Skip to main content

Diversity of active root-associated methanotrophs of three emergent plants in a eutrophic wetland in northern China


Root-associated aerobic methanotrophs play an important role in regulating methane emissions from the wetlands. However, the influences of the plant genotype on root-associated methanotrophic structures, especially on active flora, remain poorly understood. Transcription of the pmoA gene, encoding particulate methane monooxygenase in methanotrophs, was analyzed by reverse transcription PCR (RT-PCR) of mRNA isolated from root samples of three emergent macrophytes, including Phragmites australis, Typha angustifolia, and Schoenoplectus triqueter (syn. Scirpus triqueter L.) from a eutrophic wetland. High-throughput sequencing of pmoA based on DNA and cDNA was used to analyze the methanotrophic community. Sequencing of cDNA pmoA amplicons confirmed that the structure of active methanotrophic was not always consistent with DNA. A type I methanotroph, Methylomonas, was the most active group in P. australis, whereas Methylocystis, a type II methanotroph, was the dominant group in S. triqueter. In T. angustifolia, these two types of methanotroph existed in similar proportions. However, at the DNA level, Methylomonas was predominant in the roots of all three plants. In addition, vegetation type could have a profound impact on root-associated methanotrophic community at both DNA and cDNA levels. These results indicate that members of the genera Methylomonas (type I) and Methylocystis (type II) can significantly contribute to aerobic methane oxidation in a eutrophic wetland.

Key points

  1. 1.

    Root-associated Methylomonas was predominant in three macrophytes using DNA approach.

  2. 2.

    Active Methylocystis was dominant in genera Typha and Schoenoplectus but not in Phragmites.

  3. 3.

    Plant species impact on methanotrophic communities in both DNA and cDNA levels.


Wetlands can both produce and absorb greenhouse gases, which is a major component in the global climate change. Being the largest natural wetland at the same latitude of the earth, Wuliangsuhai (WLSH) is a typical eutrophication wetland in northern China (Wu et al. 2017) and plays an important role in the earth’s ecosystem, such as maintaining water resources, regulating drought climate, and providing high biodiversity, etc. (Liu et al. 2020; Yu et al. 2004). Methane, a greenhouse gas, accounts for 20–30% of the contribution of greenhouse gases to global warming (Conrad 2009). In nature, methane is normally produced by methanogens in anaerobic zone of soil (Serrano-Silva et al. 2014), but is not directly released into the atmosphere. About 90% is consumed by methanotrophic bacteria when passing through the aerobic soil layer. As a biofilter, methanotrophs became a powerful biological weapon to combat global climate change (Hornibrook et al. 2009). Aerobic methanotrophs, with the help of a series of enzymes, can eventually convert particulate methane into carbon dioxide, thus effectively reducing the greenhouse effect of methane. Methane monooxygenase (pMMO) enzyme plays an important role in this process. As a key gene encoding the β-subunit of pMMO, pmoA was found in almost all known aerobic methanotrophs. So far, the diversity of methanotrophs is typically assessed by the detection of the pmoA gene (Brablcova et al. 2015; McDonald et al. 2008; Semrau et al. 2010).

Eutrophication is a serious ecology problem in major aquatic ecosystems around the world, and aquatic macrophytes play critical roles in improving water quality (Dhote and Dixit 2009). Phragmites spp., Typha spp., and Schoenoplectus spp. are three common emergent types of macrophyte vegetation that are present worldwide (Vymazal 2013), and they mediate CH4 emissions from wetlands to the atmosphere (Grunfeld and Brix 1999). A well-developed aeration tissue inside the emergent plants can mediate the release of methane from the sediments into the atmosphere through the plants. In this process, the methanotrophs in the plant roots will have a decisive influence on the final methane gas emissions. As was observed in constructed wetlands, the existence of plants play an important role in regulating the production, consumption and transportation of CH4 (Sun et al. 2013). CH4 flux is usually measured under the condition of constructed wetland, which is disturbed by human factors (Zhang et al. 2018). For CH4 emissions, published results report lower (Bateganya et al. 2015; Maltais-Landry et al. 2009) or higher (Wang et al. 2013) in planted compared to unplanted constructed wetlands. However, the effect of different plant species on CH4 fluxes remains controversial (Chen et al. 2019). Between 18 and 90% of the produced CH4 in the root zone of emergent macrophytes in wetlands is consumed by aerobic methanotrophs (Grunfeld and Brix 1999; Laanbroek 2010). So far, many studies have focused on microorganisms in their rhizosphere sediment, including methylotroph- and heterotroph-mediated processes of carbon and other element cycles (Borruso et al. 2017). Through a 16S rRNA gene Illumina MiSeq sequencing, Pietrangelo et al. (2018) reported the bacterial community structure on the root surface of P. australis was indeed different from that of T. latifolia. In addition, Fausser et al. (2012) have suggested that methylotrophic bacteria live in the root zones of P. australis and T. latifolia. However, the community structure of root-associated methanotrophs and the relationship between the root-associated methanotrophs (functional bacteria) and plant species remain poorly understood.

