 278-292, 2019https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME19017

MOST CITED ORIGINAL PAPER PUBLISHEDIN THIS THREE YEARS [2019-2021] (18 citations)Geochemical and Metagenomic Characterization of Jinata Onsen, a ProterozoicAnalog Hot Spring, Reveals Novel Microbial Diversity including Iron-Tolerant
Phototrophs and Thermophilic Lithotrophs
Lewis M. Ward1,2,3*, Airi Idei4, Mayuko Nakagawa2, Yuichiro Ueno2,5,6, Woodward W. Fischer3,
and Shawn E. McGlynn2*
1Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138 USA; 2Earth-Life Science Institute,Tokyo Institute of Technology, Meguro, Tokyo, 152–8550, Japan; 3Division of Geological and Planetary Sciences, California
Institute of Technology, Pasadena, CA 91125 USA; 4Department of Biological Sciences, Tokyo Metropolitan University, Hachioji,
Tokyo 192–0397, Japan; 5Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Meguro, Tokyo, 152–8551,
Japan; and 6Department of Subsurface Geobiological Analysis and Research, Japan Agency for Marine-Earth Science andTechnology, Natsushima-cho, Yokosuka 237–0061, Japan(Received February 1, 2019—Accepted May 22, 2019—Published online August 14, 2019)

Hydrothermal systems, including terrestrial hot springs, contain diverse geochemical conditions that vary over short spatial
scales due to progressive interactions between reducing hydrothermal fluids, the oxygenated atmosphere, and, in some cases,
seawater. At Jinata Onsen on Shikinejima Island, Japan, an intertidal, anoxic, iron-rich hot spring mixes with the oxygenatedatmosphere and seawater over short spatial scales, creating diverse chemical potentials and redox pairs over a distance of ~10 m.
We characterized geochemical conditions along the outflow of Jinata Onsen as well as the microbial communities present inbiofilms, mats, and mineral crusts along its traverse using 16S rRNA gene amplicon and genome-resolved shotgun metagenomic
sequencing. Microbial communities significantly changed downstream as temperatures and dissolved iron concentrations
decreased and dissolved oxygen increased. Biomass was more limited near the spring source than downstream, and primary
productivity appeared to be fueled by the oxidation of ferrous iron and molecular hydrogen by members of Zetaproteobacteria
and Aquificae. The microbial community downstream was dominated by oxygenic Cyanobacteria. Cyanobacteria are abundant
and active even at ferrous iron concentrations of ~150 μM, which challenges the idea that iron toxicity limited cyanobacterial
expansion in Precambrian oceans. Several novel lineages of Bacteria are also present at Jinata Onsen, including previously
uncharacterized members of the phyla Chloroflexi and Calditrichaeota, positioning Jinata Onsen as a valuable site for the
future characterization of these clades.
Key words: geomicrobiology, Proterozoic, microaerobic, astrobiology, ferruginous, thermophiles

A major theme of environmental microbiology has been
the enumeration of microbial groups with the capacity to exploit
the diverse chemical potentials (i.e. chemical disequilibria)that occur in nature (e.g. 7, 25, 111). Hot springs, with theirvaried chemical compositions, provide reservoirs of novel
microbial diversity; environmental and geochemical conditionsin these environments select lineages and metabolic pathways
that are distinct from other Earth-surface environments (e.g.
3, 9, 26, 126, 127). In addition to their value as sources of
microbial diversity, hot springs are valuable test beds forunderstanding microbial community processes driven bydifferent suites of metabolism (e.g. 50). This in turn allows
these systems to serve as process analogs and also to provide
a window into biosphere functions during early times in Earthhistory, for example when the O2 content of surface waters
was low or non-existent. Most surface ecosystems today are
fueled entirely by oxygenic photosynthesis by plants, algae,
and Cyanobacteria; in contrast, hot spring microbial communities are commonly supported by lithotrophic or anoxygenic

24* Corresponding authors.
Lewis M. Ward: E-mail: lewis_ward@fas.harvard.edu;
Tel: +1–617–998–1750; Fax: +1–617–384–7396.
Shawn E. McGlynn: E-mail: mcglynn@elsi.jp;
Tel: +81–3–5734–3414; Fax: +81–3–5734–3416.

phototrophic organisms that derive energy and electrons forcarbon fixation by oxidizing geologically sourced electron
donors such as Fe2+, sulfide, arsenite, and molecular hydrogen (e.g. 34, 63, 69, 105, 126). These communities may
therefore provide insight into the flow of energy and massthat characterized microbial communities on the early Earthor even other planets, in which oxygenic photosynthesis may
be absent or less significant and anoxygenic photosynthetic orlithotrophic metabolisms may play a larger role, resulting inlower overall rates of primary productivity (e.g. 12, 63, 97,
131–133).
Here, we present a geomicrobiological characterization of
a novel Precambrian Earth process analog site: Jinata Onsen
on Shikinejima Island, Tokyo Prefecture, Japan. While a small
number of metagenome-assembled genomes have previously
been recovered from Jinata (125, 127), we describe here thefirst overall characterization of the geochemistry and microbial community of this site. This site supports sharp gradientsin geochemistry that in some ways recapitulate spatiallyenvironmental transitions that occurred temporally during
Proterozoic time. The modern, sulfate-rich, well-oxygenatedocean that we see today is a relatively recent state, typical of
only the last few hundred million years (e.g. 76). For the firsthalf of Earth history, until ~2.3 billion years ago (Ga), theatmosphere and oceans were anoxic (52), and the oceans wereMicrobes of Jinata Onsen

rich in dissolved iron but poor in sulfur (120). Following theGreat Oxygenation Event ~2.3 Ga, the atmosphere and surface
waters accumulated oxygen, and the oceans became morestrongly redox stratified with oxygenated surface waters andanoxic deeper waters, rich in either dissolved iron or sulfide(88). At Jinata Onsen, this range of geochemical conditions is
recapitulated over just a few meters, providing an ideal spacefor-time analog to test hypotheses of how microbial diversity
and productivity may have varied as environmental conditions
changed through Earth history.
At Jinata hot spring, anoxic, iron-rich hydrothermal fluids
feed a subaerial spring that flows into a small bay, and mixes
with seawater over the course of a few meters. Over its
course, the waters transition from low-oxygen, iron-richconditions analogous to some aspects of early Proterozoic
oceans, toward iron-poor and oxygen-rich conditions typicalof modern coastal oceans. In upstream regions of the stream
where oxygenic Cyanobacteria are at very low abundance,
biomass is visibly sparse; however, downstream, biomass
accumulates in the form of thick microbial mats containing
abundant Cyanobacteria. Visible differences in accumulation
and appearance of biomass across the temperature and redox
gradient establish the hypothesis that microbial community
composition, as well as the magnitude and metabolic driversof primary productivity, varies along the spring flow. To
begin testing this hypothesis and to provide a baseline
description of the geochemistry and microbiology of this site
in support of future investigation, we performed geochemicalmeasurements, 16S rRNA gene amplicon sequencing, andgenome-resolved metagenomic sequencing to recover draft
genomes of diverse novel microbial lineages that inhabit
Jinata Onsen.
Materials and Methods
Geological context and sedimentology of Jinata
Jinata Onsen is located at 34.318 N, 139.216 E on the island of

Fig. 1.

279
Shikinejima, Tokyo Prefecture, Japan. Shikinejima is part of the Izu
Islands, a chain of volcanic islands that formed in the last few million
years along the northern edge of the Izu-Bonin-Mariana Arc (56).
Shikinejima is formed of Late Paleopleistocene- to-Holocene
non-alkaline felsic volcanics and Late-Miocene to Pleistocene
non-alkaline pyroclastic volcanic flows, with Jinata Onsen locatedon a small bay on the southern side of the island (Fig. 1).
Sample collections
Five sites were sampled at Jinata Onsen: the Source Pool, Pool 1,
Pool 2, Pool 3, and the Outflow (Fig. 1 and 2). During the first sampling
trip in January 2016, two whole community DNA samples werecollected from each site for 16S rRNA gene amplicon sequencing.
During the second sampling trip, additional DNA was collectedfrom the Source Pool and Pool 2 for shotgun metagenomic sequencing
along with gas samples for qualitative analysis. Samples for quantitative gas analysis were collected in October 2017 and April 2018.
Samples were collected as mineral scrapings of loosely attached,fluffy iron oxide coatings from surfaces and clasts upstream (Source
Pool and Pool 1) and as samples of microbial mats downstream(Pools 2 and 3 and Outflow) using sterile forceps and spatulas (~0.25 cm3of material). Immediately after sampling, cells were lysed and DNA
was preserved with a Zymo Terralyzer BashingBead Matrix and
Xpedition Lysis Buffer. Lysis was achieved by attaching tubes to theblade of a cordless reciprocating saw (Black & Decker, Towson,
MD, USA) and operating for 1 min. Aqueous geochemistry samples
consisted of water collected with sterile syringes and filteredthrough a 0.2-μm filter. Gas samples were collected near sites of
ebullition emerging from the bottom of the Source Pool; collection
was performed into serum vials by water substitution that were thensealed underwater to prevent contamination by air.
Geochemical analysis
Dissolved oxygen (DO), pH, and temperature measurements
were performed in situ using an Extech DO700 8-in-1 Portable
Dissolved Oxygen Meter (FLIR Commercial Systems, Nashua, NH,
USA). In upstream regions at which water temperatures were higher
than the operating temperature of the oxygen probe (50°C), DO
concentrations were estimated by filling a 50-mL Falcon tube withspring water, sealing the tube to prevent gas exchange, and allowing
the water to cool to acceptable temperatures before reopening thetube and immediately measuring oxygen concentrations, thereby

Location of Jinata Onsen on Shikinejima Island, Japan, and inset overview sketch of the field site with sampling localities marked.

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Ward et al.

Fig. 2. Representative photos of Jinata. A) Panorama of the field site, with the Source Pool on the left (Pool 1 below), Pools 2 and 3 in the center,and the Outflow to the bay on the right. B) Undistorted view north up the canyon. C) Undistorted view south towards the bay, overlooking Pool 2.
D) The Source Pool, coated in floc-y iron oxides and bubbling with a gas mixture containing CO2, CH4 and trace, potentially variable, H2. E) Pool 2,
with a mixture of red iron oxides and green from Cyanobacteria-rich microbial mats. F) Close up of textured microbial mats in Pool 3. G) Close upof the Outflow, at which hot spring water mixes with ocean water. Reprinted with permission from (127).

26

minimizing the dissolution of atmospheric O2. Iron concentrations
were measured using the ferrozine assay (110) following acidification
with 40 mM sulfamic acid to inhibit iron oxidation by O2 or oxidized
nitrogen species (67). Ammonia/ammonium concentrations were
measured using a TetraTest NH3/NH4+ Kit (TetraPond, Blacksburg,
VA, USA) following the manufacturer’s instructions, but with thecolorimetry of samples and NH4Cl standards quantified with a
Thermo Scientific Nanodrop 2000c spectrophotometer (Thermo
Fisher Scientific, Waltham, MA, USA) at 700 nm to improve sensitivity and accuracy. Anion concentrations were measured via ion
chromatography on a Shimadzu Ion Chromatograph (Shimadzu,
Kyoto, Japan) equipped with a Shodex SI-90 4E anion column(Showa Denko, Tokyo, Japan).
The presence of H2 and CH4 in gas samples was initially qualitatively
assessed by comparison to standards with a Shimadzu GC-14A gas
chromatograph within 12 h of collection in order to minimize theoxidation of reduced gases. Subsequent gas samples were analyzed
according to previously described methods (112). In brief, samples
were analyzed using a gas chromatograph (GC-4000; GL Sciences,
Tokyo, Japan) equipped with both a pulsed discharge detector(PDD) and thermal conductivity detector (TCD). The GC was
equipped with a ShinCarbon ST packed column (2 m×2.2 mm ID,
50/80 mesh) connected to a HayeSepo Q packed column (2 m×2.2 mm
ID, 60/80 mesh) to separate O2, N2, CO2, and light hydrocarbons.
Temperature was maintained at 40°C for 6 min before ramping up to
200°C at 20°C min–1. This temperature was held for 6 min before
ramping up to 250°C at 50°C min–1 before a final hold for 15 min.
The values of standard errors (SE) were obtained by replicate measurements of samples. The detection limit was on the order of 1 nmol cc–1
for H2 and CH4.
Water samples for dissolved inorganic carbon (DIC) and dissolved
organic carbon (DOC) concentration measurements were collectedwith sterile syringes and transferred after filtering through a 0.2-μm

