Community Succession of Methanotrophic Bacteria Based on pmoA Gene in Rice Fields
COMMUNITY SUCCESSION OF METHANOTROPHIC
BACTERIA BASED ON pmoA GENE IN RICE FIELDS
HENDRI SUTANTO
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014
THE THESIS STATEMENT AND SOURCES OF
INFORMATION ALONG COPYRIGHT DEVOLUTION
I hereby declare that the thesis entitled “Community succession of
methanotrophic bacteria based on pmoA gene in rice fields” is true of my research
under the guidance of the supervisor committee and has not been submitted in any
form to any college. Sources of information derived or citated from published and
unpublished works from other writers have been mentioned in the text and listed
in the references in the end of this thesis.
I hereby assign the copyright of my thesis to Bogor Agricultural
University.
Bogor, August 2014
Hendri Sutanto
NIM G351130356
RINGKASAN
HENDRI SUTANTO. Suksesi Komunitas Bakteri Metanotrof Berdasarkan Gen
pmoA di Lahan Sawah. Dibimbing oleh IMAN RUSMANA dan NISA
RACHMANIA MUBARIK.
Bakteri metanotrof memainkan peranan penting dalam oksidasi gas metan di
lahan sawah. Keragaman komunitas bakteri metanotrof di lahan sawah erat
kaitannya dengan emisi metan yang akhirnya dilepas ke atmosfer setelah
diproduksi oleh arkea metanogen. Isolat bakteri metanotrof BGM 1, BGM 5,
BGM 9, dan SKM 14 diketahui mampu mengoksidasi gas metan di lahan sawah.
Suksesi komunitas bakteri metanotrof di lahan sawah seiring bertambahnya waktu
selama masa tanam padi di lahan sawah dan penggunaan piranti lunak DNDC 9.5
untuk mengestimasi emisi gas metan dari lahan sawah masih jarang dilakukan.
Oleh karena itu penelitian ini bertujuan mempelajari suksesi komunitas bakteri
metanotrof berdasarkan gen pmoA di lahan sawah serta melakukan perbandingan
pengukuran aktivitas emisi gas metan secara aktual dan estimasi menggunakan
piranti lunak DNDC 9.5.
Isolat bakteri metanotrof BGM 1, BGM 5, BGM 9, dan SKM 14 digunakan
sebagai kultur pupuk hayati di sawah. Perlakuan yang digunakan ialah kontrol,
celup, dan tidak celup. Pupuk NPK diberikan pada perlakuan kontrol. Pada
perlakuan celup diberikan pupuk NPK dengan dosis yang lebih rendah serta kultur
bakteri metanotrof yang digunakan untuk merendam rumpun padi sebelum
ditanam di sawah. Pupuk NPK dengan dosis yang sama dengan perlakuan celup
serta kultur bakteri metanotrof yang disebar langsung ke tanah sawah dilakukan
pada perlakuan tidak celup.
DNA genom dari sampel tanah sawah pada setiap perlakuan yang diambil
setiap 30 hari setelah tanam diisolasi menggunakan PowerSoilTM Soil DNA
Isolation Kit, selanjutnya gen pmoA diamplifikasi menggunakan PCR. Analisis
suksesi komunitas bakteri metanotrof dilakukan menggunakan teknik metagenom
DGGE (Denaturant Gradient Gel Electrophoresis). Pita yang dipotong dari gel
DGGE diamplifikasi kembali dengan PCR kemudian disekuen dan dianalisis
menggunakan piranti lunak MEGA 5 untuk mengkonstruksi pohon filogenetik.
Analisis perbandingan emisi gas metan pada setiap perlakuan dilakukan dengan
melakukan pengukuran aktual di lahan sawah setiap 30 hari setelah tanam serta
pengukuran estimasi menggunakan piranti lunak DNDC 9.5.
Berdasarkan analisis DGGE untuk gen pmoA, terlihat adanya suksesi
komunitas bakteri pengoksidasi metan baik pada fase vegetatif maupun generatif.
Tiga pita berhasil diamplifikasi menggunakan PCR dan menunjukkan
kekerabatannya dengan uncultured bacterium pmoA gene clone 18f_9H (identitas
maksimum 99%), uncultured bacterium pmoA gene clone 16-2000yo-B (identitas
maksimum 98%), dan uncultured bacterium pmoA gene clone 32-2000yo-B
(identitas maksimum 96%). Berdasarkan konstruksi pohon filogenetik, satu pita
menunjukkan kedekatan dengan Methylocystis sp. galur H9a sementara dua pita
lainnya menunjukkan kedekatan dengan Methylococcus capsulatus galur BL4.
Hasil analisis emisi gas metan menunjukkan emisi metan terendah diperoleh pada
perlakuan celup diikuti perlakuan tidak celup dan kontrol. Perbandingan
pengukuran emisi metan secara aktual dan estimasi menunjukkan pola yang
menyerupai yaitu emisi gas metan menurun dari fase vegetatif ke fase generatif.
Kata kunci : DGGE, gas metan, lahan sawah, metanotrof, pmoA.
SUMMARY
HENDRI SUTANTO. Microbial Community Succession of Methanotrophic
Bacteria Based on pmoA Gene in Rice Fields. Supervised by IMAN RUSMANA
and NISA RACHMANIA MUBARIK.
Methanotrophic bacteria play a role in methane oxidation in rice fields.
Methanotrophic bacterial diversity in rice fields was closely related to methane
emission whom released into the atmosphere after being produced by methanogen
archaea. Methanotrophic bacterial isolates of BGM 1, BGM 5, BGM 9, and SKM
14 were known as methane oxidizer in rice fields. Microbial community
succession of methanotrophic bacteria due to the time change during planting
season and the methane emission measurement comparison of actual and
estimation data in rice fields are still rare. Therefore, this research were aimed to
study the microbial community succession of methanotrophic bacteria based on
pmoA gene and to compare actual and estimation measurement of methane
emission in rice fields using DNDC 9.5 software.
Methanotrophic bacterial isolates of BGM 1, BGM 5, BGM 9, and 14
SKM were used as biofertilizer. The treatments used were control, soaking, and
spread treatment. NPK fertilizer was given in the control treatment. The less dose
of NPK fertilizer and methanotrophic bacterial isolates which used to soak the rice
clumps before transferred to the patch were conducted at soaking treatment. NPK
fertilizer with the same dose to soaking treatment and methanotrophic bacterial
isolates which spread directly to the patch were conducted at spread treatment.
DNA genome of rice field soil samples which taken at each treatment
every 30 days after planting were isolated using PowerSoilTM Soil DNA Isolation
Kit, furthermore pmoA gene was amplified using PCR. Analysis of microbial
community succession methanotrophic bacteria was performed using DGGE
(Denaturant Gradient Gel Electrophoresis). The bands which extracted from
DGGE gel were amplified by PCR then were sequenced and analyzed using
MEGA 5 software to construct the phylogenetic tree. Methane emission in each
treatment was analyzed by comparing the actual and estimation emission using
DNDC 9.5 software.
Based on DGGE analysis of pmoA gene, there was succession of methaneoxidizing bacterial community on both vegetative and generative phase. Three
bands were amplified successfully using PCR and showed similarity to uncultured
bacterium clone pmoA gene 18f_9H (99% of maximum identity), uncultured
bacterium clone pmoA gene 16-2000yo-B (98% of maximum identity), and
uncultured bacterium clone pmoA gene 32-2000yo-B (96% of maximum identity).
Based on phylogenetic tree construction, one band was clustered to Methylocystis
sp. strain H9a while the other two bands were clustered to Methylococcus
capsulatus strain BL4. The methane gas emission analysis showed the lowest
emission was obtained at soaking treatment followed by spread and control
treatment respectively. Comparison of actual and estimation measurement of
methane emission showed the similar pattern which methane emission was
obtained decreased from the vegetative phase to the generative phase.
Keywords: DGGE, methane gas, methanotrophic, pmoA, rice fields.
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COMMUNITY SUCCESSION OF METHANOTROPHIC
BACTERIA BASED ON pmoA GENE IN RICE FIELDS
HENDRI SUTANTO
Thesis
as one of the requirements to obtain the degree
Master of Science
on
Microbiology Major
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014
Examiner of Beyond Commission on Thesis Examination : Dr Sulistijorini MSi
Thesis Title
Name
NIM
: Community Succession of Methanotrophic Bacteria Based on
pmoA Gene in Rice Fields
: Hendri Sutanto
: G351130356
Approved by
Supervisor Commission
Dr Ir Iman Rusmana MSi
Head
Dr Nisa Rachmania Mubarik MSi
Member
Discovered by
Head of Microbiology
Major
Prof Dr Anja Meryandini, MS
Date of Examination: 17 July 2014
Dean of Graduate School
Dr Ir Dahrul Syah, MScAgr
Date of Graduation:
FOREWORD
Praise and gratitude to God for all His gifts so this thesis has been
completed successfully. The theme chosen in this research was microbial diversity
succession.
The
title
of
this
thesis
research
is
“Community Succession of Methanotrophic Bacteria Based on pmoA Gene in
Rice Fields”. The author thanks to Dr.Ir.Iman Rusmana, M.Si and Dr Nisa
Rachmania Mubarik M.Si as supervisor commission in this thesis research and
great thanks to Dr Sulistijorini MSi as examiner beyond commission on thesis
examination. The author also thanks the staff of the Laboratory of Microbiology
and Intergrated Laboratory, Department of Biology IPB.
During the college and research, the author thanks to the author family,
Alrhena, Kenny, Biology Batch 46, the first batch of fast-track student, and all of
friends on Microbiology Major Batch 2012-Graduate School of IPB who have
helped the author during this research. The author wished this research can be
beneficial for the knowledge development based on environmental microbiology
and also the environment quality improvement around the world.