As more and more molecular biology techniques are applied to the environment, reverse transcription polymerase chain reaction (RT-PCR) is another useful tool to identify active methanotrophs in the environment (Burgmann et al. 2001; Chen et al. 2007; Esson et al. 2016; Griffiths et al. 2000). Different from the measurement of methane flux, studying the transcriptional activity of functional gene pmoA can help us understand the activities of aerobic methanotrophs directly. At the same time, transcriptional analysis from natural wetland samples without laboratory culture can more accurately reflect the community characteristics of aerobic methanotrophs in their natural state.

WLSH Lake is located near the city of Bayannur in the Inner Mongolia Autonomous Region in China. This lake is the largest freshwater lake in the Yellow River watershed. Recently, the lake has become eutrophic after having received industrial wastewater with high nitrogen and phosphorus content (Wu et al. 2017). P. australis (common reed), T. angustifolia (narrow leaf cattail), and S. triqueter (bulrush) are the dominant macrophytes of WLSH (Duan et al. 2005).

Using the RT-PCR and MiSeq sequencing technique, we studied the structure of methanotrophic communities in the roots of three typical emergent plants (P. australis, T. angustifolia, and S. triqueter) in WLSH wetland, Inner Mongolia of China. This study was conducted to determine (1) whether any trends in active aerobic methanotrophs can be identified based on pmoA sequence analysis of cDNA, and (2) whether plant species influence the structure of methanotrophs. The results will be valuable for discussions and decisions related to the emission of greenhouse gas and the restoration of ecosystems by plants in WLSH wetland.

Materials and methods

Sampling sites and plant materials

Three plants of each P. australis, T. angustifolia and S. triqueter were collected from WLSH wetland (N 40°52′36″, E 108°51′16″) in 15 July 2017 (Fig. 1). The physical and chemical properties of the sampling site are shown in the attached Table (Additional file 1: Table S1). The roots of the three plants were collected from the wetland located in the naturalistic area of WLSH, then washed carefully with sterile water until all the soil was rinsed off. Some of the roots were carefully picked with sterilized forceps and divided into two equal parts which placed into 50 ml Falcon tubes (Bao et al. 2014a). One was quickly transferred to dry ice for DNA extraction, and the other was placed in a liquid nitrogen tank to extract RNA. We made three parallel lines for each sample, and quickly brought the tubes back to the lab. All samples were stored at − 80 °C prior to molecular analysis.

Fig. 1

Sampling site (a) of P. australis (b), T. angustifolia (c) and S. triqueter (d) in the natural wetland of Wuliangsuhai

DNA and RNA extraction and cDNA synthesis

The tissues of the roots were ground to powder in liquid nitrogen, and stored at − 80 °C until molecular analysis (Bao et al. 2014a). Genomic DNA was extracted from 0.5 g root by using the Fast DNA SPIN for soil kit (MP Biomedicals, Solon, OH), while RNA was by RNAprep pure Plant Kit (Tiangen Biotech Co., Beijing), and both extraction processes were done according to the manufacturer’s instructions.

Though DNase was added in the process of RNA extraction, host DNA pollution would have a great impact on the test results. The following methods (Zhao et al. 2018) were used to detect DNA contamination: primer set 27F/1492R for 16S rRNA (Martin-Laurent et al. 2001) gene was used for PCR amplification with the template of the extracted total RNA. The PCR products were analyzed as negative results by agarose gel electrophoresis and NanoVueTM Plus (GE, USA) to ensure that there was no microbial DNA in the total RNA. gDNA eraser reverse transcription kit (TaKaRa, Japan) was used to synthesize cDNA according to the manufacturer’s instructions. The first step of gDNA eraser reverse transcription kit was genomic DNA elimination reaction which can make sure no DNA left. The reagents, gDNA eraser, used in the reaction have a strong decomposition effect on DNA. The second step was reverse-transcription reaction. The processes were also followed the protocol, and the primers were RT primer mix (olige dT and random 6 mers). All the DNA and cDNA samples were stored at − 80 °C until use.