filter to pre-vacuumed 30-mL serum vials that were sealed withbutyl rubber septa and aluminum crimps.
DIC and DOC concentrations in water samples were analyzed bymeasuring CO2 in the headspace of the sampled vials after the reactionof samples with either phosphoric acid for DIC or potassium persulfate
for DOC with a Shimadzu GC-14A gas chromatograph. Sodium
bicarbonate standards and glucose standards were used to makecalibration curves for quantifying DIC and DOC concentrations,
respectively.
16S rRNA gene amplicon and metagenomic sequencing and analyses
The sequencing and analysis of 16S rRNA gene amplicon data
followed previously described methods (126). After returning to thelab, bulk environmental DNA was extracted and purified with a
Zymo Soil/Fecal DNA extraction kit. The V4–V5 region of the 16S
rRNA gene was PCR amplified using the archaeal and bacterial
primers 515F (GTGCCAGCMGCCGCGGTAA) and 926R (CCGY
CAATTYMTTTRAGTTT) (14). DNA was quantified with a Qubit
3.0 fluorimeter (Life Technologies, Carlsbad, CA, USA) following
the manufacturer’s instructions after the DNA extraction and PCR
steps. The successful amplification of all samples was verified byviewing on a gel after initial pre-barcoding PCR (30 cycles).
Duplicate PCR reactions were pooled and reconditioned for fivecycles with barcoded primers. Samples for sequencing were submitted
to Laragen (Culver City, CA, USA) for analysis on an Illumina
MiSeq platform. Sequence data were processed using QIIME version
1.9.1 (13). Raw sequence pairs were joined and quality-trimmed
using the default parameters in QIIME. Sequences were clusteredinto de novo operational taxonomic units (OTUs) with 99% similarity
using the UCLUST open reference clustering protocol (24). The
most abundant sequence was selected as the representative for eachde novo OTU (121). Taxonomic identification for each representative
sequence was assigned using the Silva-132 database (90) clustered

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Microbes of Jinata Onsen
separately at 99 and 97% similarities. Singletons and contaminants(OTUs appearing in negative control datasets) were removed. 16SrRNA gene sequences were aligned using MAFFT (60) and a phylogeny constructed using FastTree (89). Alpha diversity was estimatedusing the Shannon Index (96) and Inverse Simpson metric (1/D) (46,100). Sampling depths were estimated using Good’s Coverage (35).
All statistics were calculated using scripts in QIIME and reported at
the 99 and 97% OTU similarity levels. Multidimensional scaling(MDS) analyses and plots to evaluate similarities between different
samples and environments were produced in R using the vegan andggplot2 packages (91, 135) (Oksanen, J., F.G. Blanchet, M. Friendly,
et al. 2016. Vegan: community ecology package. R package version
2.3-5. R Foundation, Vienna, Austria).
Following initial characterization via 16S rRNA gene sequencing,
four samples were selected for shotgun metagenomic sequencing:
JP1-A and JP3-A from the first sampling trip, and JP1L-1 and JP2-1
from the second sampling trip. Purified DNA was submitted to
SeqMatic LLC (Fremont, CA, USA) for library preparation and2×100-bp paired-end sequencing via Illumina HiSeq 4000 technology.
Samples JP1-A and JP3-A shared a single lane with two samples
from another project, while JP1L-1 and JP2-1 shared a lane with one
sample from another project.
Raw sequence reads from all four samples were co-assembled
with MegaHit v. 1.02 (75) and genome bins constructed based on
nucleotide composition and differential coverage using MetaBAT(57), MaxBin (136), and CONCOCT (Alneberg, J., B.S. Bjarnason,
I. de Bruijn, M. Schirmer, J. Quick, U.Z. Ijaz, N.J. Loman, A.F.
Andersson, and C. Quince. 2013. CONCOCT: clustering contigs oncoverage and composition. arXiv preprint arXiv:1312.4038.) prior
to dereplication and refinement with DAS Tool (99) to produce
the final bin set. Genome bins were assessed for completeness, contamination, and strain-level heterogeneity using CheckM (85),tRNA sequences found with Aragorn (71), and the presence of
metabolic pathways of interest predicted with MetaPOAP (130).
Coverage was extracted using bbmap (Bushnell, B. 2016. BBMap
short read aligner. University of California, Berkeley, California.
URL: http://sourceforge.net/projects/bbmap.) and samtools (74).
Genes of interest (e.g. coding for ribosomal, photosynthesis, ironoxidation, and electron transport proteins) were identified fromassembled metagenomic data locally with BLAST+ (10) and werescreened against outlier (e.g. likely contaminant) contigs as identified
by CheckM using tetranucleotide, GC, and coding density contents.
Translated protein sequences of genes of interest were aligned with
MUSCLE (23), and alignments were manually curated in Jalview(134). Phylogenetic trees were calculated using RAxML (106) on
the Cipres science gateway (80). Node support for phylogenies wascalculated with transfer bootstraps by BOOSTER (72). Trees werevisualized with the Interactive Tree of Life viewer (73). Since thesequencing depth of each sample in the full metagenome wasuneven, the relative abundance of genes of interest between metagenomic datasets was normalized to the coverage of rpoB genes ineach raw dataset as mapped onto the co-assembly. Similar to the 16S
rRNA gene, rpoB is a highly conserved, vertically-inherited genethat is useful for the taxonomic identification of organisms, but has
the added advantage that it is only known to occur as a single copy
per genome (15) and is more readily assembled in metagenomic
datasets (e.g. 127). The presence and classification of hydrogenase
genes was performed with HydDB (103). The taxonomic assignmentof MAGs was made based on placement in a reference phylogeny
built with concatenated ribosomal protein sequences following
methods from Hug et al. (48), and confirmed using GTDB-Tk (86).
The optimal growth temperatures of MAGs were predicted based onproteome-wide 2-mer amino acid compositions following previously
described methods (Li, G., K.S. Rabe, J. Nielsen, and M.K. Engqvist.
2019. Machine learning applied to predicting microorganism growthtemperatures and enzyme catalytic optima. BioRxiv. doi: https://doi.
org/10.1101/522342).

Results
Site description
The source water of Jinata Onsen emerges with low DO
concentrations near our limit of detection, is iron-rich, andgently bubbles gas from the spring source (Fig. 1 and 2, andTable 1). Temperatures at the source are ~63°C. Water emerges
into the Source Pool, which has no visible microbial mats or
biofilms (Fig. 2D). Surfaces are instead coated with a fluffy
red precipitate, likely a poorly ordered or short range-ordered
ferric iron oxide phase such as ferrihydrite. Flow from thesource is, at least partially, tidally charged, with the highest
water levels and flow rates occurring at high tide. At low tide,flow rates drop and the water level of the Source Pool may
decrease by decimeters, with portions of the Source Poolpotentially draining during spring low tides. Spring watercollects downstream into a series of pools (Pools 1-3) (Fig.
2C, E, and F), which cool sequentially (Fig. 3 and Table S1).
Pool 1 contains iron oxides, similar to the Source Pool, butalso develops macroscopic microbial streamers that are coatedin iron oxides and thin veil-like layers of microorganisms
overlaying iron oxide sediments—structures similar to thosetypically made by marine iron-oxidizing Zetaproteobacteria(e.g. 33). Streamers are very fine (mm-scale) and delicate(break apart on contact with forceps) but can reach several
centimeters in length. Cyanobacteria in Pools 2 and 3 display
high levels of photosynthetic activity as revealed by high DO
concentrations (~234 μM), low DIC concentrations, and theaccumulation of visible O2 bubbles on the surface and within
the fabric of the mat. Downstream pools (Pools 2 and 3) mix
with seawater during high tide due to wave action; however,this seawater influence does not appear to influence the Source
Pool or Pool 1. Samples were collected and temperatures
were measured at high tide, reflecting the lowest temperatures
experienced by microbes in the pools; at low tide, hot spring
input is dominant and temperatures rise (observed range ateach site in Table S1). Subaqueous surfaces in Pools 2 and 3
are covered in thick microbial mats. In Pool 2, the mat iscoated in a layer of fluffy iron oxide similar to that in the
Source Pool, with a dense microbial mat below (Fig. 2E).
Pool 3 contains only patchy iron oxides, with mostly exposed
microbial mats displaying a finger-like morphology. These“fingers” were 0.5–1 cm in diameter, up to ~5 cm long andwere closely packed and carpeting surfaces of Pool 3 below
the high tide line, potentially related to turbulent mixing fromwave action during high tide (Fig. 2F). The Outflow is theoutlet of a channel connecting Pool 2 to the bay. Its hydrology
is dominantly marine with small admixtures of inflowing
spring water (Fig. 2G).
Table 1.
T
pH
DO
Fe2+
NH3/NH4+
Cl–
SO4–
NO3–
NO2–
HPO4–

Geochemistry of source water at Jinata Onsen.
63°C
5.4
4.7 μM
261 μM87 μM
654 mM17 mM<1.6 μM<2.2 μM
<1 μM

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Fig. 3. Summary of geochemical and microbiological trends along the flow path of Jinata Onsen. Top: Panoramic view of Jinata Onsen, with theSource Pool at the left and the flow of spring water toward the bay at the right, with sampling locations indicated. Middle: Geochemical transect
across the spring, showing temperature (°C, left axis) and dissolved Fe(II) and O2 (μM, right axis). Bottom: Stacked bar chart of the relative community
abundance of dominant microbial phyla as assessed by 16S rRNA amplicon sequencing. Sequence data were binned at the phylum level and duplicate
samples at each site were averaged. Reads that could not be assigned to a phylum were discarded; all phyla that do not make up more than 2% of thecommunity at any one site have been collapsed to “Other”. Near the source, the community is predominantly made up of iron- and/or hydrogen-oxidizingorganisms in the phyla Proteobacteria and Aquificae. As hot spring water flows downstream, it equilibrates with the atmosphere and eventually
mixes with seawater, resulting in downstream cooling, the accumulation of oxygen, and loss of dissolved iron due to biological and abiotic processes.
Oxygenic Cyanobacteria become progressively more abundant downstream. Putative hydrogen- and iron-oxidizing lithotrophs dominate near thesource, whereas phototrophic Cyanobacteria dominate downstream. Additional community diversity is found in Table S4.

28

Jinata hot spring was visited twice for observations andcommunity DNA sampling in 2016 (January and September),and again for observations and gas sampling in October 2017
and April 2018. These visits corresponded to a range of tidal
conditions, including a spring low and high tide in September
2016. The general features of the spring were consistentacross this period (including the abundance and distributionof iron minerals and microbial mats), differing primarily inapparent tidal dependence in the flow rate and water level ofthe spring and the extent of the seawater influence on Pool 3.
These differences in flow and mixing led to variations inwater temperatures of 3–10°C (Table S1). At high tide, theflow rate of the spring increases, as does seawater influx toPool 3. During the spring low tide, the spring flow stagnatedand the water level of Source Pool and Pool 1 dropped bydecimeters, with some portions draining entirely. During lessextreme low tides observed on other dates, the spring flow
was low but nonzero and the water level of the Source Pool
did not significantly drop. While marked variability wasobserved in the flow rate from the spring based on tides (andresulting shifts in water level and temperature), the overallgeochemistry of the source water and microbial community
mostly appeared to be similar between expeditions.
Geochemistry
Geochemical measurements along the flow path of Jinata
Onsen revealed a major shift from hot, low-oxygen, high-ironsource water to cooler, more oxygen-rich water with lessdissolved iron downstream. Geochemistry measurements of
Jinata source water are summarized in Table 1, while geochemical gradients along the stream outflow are shown in
Fig. 3 and Table S1. Source waters were slightly enriched inchloride relative to seawater (~23.2 g L–1 in Jinata source
water versus ~19.4 g L–1 in typical seawater) and depleted insulfate (~1.6 g L–1 in Jinata versus ~2.7 g L–1 in seawater), but
approached seawater concentrations downstream as mixing
increased. Water emerging from the source was 63°C, very
low in DO (~4.7 μM), at pH 5.4, and contained substantial
concentrations of dissolved iron (~250 μM Fe2+). DOC in thewater of Pool 1 was high (~1.31 mM). It is unknown whether
this is produced in situ or if the source water emerges withhigh DOC. DOC and DIC both decreased along the outflow