Bogor, August 2014
Hendri Sutanto
TABLE OF CONTENTS
LIST OF TABLE
LIST OF FIGURE
LIST OF APPENDIX
INTRODUCTION
Background
Issue Formularization
Aim of Research
Benefit of Research
Scope of Research
LITERATURE REVIEW
Atmospheric Methane
Methanotrophic Bacteria
PCR and DGGE
DNDC Model
METHOD
Research Framework
Time and Place of Research
Bacterial Isolates Preparation
Rice Planting
Soil Characteristic Analysis
DNA Extraction and Quantification
PCR Amplification
DGGE Analysis of the pmoA Gene
DGGE Bands Extraction and Re-PCR
Methane Gas Emission Measurement
x
x
x
1
1
2
2
2
2
3
3
3
5
6
6
6
7
7
8
8
8
8
9
9
9
RESULT AND DISCUSSION
Result
Soil Characteristic
DNA Concentration and PCR Amplification
DGGE Profile
The Phylogenetic Tree
Methane Gas Emission
10
10
10
10
11
11
13
Discussion
CONCLUSION AND RECOMMENDATION
Conclusion
Recommendation
REFERENCES
APPENDIX
BIOGRAPHY
14
17
17
17
19
22
35
LIST OF TABLE
1 Characteristic of type I, II, and X of methanotroph
2 DNA template concentration
3 Comparison of estimation and actual result of CH4 emission
5
10
14
LIST OF FIGURE
1
2
3
4
5
6
Pathways for the oxidation of methane and assimilation of formaldehyde
Research framework
PCR amplification of the pmoA gene
DGGE band profile
The phylogenetic tree constructed with pmoA gene sequences
Flux rate of CH4
4
7
11
12
12
13
LIST OF APPENDIX
1
2
3
4
Sequencing result of all bands excised from DGGE gel
Soil texture classification by USDA
BLAST result of all bands
Notification of acceptance on “Advances in Environmental Biology”
23
32
32
33
INTRODUCTION
Background
Methane gas (CH4) in the atmosphere showed increasing concentration
from 715 ppb to 1732 ppb in 1970 till early 1990 and has increased again as of
1774 ppb in 2005 (Qin et al. 2007). CH4 gas can absorb infrared radiation 25
times more effective when compared to CO2. Based on data in United States
Environmental Protection Agency's for Greenhouse Gas Report in 2010,
Indonesia is the seventh largest contributor of methane gas emission from rice
fields in the world. Setyanto (2004) reported that flooding activity in rice fields
and other wetlands are one of the sources of the emergence of CH4 emission.
Flooded soil conditions caused reductive condition in the soil then the growth of
methanogens increased. Along with the increase in rice production, CH4 emission
will also be increased if the management is not accompanied by efforts to reduce
the emission. The utilization of methanotrophic bacteria can be used to reduce the
emission.
Methanotrophs are unique bacteria which can use methane as their sole
source of carbon and energy. The ability of methanotrophs to oxidize methane is
due to the activity of the enzyme methane monooxygenase. There are two distinct
forms of this enzyme viz the cytoplasmic soluble methane monooxygenase
(sMMO) and the membrane-bound particulate methane monooxygenase (pMMO)
(Murrell et al. 2000). The pMMO is commonly found in all methanotrophs rather
than sMMO. The pMMO is coded by three contiguous genes viz pmoA, pmoB,
and pmoC. The pmoA gene is highly conserved and often used as a functional
marker for analyzing methanotrophs in the environment (Hakemian and
Rosenzwelg 2007). Based on their cell morphology, ultrastructure, phylogeny, and
metabolic pathways, methanotrophs can be divided into three taxonomic groups:
type I, type II, and type X. Type I methanotrophs include the genera
Methylobacter,
Methylomicrobium,
Methylomonas,
Methylocaldum,
Methylosphaera,
Methylothermus,
Methylosarcina,
Methylohalobius,
Methylosoma, and Methylococcus, which belong to the gamma subdivision of the
Proteobacteria. The type II methanotrophs include the genera Methylocystis,
Methylosinus, Methylocella, and Methylocapsa are in the alpha subdivision of the
Proteobacteria (Hanson and Hanson 1996). Methylococcus capsulatus is the
species of type X methanotrophs.
The CH4 oxidation activity in rice fields is closely related to the their
diversity, however ± 99% of bacteria in environment is classified as unculturable
bacteria (D'Onofrio 2010). It makes the bacteria could not be isolated in artificial
media but can be determined by using the metagenome technique. One technique
that can be used is DGGE (Denaturing Gradient Gel Electrophoresis). The
introduction of DGGE to microbial ecology provides a valuable molecular
fingerprinting technique for studying microbial community structure. DGGE
facilitates separation of mixtures of PCR-amplified gene fragments based on
2
sequence differences and allows large numbers of samples to be analyzed
simultaneously. Eventhough this technique is ideally suited for monitoring the
dynamics of microbial communities influenced by environmental changes
escpecially in rice fields but the microbial community succesion of
methanotrophic bacteria which related to pmoA gene in rice fields based on the
time change during the crop phase is still unknown. The major aims of this
research were to study microbial community succesion and also to measure the
CH4 emission in rice fields.
Issue Formularization
1. Methanotrophic bacteria can be used as methane oxidizer agent in rice fields.
2. There is community succession of methanotrophic bacteria in rice fields due to
the time change during crop season.
3. There is change of methane emission from vegetative to generative phase.
4. DNDC 9.5 software can be used to estimate the methane emission in rice
fields.
Aim of Research
The aims of this research were to study community succession of
methanotrophic bacteria based on pmoA gene using DGGE technique and also to
measure the CH4 emission in rice fields.
Benefit of Research
Analysis of the community succession of methanotrophic bacteria in rice
fields was expected to provide information about the diversity of methanotrophic
bacteria which obtained due to the time change during crop season in rice fields.
Application of methanotrophic bacterial isolates were also expected to reduce
methane emission from rice fields as well as to make the environmentally friendly
farming system. The usage of DNDC 9.5 software was expected to be a good
estimation of the methane emission when compared to the actual emission from
rice fields.
Scope of Research
The scope of this research included the DGGE analysis of community
succession of methanotrophic bacteria during the crop season and its relation to
the methane emission from rice fields. The methane emission analysis included
the comparison of actual measurement in rice fields and estimation measurement
using DNDC 9.5 software.
3
LITERATURE REVIEW
Atmospheric Methane
Methane gas is the second most important anthropogenic greenhouse gas
in the atmosphere and responsible for 20–30% of total greenhouse gas radiative
forcing since the industrial revolution (IPCC 2007). Methane is currently about
200 times less concentrated in the atmosphere than carbon dioxide, but each
molecule of methane is 25 times more potent in terms of heat-holding capacity
(Lelieveld et al. 1998). Due to changes in human activity and land use in the
world, both carbon dioxide and methane began to increase around 150 years ago,
as the industrial phase began. Maxfield et al. 2006 and Degelmann et al. 2010
stated since that beginning, atmospheric methane concentrations have increased
approximately 150% from a pre-industrial mixing ratio of about 0.7 ppm to 1.8
ppm. Methane sources are various but their number and magnitude appear to be
on the rise, while methane sinks are more uncertain. The largest global methane
sources are natural and constructed wetlands, which contribute around 33% of
annual emissions (IPCC 2007). Anthropogenic sources including rice fields,
domesticated animals, landfills, fossil fuel acquisition and burning, as well as
biomass use for energy and agriculture, total at least 307 Tg methane yr−1, which
could be over 60% of total emissions (Wang et al. 2004). There may be more
sources than have been accounted for, as methane has also been found to be
produced aerobically in the ocean (Karl et al. 2008).
Methanotrophic Bacteria
Exchange of methane within the soil and atmosphere is regulated by two
groups of microorganisms which known as methanogen and methanotrophs. The
disparate environmental requirements of these two groups, particularly oxygen
concentration, temperature, water content, and nutrient availability, determine the
net methane flux of a given ecosystem. Methanogenic archaea as methane
producer active mainly in anaerobic conditions, produce methane as a metabolic
byproduct and are the main biological source of methane in natural systems,
landfills, and agriculture. Methanotrophic bacteria as methane oxidizer are active
mainly in aerobic conditions and derive energy and carbon from the oxidation of
methane (Hanson & Hanson 1996).
Methanotrophic bacteria or methanotrophs are a subset of a physiological
group of bacteria known as methylotrophs. Methanotrophic bacteria are unique in
their ability to utilize methane as a sole carbon and energy source. Methylotrophic
bacteria are those aerobic bacteria that utilize one-carbon compounds more
reduced than formic acid as sources of carbon and energy and assimilate
formaldehyde as a major source of cellular carbon (King & Nanba 2008).
Methylotrophic bacteria utilize a variety of different one-carbon compounds
including methane, methanol, methylated amines, halomethanes, and methylated
compounds containing sulfur. Some cleave methyl groups from organic
4
compounds including choline or the pesticide carbofuran and utilize them as sole
sources of carbon and energy. Bacteria that utilize formate, cyanide, and carbon
monoxide have different modes of metabolism including pathways for the
assimilation of one-carbon units (Hanson & Hanson 1996). The use of enzymes
known as methane monooxygenases (MMO) to catalyze the oxidation of methane
to methanol is a defining characteristic of methanotrophs. There are two forms of
MMO viz particulate mono oxygenase (pMMO) and soluble monooxgenase
(sMMO). The pMMO is encoded by three contigous gene : pmoA, pmoB, and
pmoC; however, pmoA gene is highly conserved and often used as analyzing
marker for methanotrophs. Figure 1 shows the pathways for the oxidation of
methane and assimilation of formaldehyde.
Figure 1 Pathways for the oxidation of methane and assimilation of formaldehyde
The methanotrophs were separated into three types based on the criteria
shown in Table 1. Type I includes the genera Methylomonas and Methylobacter.
Type II includes the genera Methylosinus and Methylocystis. Type X was added
to accommodate methanotrophs similar to Methylococcus capsulatus that, like
type I methanotrophs, utilized ribulose monophosphate (RuMP) as the primary
pathway for formaldehyde assimilation. Type X methanotrophs were
distinguished from type I methanotrophs because they also possessed low levels
of enzymes of the serine pathway ribulosebisphosphate carboxylase, an enzyme
present in the Calvin-Benson cycle. They grew at higher temperatures than type I
and type II methanotrophs and possessed DNA with a higher moles percent G-C
content than that of most type I methanotrophs.
5
Table 1 Characteristics of Type I, II, and X of Methanotrophs
Characteristics
Cell morphology
Type I
Short rods, some
cocci or
elipsoids
Type II
Cresecentshaped rods
Type X
Cocci, often found
as pairs
Growth at 45 oC
No
No
Yes
G-C content of DNA
(mol %)
49-60
62-67
59-65
Nitrogen fixation
No
Yes
Yes
RuMP pathway
present
Yes
No
Yes
Serine pathway
present
No
Yes
Sometimes
Proteobacterial
subdivision
Gamma
Alpha
Gamma
PCR and DGGE
PCR (Polymerase Chain Reaction) is a method for making copies of a
specific segment of DNA. Initial material for PCR is a double stranded of DNA
which contain nucleotide sequences that are targeted to be copied. Primers used
for the PCR process is a synthetic single-stranded DNA molecule that is short,
which is complementary to the ends of the target DNA that determines a particular
DNA segment to be amplified (Campbell et al. 2002). The principle of PCR
includes three phases in a cycle. The first stage is the denaturation stage. It takes
place at high temperatures between 92-96 °C and intended to separate the doublestranded DNA into single-stranded chain (single strand). The separation of bond is
caused by high temperatures which trigger the rupture of hydrogen bonds in DNA.