High-throughput sequencing of pmoA genes based on both DNA and cDNA

The pmoA gene in DNA and cDNA samples from three plants root were sequenced using the Illumina MiSeq platform. The barcode primer pair A189f/mb661r (Costello and Lidstrom 1999) and reagent kit (RR902A, Premix ExTaq™, Takara Bio Inc., Japan) were used for PCR amplification. A raw sequence file was processed by using the mothur software (version 1.33.3) for quality control and sample splitting (Schloss et al. 2009). The reads were processed using the online version of FunGene Pipeline (Fish et al. 2013), then high-quality pmoA sequences of each sample were classified as known pmoA groups or lineages as described (Luke and Frenzel 2011). The nucleotide sequences of pmoA were clustered into species-level operational taxonomic units (OTUs) using the FunGene Pipeline with a distance cutoff of 0.09 (Heyer et al. 2002).

Statistical analysis

The phylogenetic tree of the OTUs was drawn with the neighborhood joining method using MEGA5.2 (Tamura et al. 2011). The display and annotation of the tree were done with ITOL( The representative sequence of OTUs were blasted in NCBI. Statistical analysis and data visualization were carried out in R (version3.6.1). Mothur software (version1.33.3) was used for calculating alpha diversity indices which were then analyzed using ANOVA (SPSS v16.0). Principal coordinates analysis (PCoA) and analysis of similarities (ANOSIM) were done by the package vegan. The package ggplot2 was used to draw the plot of PCoA. Heatmap were also plotted in R with the package pheatmap.

Nucleotide sequence accession numbers

All data from MiSeq sequencing of the pmoA have been deposited in the NCBI under the accession numbers: SRR10584604-SRR10584613.


Comparison of diversity and community of methanotrophs between DNA and cDNA amplicons

High throughput sequencing of pmoA gene was performed on root DNA and cDNA of three plants, and 109,563 high quality reads (DNA 59,742; cDNA 53,344) were obtained. The alpha diversity is shown in Table 1, Fig. 5a and Additional file 1: Fig. S1. The OTUs richness was evaluated through the Chao1 index whereas the OTUs evenness was evaluated through Shannon. P. australis had the most OTUs richness, while T. angustifolia had most diversity. In both alpha diversity measures, the methanotrophic diversity in roots of the three plants was significantly different from each other (p < 0.05). In DNA, bacterial diversity of P. australis was significantly higher than that of T. angustifolia and S. triqueter and it was lowest in T. angustifolia; while in cDNA, bacterial diversity of T. angustifolia was heights and it was significantly higher than that of P. australis and S. triqueter (Table 1, Fig. 5a).

Table 1 The diversity index of high-throughput sequencing based on pmoA of three plant roots

Figure 2 showed the community structure at the genus level between DNA and cDNA. For DNA, more than 90% aerobic methanotrophic bacteria belonged to type I methanotrophs affiliated with Methylomonas in three plants root (Fig. 2a). The relative abundance of type II in all the samples was very low (0.2–8.9%). Methylocystis (8.8%) in S. triqueter was much more than the other two plants. Unlike DNA, Methylomonas was still dominant in P. australis, and Methylocystis had the highest abundance in the S. triqueter in cDNA (Fig. 2b). The relative abundance of four main genera in both DNA and cDNA were shown in Fig. 3. As Methylomonas of all three plants root had the highest abundance in DNA (79.3–87.9%), it all significantly decreased to some extent in cDNA (10.2–52.7%); however, Methylocystis and Methylosinus had both significantly increased in S. triqueter (68%) and T. angustifolia (23.2%).

Fig. 2

Relative abundant of aerobic methanotroph community structure in different emergent plants at the genus level based on DNA (a) and cDNA (b) analysis of pmoA (PAR, P. australis root; TAR, T. angustifolia root; STR, S. triqueter root)

Fig. 3

Relative abundant of four main genera in both DNA(a) and cDNA(b)

PCoA (Fig. 5b) were used to analyze beta-diversity of root-associated methanotrophs based on DNA and cDNA analysis. It verified that samples from the same emergent plants tended to group together, while samples from different plant species were located far apart. The difference between groups was much larger than that within groups (anosim, p < 0.01). The result clearly showed that plant species had affect the community structure of methanotrophs.

Phylogeny of active methanotrophs associated with emergent plant roots

By studying the community of methanotrophs at the transcriptional level, we can reflect more directly on the role of functional flora in controlling methane emissions. The total sequences, obtained by high throughput sequencing of pmoA on root cDNA, were classified into 41 OTUs (Fig. 4; Additional file 1: Table S2) assigned to 14 genera (Fig. 2b) and 5 classes (Fig. 4). The genus Methylomonas and Methylococcaceae which were type I methanotrophs in the Gammaproteobacteria and the genus Methylocystis and Methylosinus which were type II methanotrophs in the Alphaproteobacteria accounted for more than 80% of all samples.