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Microbes of Jinata Onsenof the spring (Table S1). After emerging from the source, thespring water exchanges gases with the air due to mixing
associated with water flow and gas ebullition, and DO increased
to 39 μM at the surface of the Source Pool. As water flows
downstream from the Source Pool, it cools slightly, exchanges
gases with the atmosphere, and intermittently mixes withseawater below Pool 1.
H2 and CH4 were both qualitatively detected in bubbles
from the Source Pool following initial sampling in September
2016. However, during subsequent analyses to quantify thegas composition in October 2017 and April 2018, the gas wasfound to contain CO2, CH4, and N2 (Table S2). This subsequentnon-detection of H2 may be related to temporal variability inthe gas composition at Jinata (e.g. following tidal influence;
significant variability was observed in the CO2:N2 ratio
between two sampling dates, Table S2) or may reflect
oxidation of H2 between sampling and analysis. The detection
limit of H2 for these later measurements was ~1 nmol cc–1 (inthe gas phase of our quantitative gas analyses, or ~1 nM in the
aqueous phase [2]), well above the energetic and ecologicallimits for hydrogenotrophic metabolisms (e.g. 51) leaving
open the possibility of biologically significant H2 fluxes at
Jinata around the time of sampling. The oxidation of H2 coupled
to O2 reduction is a thermodynamically favorable process evenat very low substrate concentrations (e.g. ΔrG’ <–375 kJ mol–1
with substrate concentrations of 0.1 nM H2(aq) and 0.1 μM
O2(aq), below our limit of detection) (30). Consistent with thisthermodynamic favorability, biology has been shown tomake use of this metabolism in environments such as hot
springs with H2 concentrations near our detection limits (19)and in Antarctic soils where microbes rely on uptake of trace
atmospheric H2 at concentrations of ~190 ppbv (51). Therefore,
the trace amounts of H2 that may be present in the source
water at Jinata may be sufficient to support lithoautotrophynear the hot spring source in organisms possessing thegenetic capacity for hydrogen oxidation, as discussed below.
Metagenomic data suggest that organisms with the genetic
capacity for hydrogenotrophic metabolisms are abundant in
upstream regions of Jinata (discussed below), and so it is also
possible that low dissolved H2 concentrations may be due toactive biological consumption near the hot spring source.
Improved quantification of H2 concentrations, measurements of hydrogenase activity, and the productivity ofhydrogenotrophic microbes will be necessary to determine
the relative contribution of hydrogen oxidation to productivity
at Jinata.
16S rRNA gene amplicon and genome-resolved metagenomic
sequencing
16S rRNA gene amplicon and metagenomic sequencing of
microbial communities at Jinata Onsen revealed a highly diverse
community. In total, 16S rRNA gene amplicon sequencing
recovered 456,737 sequences from the 10 samples at Jinata(Tables S3, 4, and 5). Reads per sample following filtering for
quality and the removal of chimeras ranged between 2,076
for Pool 3 Sample B and 96,268 for Pool 1 Sample A (median
32,222, mean 35,479, and standard deviation 26,014). On
average, 65% of the microbial community was recoveredfrom Jinata samples at the 99% OTU level based on the Good’s
Coverage statistic of the 16S rRNA gene (ranging between

50% coverage in Outflow Sample A and 80% in Pool 1
Sample A) and 82% at the 97% OTU level (between 69% for
Pool 2 Sample B and 93% for Pool 1 Sample B). MDS analysis(Fig. S1) demonstrates that samples from the same site werehighly similar, and adjacent sites (e.g. Source Pool and Pool1, Outflow and Pool 3) also showed a high degree of similarity.
However, a substantial transition was observed in microbialcommunity diversity between the most distant samples (e.g.
Source Pool and Outflow).
Shotgun metagenomic sequencing of four samples from
Jinata Onsen recovered 121 GB of data, forming a 1.48-Gb
co-assembly consisting of 1,531,443 contigs with an N50 of
1,494 bp. Nucleotide composition and differential coveragebased binning of the co-assembly via multiple methods followed
by dereplication and refinement resulted in a final set of 161
medium- or high-quality metagenome-assembled genomes(MAGs) following current standards (i.e. completeness >50%
and contamination <10%) (6). These MAGs are from diverse
phyla of Bacteria and Archaea (Fig. 4); metagenome and MAG
statistics with tentative taxonomic assignments for recovered
MAGs are shown in Table S6, while MAGs of particular interest
due to their potential contribution to primary productivity at
this site or due to substantial genetic or metabolic novelty are
discussed in depth below and shown in phylogenetic trees
alongside reference strains in Fig. 5, 6, and 7.
Discussion
As Jinata spring water flows from its source to the ocean, it
transitions from hot, low-oxygen, high-iron water to cooler,iron-depleted, oxygen-rich water in downstream regions (Fig.
3). Following this geochemical transition is a major shift in
the composition of the microbial community—from a hightemperature, putatively lithotrophic community which produces
little visible biomass upstream, to a lower temperature community with well-developed, thick microbial mats downstream.
This shift in community composition is summarized in Fig. 3,
with complete diversity data in the Supplemental Information(including OTU counts per sample in Table S4 and relative
abundance binned at the class level in Table S5). Below, we
discuss the overall physiological and taxonomic trends acrossthe spring sites as inferred from diversity and genomic analysis.
Potential for lithotrophic iron and hydrogen oxidation
The hot spring water emerging at the Source Pool at Jinata
contains abundant dissolved Fe2+ and trace H2 (though measurements of gas content varied, as discussed above) (Table
1). Although rates of carbon fixation were not measured, theappearance of (presumed) zetaproteobacterial veils and streamers
together with molecular evidence for lithoautotrophic microbes
suggest that these electron donors may fuel productivity anddetermine the microbial community upstream at the Source
Pool and Pool 1, where microbial mats were not well developed.
The low accumulation of visible biomass in upstream regionsof Jinata is similar to other microbial ecosystems fueled byiron oxidation (e.g. Oku-Okuhachikurou Onsen [126], Fuschna
Spring [43], and Jackson Creek [92]), in which lithotrophic
communities appear capable of accumulating less organiccarbon than communities fueled by oxygenic photosynthesis(including those in downstream regions at Jinata).

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Fig. 4. Phylogeny of Bacteria and Archaea based on concatenated ribosomal proteins. Numbers in parentheses next to phylum labels refer to thenumber of MAGs recovered from Jinata Onsen. Labels for phyla with two or fewer MAGs recovered from Jinata were omitted for clarity. The referencealignment was modified from Hug et al. (48). A full list of MAGs recovered is available in Table S6.

30

The results of 16S rRNA gene amplicon sequencing indicatethat the most abundant organisms in the Source Pool are
members of the Aquificae family Hydrogenothermaceae(32% of reads in the Source Pool and 11.5% of reads in Pool1). Members of this family are typically marine thermophiliclithotrophs capable of iron and hydrogen oxidation as well asheterotrophy (114); at Jinata, they may be utilizing Fe2+, H2,
or DOC. The seventh most abundant OTU in the Source Poolsamples was a novel sequence that was 89% similar to astrain of Persephonella observed in an alkaline hot spring in
Papua New Guinea. Persephonella is a genus of thermophilic,microaerophilic hydrogen-oxidizing bacteria within
Hydrogenothermaceae (36). Despite their abundance asassessed by 16S rRNA gene amplicon sequencing (Fig. 3),
only four partial Aquificae MAGs were recovered from
Jinata, of which only one (J026) was reasonably complete(~94%). Two Aquificae MAGs recovered Group 1 NiFe
hydrogenase genes, which could support hydrogenotrophy;

the absence of hydrogenases from the other MAGs may berelated to their low completeness or could reflect a utilizationof iron or other electron donors and not H2 in these organisms.
The other most abundant organisms near the source aremembers of Zetaproteobacteria, a group typified by theneutrophilic, aerobic iron-oxidizing genus Mariprofundus
common in marine systems (27). Zetaproteobacteria accounted
for 24% of 16S rRNA gene sequences in the Source Pool and
26.5% in Pool 1. All Zetaproteobacteria characterized to dateare obligate iron- and/or hydrogen-oxidizing lithoautotrophs(82), suggesting that these organisms play a substantial rolein driving carbon fixation in the Source Pool and Pool 1.
Members of Mariprofundaceae have been observed tohave an upper temperature limit for growth of 30°C (28),while Zetaproteobacteria at Jinata are found at temperatures
up to 63°C. This currently represents a unique high-temperatureenvironment for these organisms. In particular, the third mostabundant OTU in the Source Pool and Pool 1 sample A is anMicrobes of Jinata Onsen

285

Fig. 5. Phylogeny of Zetaproteobacteria, rooted with Alphaproteobacteria, built with concatenated ribosomal protein sequences. Data from (77),(82), (101), and other draft genomes available on Genbank. Transfer bootstrap expectation (TBE) support values as calculated by BOOSTER (72)shown for internal nodes. In cases for which reference genomes have a unique strain name or identifier, this is included; otherwise Genbank WGS
genome prefixes are used.

Fig. 6. Phylogeny of Calditrichaeota, rooted with Bacteroidetes, built with concatenated ribosomal protein sequences. Transfer bootstrap expectation(TBE) support values as calculated by BOOSTER (72) shown for internal nodes. Data from (68) and other draft genomes available on Genbank. In
cases where reference genomes have a unique strain name or identifier, this is included; otherwise Genbank WGS genome prefixes are used.

unknown sequence that is 92% identical to a sequence from an
uncultured zetaproteobacterium from a shallow hydrothermal ventin Papua New Guinea (79). This sequence likely marks a novel
lineage of high-temperature iron-oxidizing Zetaproteobacteria.
The relative abundance of Hydrogenothermaceae decreases
to less than 1% of sequences in areas where microbial mats
are well developed downstream of Pool 1; however,Zetaproteobacteria continue to account for ~1–4% of reads
in Pools 2 and 3 in which dissolved iron concentrations arestill significant (Fig. 3). This relative abundance change may
be due more to the increased abundance of other organisms
rather than a decrease in the number of Zetaproteobacteria or
their ability to make a living oxidizing iron. This hypothesis
awaits confirmation by a technique such as qPCR. In contrast,
the absence of Hydrogenothermaceae downstream may be areal signal driven by the rapid disappearance of trace H2 as anelectron donor. However, in both cases, a drop in relative

abundance is likely related to the increasing total biomass (i.e.
number of cells) downstream as Cyanobacteria become moreproductive, leading to sequences from Hydrogenothermaceae
and Zetaproteobacteria being diluted out by increased numbersof Cyanobacteria, Chloroflexi, and other sequences.
Four MAGs affiliated with Zetaproteobacteria were recoveredfrom Jinata with completeness estimates by CheckM ranging
between ~80 and ~97% (J005, J009, J030, and J098). Whilethese MAGs did not recover 16S rRNA genes, RpoB- andconcatenated ribosomal protein-based phylogenies illustrated
that members of this group at Jinata Onsen do not belong to
the characterized genera Mariprofundus or Ghiorsea, butinstead form separate basal lineages within Zetaproteobacteria(Fig. 5). Despite their phylogenetic distinctness, these MAGs
largely recovered genes associated with aerobic iron oxidation as expected based on the physiology of other
Zetaproteobacteria. These include a terminal O2 reductase

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Fig. 7. Detailed phylogeny of the phylum Chloroflexi, with class-level clades highlighted in gray, built with concatenated ribosomal protein
sequences. The large basal class Dehalococcoidia, which was not observed in 16S rRNA or metagenome data from Jinata, is omitted for clarity. The
phylogeny contains MAGs reported here, members of the phylum Chloroflexi previously described (16, 20, 38, 41, 43–45, 62, 70, 83, 104, 122, 123,
127–129), and members of the closely related phylum Armatimonadetes as an outgroup (21, 125). MAGs described here are highlighted in green,and MAGs previously reported from Jinata Onsen are highlighted in pink. Transfer bootstrap expectation (TBE) support values as calculated by
BOOSTER (72) shown for internal nodes. In cases for which reference genomes have a unique strain name or identifier, this is included; otherwise
Genbank WGS genome prefixes are used.