After the DNA becomes single-stranded chain, then the DNA is ready to be used
as template for primer set (short chain of nucleotide or oligonucleotide which the
sequence of its nitrogen bases are known). The second stage is annealing which
takes place at temperatures between 42-65 °C. Primer attaches to the DNA
template whom are complementary with its nitrogen bases sequence. This
attachment stage is very specific and wrong temperature will not lead the primer
attach to the target and result in a attachment to any place. The duration of this
stage usually depends on the primers used. The third stage is the elongation. The
temperature for this stage depends on the type of DNA polymerase used in the
reaction. The enzyme used is DNA taq-polymerase. The enzyme relatively works
more stable at high temperatures and is not denatured quickly which generally
performed at 72 °C. The cycle goes over and over again until the target sequence
has been duplicated many times. Until approximately minimum 20 cycles, almost
all DNA molecules generated will consist of appropriate target sequence
(Campbell et al. 2002). Therefore it happens repeatedly then will produce an
abundance of DNA which will be resulted as amplicons or called as PCR product.
6
This amplicon can be used for various purposes in molecular analysis (Hapwood
et al. 1985 ; Sivakumar 2001).
Denaturing gradient gel electrophoresis (DGGE) is a commonly used
technique in molecular biology and has become a staple of environmental
microbiology for characterization of population structure and dynamics. DGGE
analyses are employed for the separation of double-stranded DNA fragments that
are identical in length, but differ in sequence (Muyzer and Smalla 1998). In
practice, this refers to the separation of DNA fragments produced via PCR
amplification. The technique exploits (among other factors) the difference in
stability of G-C pairing (three hydrogen bonds per pairing) as opposed to A-T
pairing (two hydrogen bonds). A mixture of DNA fragments of different sequence
are electrophoresed in an acrylamide gel containing a gradient of increasing DNA
denaturants. In general, DNA fragments richer in GC will be more stable and
remain double-stranded until reaching higher denaturant concentrations. Doublestranded DNA fragments migrate better in the acrylamide gel, while denatured
DNA molecules become effectively larger and slow down or stop in the gel. In
this manner, DNA fragments of differing sequence can be separated in an
acrylamide gel.
DNDC Model
The Denitrification-Decomposition (DNDC) model is a process-oriented
computer simulation model of carbon and nitrogen biogeochemistry in
agroecosystems. This model was introduced by Institute for the Study of Earth,
Oceans and Space, University of New Hampshire. The model consists of two
components. The first component, consisting of the soil climate, crop growth and
decomposition sub-models, predicts soil temperature, moisture, pH, redox
potential (Eh) and substrate concentration profiles driven by ecological drivers
(e.g., climate, soil, vegetation and anthropogenic activity). The second
component, consisting of the nitrification, denitrification and fermentation submodels, predicts emissions of carbon dioxide (CO2), methane (CH4), ammonia
(NH3), nitric oxide (NO), nitrous oxide (N2O) and dinitrogen (N2) from the plantsoil systems. Classical laws of physics, chemistry and biology, as well as
empirical equations generated from laboratory studies, have been incorporated in
the model to parameterize each specific geochemical or biochemical reaction. Cai
et al. (2003) stated that the use of DNDC can be valuable to simulate the methane
emission based on the farming management practice used in the rice fields.
METHOD
Research Framework
The research framework (Figure 2) generally included the biofertilizer
preparation, rice planting in the rice fields, PCR amplification, DGGE analysis,
7
and comparison of methane emission based on actual data from the rice fields and
simulated data which analyzed by DNDC 9.5 software.
Figure 2 Research framework
Time and Place of Research
This research was observed from May 2013 to May 2014. The field
research was observed at Cidahu Village, Sukabumi, West Java. The molecular
research was done at Microbiology and Intergrated Laboratory, Department of
Biology, Faculty of Mathematics and Natural Science, Bogor Agricultural
University.
Bacterial Isolates Preparation
Isolates of methanotrophic bacteria BGM 1, BGM 5, BGM 9, and SKM 14
(Hapsari 2008) were purified in Nitrate Mineral Salt (NMS) +1% methanol. Each
8
isolate of bacteria was incubated at 27oC for 3-7 days. One loop of the bacteria
was cultured in 300 ml media. The culture then incubated in rotary shaker (±
37oC) for 10 days until the total of cell was 108 cell ml-1.
Rice Planting
Rice planting was done at Cidahu Village, Sukabumi, West Java. Each
plot measured approximately 150 m2 with 2020 cm of planting distance. Control
treatment used a patch while soaking and spread treatments respectively used four
patch. The rice clump then planted in rice fields. In the control treatment, the
patch was given by 300 kg ha-1 of NPK fertilizer. In the soaking and spread
treatment, the patch was given by 200 kg ha-1 of NPK fertilizer and biofertilizer
but in the soaking treatment the clumps were soaked with biofertilizer liquid for
15 minutes before being transferred to the patch.
Soil Characteristic Analysis
One kilogram of soil samples were taken from each plot then sent to the
Soil Laboratory at Center for Agricultural Land Resources, Soil Research
Institute, Bogor to be analyzed its soil type and characteristic (Appendix 2).
DNA Extraction and Quantification
Soil samples which taken at 30, 60, and 90 days after planting (DAP) from
each treatment were extracted to get the DNA template using PowerSoilTM Soil
DNA Isolation Kit, MO BIO Laboratories, USA. DNA template was quantificated
using Nanodrop.
PCR Amplification
PCR amplification reactions were performed in 25 µL (total volume)
reaction mixtures in 0.2 ml PCR tubes using ESCO DNA thermal cycler. All PCR
amplification of the pmoA gene used the GC-A189f (5’- CCC-CCC-CCC-CCCCGC-CCC-CCG-CCC-CCC-GCC-CCC-GCC-GCC-CGG-NGA-CTG-GGACTT-CTGG-3’) and A682r primer (5’-GAA-SGC-NGA-GAA-GAA-SGC-3’)
(Henckel et al. 1999). Individual reagents and their concentrations of amounts
were as follows : 12.5 µL of Taq DNA polymerase (supplied by KAPA 2G
Robust Hot-Start, KAPA Biosystems USA), 1.25 µL of each primer (20 pmol), 3
µL of DNA template (~100 ng µL-1), and 7 µL of ddH2O. The PCR steps were
optimized and consisted as follows : 95 oC for 1 min, followed by 30 cycles of 95
o
C for 15 s, 59 oC for 15 s, 72 oC for 15 s, and final extension at 72 oC for 10 min.
PCR products were electrophoresed on 1.5% agarose gel at 80 V for 60 min.
Agarose gel then stained using 1% EtBr for 15 min and visualized by Gel Doc
1000 (BIO-RAD, USA).
9
9
DGGE Analysis of the pmoA Gene
PCR products amplified with GC-A189f and A682r primer set were
separated on a gradient of 35-80% (Henckel et al. 1999) using a DCodeTM
Universal Mutation Detection System (BIO-RAD, USA) on 1-mm-thick
polyacrylamide gels (6% [wt/vol]) acrylamide-bisacrylamide [37.5:1]) (BIORAD, USA). The gel was electrophoresed in 1XTAE at 60 oC and 150 V for 6
hours. The gel then stained using SYBR Safe for 60 min and scanned by Gel Doc
1000 (BIO-RAD, USA).
DGGE Bands Extraction and Re-PCR
Samples of individual DGGE bands were excised from ployacrylamide gel
using sterile scalpel then inserted to a microtube consisted of 50 µL of sterile
biquadest. The microtube was incubated at 4 oC overnight and 60 oC for 2 hours
(Coelho et al. 2009; Perez et al. 2014). A 10 µL (~30 ng µL-1) of template was
used for re-PCR reaction. The reaction used A189f (without GC Clamp) and
A682r primer set. The PCR steps were used as the protocol from KAPA 2G
Robust Hot-Start and set as follows : 95 oC for 1 min, followed by 30 cycles of 95
o
C for 15 s, 55 oC for 15 s, 72 oC for 15 s, and final extension at 72 oC for 10 min.
The PCR nested products were sent to 1st BASE Malaysia to be sequenced.
Nucleotide sequences were blasted in www.ncbi.nlm.nih.gov and analyzed to get
the phylogenetic tree using the neighbour joining program in MEGA 5 software.
Methane Gas Emission Measurement
Gas sampling was taken by closing the sample plots in the fields with the
lid box. A box was placed in each treatment. Gas samples then taken from the
inside the box using a 100 ml syringe then stored in vacuum glass bottle. Gas
sampling was conducted on vegetative and generative phase. At 30, 60, and 90
DAP (Days After Planting), the gas was taken then sent to Greenhouse Gas
Laboratory, Environmental Research Institute of Agriculture, Jakenan Pati,
Central Java to be analyzed the CH4 concentration. The CH4 concentration results
converted to obtain the flux rate of gas change emission during the cropping time.
The CH4 emission was also simulated by DNDC 9.5 software to obtain the flux
rate of the gas concentration through the year of cropping.
10
RESULT AND DISCUSSION
Result
Soil Characteristic
Soil characteristic plays an important role on biogeochemical systems in
nature. Based on the United States of Department of Agriculture soil criteria, the
analysis result showed that the composition of the soil with 16% of sand, 33% of
silt and 51% of clay then categorized as clay loam soil. Soil contained 1.81% of
C. Based on the result obtained, it can be seen that the carbon content in soil was
classified as low category. In addition to the soil organic content, the pH value
also played an important role in influencing the development of microorganisms.
Soil pH value obtained was 5.2 and categorized as acidic soil pH.
DNA Concentration and PCR Amplification
Quantification results obtained from the DNA template extraction process
indicated that the DNA concentration of all samples were not much different. The
result showed that the concentration ranged from 24.4 to 28 ng µL-1 (Table 2).
Table 2 DNA template concentration
Control
30
60
90
DNA
concentration
(ng µL -1)
26.0
24.4
24.5
Soaking
30
60
90
28.0
24.4
27.9
1.94
1.95
1.98
Spread
30
60
90
27.4
26.6
27.2
1.99
1.92
1.98
Treatment
Day
A260/A280
1.96
1.97
1.90
The average concentration of DNA template observed was around 26.27 ng
µL-1. The A260/A280 ratio showed the purity of DNA template which around
1.90-1.99. The PCR amplification using A189f and A682r primer set also showed
that all samples were successfully amplificated and after being electrophoresed on
1.5% agarose gel, the PCR product size was around 508 bp (Figure 3).
11
Figure 3 PCR amplification of pmoA gene. K (control), C (soaking treatment),
and TC (spread treatment). The following number 1, 2, 3 : 30, 60, and
90 days after planting respectively
DGGE Profile
The DGGE analysis showed some bands which appeared on the gel (Fig
4a,b). Overall the soaking treatment showed the highest diversity compared than
other treatments. Due to the crop phase, the highest diversity at 30 DAP was
obtained at the soaking treatment followed by control and spread treatment. The
same pattern was also obtained at the soaking treatment which showed the highest
diversity than other treatments at 60 DAP and 90 DAP. The control treatment
showed the second highest diversity at 60 DAP but became the lowest diversity at
90 DAP. There were twelve different bands obtained; however, there were only
four bands (band no 1, 4, 8, and 12) chosen to be sequenced in this research
(Appendix 1). The band no 8 was recalcitrant to be extracted from gel. The blast
result showed the band no 1 had 99% similarity to uncultured bacterium pmoA
gene clone 18f_9H while band no 4 and 12 had 98% and 96% similarity to
uncultured bacterium pmoA gene clone 16-2000yo-B and uncultured bacterium
pmoA gene clone 32-2000yo-B respectively (Appendix 3).