Fig. 4

Neighbor-joining tree of methanotrophic phylotypes detected in the emergent plants based on the cDNA of pmoA genes. The blue part of the inner circle represents type I; the pink part represents type II. The different colors in the outer circle represent the corresponding class of OTU. Multi value bar charts represent the relative abundance of OTU in different plants

The community structure varied greatly in different plants root (Fig. 2b). For P. australis, type I methanotrophs, including three main OTUs of Methylomonas sp. LW13 (Lontoh et al. 2000), Methyloglobulus morosus (Lontoh et al. 2000) and Methylomonas sp. (QBB78506.1), represented 75.1%; however, in S. triqueter, the most OTU of Methylocystis hirsute (Lontoh et al. 2000) affiliated with type II accounted for 68.0%. The proportions of type I and type II methanotrophs were almost equal in T. angustifolia. Two main OTUs, Methylomonas of Type I and Methylocystis of type II, accounted for 24.6% and 23.1%, respectively.


Research has shown that type I methanotrophs are always found in soils with a limited CH4 supply because they grow better than type II methanotrophs in a low-CH4 environment (Hanson and Hanson 1996). On the other hand, type II methanotrophs such as Methylocystis are usually found in high-CH4 systems (Shiau et al. 2018a, b). Recently, Kits et al. (2015) have used complete genome analysis to show that the type I methanotrophs Methylomonas denitrificans FJG1 possess related denitrification genes and demonstrate denitrification activity under hypoxic conditions. In addition, Methylocystis and Methylosinus of type II methanotrophs were found to be the predominant root-associated methanotrophs in rice paddy field (Bao et al. 2014b; Eller and Frenzel 2001; Qiu et al. 2009; Shinoda et al. 2019) and were identified as diazotrophic methanotrophs in rice root (Bao et al. 2014b; Shinoda et al. 2019). These results support that members of type I and type II methanotrophs inhabiting in aquatic plants in wetland. This study showed that type I methanotrophs dominate the root systems of the three species of emergent plants, which could easily be explained by the fact that the three species lived in the same water area and their aerenchyma was conducive to the proliferation of CH4, resulting in a low concentration of CH4 in this water area. However, when analyzing the active methanotrophs in cDNA, we found that the structure was significantly different from the DNA level, especially S. triqueter, whose active communities were mainly Methylocystis of type II methanotrophs. This leads us to the question: what factors affect the transcriptional activity of different methanotrophs? Methylomonas has been shown to be active in methane oxidation in the environments that are more neutral to alkaline in pH, such as a cave sys-tem, soda lake, and landfill cover soil (Cebron et al. 2007; Hutchens et al. 2004; Lin et al. 2004). On the other hand, Chen et al. (2008) determined that Methylocystis populations were predominant in the active methanotrophs in a range of peatlands in the United Kingdom. These results showed that environmental factors (e.g., pH) affect the activity of different groups of methanotrophs. The pH of sediments in WLSH was 8.0 (Additional file 1: Table S1), and all three plants in this study grew in the same slightly alkaline environment which is suitable for the habitation of Methylomonas (Liu et al. 2020). Nevertheless, the main active groups of these plant roots varied with different plant species in this research, which may be due to the transcriptional activity of methanotrophs being more sensitive to the change of microenvironment (root secretion, etc.) of the plant root. Rhizodermis cells secrete a wide range of compounds, including organic acid ions, inorganic ions, phytosiderophores, sugars, vitamins, amino acids, purines, and nucleosides, and the root cap produces polysaccharide mucilage (Dakora and Phillips 2002). These exudates have a great influence on root microhabitats. The production of organic acids, for example, may alter the pH of plant roots. Though the root exudates of emergent plants might get diluted in the wetland environment, the ability of the microbial capacity to adhere to the root of different plants may vary (Pietrangelo et al. 2018). Root secretion of P. australis may have little effect on the pH of the environment, but to S. triqueter and T. angustifolia, it could change the pH of the root surface, thereby affecting the community of active methanotrophs. CH4 emission may be one of the factors affecting the transcriptional activity of aerobic methanotrophs. As root-associated methanotrophs varies with plant species, it may affect the fluxes of plant mediated methane. Maltais-Landry et al. (2009) reported higher CH4 emissions from constructed wetlands planted with P. australis and P. arundinacea than for constructed wetlands planted with T. angustifolia. Therefore, the community structure of active aerobic methanotrophs is more valuable for the study of natural wetland CH4 emission.