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from the C-family of heme copper oxidoreductases for respiration at low O2 concentrations and Cyc2 cytochrome genes
implicated in ferrous iron oxidation in Zetaproteobacteria
and other taxa (e.g. Chlorobi) (39, 40, 59). Hydrogenasecatalytic subunit genes (neither [NiFe] nor [FeFe]) were notrecovered in zetaproteobacterial MAGs even at high completeness, suggesting that these organisms are not hydrogenotrophic. Consistent with the obligately autotrophic lifestyle ofpreviously characterized Zetaproteobacteria, J009 and J098
encode carbon fixation via the Calvin cycle. However, J005
and J030 did not recover genes for carbon fixation via theCalvin cycle such as the large and small subunits of rubisco,
phosphoribulose kinase, or carboxysome proteins. The highcompleteness of these MAGs (~94–97%) makes it unlikelythat these genes would all fail to be recovered (MetaPOAP
False Negative estimates 10–5–10–7). The absence of carbon
fixation pathways from these genomes together with theavailability of abundant DOC in Pool 1 (~1.3 mM) suggestthat these organisms may be heterotrophic, a lifestyle notpreviously observed for members of Zetaproteobacteria.
Seven MAGs were recovered from the enigmatic bacterial
phylum Calditrichaeota (J004, J008, J042, J070, J075, J140,
and J141) (Fig. 6). While few members of Calditrichaeota
have been isolated or sequenced, the best known of these is
Caldithrix abyssi (81); this taxon was characterized as ananaerobic thermophile capable of lithoheterotrophic H2 oxidation coupled to denitrification and organoheterotrophic
fermentation (1, 78). The Calditrichaeota MAGs reported
here are up to 97% complete (J004) and contain memberswith variable putative metabolic capabilities, potentially
including aerobic hydrogen- or iron-oxidizing lithoautotrophy.
In the Calditrichaeota MAGs recovered from Jinata Onsen,
aerobic respiration via A-family heme copper oxidoreductases
could potentially be coupled to autotrophic hydrogen oxidation(via the Group 1d NiFe hydrogenase in J042) or iron oxidation(via the pioA gene in J075); however, C. abyssi appears
incapable of aerobic respiration despite encoding an A-familyheme copper oxidoreductase (68). A MAG from a member of
Calditrichaeota has previously been recovered from Chocolate
Pots hot spring in Yellowstone National Park (32); together
with the data presented here this suggests that this phylum
may be a common member of microbial communities in ironrich hot springs. Unlike previously described Calditrichaeota
which are all heterotrophic (78), most of the Calditrichaeota
MAGs reported here possess a putative capacity for carbon
fixation via the Calvin cycle. J004 is closely related to C.
abyssi, while the other MAGs form two distinct but related
clades (Fig. 6).
Oxygenic photosynthesis
Cyanobacteria are nearly absent from the Source Pool(<0.15% relative abundance), but are observed at low numbers
in Pool 1 and become abundant starting in Pool 2. The mostabundant Cyanobacteria present are predominantly membersof Nostocales. This group includes Leptolyngbya and Phormidium,
genera of filamentous non-heterocystous Cyanobacteria thatare present in other hot springs of similar temperatures (e.g.
5, 94, 126). Diverse cyanobacterial MAGs were recovered,including members of the orders Pleurocapsales (J083),
Chroococcales (J003 and J149), and Oscillatoriales (J007,

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J055, and J069). In Outflow samples, chloroplast sequences
related to the diatom Melosira were abundant.
Cyanobacteria are sometimes underrepresented in 16SrRNA gene amplicon sequencing datasets as a result of a poor
DNA yield or amplification biases (e.g. 84, 118); however,the low abundance of Cyanobacteria near the Source Poolwas confirmed by fluorescent microscopy, in which cells
displaying cyanobacterial autofluorescence were abundant indownstream samples, but not in samples from the Source
Pool (Fig. S2). Thick microbial mats initially appear in Pool
2 when Cyanobacteria become abundant, suggesting thatoxygenic photosynthesis fuels more net carbon fixation thanlithotrophy in these environments.
It has been suggested that high ferrous iron concentrations
are toxic to Cyanobacteria, and that this would have greatly
reduced their productivity under ferruginous ocean conditions
such as those that may have persisted through much of theArchean era (113). The abundant Cyanobacteria observed tobe active at Jinata under high iron concentrations suggest that
Cyanobacteria can adapt to ferruginous conditions, and therefore iron toxicity might not inhibit Cyanobacteria over geologicaltimescales. Indeed, the soluble iron concentrations observed
at Jinata are higher (150–250 μM) than predicted for the Archean
oceans (<120 μM, 47) or observed at other iron-rich hot springs(~100–200 μM, 87, 126), making Jinata an excellent test casefor determining the ability of Cyanobacteria to adapt to highiron concentrations. Culture-based physiological experiments
may be useful to determine whether Jinata Cyanobacteria
utilize similar strategies to other iron-tolerant strains (e.g. by
those in Chocolate Pots Hot Spring, 87, or the ferric iron-tolerant
Leptolyngbya-relative Marsacia ferruginose, 8) or whether
Jinata strains possess unique adaptations that allow them togrow at higher iron concentrations than known for otherenvironmental Cyanobacteria strains. This will in turn provide
insight into whether iron tolerance is due to evolutionarily
conserved strategies or whether this is a trait that has evolved
convergently multiple times.
Diverse novel Chloroflexi from Jinata Onsen
In addition to the primary phototrophic and lithotrophic
carbon fixers at Jinata, 16S rRNA gene and metagenomic
data sets revealed diverse novel lineages within the phylum
Chloroflexi. Twenty-three Chloroflexi MAGs were recovered,introducing substantial genetic and metabolic diversity that
expands our understanding of this group. While the best
known members of this phylum are Type 2 Reaction Centercontaining lineages such as Chloroflexus and Roseiflexus
within the class Chloroflexia (e.g. 117), phototrophy is not asynapomorphy of the phylum Chloroflexi or even the class
Chloroflexia (e.g. 122) and most of the diversity of the phylum
belongs to several other classes primarily made up of
non-phototrophic lineages (127). The bulk of Chloroflexi
diversity recovered from Jinata belongs to “subphlyum I”, a
broad group of predominantly non-phototrophic lineages thatwas originally described based on the class- or order-levellineages Anaerolineae and Caldilineae (137), but also
encompasses the related groups Ardenticatenia, Thermoflexia,
and Candidatus Thermofonsia (20, 61, 127).
16S rRNA gene analysis indicated that members ofAnaerolineae and Ca. Thermofonsia (annotated by Silva and

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34

GTDB-Tk as the order SBR1031) were fairly abundant at
Jinata, with Anaerolineae at ~3% relative abundance in the
Source Pool and Pool 1 and Ca. Thermofonsia at ~3.5% relative
abundance in Pools 2 and 3. Three MAGs recovered from Jinata(J082, J097, and J130) are associated with the Anaerolineae
class, as determined by RpoB and concatenated ribosomal
protein phylogenies, along with seven associated with Ca.
Thermofonsia (J027, J033, J036, J038, J039, J064, and J076).
Of particular interest among these is J036, a close relative of
the phototrophic Ca. Roseilinea gracile (66, 115, 116). J036
contains a 16S rRNA gene that is 96% similar to that of Ca.
Roseilinea gracile, and two-way AAI estimates (93) showed
73.6% similarity between the two strains, indicating thatthese strains are probably best classified as distinct species
within the same genus. Unlike other phototrophs in the phylum
Chloroflexi that are capable of photoautotrophy via the
3-hydroxypropionate bicycle or the Calvin cycle (65, 98),
J036 and Ca. Roseilinea gracile do not encode carbon fixation
and are likely photoheterotrophic. Previous studies suggested
that the Roseilinea lineage belongs to Anaerolineae (66) or
Thermofonsia (127). However, our updated phylogeny presented
here places J036 and Roseilinea in a separate lineage alongwith J033 and J162, diverging just outside of the Anaerolineae+
Thermofonsia clade. This suggests that these strains may instead
be yet another class- or order-level lineage within the broader“subphylum I” of Chloroflexi (Fig. 7), an interpretation
supported by analysis via GTDB-Tk that places these genomes
outside of characterized clades (Table S6).
The Chloroflexi class Ardenticatenia was first describedfrom an isolate from an iron-rich Japanese hydrothermal field(61) and has since also been recovered from sulfidic hot springs(128). Members of Ardenticatenia were present at up to 1.2%
relative abundance in Pool 3 in 16S amplicon data. One MAG
recovered from Jinata Onsen, J129, was closely related to
Ardenticatena maritima. While A. maritima 110S contains acomplete denitrification pathway (45), MAG J129 did notrecover any denitrification genes. This may be related to therelatively low completeness of this MAG (~70%); however,
MetaPOAP (131) False Negative estimates for the probability
that all four steps in the canonical denitrification pathway
would fail to be recovered in J129 given their presence in thesource genome is less than 0.8%. This suggests that most, if
not all denitrification genes are absent from the J129 genome
and that the capacity for denitrification is not universal within
members of Ardenticatena. This would be consistent with broad
trends in the apparently frequent modular horizontal gene transferof partial denitrification pathways between disparate microbial
lineages to drive rapid adaption and metabolic flexibility of
aerobic organisms in microoxic and anoxic environments, forreasons that are still not well established (17, 109).
Members of the Chloroflexi class Caldilineae were present
at up to 0.5% abundance at Jinata in the 16S rRNA genedataset. Members of Caldilineae have previously been isolated
from intertidal hot springs in Iceland (55) and Japanese hotsprings (95). Characterized organisms in this class are filamentous, anaerobic, or facultatively aerobic heterotrophs (37,55, 95); therefore, these taxa may play a role in degrading
biomass within low-oxygen regions of microbial mats at
Jinata. Three MAGs were recovered that form a deeply
branching lineage within the Caldilineae class (J095, J111, andWard et al.

J123), sister to the previously characterized genera Caldilinea
and Litorilinea. Like other members of Caldilineae, thesestrains encode aerobic respiration via A-family heme copperoxidoreductases and both a bc complex III and alternativecomplex III, and are therefore likely at least facultatively
aerobic. J095 also encodes carbon fixation via the Calvincycle as well as a Group 1f NiFe hydrogenase, suggesting a
potential capability for hydrogenotrophic lithoautotrophy,
expanding the known metabolic diversity of this class and the
Chloroflexi phylum as a whole.
MAG J114 branches at the base of subphylum I of
Chloroflexi, potentially the first member of a novel class-levellineage. The divergence between Anaerolineae and Caldilineae
has been estimated to have occurred on the order of 1.7 Ga(98). The phylogenetic placement of J114 suggests that it
diverged from other members of subphylum I even earlier,and it may be a good target for future investigation to assess
aspects of the early evolution of the phylum Chloroflexi. J114
encodes aerobic respiration via an A-family heme copperoxidoreductase and an alternative complex III like manyother non-phototrophic Chloroflexi lineages (e.g. 122, 127) as
well as a Group 1f NiFe hydrogenase and carbon fixation via
the Calvin cycle, suggesting the capacity for aerobichydrogen-oxidizing autotrophy—a lifestyle not previously
described for members of Chloroflexi.
Conclusions
To our knowledge, this is the first overall geomicrobiologicalcharacterization of Jinata Onsen, providing baseline descriptionsof geochemistry and microbial diversity in order to establish
a series of testable hypotheses that can be addressed by futurestudies. We have also provided genome-resolved metagenomic
sequencing of this site focusing on members of the microbialcommunity predicted to be responsible for the bulk of primary
productivity in this system along with other organisms belonging
to novel or under-characterized lineages. However, this is justa subset of the diverse microbial populations at Jinata Onsen;
many more MAGs from across the tree of life were recovered
than are discussed in detail here but which may be of use toothers (Fig. 4 and Table S6).
The diversity of iron-oxidizing bacteria at Jinata is different
than in other Fe2+-rich springs and environments. For example,
in freshwater systems such as Oku-Okuhachikurou Onsen in
Akita Prefecture (126) and Budo Pond in Hiroshima Prefecture,
Japan (58), iron oxidation is primarily driven by the activityof chemoautotrophs such as members of Gallionellaceae. In
contrast, at Chocolate Pots hot spring in Yellowstone National
Park, USA, iron oxidation is primarily abiotic, driven by O2
produced by Cyanobacteria, with only a small contribution
from iron-oxidizing bacteria (32, 119). The distinct ironoxidizing community at Jinata Onsen may be related to thesalinity of the spring water, or biogeographically by access tothe ocean, as Zetaproteobacteria are typically found inmarine settings, particularly in deep ocean basins associatedwith hydrothermal iron sources (28). Despite the taxonomicallydistinct iron oxidizer communities between Jinata and OkuOkuhachikurou Onsen, both communities support only limited
visible biomass in regions dominated by iron oxidizers (126),
perhaps reflecting the shared biochemical and bioenergetic