The Phylogenetic Tree
There were five genera of methanotroph used as clustering comparation in
this phylogenetic tree. They were Methylomonas, Methylobacter, Methylococcus,
Methylosinus, and Methylocystis. The band no 1 was closely related to
Methylocystis sp. strain H9a while the band no 4 and 12 were closely related to
the Methylococcus capsulatus strain BL4 (Fig 5). The phylogenetic tree also
showed Methanogen sp. TM 20-1 as outgroup of this clustering result.
12
1
4
(a)
(a)
8
12
(b)
Figure 4 DGGE band profile on the polyacrylamide gel (a) and the band pattern
illustration (b) with the pmoA primer set. The thick bands referred to
the sequenced band. K (control), C (soaking treatment), and TC (spread
treatment). The following number 1, 2, 3 : 30, 60, and 90 days after
planting respectively
98
99
51
51
90
Methylomonas sp. MG30 strain DSM 24973 (HE801217.1)
Methylomonas methanica (U31653.1)
68
Methylobacter psychrophilus (AY945762.1)
Methylobacter sp. HG-1 (AF495888.1)
Uncultured Methylococcus sp. clone Xh pmoA CA65 (JQ038177.1)
Methylococcus capsulatus strain BL4 (AF533666.1)
Band 12
81
99
Band 4
Methylosinus sporium strain ATCC 35069 (FJ713041.1)
Methylosinus sporium (DQ119048.1)
Methylosinus acidophilus (DQ076755.1)
72
Methylosinus sp. B3R (AB636306.1)
Uncultured Methylocystis sp. clone 5 (FJ930096.1)
Methylocystis sp. strain H9a (AJ459027.1)
Band 1
Methanogen sp. TM20-1 (AB062404.1)
0.2
Figure 5
The phylogenetic tree constructed with pmoA gene sequences. The
scale bar indicates the estimated number of base changes per
nucleotide sequence position
13
Methane Gas Emission
The lowest methane gas emission in the generative phase was obtained at
the soaking then followed by the spread and control treatment respectively (Fig 6).
The estimation gas emission rate was increased at the early day of planting phase
then decreased significantly. The rate was showed to increase again till around 35
days after planting then decreased slightly till the generative phase. The soaking
treatment showed 12.29% lower emission of methane while the spread treatment
only showed 2.29% lower emission of methane compared than control treatment
(Table 3).
Figure 6
Flux rate of CH4. Symbols: (
) the estimation result of the control
treatment; ( ) the estimation result of the soaking treatment; ( ) the
estimation result of the spread treatment; (
) the actual result of the
control treatment; ( ) the actual result of the soaking treatment; ( )
the actual result of the spread treatment
The decreasing percentage comparation of methane emission from vegetative
phase to generative phase was not slightly different at all treatments. The control
treatment showed decreasing actual methane emission from 30 DAP to 90 DAP
(19.66 to 12.21 kg C ha-1 day-1). The spread treatment showed decreasing actual
methane emission from 30 DAP to 90 DAP (17.91 to 11.93 kg C ha-1 day-1) while
the lowest actual methane emission obtained at soaking treatment (12.75 to 9.71 kg
C ha-1 day-1). The methane gas oxidation at the soaking treatment showed the better
percentage than spread treatment compared than control treatment. It can be seen
from the methane gas emission which produced from all treatments. The soaking
treatment showed 12.29% lower emission of methane while the spread treatment
only showed 2.29% lower emission of methane compared than control treatment.
14
Table 3 Comparison of estimation and actual result of CH4 emission
Treatment
Day
Estimation result
(kg C ha-1 day-1)
Actual result
(kg C ha-1 day-1)
Total estimation result
(kg C ha-1 yr-1)
Control
30
60
90
20.72±0.75
13.66±0.56
12.32±0.08
19.66
12.87
12.21
2527
Soaking
30
60
90
16.72±2.81
9.60±0.94
8.32±0.98
12.75
10.93
9.71
2256
Spread
30
60
90
19.22±0.93
12.10±0.02
10.82±0.08
17.91
12.13
11.93
2469
Discussion
Soil characteristic plays an important role on biogeochemical systems in
nature. Rice fields soil in Sukabumi, West Java showed that the soil was
categorized as clay loam soil type. The characteristic of clay loam soil type are
slippery structure, easy to be attached, firm ball may be formed, and a clot can be
formed rather easily destroyed. Clay loam soil was known suited for rice fields
because the clay particle acts a storehouse of plant nutrients that are retained by
soil and gradually released for uptake as cations (Thapa 2010). Based on the result
obtained, it can be seen that the carbon content in soil was classified as low
category. Jain et al. (2004) reported the positive correlation between methane
emission and C content in soil. Methane emission will be decreased when the
carbon content in soil decrease. Yagi and Minami (1990), on the contrary,
reported there’s no correlation between methane production and emission with
total carbon in rice fields soil. This statement was also supported by Wang et al.
(1993) which observed no correlation obtained with soil organic carbon content
and suggested that other factors such as bacterial population of the soil were more
important in production and emission of methane. In addition to the soil organic
content, the pH value also played an important role in influencing the
development of microorganisms. Soil pH value was categorized as acidic soil pH;
however, this is an appropriate pH for the growth and development of bacteria.
This result was supported by Jain et al. (2004) who reported that below pH 5.8
and above 8.8, methane emission in the soil suspension was lower compared than
methane emission in a pH range between 6.5 and 7.5.
Quantification results obtained from the DNA template extraction process
indicated that the DNA concentration of all samples were not much different. The
average value was 26.27 ng µL -1. The DNA template was pure enough based on
the spectrophotometer ratio of A260/280. This purity was important due to the
template used in PCR step. PCR amplification will be effective if the DNA
template used is pure. PCR amplification reaction using A189f and A682r primers
set showed that nine samples can be amplified well with amplicon length reached
was around 508 bp. It was similar to the report by Holmes et al. (1999) and
15
McDonald et al. (2008) who stated that the PCR amplification which targeted
pmoA gene using A189f and A682r primer sets will generate amplicon with the
size of 525 bp.
The diversity of microbial communities related to the methane oxidation
based on pmoA functional gene was able to be represented using DGGE
technique. The result showed there were different patterns of microbial
community diversity when all of them compared due to the crop phase and
treatment used. Due to the crop phase, the highest diversity at 30 DAP was
obtained at the soaking treatment followed by control and spread treatment. It
showed the correlation to the lower methane emission obtained at the soaking
treatment than control and spread treatment. The same pattern was also obtained
at the soaking treatment which showed the highest diversity than other treatments
at 60 DAP and 90 DAP. The control treatment showed the second highest
diversity at 60 DAP but became the lowest diversity at 90 DAP. This result was
related to the not-flooded condition on top soil at 60 DAP then the soil condition
was still aerobic enough and supported the growth of indogenous methanotrophic
bacteria. The uncultured bacterium pmoA gene clone 18f_9H and uncultured
bacterium pmoA gene clone 32-2000yo-B were appeared almost constant from 30
DAP to 90 DAP while uncultured bacterium pmoA gene clone 16-2000yo-B
appeared clearly at 60 DAP. It can be seen that the diversity was increased from
vegetative phase to early of generative phase. It was consistent with Das and
Adhya (2012) who stated that the methanogenic archaea population will decline
during the vegetative to generative phase then the methanotrophic bacterial
population will be increased. The increased methanotrophic bacterial population
was also due to the wetland drainage activities in the generative phase so the
wetland conditions become more aerobic and unsuitable habitat for methanogenic
archaea growth.
The blast result of band no 1, 4, and 12 showed the consistency for both
forward and reverse sequence which analyzed in www.ncbi.nlm.nih.gov. The
phylogenetic tree showed the clustering view of samples analyzed and
methanotrophic bacteria. The band no 1 was closely related into type II
methanotroph in genera of Methylocystis while the band no 4 and 12 were closely
related to the type I methanotroph in genera of Methylococcus. These results were
correlated to Hoffman et al. (2002) ; Mohanty et al. (2007) ; Zheng et al. (2008)
who reported that the dominance of Methylobacter, Methylomicrobium,
Methylococcus, Methylocaldium, Methylocystis, and Methylosinus in the rice
fields soil. Horz et al. (2001) also stated the different pattern of methanotroph
bacteria diversity between rice field soil and rice plant roots. Genera of
Methylomonas, Methylobacter, Methylococcus were found in the rice plant roots
while genera of Methylocystis and Methylosinus can be found in both rice fields
soil and rice plant roots. Different diversity of methanotrophic bacteria between
flooded rice fields and drained rice fields also reported by Mayumi et al. (2010)
that flooded rice fields were dominated by Methylocystis and Methylosinus while
drained rice fields were dominated by Methylomonas, Methylosarcina, and
Methylomicrobium.
The observation result of methane gas emission measurement seen to be
higher in the vegetative phase, while the gas emission began to decline in
generative phase. This was similar to Das and Adhya (2012) who stated that
16
methane gas emission increased in the vegetative phase and decrease towards the
generative phase. The soil which begins to dry in the generative phase decreases
the population of methanogenic archaea. This effect will cause the rate of methane
production and emissions go down. Setyanto and Susilawati (2007) also reported
the high methane was produced during the vegetative phase, especially at
maximum tiller, and tended to go down in the generative phase. The decrease was
caused by use of plant photosynthate at the process leading to formation of
flowers womb and also root exudates in soil were low in the generative phase. The
lower content of root exudates was the higher inhibition of methanogenesis
process so that flux of methane was down. Root exudates are organic compounds
consisting of sugars, amino acids, and organic acids as constituent materials
immediately available for methanogenic archaea. In the flooded condition,
methane emission was higher than that in dry condition (Kimura et al. 1991;
Wihardjaka 2005).
The comparison of actual and estimation emission of methane showed the
same trend from 30 DAP to 90 DAP. The methane gas measurement system used
in this research was be able to represent the methane gas emission simulated by
DNDC 9.5 software. Cai et al. (2003) and Li et al. (2003) also stated that the
estimation of methane gas emissions in China and eastern Asia were able to be
represented using DNDC 9.5 software. The decreasing percentage comparison of
methane emission from vegetative phase to generative phase was not slightly
different at all treatments. The methane gas oxidation at the soaking treatment
showed the better percentage than spread treatment compared than control
treatment. It can be seen from the methane gas emission which produced from all
treatments. The soaking treatment showed 12.29% lower emission of methane
while the spread treatment only showed 2.29% lower emission of methane
compared than control treatment. This result could be correlated to the treatment
used. When the methanotrophic bacterial isolates were soaked to the rice clumps
before planting step, the methane oxidation in rice fields will be more effective
than the soil which only given by inorganic fertilizer or spread by bacterial
isolates. It was because there was association between rice plants root and
methanotrophic bacterial isolates after the soaking step. The methane gas which
resulted from methanogenic archaea will be oxidized by the methanotrophic
bacteria who lived near the rice plants root before being transferred into
aerenchyme and atmosphere. The estimation result of total methane emmision at
soaking and spread treatment were 2256 kg C ha-1 yr-1 and 2469 kg C ha-1 yr-1
respectively. It was lower than total methane emission reported by Khalil et al.