As the microhabitat of bacterial life, the roots of emergent plants can be affected by various factors, such as the physical and chemical properties of sediments (Chen et al. 2018; Cui et al. 2017; Shiau et al. 2018a, b), vegetation types (Chen et al. 2019; Yoshida et al. 2014), growth period, and the change of microenvironment is bound to lead the change of root-associated bacterial community. Bulgarelli et al. (2013) found that the microbial community in different plant roots had its distinctive phylogenetic structure. Is the aerobic methanotrophs affected by plant host? Studies on rhizosphere sediments have shown that vegetation types affected the community structure of methanotrophs. Zhang et al. (2018) reported that plant species had a profound impact on methanotrophic communities, and each plant species in constructed wetlands contained a specific group of methanotrophs. Yoshida et al. (2014) studied the communities of methanotrophs in the leaves, submerged part and emerged part of different aquatic plants, and drew the same conclusion. Little work was done on root-associated methanotrophs in natural wetland. In this study, beta-diversity based on both pmoA DNA and cDNA (Fig. 5b) showed that the aerobic methanotrophic bacteria of three plants root vary with plant species. The root exudates of emergent plants in the wetland spread with water to other parts of the plant and surrounding sediments, which may be one reason why methanotrophic communities were different in rhizosphere and emerged part. In addition, aerobic methanotrophs form an important bridge between the global carbon and nitrogen cycles including denitrification and nitrogen fixation (Bao et al. 2014b; Stein and Klotz 2011). Further investigation should be conducted, such as transcriptomic and/or metaproteomic analysis of root-associated methanotrophs to clarify whether aerobic methanotrophs play a critical role to methane oxidation and denitrification or nitrogen fixation in natural wetland.

Fig. 5

Alpha diversity and beta-diversity of aerobic methanotroph calculation based on pmoA. The OTUs evenness of root microbiotas is shown by Shannon(a). The red boxplot based on DNA; the green boxplot based on cDNA. Error bars represent the standard error of the mean. Bars with the different letter (a, b or c) within a panel (DNA or cDNA) are significantly different (p < 0.05). The distance between samples of principal coordinates analysis (PCoA) (b) calculated using Bray–Curtis index

In this study, we demonstrated that the root-microhabitats of wetland emergent plants have significant influence on both the community structure and the active structure of aerobic methanotrophs. Methylomonas of type I methanotrophs was predominant in the root of three plant species in DNA level analysis. However, active root-associated Methylocystis of type II methanotrophs was predominant in the root of S. triqueter, as well as in T. angustifolia. The diversity and composition of active methanotrophs were dependent on plants species. The community of active methanotrophs were affected by the host plant. These results will lay the foundation for further studies on plant-mediated methane emissions from natural wetlands.

Availability of data and materials

We declared that materials described in the manuscript, including all relevant raw data, will be freely available to any scientist wishing to use them for non-commercial purposes, without breaching participant confidentiality.


  1. Bao Z, Watanabe A, Sasaki K, Okubo T, Tokida T, Liu D, Ikeda S, Imaizumi-Anraku H, Asakawa S, Sato T, Mitsui H, Minamisawa K (2014a) A rice gene for microbial symbiosis, Oryza sativa CCaMK, reduces CH4 flux in a paddy field with low nitrogen input. Appl Environ Microbiol 80:1995–2003.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. Bao Z, Okubo T, Kubota K, Kasahara Y, Tsurumaru H, Anda M, Ikeda S, Minamisawa K (2014b) Metaproteomic identification of diazotrophic methanotrophs and their localization in root tissues of field-grown rice plants. Appl Environ Microbiol 80:5043–5052.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Bateganya NL, Mentler A, Langergraber G, Busulwa H, Hein T (2015) Carbon and nitrogen gaseous fluxes from subsurface flow wetland buffer strips at mesocosm scale in East Africa. Ecol Eng 85:173–184.

    Article  Google Scholar 

  4. Borruso L, Esposito A, Bani A, Ciccazzo S, Papa M, Zerbe S, Brusetti L (2017) Ecological diversity of sediment rhizobacteria associated with Phragmites australis along a drainage canal in the Yellow River watershed. J Soil Sediment 17:253–265.

    CAS  Article  Google Scholar 

  5. Brablcova L, Buriankova I, Badurova P, Chaudhary PP, Rulik M (2015) Methanogenic archaea diversity in hyporheic sediments of a small lowland stream. Anaerobe 32:24–31.

    CAS  Article  PubMed  Google Scholar 

  6. Bulgarelli D, Schlaeppi K, Spaepen S, Loren Ver, van Themaat E, Schulze-Lefert P (2013) Structure and functions of the bacterial microbiota of plants. Annu Rev Plant Biol 64:807–838.