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challenges of iron oxidation incurred by diverse iron-oxidizing
bacteria including Gallionellaceae and Zetaproteobacteria(4, 28, 126). Future work focused on isolation and physiologicalcharacterization of these microbes, quantification of rates anddetermination of microbial drivers of carbon fixation and aerobic
and anaerobic heterotrophy, and carbon isotope profiling oforganic and inorganic species along the flow path of the hotspring will be necessary to fully characterize the activity of
microbes at Jinata and to fully compare this system to otherareas with high dissolved ferrous iron concentrations (e.g.
Oku-Okuhachikurou Onsen [126], Fuschna Spring [42], Jackson
Creek [92], and Chocolate Pots Hot Spring [32, 119]).
Future work aimed at a more complete understanding of thegeochemistry of Jinata and its impact on microbial communities
will be valuable. For example, a quantitative determination ofbudgets and variability of gas chemistry and DOC will bebeneficial. DOC may stimulate heterotrophic activity by the
microbial community at Jinata, coupled to aerobic or anaerobic
respiration (such as dissimilatory iron reduction, as observedin other iron-rich hot springs, e.g. 31), resulting in the drawdown
of DOC downstream. Since the source of this DOC is unclear,
future work will be necessary to determine whether DOC ispresent in the source water or if it is produced in situ by the
microbial community in the Source Pool and Pool 1. Future
work is also needed to evaluate the potential for dissimilatoryiron reduction and other anaerobic metabolisms including
sulfate reduction and methanogenesis.
Throughout Earth history, the metabolic opportunities
available to life, and the resulting organisms and metabolisms
responsible for driving primary productivity, have been
shaped by the geochemical conditions of the atmosphere andoceans. The modern, sulfate-rich, well-oxygenated oceans we
see today reflect a relatively recent state—one typical of only
the last few hundred million years (e.g. 76). For the first halfof Earth history, until ~2.3 Ga, the atmosphere and oceans
were anoxic (52), and seawater was rich in dissolved iron but
poor in sulfur (120). At this time, productivity was low andfueled by metabolisms such as methanogenesis and anoxygenic
photosynthesis (12, 64, 131). Following the expansion ofoxygenic photosynthesis by Cyanobacteria and higher primary
productivity around the Great Oxygenation Event ~2.3 Ga(18, 29, 124, 133), the atmosphere and surface ocean accumulated some oxygen, and the ocean transitioned into a statewith oxygenated surface waters but often anoxic deeper waters,rich in either dissolved iron or sulfide (11, 53, 54, 88). At
Jinata Onsen, this range of geochemical conditions is recapitulated over just a few meters, providing a useful test case forprobing the shifts in microbial productivity over the course of
Earth history. In particular, the concomitant increase in visible
biomass at Jinata as the community shifts from lithotrophy
toward water-oxidizing phototrophy (i.e. oxygenic photosynthesis) is consistent with estimates for greatly increased
primary production following the evolution and expansion of
Cyanobacteria around the GOE (18, 97, 102, 124, 131, 133).
The dynamic abundances of redox-active compounds
including oxygen, iron, and hydrogen at Jinata may not only
be analogous to conditions on the early Earth, but may haverelevance for potentially habitable environments on Mars as
well. Early Mars is thought to have supported environmentswith metabolic opportunities provided by the redox gradient

between the oxidizing atmosphere and abundant electron
donors such as ferrous iron and molecular hydrogen sourced
from water/rock interactions (e.g. 49), and production ofthese substrates may continue today (22, 107), potentially
supporting past or present life in the Martian subsurface(108). Understanding the potential productivity of microbialcommunities fueled by lithotrophic metabolisms is critical for
setting expectations for the presence and size of potential
biospheres on other worlds and early in Earth history (e.g.
131–133). Uncovering the range of microbial metabolisms
present under the environmental conditions at Jinata, andtheir relative contributions to primary productivity, may
therefore find application to predicting environments on Mars
most able to support productive microbial communities.
Data availability
Raw 16S rRNA gene amplicon data, raw metagenomic
sequence data, and MAGs have been uploaded and madepublicly available on NCBI under Project Number
PRJNA392119 (genome accession numbers are found in
Table S6).
Acknowledgements
LMW acknowledges support from NASA NESSF (#NNX16AP39H),
NSF (#OISE 1639454), NSF GROW (#DGE 1144469), the Lewis
and Clark Fund for Exploration and Field Research in Astrobiology,
the ELSI Origins Network, and an Agouron Institute postdoctoral
fellowship. WWF acknowledges the support of NASA Exobiology
award #NNX16AJ57G and the Simons Foundation Collaboration on
the Origins of Life. MN and YU acknowledge the support of theJapan Society for the Promotion of Science (JP17K14412, JP17H06105)
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https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME14054

MOST CITED ORIGINAL PAPER PUBLISHEDIN THIS TEN YEARS [2012-2021] (201 citations)
MOST CITED ORIGINAL PAPER IN 2021 (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

39

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.

40

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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Microbes Environ. 35(2), 2020

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Minireview

MOST CITED REVIEW PAPER PUBLISHEDIN THIS THREE YEARS [2019-2021] (11 citations)

Size Matters: Ultra-small and Filterable Microorganisms in the Environment
Ryosuke Nakai1*†
Applied Molecular Microbiology Research Group, Bioproduction Research Institute, National Institute of Advanced Industrial
Science and Technology (AIST), 2–17–2–1, Tsukisamu-Higashi, Sapporo, 062–8517, Japan
1(Received February 29, 2020—Accepted April 17, 2020—Published online June 3, 2020)

Ultra-small microorganisms are ubiquitous in Earth’s environments. Ultramicrobacteria, which are defined as having acell volume of <0.1 μm3, are often numerically dominant in aqueous environments. Cultivated representatives among thesebacteria, such as members of the marine SAR11 clade (e.g., “Candidatus Pelagibacter ubique”) and freshwater
Actinobacteria and Betaproteobacteria, possess highly streamlined, small genomes and unique ecophysiological traits.
Many ultramicrobacteria may pass through a 0.2-μm-pore-sized filter, which is commonly used for filter sterilization invarious fields and processes. Cultivation efforts focusing on filterable small microorganisms revealed that filtered fractions
contained not only ultramicrocells (i.e., miniaturized cells because of external factors) and ultramicrobacteria, but also
slender filamentous bacteria sometimes with pleomorphic cells, including a special reference to members of Oligoflexia, theeighth class of the phylum Proteobacteria. Furthermore, the advent of culture-independent “omics” approaches to filterable
microorganisms yielded the existence of candidate phyla radiation (CPR) bacteria (also referred to as “Ca. Patescibacteria”)
and ultra-small members of DPANN (an acronym of the names of the first phyla included in this superphyla) archaea.
Notably, certain groups in CPR and DPANN are predicted to have minimal or few biosynthetic capacities, as reflected bytheir extremely small genome sizes, or possess no known function. Therefore, filtered fractions contain a greater variety andcomplexity of microorganisms than previously expected. This review summarizes the broad diversity of overlooked
filterable agents remaining in “sterile” (<0.2-μm filtered) environmental samples.
Key words: filterable microorganisms, ultramicrocells, ultramicrobacteria, candidate phyla radiation, minimal cell

How small may actual organisms be? This question has
long fascinated scientists in various fields. Prokaryotic
microorganisms (Archaea and Bacteria) constitute thesmallest life forms. Bacterial cells range in volume fromultramicrobacteria (UMB; <0.1 μm3; Duda et al., 2012) tothe typical bacterium Escherichia coli (1.6 μm3; Moore,1999) and the giant bacterium Epulopiscium fishelsoni(3.0×106 μm3; Schulz and Jørgensen, 2001; note that thecells of Thiomargarita namibiensis are larger [2.2×108 μm3],
but are occupied by a liquid vacuole, that is, they do nothave large cytoplasmic bodies; Schulz et al., 1999). Thus,
bacteria exhibit cell-size plasticity by varying cell volume
by more than seven orders of magnitude in different species.
UMB may pass through membrane filters down to 0.2-μmpore-size, which is commonly used for filter sterilization in
research laboratories as well as in medical, food, and indus‐
trial processes (Levy and Jornitz, 2006). In fact, efforts toculture microorganisms remaining in the 0.2-μm filtrate(hereafter called filterable microorganisms) of environmen‐
tal samples have yielded diverse UMB members. The sev‐
eral isolates were affiliated with unique lineages, such as* Corresponding author. E-mail: nakai-ryosuke@aist.go.jp;
Tel: +81–11–857–8408; Fax: +81–11–857–8980.
† Present address: Microbial Ecology and Technology ResearchGroup, Bioproduction Research Institute, National Institute ofAdvanced Industrial Science and Technology (AIST), 2–17–2–1,
Tsukisamu-Higashi, Sapporo, 062–8517, Japan.
Citation: Nakai, R. (2020) Size Matters: Ultra-small and Filterable
Microorganisms in the Environment. Microbes Environ 35: ME20025.
https://doi.org/10.1264/jsme2.ME20025

cosmopolitan
freshwater
Actinobacteria
andBetaproteobacteria (Hahn, 2003; Hahn et al., 2003) as wellas the candidate phylum termite group 1 (TG1) described as
Elusimicrobia (Geissinger et al., 2009). The existence of
UMB has expanded our knowledge of microbial life at thelower size limit.
In the last five years, filterable microorganisms have been
attracting increasing interest with the discovery of otherultra-small members: the candidate phyla radiation (CPR)
bacteria, also referred to as “Candidatus Patescibacteria”(hereafter described as CPR/Patescibacteria; Rinke et al.,2013; Brown et al., 2015), and some members of DPANN(an acronym of the names of the first phyla included in thissuperphyla, “Ca. Diapherotrites”, “Ca. Parvarchaeota”, “Ca.
Aenigmarchaeota”, Nanoarchaeota, and “Ca. Nanohalorch‐aeota”; Rinke et al., 2013; Dombrowski et al., 2019). Sev‐
eral CPR members have an extremely small cell volume(approximately 0.01 μm3) that was unveiled by cryotransmission electron microscopy imaging (Luef et al.,2015). Moreover, the emergence of these ultra-small prokar‐
yotes has re-opened debate on the tree of life (Hug et al.,2016; Parks et al., 2018; Zhu et al., 2019). These members
are ubiquitous in the environment and recent studies haveprovided insights into their contribution to the material
cycle (e.g., carbon and nitrogen cycles; Danczak et al.,2017; Lannes et al., 2019). This review focuses on the phy‐logenetic diversity and complexity of filterable microorgan‐
isms in natural systems, with specific references to UMB
and pleomorphic bacteria. Other reviews presented aspects
of ultra-small microorganisms including CPR/Patescibacte‐
ria and DPANN members (e.g., terminology, biogeography,
Article ME20025