(2008) which around 3494 kg C ha-1 yr-1. This result could be correlated to the
methane-oxidizer bacterial diversity on DGGE gel. It was described before that
microbial community at soaking treatment from vegetative phase to generative
phase always clearly showed the highest diversity compared than spread and
control treatment. That higher number of methane-oxidizer microbial community
gave the positive correlation to its methane emission. The higher of diversity
made the lower of methane emission. This result could be also related to the
methanotrophic bacterial isolates which given in this research then the total
methane emission became lower due to the methane oxidation performed by
methanotrophic bacterial isolates. The role of given methanotrophic bacterial
isolates (BGM 1, BGM 5, BGM 9, and SKM 14) showed clear effect in methane
17
gas emission reduction. Pingak et al. (2014) also reported that the application of
methanotrophic bacterial isolates in rice fields were known to decrease the rate of
gas emission compared with soil which only given by the inorganic fertilizer.
Methane gas emission can be reduced by methanotrophic bacterial isolates
because the combination of these bacteria (BGM 1, BGM 5, BGM 9, and SKM
14) were confirmed to have the methane monooxygenase (MMO) enzyme
acitivity which play roles in the methane gas oxidation (Maharani 2011). MMO is
an enzyme that plays role in the oxidation of methane to methanol (Hanson and
Hanson 1996). The control treatment was the highest among others because the
use of inorganic fertilizer with excessive dosing while the lowest emission
obtained at the soaking treatment result can be related to the better methane
oxidation activity performed by methanotrophic bacteria isolates of BGM 1, BGM
5, BGM 9, and SKM 14 which soaked to the rice clumps at the soaking treatment
compared to other treatments.
CONCLUSION AND RECOMMENDATION
Conclusion
The DGGE showed methane-oxidizing microbial community succession
during the vegetative phase to generative phase. The treatment which rice clumps
was soaked by biofertilizer showed both the highest diversity and the lowest
emission than the control treatment as well as the treatment which only spread by
biofertilizer. The blast result of DGGE bands were closely related to uncultured
bacterium pmoA gene clone 18f_9H, uncultured bacterium pmoA gene clone 162000yo-B, and uncultured bacterium pmoA gene clone 32-2000yo-B. The actual
result of methane gas emission measurement in this study had similar trend to the
estimation result analyzed by DNDC 9.5 software. Methane gas emission rate was
decreased from the vegetative phase to the generative phase.
Recommendation
The recommendation from this research are the test in lowland rice fields
to see the effectivity and stability of methanotrophic bacterial isolates used in
reducing methane emission and the methanotrophic bacteria community
succession can also be obtained with RNA analysis.
18
19
REFERENCES
Cai Z, Sawamoto T, Li C, Kang G, Boonjawat J, Mosier A, Wassmann R, Tsuruta
H. 2003. Field validation of the DNDC model for greenhouse gas emissions in
East Asian cropping systems. Glob Biogeochem Cycles. : 17(4) : 1107-1118.
doi:10.1029/2003GB002046.
Campbell NA, Reece JB, Mitchell LG. 2002. Biologi. Edisi Kelima. Lestari R,
translator. Jakarta (ID): Erlangga. Translated from : Biology. Fifth Edition.
Coelho MRR, Marriel IE, Jenkins SN, Langon CV, Seldin L, O’Donnell AG.
2009. Molecular detection and quantification of nifH gene sequences in the
rhizosphere of sorghum (S
BACTERIA BASED ON pmoA GENE IN RICE FIELDS
HENDRI SUTANTO
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014
THE THESIS STATEMENT AND SOURCES OF
INFORMATION ALONG COPYRIGHT DEVOLUTION
I hereby declare that the thesis entitled “Community succession of
methanotrophic bacteria based on pmoA gene in rice fields” is true of my research
under the guidance of the supervisor committee and has not been submitted in any
form to any college. Sources of information derived or citated from published and
unpublished works from other writers have been mentioned in the text and listed
in the references in the end of this thesis.
I hereby assign the copyright of my thesis to Bogor Agricultural
University.
Bogor, August 2014
Hendri Sutanto
NIM G351130356
RINGKASAN
HENDRI SUTANTO. Suksesi Komunitas Bakteri Metanotrof Berdasarkan Gen
pmoA di Lahan Sawah. Dibimbing oleh IMAN RUSMANA dan NISA
RACHMANIA MUBARIK.
Bakteri metanotrof memainkan peranan penting dalam oksidasi gas metan di
lahan sawah. Keragaman komunitas bakteri metanotrof di lahan sawah erat
kaitannya dengan emisi metan yang akhirnya dilepas ke atmosfer setelah
diproduksi oleh arkea metanogen. Isolat bakteri metanotrof BGM 1, BGM 5,
BGM 9, dan SKM 14 diketahui mampu mengoksidasi gas metan di lahan sawah.
Suksesi komunitas bakteri metanotrof di lahan sawah seiring bertambahnya waktu
selama masa tanam padi di lahan sawah dan penggunaan piranti lunak DNDC 9.5
untuk mengestimasi emisi gas metan dari lahan sawah masih jarang dilakukan.
Oleh karena itu penelitian ini bertujuan mempelajari suksesi komunitas bakteri
metanotrof berdasarkan gen pmoA di lahan sawah serta melakukan perbandingan
pengukuran aktivitas emisi gas metan secara aktual dan estimasi menggunakan
piranti lunak DNDC 9.5.
Isolat bakteri metanotrof BGM 1, BGM 5, BGM 9, dan SKM 14 digunakan
sebagai kultur pupuk hayati di sawah. Perlakuan yang digunakan ialah kontrol,
celup, dan tidak celup. Pupuk NPK diberikan pada perlakuan kontrol. Pada
perlakuan celup diberikan pupuk NPK dengan dosis yang lebih rendah serta kultur
bakteri metanotrof yang digunakan untuk merendam rumpun padi sebelum
ditanam di sawah. Pupuk NPK dengan dosis yang sama dengan perlakuan celup
serta kultur bakteri metanotrof yang disebar langsung ke tanah sawah dilakukan
pada perlakuan tidak celup.
DNA genom dari sampel tanah sawah pada setiap perlakuan yang diambil
setiap 30 hari setelah tanam diisolasi menggunakan PowerSoilTM Soil DNA
Isolation Kit, selanjutnya gen pmoA diamplifikasi menggunakan PCR. Analisis
suksesi komunitas bakteri metanotrof dilakukan menggunakan teknik metagenom
DGGE (Denaturant Gradient Gel Electrophoresis). Pita yang dipotong dari gel
DGGE diamplifikasi kembali dengan PCR kemudian disekuen dan dianalisis
menggunakan piranti lunak MEGA 5 untuk mengkonstruksi pohon filogenetik.
Analisis perbandingan emisi gas metan pada setiap perlakuan dilakukan dengan
melakukan pengukuran aktual di lahan sawah setiap 30 hari setelah tanam serta
pengukuran estimasi menggunakan piranti lunak DNDC 9.5.
Berdasarkan analisis DGGE untuk gen pmoA, terlihat adanya suksesi
komunitas bakteri pengoksidasi metan baik pada fase vegetatif maupun generatif.
Tiga pita berhasil diamplifikasi menggunakan PCR dan menunjukkan
kekerabatannya dengan uncultured bacterium pmoA gene clone 18f_9H (identitas
maksimum 99%), uncultured bacterium pmoA gene clone 16-2000yo-B (identitas
maksimum 98%), dan uncultured bacterium pmoA gene clone 32-2000yo-B
(identitas maksimum 96%). Berdasarkan konstruksi pohon filogenetik, satu pita
menunjukkan kedekatan dengan Methylocystis sp. galur H9a sementara dua pita
lainnya menunjukkan kedekatan dengan Methylococcus capsulatus galur BL4.
Hasil analisis emisi gas metan menunjukkan emisi metan terendah diperoleh pada
perlakuan celup diikuti perlakuan tidak celup dan kontrol. Perbandingan
pengukuran emisi metan secara aktual dan estimasi menunjukkan pola yang
menyerupai yaitu emisi gas metan menurun dari fase vegetatif ke fase generatif.
Kata kunci : DGGE, gas metan, lahan sawah, metanotrof, pmoA.
SUMMARY
HENDRI SUTANTO. Microbial Community Succession of Methanotrophic
Bacteria Based on pmoA Gene in Rice Fields. Supervised by IMAN RUSMANA
and NISA RACHMANIA MUBARIK.
Methanotrophic bacteria play a role in methane oxidation in rice fields.
Methanotrophic bacterial diversity in rice fields was closely related to methane
emission whom released into the atmosphere after being produced by methanogen
archaea. Methanotrophic bacterial isolates of BGM 1, BGM 5, BGM 9, and SKM
14 were known as methane oxidizer in rice fields. Microbial community
succession of methanotrophic bacteria due to the time change during planting
season and the methane emission measurement comparison of actual and
estimation data in rice fields are still rare. Therefore, this research were aimed to
study the microbial community succession of methanotrophic bacteria based on
pmoA gene and to compare actual and estimation measurement of methane
emission in rice fields using DNDC 9.5 software.
Methanotrophic bacterial isolates of BGM 1, BGM 5, BGM 9, and 14
SKM were used as biofertilizer. The treatments used were control, soaking, and
spread treatment. NPK fertilizer was given in the control treatment. The less dose
of NPK fertilizer and methanotrophic bacterial isolates which used to soak the rice
clumps before transferred to the patch were conducted at soaking treatment. NPK
fertilizer with the same dose to soaking treatment and methanotrophic bacterial
isolates which spread directly to the patch were conducted at spread treatment.
DNA genome of rice field soil samples which taken at each treatment
every 30 days after planting were isolated using PowerSoilTM Soil DNA Isolation
Kit, furthermore pmoA gene was amplified using PCR. Analysis of microbial
community succession methanotrophic bacteria was performed using DGGE
(Denaturant Gradient Gel Electrophoresis). The bands which extracted from
DGGE gel were amplified by PCR then were sequenced and analyzed using
MEGA 5 software to construct the phylogenetic tree. Methane emission in each
treatment was analyzed by comparing the actual and estimation emission using
DNDC 9.5 software.
Based on DGGE analysis of pmoA gene, there was succession of methaneoxidizing bacterial community on both vegetative and generative phase. Three
bands were amplified successfully using PCR and showed similarity to uncultured
bacterium clone pmoA gene 18f_9H (99% of maximum identity), uncultured
bacterium clone pmoA gene 16-2000yo-B (98% of maximum identity), and
uncultured bacterium clone pmoA gene 32-2000yo-B (96% of maximum identity).