    CAS  Article  PubMed  Google Scholar 

  7. Burgmann H, Pesaro M, Widmer F, Zeyer J (2001) A strategy for optimizing quality and quantity of DNA extracted from soil. J Microbiol Meth 45:7–20

    CAS  Article  Google Scholar 

  8. Cebron A, Bodrossy L, Chen Y, Singer AC, Thompson IP, Prosser JI, Murrell JC (2007) Identity of active methanotrophs in landfill cover soil as revealed by DNA-stable isotope probing. FEMS Microbiol Ecol 62:12–23.

    CAS  Article  PubMed  Google Scholar 

  9. Chen Y, Dumont MG, Cebron A, Murrell JC (2007) Identification of active methanotrophs in a landfill cover soil through detection of expression of 16S rRNA and functional genes. Environ Microbiol 9:2855–2869.

    CAS  Article  PubMed  Google Scholar 

  10. Chen Y, Dumont MG, Neufeld JD, Bodrossy L, Stralis-Pavese N, McNamara NP, Ostle N, Briones MJ, Murrell JC (2008) Revealing the uncultivated majority: combining DNA stable-isotope probing, multiple displacement amplification and metagenomic analyses of uncultivated Methylocystis in acidic peatlands. Environ Microbiol 10:2609–2622.

    CAS  Article  PubMed  Google Scholar 

  11. Chen S, Chen J, Chang S, Yi H, Huang D, Xie S, Guo Q (2018) Aerobic and anaerobic methanotrophic communities in urban landscape wetland. Appl Microbiol Biotechnol 102:433–445.

    CAS  Article  PubMed  Google Scholar 

  12. Chen X, Zhu H, Yan B, Shutes B, Xing D, Banuelos G, Cheng R, Wang X (2019) Greenhouse gas emissions and wastewater treatment performance by three plant species in subsurface flow constructed wetland mesocosms. Chemosphere 239:124795.

    CAS  Article  PubMed  Google Scholar 

  13. Conrad R (2009) The global methane cycle: recent advances in understanding the microbial processes involved. Environ Microbiol Rep 1:285–292.

    CAS  Article  PubMed  Google Scholar 

  14. Costello AM, Lidstrom ME (1999) Molecular characterization of functional and phylogenetic genes from natural populations of methanotrophs in lake sediments. Appl Environ Microbiol 65:5066–5074

    CAS  Article  Google Scholar 

  15. Cui H, Su X, Wei S, Zhu Y, Lu Z, Wang Y, Li Y, Liu H, Zhang S, Pang S (2017) Comparative analyses of methanogenic and methanotrophic communities between two different water regimes in controlled wetlands on the Qinghai-Tibetan Plateau, China. Curr Microbiol 75:484–491.

    CAS  Article  PubMed  Google Scholar 

  16. Dakora FD, Phillips DA (2002) Root exudates as mediators of mineral acquisition in low-nutrient environments. Plant Soil 245:35–47.

    CAS  Article  Google Scholar 

  17. Dhote S, Dixit S (2009) Water quality improvement through macrophytes—a review. Environ Monit Assess 152:149–153.

    CAS  Article  PubMed  Google Scholar 

  18. Duan XN, Wang XK, Mu YJ, Ouyang ZY (2005) Seasonal and diurnal variations in methane emissions from Wuliangsu Lake in arid regions of China. Atmos Environ 39:4479–4487.

    CAS  Article  Google Scholar 

  19. Eller G, Frenzel P (2001) Changes in activity and community structure of methane-oxidizing bacteria over the growth period of rice. Appl Environ Microbiol 67:2395–2403.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Esson KC, Lin X, Kumaresan D, Chanton JP, Murrell JC, Kostka JE (2016) Alpha- and gammaproteobacterial methanotrophs codominate the active methane-oxidizing communities in an acidic boreal peat bog. Appl Environ Microbiol 82:2363–2371.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Fausser AC, Hoppert M, Walther P, Kazda M (2012) Roots of the wetland plants Typha latifolia and Phragmites australis are inhabited by methanotrophic bacteria in biofilms. Flora 207:775–782.

    Article  Google Scholar 

  22. Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, Cole JR (2013) FunGene: the functional gene pipeline and repository. Front Microbiol 4:291.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Griffiths RI, Whiteley AS, O’Donnell AG, Bailey MJ (2000) Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl Environ Microbiol 66:5488–5491.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Grunfeld S, Brix H (1999) Methanogenesis and methane emissions: effects of water table, substrate type and presence of Phragmites australis. Aquat Bot 64:63–75.