43

Nakaigenomic diversity, and metabolic variety; Duda et al., 2012;
Castelle et al., 2018; Ghuneim et al., 2018; Dombrowski etal., 2019). In this review, archaea with a cell volume of <0.1
μm3 are specifically referred to as ultramicroarchaea (UMA)
to distinguish them from UMB.
Filterable microorganisms
To date, many studies have reported the presence of filter‐
able microorganisms in various environments (mainly aque‐ous environments) including seawater (Haller et al., 2000;
Elsaied et al., 2001; Lannes et al., 2019; Obayashi and
Suzuki, 2019), lake water (Hahn, 2003; Hahn et al., 2003;
Watanabe et al., 2009; Fedotova et al., 2012; Maejima et al.,2018; Vigneron et al., 2019), terrestrial aquifers (Miyoshi etal., 2005; Luef et al., 2015), glacier ice and the ice cover oflakes (Miteva and Brenchley, 2005; Kuhn et al., 2014),
deep-sea hydrothermal fluids (Naganuma et al., 2007; Nakai
et al., 2011), and soil and sand (Nakai et al., 2013). How‐ever, the use of membrane filters with a small pore size
(approximately 0.2 μm) was traditionally recommended forthe retention of bacteria in the field of marine microbial
ecology in the 1960s (e.g., Anderson and Heffernan, 1965)
and is still widely practiced today in various fields. The
existence of very small microorganisms has been well rec‐ognized since the 1980s. The term “ultramicrobacteria” wasfirst used by Torrella and Morita (1981) to describe verysmall coccoid cell forms of <0.3 μm in diameter from sea‐water. MacDonell and Hood (1982) subsequently isolated
and characterized viable filterable microorganisms poten‐
tially belonging to the genera Vibrio, Aeromonas,
Pseudomonas, and Alcaligenes from estuarine waters. They
concluded that these filterable microorganisms represented astate of dormancy for adaptation to low nutrient conditions
and were not completely novel bacteria. Other studies also
reported that external factors reduced cell sizes, such as
Staphylococcus aureus and Pseudomonas syringae (~50%
reduction in size as described in Table 1; Watson et al.,
1998; Monier and Lindow, 2003). Therefore, the cells of
miniaturized microorganisms need to be distinguished fromtrue UMB and are described in this review as “ultramicro‐
cells”, which has the synonyms dwarf cells and midget cells,
according to Duda et al. (2012). Schut et al. (1997) andDuda et al. (2012) subsequently defined a cell volume index
of <0.1 μm3 as being characteristic of true UMB.
Based on previous studies, filterable microorganisms
have been classified into five groups (Fig. 1): (I) ultramicro‐
cells that are miniaturized microorganisms because of exter‐
nal factors (e.g., environmental stress) as described above;(II) obligate UMB that maintain small cell volumes(<0.1 μm3) regardless of their growth conditions; (III) facul‐
tative UMB that contain a small proportion of larger cellswith a cell volume >0.1 μm3 (note that the definitions of theterms “obligate” and “facultative” UMB follow those of
Duda et al. [2012]); (IV) slender filamentous bacteria; and(V) ultra-small members among CPR/Patescibacteria bacte‐
ria and DPANN archaea. In contrast to UMB strains, the cell
shapes and morphological characteristics of members ingroup V are largely unknown under different environmental
or culture conditions because all of the members of CPR
44

and DPANN are uncultivated, with a few exceptions ofmembers belonging to the phyla “Ca. Saccharibacteria”(former TM7) and Nanoarchaeota (e.g., Huber et al., 2002;
He et al., 2015). Incidentally, the groups presented in thisreview do not include filterable cell-wall-less mycoplasmas
as well as “nanobacteria” or “nannobacteria” as microfos‐sils, which are often referred to in geological literature(Folk, 1999), or as calcium carbonate nanoparticles in thehuman body, as reported in medical literature (Martel andYoung, 2008). Representative cases of groups II to V are
described below and Table 1 shows a summarized list.
Obligate UMB
Obligate UMB are often reported from aqueous environ‐ments. One of the most prominent representatives is“Candidatus Pelagibacter ubique” HTCC1062, which is a
SAR11 clade bacterium that is ubiquitous in marine envi‐
ronments. Previous studies found that SAR11 members con‐sistently dominated ribosomal RNA gene clone libraries
derived from seawater DNA and estimated their global pop‐
ulation size as 2.4×1028 cells—approximately 25% of all
prokaryotic cells—in oceans (Giovannoni et al., 1990;
Morris et al., 2002). Despite their ubiquitous and abundantpresence, it was not possible to isolate them. However, thefirst cultivated strain HTCC1062 was established in 2002
using a high-throughput dilution-to-extinction culturing(HTC) technique (Rappé et al., 2002). This HTC techniqueinvolves cultivation with serial dilutions of natural seawater
samples into very low nutrient media (Connon and
Giovannoni, 2002). The cell volume (approximately
0.01 μm3) of “Ca. P. ubique” was reported as one of thesmallest free-living cells known. Subsequent studies charac‐terized the SAR11 clade with the small, streamlined
genomes (<1.5 Mbp) described below, an unusual mode ofglycine auxotrophy, a light-dependent proton pump known
as proteorhodopsin, and the ability to utilize various onecarbon compounds (reviewed in Tripp, 2013; Giovannoni,2017). The SAR11 clade is highly divergent with multiple
ecotypes and has freshwater members known as LD12 clas‐sified in SAR11 subclade IIIb (Grote et al., 2012). An LD12
cultivated representative, “Ca. Fonsibacter ubiquis” strain
LSUCC0530, was subsequently established (Henson et al.,2018), and its genomic characteristics promoted the hypoth‐esis that gene losses for osmolyte uptake were related to theevolutionary transition, or metabolic tuning, of freshwater
SAR11 (LD12) from a salt to freshwater habitat.
Another marine ultramicrobacterium, Sphingopyxis
alaskensis (formerly known as Sphingomonas alaskensis)
RB2256 was intensively investigated before the study of the
SAR11 clade (e.g., Eguchi et al., 1996; Schut et al., 1997).
This strain was also characterized as an obligate UMB(Duda et al., 2012). When the cultivation of this strain tran‐
sitioned from low-carbon to highly-enriched media, the cellvolume of S. alaskensis remained at <0.1 μm3 in mostmedia; however, larger elongated cells, not UMB cells, wereobserved in trypticase soy agar medium (Vancanneyt et al.,2001). Furthermore, this strain possesses a larger genome of
3.3 Mb (DDBJ/ENA/GenBank accession no. CP000356)
than other UMB (Table 1).

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Diversity of Filterable Microorganisms
Table 1.

An overview of ultra-small and filterable microorganisms in the environment

Phylum (and classfor Proteobacteria)

Isolation source

Cell shape

Cell size (length×width Genome
and/or volume)
size (Mbp)

Physiological and ecologicaltrait(s) or its potential

Reference

Staphylococcus aureus 8325-4

Firmicutes

derivative of S.
aureus NCTC8325(patient’s strain)

coccicell size reduction
from 0.69±0.08
to 0.41±0.08 μm

n.d.

host cell invasion, starvationassociated cell size reduction

Watson et al. (1998)

Pseudomonas syringae pv. syringae
B728aProteobacteria(γ-proteobacteria)

snap bean leaflet

rods

cell length reduction
from ~2.5 to ~1.2 μm

6.09

host cell invasion, leaf
environment-induced cell size
reduction

Monier and Lindow (2003);
Feil et al. (2005)

Taxa
Ultramicrocells

Obligate ultramicrobacteria and related candidates“Candidatus Pelagibacter ubique”
HTCC1062

Proteobacteria(α-proteobacteria)

coastal sea

curved rods

0.01 μm3

1.31“Candidatus Fonsibacter ubiquis”
LSUCC0530

Proteobacteria(α-proteobacteria)

coastal lagoon

curved rods

1.0×0.1 μm

1.16

Sphingopyxis alaskensis RB2256

Proteobacteria(α-proteobacteria)

fjord estuary

short rods

0.05–0.09 μm3

3.35

Aurantimicrobium minutum KNCT

Actinobacteria

freshwater rivercurved rods

1.62

Rhodoluna lacicola MWH-Ta8T

Actinobacteria

freshwater lake

curved rods

0.7–0.8×0.3 μm;
0.04–0.05 μm3
0.85×0.30 μm;
0.053 μm3

Rhodoluna limnophila 27D-LEPIT

Actinobacteria

freshwater pond

short rods

0.49×0.28 μm

1.40

glycine auxotrophy, rhodopsinbased photometabolism,
utilization of one-carbon
compounds
glycine auxotrophy, rhodopsinbased photometabolism,
tetrahydrafolate metabolism**
utilization of various aminoacids, resistance to heat shock,
H2O2, and ethanol
rhodopsin-based
photometabolism**rhodopsin-based
photometabolism
nitrate uptake and nitrite
excretion system**Actinobacteria

freshwater lake

curved rods

0.041 μm3

1.35

catalase-dependent growth

Actinobacteria

freshwater lake

curved rods

0.061 μm3

1.46

catalase-dependent growth

Kim et al. (2019)

utilization of low-molecularweight substrates
utilization of sugars and sugar
polymers, strict fermentative
metabolism, oxygen tolerance

Hahn et al. (2012);
Meincke et al. (2012)

1.43

Rappé et al. (2002); Tripp(2013); Giovannoni (2017)
Henson et al. (2018)
Eguchi et al. (1996);
Schut et al. (1997)
Nakai et al. (2015, 2016b)
Hahn et al. (2014);
Keffer et al. (2015)
Pitt et al. (2019)“Candidatus Planktophila rubra”
IMCC25003“Candidatus Planktophila aquatilis”
IMCC26103
Polynucleobacter necessarius subsp.
asymbioticus QLW-P1DMWA-1T

Proteobacteria(β-proteobacteria)

freshwater pond

straight rods

0.7–1.2×0.4–0.5 μm

2.16

Opitutus sp. VeCb1

Verrucomicrobia

rice paddy soil

ellipsoids

0.49×0.33 μm;
0.030 μm3n.d.

Endomicrobium proavitum Rsa215

Elusimicrobia

gut homogenate of
Reticulitermes
santonensis

0.3–0.5 μm (for cocci);
0.5–3.5×0.15–0.30 μm 1.59
(for rods)

nitrogen fixation

Zheng and Brune (2015);
Zheng et al. (2016)

Chryseobacterium solincola NF4Bacteroidetes

lake sedimentcocci, rods
showing budding
cell division
cocci, rods
showing budding
cell division orcell septation

0.004–0.04 μm3 (forcocci); 0.1–0.3 μm3(for rods)~1.7

ectoparasite of Bacillus subtilis

Suzina et al. (2011);
Duda et al. (2012)

Proteobacteria(β-proteobacteria)

freshwater

0.12 μm3 (smallest
width=0.2 μm)

n.d.

n.d.

Wang et al. (2007, 2008)

Oligoflexus tunisiensis Shr3T

Proteobacteria(Oligoflexia)*

desert sandvarious lengths×0.4–
0.8 μm (for filaments)

7.57

multidrug resistance, incomplete Nakai et al. (2014, 2016a)
denitrification**

Silvanigrella aquatica MWH-NonnenW8redTProteobacteria(Oligoflexia)*

freshwater lake

3.6×0.6 μm (for rods)

3.51

antimicrobial peptides, plasmidencoded type IV secretion
Hahn et al. (2017)
systems**various lengths

3.94

utilization of limited substrates

Pitt et al. (2020)

various lengths

3.39

violacein-like production

Pitt et al. (2020)

cocci or ovalshaped

0.009±0.002 μm3

0.69–1.05

potential interaction with otherbacterial cells via pili-likestructures

Luef et al. (2015)

straight rods

1.6–1.9×0.5–0.6 μm

n.d.

endoplasmic symbiont of theciliate P. bursaria

Gong et al. (2014)

Facultative ultramicrobacteria

Slender filamentous bacteria
Hylemonella gracilis CB

Silvanigrella paludirubra SP-Ram-0.45- Proteobacteria
freshwater pond
(Oligoflexia)*
NSY-1T
Proteobacteria
Tfreshwater creek
Fluviispira multicolorata 33A1-SZDP (Oligoflexia)*
CPR/Patescibacteria bacteria
candidate division WWE3,“Candidatus
WWE3-OP11-OD1 bacteria
Microgenomates” (OP11), deep aquifer“Candidatus
Parcubacteria” (OD1)ciliated protist“Candidatus“Candidatus Sonnebornia yantaiensis” Parcubacteria” (OD1)
Paramecium
bursaria

spirals
pleomorphic (rods,
filaments, spirals,
and spherical [orcurled] cells)
pleomorphic (rods,
filaments, andspirals)
pleomorphic (rodsand filaments)
pleomorphic (rodsand filaments)

Kim et al. (2019)

Janssen et al. (1997);
Chin et al. (2001)“Candidatus
Saccharibacteria” (TM7)human oral cavity

cocci

0.2–0.3 μm

0.71ectosymbiont of Actinomycesodontolyticus

He et al. (2015)

Nanoarchaeum equitans

Nanoarchaeota

submarine hot ventcocci

0.4 μm~0.5

Huber et al. (2002)“Candidatus Nanopusillus acidilobi”

Nanoarchaeota

hot spring

cocci

0.1–0.3 μm

0.61“Candidatus Nanoclepta minutus”
Ncl-1

Nanoarchaeota

hot spring

flagellated cocci~0.2 μm

0.58

hypersaline lake

cocci-like

0.6 μm1.23

possible free-living lifestyle

Narasingarao et al. (2012)“Candidatus Nanosalinarum” sp.
J07AB56“Candidatus
Nanohaloarchaeota”“Candidatus
Nanohaloarchaeota”

ectosymbiont of Ignicoccus
hospitalis
ectosymbiont of Acidilobus
species
ectosymbiont of Zestosphaera
tikiterensis

hypersaline lake

cocci-like

0.6 μm1.22

possible free-living lifestyle

Narasingarao et al. (2012)

ARMAN-2, -4, and -5“Candidatus
Micrarchaeota”

acid mine drainage

cocci~0.5 μm~1.0“Candidatus Mancarchaeum
acidiphilum” Mia14“Candidatus
Micrarchaeota”

acid mine drainage

n.d.

n.d.