Based on phylogenetic tree construction, one band was clustered to Methylocystis
sp. strain H9a while the other two bands were clustered to Methylococcus
capsulatus strain BL4. The methane gas emission analysis showed the lowest
emission was obtained at soaking treatment followed by spread and control
treatment respectively. Comparison of actual and estimation measurement of
methane emission showed the similar pattern which methane emission was
obtained decreased from the vegetative phase to the generative phase.
Keywords: DGGE, methane gas, methanotrophic, pmoA, rice fields.
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COMMUNITY SUCCESSION OF METHANOTROPHIC
BACTERIA BASED ON pmoA GENE IN RICE FIELDS
HENDRI SUTANTO
Thesis
as one of the requirements to obtain the degree
Master of Science
on
Microbiology Major
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014
Examiner of Beyond Commission on Thesis Examination : Dr Sulistijorini MSi
Thesis Title
Name
NIM
: Community Succession of Methanotrophic Bacteria Based on
pmoA Gene in Rice Fields
: Hendri Sutanto
: G351130356
Approved by
Supervisor Commission
Dr Ir Iman Rusmana MSi
Head
Dr Nisa Rachmania Mubarik MSi
Member
Discovered by
Head of Microbiology
Major
Prof Dr Anja Meryandini, MS
Date of Examination: 17 July 2014
Dean of Graduate School
Dr Ir Dahrul Syah, MScAgr
Date of Graduation:
FOREWORD
Praise and gratitude to God for all His gifts so this thesis has been
completed successfully. The theme chosen in this research was microbial diversity
succession.
The
title
of
this
thesis
research
is
“Community Succession of Methanotrophic Bacteria Based on pmoA Gene in
Rice Fields”. The author thanks to Dr.Ir.Iman Rusmana, M.Si and Dr Nisa
Rachmania Mubarik M.Si as supervisor commission in this thesis research and
great thanks to Dr Sulistijorini MSi as examiner beyond commission on thesis
examination. The author also thanks the staff of the Laboratory of Microbiology
and Intergrated Laboratory, Department of Biology IPB.
During the college and research, the author thanks to the author family,
Alrhena, Kenny, Biology Batch 46, the first batch of fast-track student, and all of
friends on Microbiology Major Batch 2012-Graduate School of IPB who have
helped the author during this research. The author wished this research can be
beneficial for the knowledge development based on environmental microbiology
and also the environment quality improvement around the world.
Bogor, August 2014
Hendri Sutanto
TABLE OF CONTENTS
LIST OF TABLE
LIST OF FIGURE
LIST OF APPENDIX
INTRODUCTION
Background
Issue Formularization
Aim of Research
Benefit of Research
Scope of Research
LITERATURE REVIEW
Atmospheric Methane
Methanotrophic Bacteria
PCR and DGGE
DNDC Model
METHOD
Research Framework
Time and Place of Research
Bacterial Isolates Preparation
Rice Planting
Soil Characteristic Analysis
DNA Extraction and Quantification
PCR Amplification
DGGE Analysis of the pmoA Gene
DGGE Bands Extraction and Re-PCR
Methane Gas Emission Measurement
x
x
x
1
1
2
2
2
2
3
3
3
5
6
6
6
7
7
8
8
8
8
9
9
9
RESULT AND DISCUSSION
Result
Soil Characteristic
DNA Concentration and PCR Amplification
DGGE Profile
The Phylogenetic Tree
Methane Gas Emission
10
10
10
10
11
11
13
Discussion
CONCLUSION AND RECOMMENDATION
Conclusion
Recommendation
REFERENCES
APPENDIX
BIOGRAPHY
14
17
17
17
19
22
35
LIST OF TABLE
1 Characteristic of type I, II, and X of methanotroph
2 DNA template concentration
3 Comparison of estimation and actual result of CH4 emission
5
10
14
LIST OF FIGURE
1
2
3
4
5
6
Pathways for the oxidation of methane and assimilation of formaldehyde
Research framework
PCR amplification of the pmoA gene
DGGE band profile
The phylogenetic tree constructed with pmoA gene sequences
Flux rate of CH4
4
7
11
12
12
13
LIST OF APPENDIX
1
2
3
4
Sequencing result of all bands excised from DGGE gel
Soil texture classification by USDA
BLAST result of all bands
Notification of acceptance on “Advances in Environmental Biology”
23
32
32
33
INTRODUCTION
Background
Methane gas (CH4) in the atmosphere showed increasing concentration
from 715 ppb to 1732 ppb in 1970 till early 1990 and has increased again as of
1774 ppb in 2005 (Qin et al. 2007). CH4 gas can absorb infrared radiation 25
times more effective when compared to CO2. Based on data in United States
Environmental Protection Agency's for Greenhouse Gas Report in 2010,
Indonesia is the seventh largest contributor of methane gas emission from rice
fields in the world. Setyanto (2004) reported that flooding activity in rice fields
and other wetlands are one of the sources of the emergence of CH4 emission.
Flooded soil conditions caused reductive condition in the soil then the growth of
methanogens increased. Along with the increase in rice production, CH4 emission
will also be increased if the management is not accompanied by efforts to reduce
the emission. The utilization of methanotrophic bacteria can be used to reduce the
emission.
Methanotrophs are unique bacteria which can use methane as their sole
source of carbon and energy. The ability of methanotrophs to oxidize methane is
due to the activity of the enzyme methane monooxygenase. There are two distinct
forms of this enzyme viz the cytoplasmic soluble methane monooxygenase
(sMMO) and the membrane-bound particulate methane monooxygenase (pMMO)
(Murrell et al. 2000). The pMMO is commonly found in all methanotrophs rather
than sMMO. The pMMO is coded by three contiguous genes viz pmoA, pmoB,
and pmoC. The pmoA gene is highly conserved and often used as a functional
marker for analyzing methanotrophs in the environment (Hakemian and
Rosenzwelg 2007). Based on their cell morphology, ultrastructure, phylogeny, and
metabolic pathways, methanotrophs can be divided into three taxonomic groups:
type I, type II, and type X. Type I methanotrophs include the genera
Methylobacter,
Methylomicrobium,
Methylomonas,
Methylocaldum,
Methylosphaera,
Methylothermus,
Methylosarcina,
Methylohalobius,
Methylosoma, and Methylococcus, which belong to the gamma subdivision of the
Proteobacteria. The type II methanotrophs include the genera Methylocystis,
Methylosinus, Methylocella, and Methylocapsa are in the alpha subdivision of the
Proteobacteria (Hanson and Hanson 1996). Methylococcus capsulatus is the
species of type X methanotrophs.
The CH4 oxidation activity in rice fields is closely related to the their
diversity, however ± 99% of bacteria in environment is classified as unculturable
bacteria (D'Onofrio 2010). It makes the bacteria could not be isolated in artificial
media but can be determined by using the metagenome technique. One technique
that can be used is DGGE (Denaturing Gradient Gel Electrophoresis). The
introduction of DGGE to microbial ecology provides a valuable molecular
fingerprinting technique for studying microbial community structure. DGGE
facilitates separation of mixtures of PCR-amplified gene fragments based on
2
sequence differences and allows large numbers of samples to be analyzed
simultaneously. Eventhough this technique is ideally suited for monitoring the
dynamics of microbial communities influenced by environmental changes
escpecially in rice fields but the microbial community succesion of
methanotrophic bacteria which related to pmoA gene in rice fields based on the
time change during the crop phase is still unknown. The major aims of this
research were to study microbial community succesion and also to measure the
CH4 emission in rice fields.
Issue Formularization
1. Methanotrophic bacteria can be used as methane oxidizer agent in rice fields.
2. There is community succession of methanotrophic bacteria in rice fields due to
the time change during crop season.
3. There is change of methane emission from vegetative to generative phase.
4. DNDC 9.5 software can be used to estimate the methane emission in rice
fields.
Aim of Research
The aims of this research were to study community succession of
methanotrophic bacteria based on pmoA gene using DGGE technique and also to
measure the CH4 emission in rice fields.
Benefit of Research
Analysis of the community succession of methanotrophic bacteria in rice
fields was expected to provide information about the diversity of methanotrophic
bacteria which obtained due to the time change during crop season in rice fields.
Application of methanotrophic bacterial isolates were also expected to reduce
methane emission from rice fields as well as to make the environmentally friendly
farming system. The usage of DNDC 9.5 software was expected to be a good
estimation of the methane emission when compared to the actual emission from
rice fields.
Scope of Research
The scope of this research included the DGGE analysis of community
succession of methanotrophic bacteria during the crop season and its relation to
the methane emission from rice fields. The methane emission analysis included
the comparison of actual measurement in rice fields and estimation measurement
using DNDC 9.5 software.
3
LITERATURE REVIEW
Atmospheric Methane
Methane gas is the second most important anthropogenic greenhouse gas
in the atmosphere and responsible for 20–30% of total greenhouse gas radiative
forcing since the industrial revolution (IPCC 2007). Methane is currently about
200 times less concentrated in the atmosphere than carbon dioxide, but each
molecule of methane is 25 times more potent in terms of heat-holding capacity
(Lelieveld et al. 1998). Due to changes in human activity and land use in the
world, both carbon dioxide and methane began to increase around 150 years ago,
as the industrial phase began. Maxfield et al. 2006 and Degelmann et al. 2010
stated since that beginning, atmospheric methane concentrations have increased
approximately 150% from a pre-industrial mixing ratio of about 0.7 ppm to 1.8
ppm. Methane sources are various but their number and magnitude appear to be
on the rise, while methane sinks are more uncertain. The largest global methane
sources are natural and constructed wetlands, which contribute around 33% of
annual emissions (IPCC 2007). Anthropogenic sources including rice fields,
domesticated animals, landfills, fossil fuel acquisition and burning, as well as
biomass use for energy and agriculture, total at least 307 Tg methane yr−1, which
could be over 60% of total emissions (Wang et al. 2004). There may be more
sources than have been accounted for, as methane has also been found to be
produced aerobically in the ocean (Karl et al. 2008).
Methanotrophic Bacteria
Exchange of methane within the soil and atmosphere is regulated by two
groups of microorganisms which known as methanogen and methanotrophs. The
disparate environmental requirements of these two groups, particularly oxygen
concentration, temperature, water content, and nutrient availability, determine the
net methane flux of a given ecosystem. Methanogenic archaea as methane
producer active mainly in anaerobic conditions, produce methane as a metabolic
byproduct and are the main biological source of methane in natural systems,
landfills, and agriculture. Methanotrophic bacteria as methane oxidizer are active
mainly in aerobic conditions and derive energy and carbon from the oxidation of
methane (Hanson & Hanson 1996).
Methanotrophic bacteria or methanotrophs are a subset of a physiological
group of bacteria known as methylotrophs. Methanotrophic bacteria are unique in
their ability to utilize methane as a sole carbon and energy source. Methylotrophic
bacteria are those aerobic bacteria that utilize one-carbon compounds more
reduced than formic acid as sources of carbon and energy and assimilate
formaldehyde as a major source of cellular carbon (King & Nanba 2008).