    CAS  Article  Google Scholar 

  25. Hanson RS, Hanson TE (1996) Methanotrophic bacteria. Microbiol Rev 60:439–471

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Heyer J, Galchenko VF, Dunfield PF (2002) Molecular phylogeny of type II methane-oxidizing bacteria isolated from various environments. Microbiology 148:2831–2846.

    CAS  Article  PubMed  Google Scholar 

  27. Hornibrook ERC, Bowes HL, Culbert A, Gallego-Sala AV (2009) Methanotrophy potential versus methane supply by pore water diffusion in peatlands. Biogeosciences 6:1490–1504

    Article  Google Scholar 

  28. Hutchens E, Radajewski S, Dumont MG, McDonald IR, Murrell JC (2004) Analysis of methanotrophic bacteria in Movile Cave by stable isotope probing. Environ Microbiol 6:111–120

    CAS  Article  Google Scholar 

  29. Kits KD, Campbell DJ, Rosana AR, Stein LY (2015) Diverse electron sources support denitrification under hypoxia in the obligate methanotroph Methylomicrobium album strain BG8. Front Microbiol 6:1072.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Laanbroek HJ (2010) Methane emission from natural wetlands: interplay between emergent macrophytes and soil microbial processes A mini-review. Ann Bot-London 105:141–153.

    CAS  Article  Google Scholar 

  31. Lin JL, Radajewski S, Eshinimaev BT, Trotsenko YA, McDonald IR, Murrell JC (2004) Molecular diversity of methanotrophs in Transbaikal soda lake sediments and identification of potentially active populations by stable isotope probing. Environ Microbiol 6:1049–1060.

    CAS  Article  PubMed  Google Scholar 

  32. Liu J, Bao Z, Cao W, Han J, Zhao J, Kang Z, Wang L, Zhao J (2020) Enrichment of type I methanotrophs with nirs genes of three emergent macrophytes in a eutrophic wetland in China. Microbes Environ.

    Article  PubMed  Google Scholar 

  33. Lontoh S, DiSpirito AA, Krema CL, Whittaker MR, Hooper AB, Semrau JD (2000) Differential inhibition in vivo of ammonia monooxygenase, soluble methane monooxygenase and membrane-associated methane monoxygenase by phenylacetylene. Environ Microbiol 2:485–494.

    CAS  Article  PubMed  Google Scholar 

  34. Luke C, Frenzel P (2011) Potential of pmoA amplicon pyrosequencing for methanotroph diversity studies. Appl Environ Microbiol 77:6305–6309.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Maltais-Landry G, Maranger R, Brisson J, Chazarenc F (2009) Greenhouse gas production and efficiency of planted and artificially aerated constructed wetlands. Environ Pollut 157:748–754.

    CAS  Article  PubMed  Google Scholar 

  36. Martin-Laurent F, Philippot L, Hallet S, Chaussod R, Germon JC, Soulas G, Catroux G (2001) DNA extraction from soils: old bias for new microbial diversity analysis methods. Appl Environ Microbiol 67:2354–2359.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. McDonald IR, Bodrossy L, Chen Y, Murrell JC (2008) Molecular ecology techniques for the study of aerobic methanotrophs. Appl Environ Microbiol 74:1305–1315.

    CAS  Article  PubMed  Google Scholar 

  38. Pietrangelo L, Bucci A, Maiuro L, Bulgarelli D, Naclerio G (2018) Unraveling the composition of the root-associated bacterial microbiota of phragmites australis and Typha latifolia. Front Microbiol 9:1650.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Qiu Q, Conrad R, Lu Y (2009) Cross-feeding of methane carbon among bacteria on rice roots revealed by DNA-stable isotope probing. Environ Microbiol Rep 1:355–361.

    CAS  Article  PubMed  Google Scholar 

  40. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Semrau JD, DiSpirito AA, Yoon S (2010) Methanotrophs and copper. FEMS Microbiol Rev 34:496–531.

    CAS  Article  PubMed  Google Scholar 

  42. Serrano-Silva N, Sarria-Guzman Y, Dendooven L, Luna-Guido M (2014) Methanogenesis and Methanotrophy in Soil: a Review. Pedosphere 24:291–307.

    CAS  Article  Google Scholar 

  43. Shiau Y-J, Cai Y, Jia Z, Chen C-L, Chiu C-Y (2018a) Phylogenetically distinct methanotrophs modulate methane oxidation in rice paddies across Taiwan. Soil Biol Biochem 124:59–69.