0.95

TM7x bacterium
DPANN archaea“Candidatus Nanosalina” sp. J07AB43

potential interaction with
Thermoplasmatales cells via
pili-like structuresectoparasite of Cuniculiplasma
divulgatum

Wurch et al. (2016)
John et al. (2019)

Baker et al. (2010)
Golyshina et al. (2017)

n.d.: no data.
* The proteobacterial class Oligoflexia is classified in the candidate phylum “Bdellovibrionota” in the Genome Taxonomy Database (GTDB).
** Putative physiological traits are inferred from their genomic and plasmid annotation.

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Article ME20025

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NakaiFig. 1. Diagram showing filterable microorganisms in the environment. (I) ultramicrocells; (II) obligate ultramicrobacteria; (III) facultative
ultramicrobacteria; (IV) slender filamentous bacteria; (V) ultra-small members of CPR bacteria (also referred to as “Candidatus Patescibacteria”)
and DPANN archaea indicated by the arrow in this Figure. See details in the text. This figure was created with BioRender (https://biorender.com/).

Other prominent representatives of obligate UMB are
freshwater actinobacterial strains. Typically, actinobacteria
are among the numerically dominant groups in freshwaterand their cells are found in smaller size fractions (Glöckner
et al., 2000; Sekar et al., 2003). Hahn et al. (2003) first iso‐lated nine filterable UMB of the class Actinobacteria from
freshwater habitats and newly described a novel phyloge‐
netic cluster (Luna cluster). This isolation was achieved by
the “filtration-acclimatization” method of filter separation
combined with an acclimatization procedure, which is a
stepwise transition from low substrate conditions to artifi‐
cial culture conditions. The important features of Luna clus‐
ter strains are their wide distribution in freshwater systems(Hahn and Pöckl, 2005) and their small cell sizes are stable
and maintained in nutrient-rich media (Hahn et al., 2003).
Our group also isolated an ultamicrosize actinobacterium
related to Luna strains from river water in Japan and named
it Aurantimicrobium minutum KNCT (Fig. 2; Nakai et al.,2015). This strain showed high 16S rRNA gene sequencesimilarity (>99%) to strains isolated from freshwater sys‐
tems in other places in Japan as well as in Austria, Australia,
China, Nicaragua, and Uganda (accession nos. AB278121,
AB599783, AJ507461, AJ507467, AJ565412, AJ565413,
and AJ630367), suggesting its cosmopolitan distribution in
freshwater.
46

Fig. 2. Scanning electron micrograph of c-shaped cells of
Aurantimicrobium minutum KNCT. Cells were cultured in organic
NSY (nutrient broth, soytone, and yeast extract; Hahn et al., 2004)
medium for two weeks. Scale bar: 200 nm. This micrograph is an
unpublished figure from the author; other micrographs of this species
are shown in Nakai et al. (2013, 2015).

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Diversity of Filterable Microorganisms

The other freshwater bacterium belonging to the Lunacluster, Rhodoluna lacicola MWH-Ta8T, was also described
as an obligate UMB (Hahn et al., 2014); an additional three
Rhodoluna strains smaller than R. lacicola were subse‐
quently reported (Pitt et al., 2019). From an ecophysiological point of view, the genomes of freshwater
actinobacteria
possess
rhodopsin
photosystems(Neuenschwander et al., 2018), while R. lacicola has an
unconventional proton-pumping rhodopsin that requires
external supplementation with the cofactor retinal (Keffer etal., 2015). The underlying cause is considered to be an
inability to biosynthesize the cofactor (Neuenschwander etal., 2018), suggesting that R. lacicola obtains retinal fromthe surrounding environment. One potential source in fresh‐water appears to be retinoids produced and released by cya‐nobacteria (Ruch et al., 2005; Wu et al., 2013).
Freshwater actinobacteria, including UMB strains, werepreviously shown to be phylogenetically diverse and subse‐
quent studies yielded nine lineages (acI, acTH1, acSTL,
Luna1, acIII, Luna3, acTH2, acIV, and acV; Newton et al.,2011). Among these lineages, acI containing multiple tribes
is considered to be the most successful and ubiquitous groupin the environment (Zwart et al., 2002; Warnecke et al.,
2004; Kang et al., 2017), although pure cultures had notbeen established despite various cultivation trials. However,Kim et al. (2019) recently reported the first two pure acI
cultures with very small sizes (volume, 0.04–0.06 μm3;
Table 1), which are assumed to be obligate UMB. A key
factor for their growth was the supplementation of a“helper” catalase, an enzyme that degrades hydrogen perox‐ide (H2O2), to the culture medium. Previous studies showedthat H2O2 generated in medium affected the culture effi‐
ciency of microorganisms sensitive to oxidative stress(Kawasaki and Kamagata, 2017) and that the growth of the
cyanobacterium Prochlorococcus was promoted by the pres‐ence of H2O2-scavenging microbes (Morris et al., 2011).
These findings demonstrated that a catalase-supplemented
cultivation strategy may facilitate the successful isolation ofpreviously uncultured freshwater UMB.
Freshwater habitats also harbor another obligate UMB
belonging to the genus Polynucleobacter in the class
Betaproteobacteria. Similar to some actinobacteria descri‐
bed earlier, UMB members of this genus also showed a cos‐
mopolitan distribution in freshwater systems (Hahn, 2003).
The relative abundance of the subspecies named PnecC was
high, ranging between <1% and 67% (average 14.5%) of
total bacterial numbers, in more than 130 lakes studied inCentral Europe, as assessed by fluorescent in situ hybridiza‐tion (Jezberová et al., 2010). Culture experiments andgenomic characterization suggested that PnecC bacteria innature can utilize low-molecular-weight products derived
from photooxidation and/or the direct enzymatic cleavage ofhigh-molecular-weight substrates, such as humic substances(Watanabe et al., 2009; Hahn et al., 2012). Certain PnecC
strains sharing ≥99% similarity in 16S rRNA gene sequen‐ces differed in their ecophysiological and genomic features(e.g., the presence/absence of iron transporter genes), sug‐gesting cryptic diversity among the abundant lineage notcovered by 16S rRNA gene-based typing (Hahn et al.,2016).

The obligate UMB inhabiting sea and freshwaters descri‐
bed above were characterized by minute cell sizes, but also
small genome sizes (<2 Mbp) with a low genomic guaninecytosine (GC) content: this genome “streamlining” is con‐sidered to reflect an adaptation to nutrient-limited conditions(e.g., SAR11 members; 1.16–1.46 Mb; Giovannoni et al.,2005; Grote et al., 2012; Henson et al., 2018) (Table 1).
This phenomenon of a reduced genome size with gene loss
also indicates metabolic dependencies on co-existing micro‐organisms in nature, as described by the “Black Queen
Hypothesis” (Morris et al., 2012). As another example, thereconstructed genomes of ultra-small and uncultivatedmarine actinobacteria (“Candidatus Actinomarinidae”) werevery small (<1 Mb) and had a very low GC content of
33% (Ghai et al., 2013). In addition, known obligate UMB
of different lineages, such as “Ca. P. ubique”(Alphaproteobacteria),
Polynucleobacterstrains(Betaproteobacteria), and A. minutum and R. lacicola(Actinobacteria), showed similar “c-shaped” (curved-rod)
cells (Table 1; A. minutum for Fig. 2; Hahn, 2003). This
unique shape may be advantageous for the efficient acquisi‐tion of substances because of their increased surface-tovolume ratio of cells or grazing resistance against
bacteriovorus protists for planktonic life in waters.
In contrast to aquatic environments, limited information
is currently available on UMB, including the obligate type,from soil habitats. Janssen et al. (1997) previously reported
anaerobic obligate UMB with very small ellipsoid to nearly
spherical shapes (e.g., Opitutus sp. VeCb1 with a cell vol‐ume of 0.030 μm3) belonging to the Verrucomicrobiales lin‐eage from rice paddy soil using dilution culture techniques.
Nakai et al. (2013) isolated and cultivated filterable strainsfrom soil and sand suspensions; however, obligate UMB
were not found among these strains. High-throughput
sequencing of the 16S rRNA gene revealed that the smaller
size fractions in soils were more likely to harbor rare or
poorly characterized bacterial and archaeal taxa, such as
Acidobacteria,
Gemmatimonadetes,
Elusimicrobia,
Verrucomicrobia, and Crenarchaeota (Portillo et al., 2013).
However, further studies are needed to clarify whether themembers detected in the small fractions contain UMB.
Facultative UMB
Facultative UMB that contain a small proportion of larger
cells with a cell volume >0.1 μm3 have not yet been charac‐terized in detail (Table 1) because morphological changes
throughout the growth cycle have only been examined in alimited number of UMB. Endomicrobium proavitum Rsa215(now deposited as DSM29378T=JCM32103T) belonging to
the phylum Elusimicrobia appears to be a well-studied
example of facultative UMB. The phylum Elusimicrobia(former termite group 1 candidate phylum) was initiallyestablished with the cultivated ultramicrobacterium of
Elusimicrobium minutum strain Pei191T from the 0.2 μmfiltered filtrate—originally prepared as a growth promoting
supplement for gut bacteria—of the gut homogenates of ascarab beetle larva (Geissinger et al., 2009; Herlemann etal., 2009). E. proavitum Rsa215 was isolated from the fil‐trate of the gut homogenate and was identified as a free-

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Nakailiving bacterium of a novel class-level lineage in
Elusimicrobia (Zheng et al., 2016). E. proavitum has an
unusual cell cycle that involves different cell forms, i.e.,
cocci, rods, and budding-like cells, during the cell cycle.
Under laboratory cultivation conditions, before growth com‐mences, the cell population is comprised of a large popula‐tion of UMB coccoid cells with a few rod-shaped cells(~3.5 μm in length); small cocci are formed from a bud-likeswelling at one pole of the rod-shaped cells during growth.
Although its morphological variation in the host gut cur‐rently remains unclear, cell characteristics as observed in thelaboratory result in the classification of facultative UMB.
Another important trait for E. proavitum is the ability to fix
nitrogen gas with a group IV nitrogenase, which was con‐sidered to harbor functions other than nitrogen fixation (DosSantos et al., 2012).
Slender filamentous bacteria
In addition to ultramicrocells and UMB, slender filamen‐
tous bacteria have frequently been found in 0.2 μm-filteredfractions of environmental samples. Slender spirillumshaped Hylemonella gracilis was isolated from filtrates offreshwater samples (e.g., Hahn et al., 2004; Nakai et al.,2013) and passes through membrane filters with small poresizes of not only 0.22–0.45 μm, but also 0.1 μm (Wang etal., 2007). The smallest widths of H. gracilis cells areapproximately 0.2 μm and close to filter pore sizes, which
may allow its slender cells to “squeeze” through these pores.
Regarding the quality control and assessment of filter sterili‐
zation, Wang et al. (2008) proposed that filterable slenderbacteria, such as H. gracilis with small cell widths, may be
used for the microbiological validation of membrane filters
instead of Brevundimonas diminuta, which is the current
standard strain tested.
During a screening of UMB, our group isolated a slenderfilamentous bacterium from the filtrate of a suspension ofdesert sands collected in Tunisia, and described Oligoflexus
tunisiensis Shr3T, which represents the eighth novel classnamed Oligoflexia within the phylum Proteobacteria (Nakai
et al., 2014; 2016a). The cell shape of this species is mainly
slender, filamentous, and of variable lengths, but shows apleomorphism with other shapes, such as a spiral, spherical(or curled), or curved rod morphology (Fig. 3; Nakai andNaganuma, 2015). This polymorphic flexibility of cells withsmall widths down to 0.4 μm appears to be related to theirability to pass through membrane filters; however, it has not
yet been clarified whether each morphological shape isassociated with a resting state or other states. Regarding fil‐amentous formation, this shape may be related to resistanceto protozoan grazing, as reported in previous studies (e.g.,
Jürgens et al., 1999; Suzuki et al., 2017a). The environmen‐
tal sequences closely related (>97%) to the 16S rRNA genesequence of O. tunisiensis were recovered from paddy soil,
cyanobacterial bloom in lake water, bioreactors, and humanskin using culture-independent approaches; however, theirdetection frequency was low, with at most ~0.6% (Nakai
and Naganuma, 2015). Thus, O. tunisiensis and its relatives
appear to be rare species, and their ecological roles are cur‐rently unclear; one possible role for O. tunisiensis may be
48