Methylotrophic bacteria utilize a variety of different one-carbon compounds
including methane, methanol, methylated amines, halomethanes, and methylated
compounds containing sulfur. Some cleave methyl groups from organic
4
compounds including choline or the pesticide carbofuran and utilize them as sole
sources of carbon and energy. Bacteria that utilize formate, cyanide, and carbon
monoxide have different modes of metabolism including pathways for the
assimilation of one-carbon units (Hanson & Hanson 1996). The use of enzymes
known as methane monooxygenases (MMO) to catalyze the oxidation of methane
to methanol is a defining characteristic of methanotrophs. There are two forms of
MMO viz particulate mono oxygenase (pMMO) and soluble monooxgenase
(sMMO). The pMMO is encoded by three contigous gene : pmoA, pmoB, and
pmoC; however, pmoA gene is highly conserved and often used as analyzing
marker for methanotrophs. Figure 1 shows the pathways for the oxidation of
methane and assimilation of formaldehyde.
Figure 1 Pathways for the oxidation of methane and assimilation of formaldehyde
The methanotrophs were separated into three types based on the criteria
shown in Table 1. Type I includes the genera Methylomonas and Methylobacter.
Type II includes the genera Methylosinus and Methylocystis. Type X was added
to accommodate methanotrophs similar to Methylococcus capsulatus that, like
type I methanotrophs, utilized ribulose monophosphate (RuMP) as the primary
pathway for formaldehyde assimilation. Type X methanotrophs were
distinguished from type I methanotrophs because they also possessed low levels
of enzymes of the serine pathway ribulosebisphosphate carboxylase, an enzyme
present in the Calvin-Benson cycle. They grew at higher temperatures than type I
and type II methanotrophs and possessed DNA with a higher moles percent G-C
content than that of most type I methanotrophs.
5
Table 1 Characteristics of Type I, II, and X of Methanotrophs
Characteristics
Cell morphology
Type I
Short rods, some
cocci or
elipsoids
Type II
Cresecentshaped rods
Type X
Cocci, often found
as pairs
Growth at 45 oC
No
No
Yes
G-C content of DNA
(mol %)
49-60
62-67
59-65
Nitrogen fixation
No
Yes
Yes
RuMP pathway
present
Yes
No
Yes
Serine pathway
present
No
Yes
Sometimes
Proteobacterial
subdivision
Gamma
Alpha
Gamma
PCR and DGGE
PCR (Polymerase Chain Reaction) is a method for making copies of a
specific segment of DNA. Initial material for PCR is a double stranded of DNA
which contain nucleotide sequences that are targeted to be copied. Primers used
for the PCR process is a synthetic single-stranded DNA molecule that is short,
which is complementary to the ends of the target DNA that determines a particular
DNA segment to be amplified (Campbell et al. 2002). The principle of PCR
includes three phases in a cycle. The first stage is the denaturation stage. It takes
place at high temperatures between 92-96 °C and intended to separate the doublestranded DNA into single-stranded chain (single strand). The separation of bond is
caused by high temperatures which trigger the rupture of hydrogen bonds in DNA.
After the DNA becomes single-stranded chain, then the DNA is ready to be used
as template for primer set (short chain of nucleotide or oligonucleotide which the
sequence of its nitrogen bases are known). The second stage is annealing which
takes place at temperatures between 42-65 °C. Primer attaches to the DNA
template whom are complementary with its nitrogen bases sequence. This
attachment stage is very specific and wrong temperature will not lead the primer
attach to the target and result in a attachment to any place. The duration of this
stage usually depends on the primers used. The third stage is the elongation. The
temperature for this stage depends on the type of DNA polymerase used in the
reaction. The enzyme used is DNA taq-polymerase. The enzyme relatively works
more stable at high temperatures and is not denatured quickly which generally
performed at 72 °C. The cycle goes over and over again until the target sequence
has been duplicated many times. Until approximately minimum 20 cycles, almost
all DNA molecules generated will consist of appropriate target sequence
(Campbell et al. 2002). Therefore it happens repeatedly then will produce an
abundance of DNA which will be resulted as amplicons or called as PCR product.
6
This amplicon can be used for various purposes in molecular analysis (Hapwood
et al. 1985 ; Sivakumar 2001).
Denaturing gradient gel electrophoresis (DGGE) is a commonly used
technique in molecular biology and has become a staple of environmental
microbiology for characterization of population structure and dynamics. DGGE
analyses are employed for the separation of double-stranded DNA fragments that
are identical in length, but differ in sequence (Muyzer and Smalla 1998). In
practice, this refers to the separation of DNA fragments produced via PCR
amplification. The technique exploits (among other factors) the difference in
stability of G-C pairing (three hydrogen bonds per pairing) as opposed to A-T
pairing (two hydrogen bonds). A mixture of DNA fragments of different sequence
are electrophoresed in an acrylamide gel containing a gradient of increasing DNA
denaturants. In general, DNA fragments richer in GC will be more stable and
remain double-stranded until reaching higher denaturant concentrations. Doublestranded DNA fragments migrate better in the acrylamide gel, while denatured
DNA molecules become effectively larger and slow down or stop in the gel. In
this manner, DNA fragments of differing sequence can be separated in an
acrylamide gel.
DNDC Model
The Denitrification-Decomposition (DNDC) model is a process-oriented
computer simulation model of carbon and nitrogen biogeochemistry in
agroecosystems. This model was introduced by Institute for the Study of Earth,
Oceans and Space, University of New Hampshire. The model consists of two
components. The first component, consisting of the soil climate, crop growth and
decomposition sub-models, predicts soil temperature, moisture, pH, redox
potential (Eh) and substrate concentration profiles driven by ecological drivers
(e.g., climate, soil, vegetation and anthropogenic activity). The second
component, consisting of the nitrification, denitrification and fermentation submodels, predicts emissions of carbon dioxide (CO2), methane (CH4), ammonia
(NH3), nitric oxide (NO), nitrous oxide (N2O) and dinitrogen (N2) from the plantsoil systems. Classical laws of physics, chemistry and biology, as well as
empirical equations generated from laboratory studies, have been incorporated in
the model to parameterize each specific geochemical or biochemical reaction. Cai
et al. (2003) stated that the use of DNDC can be valuable to simulate the methane
emission based on the farming management practice used in the rice fields.
METHOD
Research Framework
The research framework (Figure 2) generally included the biofertilizer
preparation, rice planting in the rice fields, PCR amplification, DGGE analysis,
7
and comparison of methane emission based on actual data from the rice fields and
simulated data which analyzed by DNDC 9.5 software.
Figure 2 Research framework
Time and Place of Research
This research was observed from May 2013 to May 2014. The field
research was observed at Cidahu Village, Sukabumi, West Java. The molecular
research was done at Microbiology and Intergrated Laboratory, Department of
Biology, Faculty of Mathematics and Natural Science, Bogor Agricultural
University.
Bacterial Isolates Preparation
Isolates of methanotrophic bacteria BGM 1, BGM 5, BGM 9, and SKM 14
(Hapsari 2008) were purified in Nitrate Mineral Salt (NMS) +1% methanol. Each
8
isolate of bacteria was incubated at 27oC for 3-7 days. One loop of the bacteria
was cultured in 300 ml media. The culture then incubated in rotary shaker (±
37oC) for 10 days until the total of cell was 108 cell ml-1.
Rice Planting
Rice planting was done at Cidahu Village, Sukabumi, West Java. Each
plot measured approximately 150 m2 with 2020 cm of planting distance. Control
treatment used a patch while soaking and spread treatments respectively used four
patch. The rice clump then planted in rice fields. In the control treatment, the
patch was given by 300 kg ha-1 of NPK fertilizer. In the soaking and spread
treatment, the patch was given by 200 kg ha-1 of NPK fertilizer and biofertilizer
but in the soaking treatment the clumps were soaked with biofertilizer liquid for
15 minutes before being transferred to the patch.
Soil Characteristic Analysis
One kilogram of soil samples were taken from each plot then sent to the
Soil Laboratory at Center for Agricultural Land Resources, Soil Research
Institute, Bogor to be analyzed its soil type and characteristic (Appendix 2).
DNA Extraction and Quantification
Soil samples which taken at 30, 60, and 90 days after planting (DAP) from
each treatment were extracted to get the DNA template using PowerSoilTM Soil
DNA Isolation Kit, MO BIO Laboratories, USA. DNA template was quantificated
using Nanodrop.
PCR Amplification
PCR amplification reactions were performed in 25 µL (total volume)
reaction mixtures in 0.2 ml PCR tubes using ESCO DNA thermal cycler. All PCR
amplification of the pmoA gene used the GC-A189f (5’- CCC-CCC-CCC-CCCCGC-CCC-CCG-CCC-CCC-GCC-CCC-GCC-GCC-CGG-NGA-CTG-GGACTT-CTGG-3’) and A682r primer (5’-GAA-SGC-NGA-GAA-GAA-SGC-3’)
(Henckel et al. 1999). Individual reagents and their concentrations of amounts
were as follows : 12.5 µL of Taq DNA polymerase (supplied by KAPA 2G
Robust Hot-Start, KAPA Biosystems USA), 1.25 µL of each primer (20 pmol), 3
µL of DNA template (~100 ng µL-1), and 7 µL of ddH2O. The PCR steps were
optimized and consisted as follows : 95 oC for 1 min, followed by 30 cycles of 95
o
C for 15 s, 59 oC for 15 s, 72 oC for 15 s, and final extension at 72 oC for 10 min.
PCR products were electrophoresed on 1.5% agarose gel at 80 V for 60 min.
Agarose gel then stained using 1% EtBr for 15 min and visualized by Gel Doc
1000 (BIO-RAD, USA).
9
9
DGGE Analysis of the pmoA Gene
PCR products amplified with GC-A189f and A682r primer set were
separated on a gradient of 35-80% (Henckel et al. 1999) using a DCodeTM
Universal Mutation Detection System (BIO-RAD, USA) on 1-mm-thick
polyacrylamide gels (6% [wt/vol]) acrylamide-bisacrylamide [37.5:1]) (BIORAD, USA). The gel was electrophoresed in 1XTAE at 60 oC and 150 V for 6
hours. The gel then stained using SYBR Safe for 60 min and scanned by Gel Doc
1000 (BIO-RAD, USA).
DGGE Bands Extraction and Re-PCR
Samples of individual DGGE bands were excised from ployacrylamide gel
using sterile scalpel then inserted to a microtube consisted of 50 µL of sterile
biquadest. The microtube was incubated at 4 oC overnight and 60 oC for 2 hours
(Coelho et al. 2009; Perez et al. 2014). A 10 µL (~30 ng µL-1) of template was
used for re-PCR reaction. The reaction used A189f (without GC Clamp) and
A682r primer set. The PCR steps were used as the protocol from KAPA 2G
Robust Hot-Start and set as follows : 95 oC for 1 min, followed by 30 cycles of 95
o
C for 15 s, 55 oC for 15 s, 72 oC for 15 s, and final extension at 72 oC for 10 min.