    CAS  Article  Google Scholar 

  44. Shiau YJ, Cai Y, Lin YT, Jia Z, Chiu CY (2018b) Community structure of active aerobic methanotrophs in red mangrove (Kandelia obovata) soils under different frequency of tides. Microb Ecol 75:761–770.

    Article  PubMed  Google Scholar 

  45. Shinoda R, Bao Z, Minamisawa K (2019) CH4 oxidation-dependent 15N2 fixation in rice roots in a low-nitrogen paddy field and in Methylosinus sp. strain 3S-1 isolated from the roots. Soil Biol Biochem 132:40–46.

    CAS  Article  Google Scholar 

  46. Stein LY, Klotz MG (2011) Nitrifying and denitrifying pathways of methanotrophic bacteria. Biochem Soc T 39:1826–1831.

    CAS  Article  Google Scholar 

  47. Sun HY, Zhang CB, Song CB, Chang SX, Gu BJ, Chen ZX, Peng CH, Chang J, Ge Y (2013) The effects of plant diversity on nitrous oxide emissions in hydroponic microcosms. Atmos Environ 77:544–547.

    CAS  Article  Google Scholar 

  48. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. Vymazal J (2013) Emergent plants used in free water surface constructed wetlands: a review. Ecol Eng 61:582–592.

    Article  Google Scholar 

  50. Wang Y, Yang H, Ye C, Chen X, Xie B, Huang C, Zhang J, Xu M (2013) Effects of plant species on soil microbial processes and CH4 emission from constructed wetlands. Environ Pollut 174:273–278.

    CAS  Article  PubMed  Google Scholar 

  51. Wu LH, Wang XY, Li JY, Yu RH, Wang LX, Zhao J (2017) Tag-encoded pyrosequencing analysis of bacterial communities in the sediments of a eutrophic lake on the inner Mongolian Plateau. J Biobased Mater Bio 11:271–277.

    CAS  Article  Google Scholar 

  52. Yoshida N, Iguchi H, Yurimoto H, Murakami A, Sakai Y (2014) Aquatic plant surface as a niche for methanotrophs. Front Microbiol.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Yu R, Li C, Liu T, Xu Y (2004) The environment evolution of wuliangsuhai wetland. J Geogr Sci 14:456–464.

    Article  Google Scholar 

  54. Zhang K, Luo H, Zhu Z, Chen W, Chen J, Mo Y (2018) CH4 flux and methanogen community dynamics from five common emergent vegetations in a full-scale constructed wetland. Environ Sci Pollut R 25:26433–26445.

    CAS  Article  Google Scholar 

  55. Zhao J, Mo Y, Jia Z (2018) Assessment of method-specific bias associated with RNA extractions for metatranscriptomics in three geographically distinct paddy soils with different origin of parent materials. Acta Microbiologica Sinica 58:724–743.

    Article  Google Scholar 

Download references


Authors acknowledge the member’s input of the Microbial Ecology Research Team of School of Ecology and Environments of Inner Mongolia University during the sampling periods and sample analysis.


This study was funded by the National Natural Science Foundation of China Grants 41563009 and 31160129, and the Science and Technology Major Project on Lakes of Inner Mongolia Grant ZDZX2018054.

Author information




ZB and JZ conceived and designed the research. JZ, ZB, JC, JL and WC collected samples. JC, ZW and WC conducted molecular biology experiments. SZ and JL measured the physical and chemical properties of sediment. JC and ZW analyzed data. JC and ZB wrote the manuscript. ZB and JZ are joint corresponding authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Ji Zhao or Zhihua Bao.

Ethics declarations

Ethics approval and consent to participate

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent for publication

All authors gave their consent for publication.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1: Table S1. physicochemical characteristics of sediments and three plants roots in Wuliangsuhai wetland. Table S2. The most closely related pmoA sequences of OUTs in phylogenetic tree. Fig. S1. Alpha diversity of aerobic methanotroph calculation based on pmoA gene. The OTUs richness and OTUs evenness of root microbiotas is shown by chao (a) and Simpson index (b). The red boxplot based on DNA; the green boxplot based on cDNA. Error bars represent the standard error of the mean. Asterisks denote statistically significant differences between samples (**p < 0.01, *p < 0.05). Bars with the different letter (a, b or c) within a panel are significantly different between groups (p < 0.05). Fig. S2. Comparison of differences between groups is shown by ANOSIM. Distance calculated on OUT level of each sample groups.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cui, J., Zhao, J., Wang, Z. et al. Diversity of active root-associated methanotrophs of three emergent plants in a eutrophic wetland in northern China. AMB Expr 10, 48 (2020).

Download citation


  • Active methanotrophs
  • High-throughput sequencing
  • Emergent plant
  • Microhabitat