Fig. 3. Micrograph of pleomorphic cells of Oligoflexus tunisiensis
Shr3T. Cells were cultured in R2A medium for more than two weeks.
This micrograph is slightly modified from the figure originallypublished in Nakai and Naganuma (2015). Scale bar: 10 μm.

incomplete denitrification to nitrous oxide, as inferred fromits genome sequence (Nakai et al., 2016a).
Despite the potential rarity of its occurrence, the size fil‐tration method led to the isolation of an additional slenderfilamentous strain, Silvanigrella aquatica MWH-NonnenW8redT, with a pleomorphic morphology in the class (Hahn
et al., 2017). Hahn et al. (2017) reclassified the order
Bdellovibrionales, including Bdellovibrio spp. known as
small “bacteria-eating” bacteria (reviewed in Sockett, 2009),
from the class Deltaproteobacteria to the class Oligoflexia
based on in-depth phylogenetic analyses. Incidentally, 0.45μm filtrates of environmental samples are frequently used
for the enrichment culture of Bdellovibrio predatory bacte‐ria. In the Genome Taxonomy Database (GTDB) based on
genome phylogeny (https://gtdb.ecogenomic.org/; Parks etal., 2018), the class Oligoflexia belongs to the candidate
phylum “Bdellovibrionota”, named after the genus
Bdellovibrio, and not the phylum Proteobacteria; its taxo‐
nomic assignment will be discussed in future studies.
Oligoflexia very recently gained two more species,
Fluviispira multicolorata 33A1-SZDPT and Silvanigrella
paludirubra SP-Ram-0.45-NSY-1T, from freshwater habitats(Pitt et al., 2020). Silvanigrella spp. are phylogeneticallyclosely aligned with “Candidatus Spirobacillus cienkow‐skii” (Pitt et al., 2020), which is an uncultured pathogen ofwater fleas (Daphnia spp.) described morphologicallyalmost 130 years ago (Metchnikoff, 1889). Since
Silvanigrella spp. are isolated from the filtrates of micropore
filtration, size fractionation may be an effective method for
isolating the uncultivated pathogen as well as additionallyoverlooked agents in Oligoflexia. A detailed comparison
within members of this class will also be important for pur‐suing the evolutionary acquisition and divergence of preda‐
tory and pathogenic behaviors.
Diverse ultra-small members and their potentials
Metagenomic investigations on microbial communities
have generated genomes for an astounding diversity of bac‐

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teria and archaea; CPR/Patescibacteria inhabiting ground‐water has attracted increasing attention in recent years.
Traditionally, certain types of groundwater bacteria were
known to pass through a micropore filter (e.g., Shirey andBissonnette, 1991). Additionally, Miyoshi et al. (2005) phy‐logenetically characterized filterable microorganisms cap‐
tured by 0.1-μm-pore-sized filters from deep aquifers of the
Tono uranium mine, Japan and then discovered candidate
divisions OD1 and OP11 (now recognized as candidate
phyla “Ca. Parcubacteria” and “Ca. Microgenomates”,
respectively) enriched by approximately 44% in 16S rRNA
gene clones from the filtered fraction. The specific occur‐
rence of “Ca. Parcubacteria” (OD1) in the 0.2-μm filtrate
was also detected in deep-sea hydrothermal fluid(Naganuma et al., 2007). It was previously unclear whether
members of these candidate divisions were UMB. In subse‐
quent studies using cryo-imaging, ultra-small cells (approxi‐
mately 0.009±0.002 μm3) were reported in the filtrate of anaquifer water near Colorado, USA, which were enrichedwith the candidate divisions WWE3, OD1, and OP11, allrecently belonging to CPR/Patescibacteria (Luef et al.,2015).
Metagenomics was then used to reconstruct the genomesof filterable members in the aquifer system, representing>35 candidate phyla named CPR (Brown et al., 2015). Thishighly diversified group of uncultivated bacteria may subdi‐
vide the domain Bacteria (Hug et al., 2016); however, this
scenario remains controversial (e.g., Parks et al., 2018; Zhu
et al., 2019). Importantly, measurements of replication rates(Brown et al., 2016; Suzuki et al., 2017b) and cryotransmission electron microscopy images showing a divid‐
ing cell (Luef et al., 2015) indicated that the extremely
small cells of CPR/Patescibacteria are metabolically active
and not simply ultramicrocells during starvation. Moreover,
CPR/Patescibacteria genomes have been recovered fromother environments, such as highly alkaline groundwater(Suzuki et al., 2017b; Sato et al., 2019), lakes (Vigneron etal., 2019), soil (Starr et al., 2018), and marine sediment(Orsi et al., 2018) as well as the human microbiome (He etal., 2015) and dolphin mouse (Dudek et al., 2017), suggest‐
ing a wide distribution across environments. Besides
describing ultra-small life forms with high phylogenetic
novelty, genomic analyses of CPR/Patescibacteria members
have provided information on their small genomes, fermen‐
tative metabolism, and other unusual features (e.g., selfsplicing introns varying in length and proteins encodedwithin their 16S rRNA genes; Brown et al., 2015; Castelle
et al., 2018). Divergent 16S rRNA gene sequences prevent
many specific phyla (e.g., ~50% of “Ca. Microgenomates”[OP11] and 60% of candidate division WWE3) from being
detected by typical PCR surveys with the universal bacterial
primer set 515F and 806R (Brown et al., 2016). The small
genome sizes observed (often <1 Mb) appear to be a reflec‐tion of a symbiotic lifestyle and/or high in situ selection
pressure in a stable environment, rather than the genome
streamlining of free-living obligate UMB, as described ear‐lier, assuming streamlining characteristics (e.g., highly con‐served core genomes with few pseudogenes; Giovannoni etal., 2014). Although the CPR/Patescibacteria genomes stud‐
ied to date possess incomplete biosynthetic pathways fortheir cellular building blocks (e.g., nucleotides and fatty
acids; Castelle et al., 2018), the possibility of their ability tode novo synthesize them by unknown pathways cannot be
ruled out. Furthermore, their host-associated distribution
was reported: “Candidatus Sonnebornia yantaiensis” of“Ca. Parcubacteria” (OD1) as an endoplasmic symbiont of
the protist (Gong et al., 2014) and TM7x bacterium of “Ca.
Saccharibacteria” (TM7) attached to Actinomycesodontolyticus (He et al., 2015), as shown in Table 1.
The features of small cell sizes and small genomes
observed in CPR/Patescibacteria are shared by some mem‐bers of the DPANN archaea, particularly Nanoarchaeota(Huber et al., 2002), “Ca. Nanohalorchaeota” (Narasingarao
et al., 2012), and so-called ARMAN (archaeal RichmondMine acidophilic nano-organisms; Baker et al., 2010).
DPANN including these UMA has been expanded by theaddition of novel phylum-level groups, and, at the time ofwriting, encompasses at least ten different lineages(reviewed in Dombrowski et al., 2019). In several cases,
except for the members of “Ca. Nanohalorchaeota”, as with
CPR/Patescibacteria, DPANN-affiliated UMA showed an
ectosymbiotic localization: Nanoarchaeum equitans
attached to Ignicoccus hospitalis (Huber et al., 2002), “Ca.
Nanopusillus acidilobi” and its host Acidilobus species(Wurch et al., 2016), and “Ca. Mancarchaeum acidiphilum”
Mia14 (ARMAN-2-related organism) and its hostCuniculiplasma divulgatum (Golyshina et al., 2017) (otherdata in Table 1). Additionally, DPANN organisms lack theability to biosynthesize their building blocks (Castelle et al.,2018). Although it is still unclear whether these symbiotic
or parasitic lifestyles represent a way of life for the CPR/
Patescibacteria and DPANN groups, the cases describedabove indicate that several members of these groups appear
to be important in organism-organism interactions.
The characterization of ultra-small life forms may provide
a new perspective for minimal cells and synthetic cells. In
the field of synthetic biology, the top-down approach has
been employed to reduce and simplify the genomes of
microbial cells by genetic engineering, and then to identify
essential genes for living systems; the bottom-up approach,which is the opposite of the top-down approach, has beenused to examine what is sufficient for living systems byassembling non-living components, such as nucleic acids,
proteins, and lipids (e.g., Matsuura et al., 2011; Xu et al.,2016). In this context, DeWall and Cheng (2011) pointed outthat the small genomes of microorganisms in nature may bemodels for the identification of a minimal genome. Since
the ultra-small members described here as well as freeliving obligate UMB already harbor small and sometimes
streamlined genome structures (<2 Mb) through the loss of
unnecessary components, the “middle-out” approach, refer‐ring to the metabolic pathway of these members (Fig. 4),
which effectively combines traditional top-down andbottom-up approaches, will be useful for the rational designof artificial cells.
Conclusions
Numerous cultivation efforts have clearly shown thatsome previously uncultured members remain viable in

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49

NakaiFig. 4. A schematic diagram of the “middle-out” approach toward the development of minimal cells or synthetic cells. This approach, inspiredby the unusual biology of ultra-small life forms, may provide a new perspective to traditional top-down or bottom-up approaches. This figure wascreated with BioRender (https://biorender.com/).

small-size fractions. Some obligate UMB are ubiquitous anddominant in water systems and may play important roles innatural microbiome functions. In parallel, the advent ofhigh-throughput sequencing technology has greatly
expanded our knowledge of ultra-small microbial diversity.
Future studies are required to shed light on small microor‐ganisms hidden in various environmental samples (e.g.,soils and sediments) other than aqueous environments, andon the ecophysiological traits and biogeochemical roles ofthese members, including CPR/Patescibacteria and DPANN.
Further studies on “extreme” microorganisms at the lower
size limit will undoubtedly lead to new conundrums aboutlife on Earth.
Acknowledgement
I would like to thank Dr. K. Takai (JAMSTEC) and one anony‐mous reviewer for their helpful comments and suggestions on an
earlier draft of this review.
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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) and54* 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

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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 the55

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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

56

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

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

57

304

58

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

305
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

59Hillman et al.

306
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)60

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 that61

Hillman et al.

308

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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Microbes and Environments
EDITORIAL BOARD (2022)

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Ken Kurokawa, National Institute of Genetics, Mishima, Japan
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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
Masaaki Konishi, Kitami Institute of Technology, Kitami, Japan
Satoshi Wakai, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan

JAPANESE SOCIETY OF MICROBIAL ECOLOGY

President (2021–2022): Satoshi OKABE, Hokkaido University
Secretary/Treasurer (2021–2022): Mamoru OSHIKI, Hokkaido University
Business aﬀairs (2021–2022): Tomonori KINDAICHI, Hiroshima University
Business aﬀairs (2021–2022): Takashi NARIHIRO, National Institute of Advanced Industrial Science and Technology (AIST)
Business aﬀairs (2021–2022): Hideomi ITO, National Institute of Advanced Industrial Science and Technology (AIST)
Treasurer (2021–2022): Tsukasa ITO, Gunma University
Treasurer (2021–2022): Shigeyuki KAKIZAWA, National Institute of Advanced Industrial Science and Technology (AIST)JAPANESE SOCIETY OF SOIL MICROBIOLOGY

President (2021–2023): Masahiro SHISHIDO, Chiba University
Vice President (2021–2023): Koki TOYOTA, Tokyo University of Agriculture and Technology
Secretary (2021–2023): Naoto OGAWA, Shizuoka University
Treasurer (2021–2023): Reiko SAMESHIMA, Shizuoka University

TAIWAN SOCIETY OF MICROBIAL ECOLOGY

President (2020–2022): Chang-Ping YU, National Taiwan University
Vice President (2020–2022): Pi-Han WANG, Tunghai University
International Business Aﬀairs (2020–2022): Shiu-Mei LIU, National Taiwan Ocean University
International Business Aﬀairs (2020–2022): Shir-Ly HUANG, National Yang Ming Chiao Tung University
Secretary-General (2020–2022): Chien-Sen LIAO, I-Shou University

JAPANESE SOCIETY OF PLANT MICROBE INTERACTIONS

Chair of Society: Kiwamu MINAMISAWA, Tohoku University
Secretary-General: Shusei SATO, Tohoku University
Treasurer: Hisayuki MITSUI, Tohoku University
General aﬀairs: Masayuki SUGAWARA, Obihiro University of Agriculture and Veterinary Medicine

JAPANESE SOCIETY FOR EXTREMOPHILES

President (2022–2023): Tairo OSHIMA, Kyowa-kako Co., Ltd
Vice President (2022–2023): Satoshi NAKAMURA, Tokyo Institute of Technology
Vice President (2022–2023): Yoshizumi ISHINO, Kyushu University