The PCR nested products were sent to 1st BASE Malaysia to be sequenced.
Nucleotide sequences were blasted in www.ncbi.nlm.nih.gov and analyzed to get
the phylogenetic tree using the neighbour joining program in MEGA 5 software.
Methane Gas Emission Measurement
Gas sampling was taken by closing the sample plots in the fields with the
lid box. A box was placed in each treatment. Gas samples then taken from the
inside the box using a 100 ml syringe then stored in vacuum glass bottle. Gas
sampling was conducted on vegetative and generative phase. At 30, 60, and 90
DAP (Days After Planting), the gas was taken then sent to Greenhouse Gas
Laboratory, Environmental Research Institute of Agriculture, Jakenan Pati,
Central Java to be analyzed the CH4 concentration. The CH4 concentration results
converted to obtain the flux rate of gas change emission during the cropping time.
The CH4 emission was also simulated by DNDC 9.5 software to obtain the flux
rate of the gas concentration through the year of cropping.
10
RESULT AND DISCUSSION
Result
Soil Characteristic
Soil characteristic plays an important role on biogeochemical systems in
nature. Based on the United States of Department of Agriculture soil criteria, the
analysis result showed that the composition of the soil with 16% of sand, 33% of
silt and 51% of clay then categorized as clay loam soil. Soil contained 1.81% of
C. Based on the result obtained, it can be seen that the carbon content in soil was
classified as low category. In addition to the soil organic content, the pH value
also played an important role in influencing the development of microorganisms.
Soil pH value obtained was 5.2 and categorized as acidic soil pH.
DNA Concentration and PCR Amplification
Quantification results obtained from the DNA template extraction process
indicated that the DNA concentration of all samples were not much different. The
result showed that the concentration ranged from 24.4 to 28 ng µL-1 (Table 2).
Table 2 DNA template concentration
Control
30
60
90
DNA
concentration
(ng µL -1)
26.0
24.4
24.5
Soaking
30
60
90
28.0
24.4
27.9
1.94
1.95
1.98
Spread
30
60
90
27.4
26.6
27.2
1.99
1.92
1.98
Treatment
Day
A260/A280
1.96
1.97
1.90
The average concentration of DNA template observed was around 26.27 ng
µL-1. The A260/A280 ratio showed the purity of DNA template which around
1.90-1.99. The PCR amplification using A189f and A682r primer set also showed
that all samples were successfully amplificated and after being electrophoresed on
1.5% agarose gel, the PCR product size was around 508 bp (Figure 3).
11
Figure 3 PCR amplification of pmoA gene. K (control), C (soaking treatment),
and TC (spread treatment). The following number 1, 2, 3 : 30, 60, and
90 days after planting respectively
DGGE Profile
The DGGE analysis showed some bands which appeared on the gel (Fig
4a,b). Overall the soaking treatment showed the highest diversity compared than
other treatments. Due to the crop phase, the highest diversity at 30 DAP was
obtained at the soaking treatment followed by control and spread treatment. The
same pattern was also obtained at the soaking treatment which showed the highest
diversity than other treatments at 60 DAP and 90 DAP. The control treatment
showed the second highest diversity at 60 DAP but became the lowest diversity at
90 DAP. There were twelve different bands obtained; however, there were only
four bands (band no 1, 4, 8, and 12) chosen to be sequenced in this research
(Appendix 1). The band no 8 was recalcitrant to be extracted from gel. The blast
result showed the band no 1 had 99% similarity to uncultured bacterium pmoA
gene clone 18f_9H while band no 4 and 12 had 98% and 96% similarity to
uncultured bacterium pmoA gene clone 16-2000yo-B and uncultured bacterium
pmoA gene clone 32-2000yo-B respectively (Appendix 3).
The Phylogenetic Tree
There were five genera of methanotroph used as clustering comparation in
this phylogenetic tree. They were Methylomonas, Methylobacter, Methylococcus,
Methylosinus, and Methylocystis. The band no 1 was closely related to
Methylocystis sp. strain H9a while the band no 4 and 12 were closely related to
the Methylococcus capsulatus strain BL4 (Fig 5). The phylogenetic tree also
showed Methanogen sp. TM 20-1 as outgroup of this clustering result.
12
1
4
(a)
(a)
8
12
(b)
Figure 4 DGGE band profile on the polyacrylamide gel (a) and the band pattern
illustration (b) with the pmoA primer set. The thick bands referred to
the sequenced band. K (control), C (soaking treatment), and TC (spread
treatment). The following number 1, 2, 3 : 30, 60, and 90 days after
planting respectively
98
99
51
51
90
Methylomonas sp. MG30 strain DSM 24973 (HE801217.1)
Methylomonas methanica (U31653.1)
68
Methylobacter psychrophilus (AY945762.1)
Methylobacter sp. HG-1 (AF495888.1)
Uncultured Methylococcus sp. clone Xh pmoA CA65 (JQ038177.1)
Methylococcus capsulatus strain BL4 (AF533666.1)
Band 12
81
99
Band 4
Methylosinus sporium strain ATCC 35069 (FJ713041.1)
Methylosinus sporium (DQ119048.1)
Methylosinus acidophilus (DQ076755.1)
72
Methylosinus sp. B3R (AB636306.1)
Uncultured Methylocystis sp. clone 5 (FJ930096.1)
Methylocystis sp. strain H9a (AJ459027.1)
Band 1
Methanogen sp. TM20-1 (AB062404.1)
0.2
Figure 5
The phylogenetic tree constructed with pmoA gene sequences. The
scale bar indicates the estimated number of base changes per
nucleotide sequence position
13
Methane Gas Emission
The lowest methane gas emission in the generative phase was obtained at
the soaking then followed by the spread and control treatment respectively (Fig 6).
The estimation gas emission rate was increased at the early day of planting phase
then decreased significantly. The rate was showed to increase again till around 35
days after planting then decreased slightly till the generative phase. The soaking
treatment showed 12.29% lower emission of methane while the spread treatment
only showed 2.29% lower emission of methane compared than control treatment
(Table 3).
Figure 6
Flux rate of CH4. Symbols: (
) the estimation result of the control
treatment; ( ) the estimation result of the soaking treatment; ( ) the
estimation result of the spread treatment; (
) the actual result of the
control treatment; ( ) the actual result of the soaking treatment; ( )
the actual result of the spread treatment
The decreasing percentage comparation of methane emission from vegetative
phase to generative phase was not slightly different at all treatments. The control
treatment showed decreasing actual methane emission from 30 DAP to 90 DAP
(19.66 to 12.21 kg C ha-1 day-1). The spread treatment showed decreasing actual
methane emission from 30 DAP to 90 DAP (17.91 to 11.93 kg C ha-1 day-1) while
the lowest actual methane emission obtained at soaking treatment (12.75 to 9.71 kg
C ha-1 day-1). The methane gas oxidation at the soaking treatment showed the better
percentage than spread treatment compared than control treatment. It can be seen
from the methane gas emission which produced from all treatments. The soaking
treatment showed 12.29% lower emission of methane while the spread treatment
only showed 2.29% lower emission of methane compared than control treatment.
14
Table 3 Comparison of estimation and actual result of CH4 emission
Treatment
Day
Estimation result
(kg C ha-1 day-1)
Actual result
(kg C ha-1 day-1)
Total estimation result
(kg C ha-1 yr-1)
Control
30
60
90
20.72±0.75
13.66±0.56
12.32±0.08
19.66
12.87
12.21
2527
Soaking
30
60
90
16.72±2.81
9.60±0.94
8.32±0.98
12.75
10.93
9.71
2256
Spread
30
60
90
19.22±0.93
12.10±0.02
10.82±0.08
17.91
12.13
11.93
2469
Discussion
Soil characteristic plays an important role on biogeochemical systems in
nature. Rice fields soil in Sukabumi, West Java showed that the soil was
categorized as clay loam soil type. The characteristic of clay loam soil type are
slippery structure, easy to be attached, firm ball may be formed, and a clot can be
formed rather easily destroyed. Clay loam soil was known suited for rice fields
because the clay particle acts a storehouse of plant nutrients that are retained by
soil and gradually released for uptake as cations (Thapa 2010). Based on the result
obtained, it can be seen that the carbon content in soil was classified as low
category. Jain et al. (2004) reported the positive correlation between methane
emission and C content in soil. Methane emission will be decreased when the
carbon content in soil decrease. Yagi and Minami (1990), on the contrary,
reported there’s no correlation between methane production and emission with
total carbon in rice fields soil. This statement was also supported by Wang et al.
(1993) which observed no correlation obtained with soil organic carbon content
and suggested that other factors such as bacterial population of the soil were more
important in production and emission of methane. In addition to the soil organic
content, the pH value also played an important role in influencing the
development of microorganisms. Soil pH value was categorized as acidic soil pH;
however, this is an appropriate pH for the growth and development of bacteria.
This result was supported by Jain et al. (2004) who reported that below pH 5.8
and above 8.8, methane emission in the soil suspension was lower compared than
methane emission in a pH range between 6.5 and 7.5.
Quantification results obtained from the DNA template extraction process
indicated that the DNA concentration of all samples were not much different. The
average value was 26.27 ng µL -1. The DNA template was pure enough based on
the spectrophotometer ratio of A260/280. This purity was important due to the
template used in PCR step. PCR amplification will be effective if the DNA
template used is pure. PCR amplification reaction using A189f and A682r primers
set showed that nine samples can be amplified well with amplicon length reached
was around 508 bp. It was similar to the report by Holmes et al. (1999) and
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McDonald et al. (2008) who stated that the PCR amplification which targeted
pmoA gene using A189f and A682r primer sets will generate amplicon with the
size of 525 bp.
The diversity of microbial communities related to the methane oxidation
based on pmoA functional gene was able to be represented using DGGE
technique. The result showed there were different patterns of microbial
community diversity when all of them compared due to the crop phase and
treatment used. Due to the crop phase, the highest diversity at 30 DAP was
obtained at the soaking treatment followed by control and spread treatment. It
showed the correlation to the lower methane emission obtained at the soaking
treatment than control and spread treatment. The same pattern was also obtained
at the soaking treatment which showed the highest diversity than other treatments
at 60 DAP and 90 DAP. The control treatment showed the second highest
diversity at 60 DAP but became the lowest diversity at 90 DAP. This result was
related to the not-flooded condition on top soil at 60 DAP then the soil condition
was still aerobic enough and supported the growth of indogenous methanotrophic
bacteria. The uncultured bacterium pmoA gene clone 18f_9H and uncultured
bacterium pmoA gene clone 32-2000yo-B were appeared almost constant from 30
DAP to 90 DAP while uncultured bacterium pmoA gene clone 16-2000yo-B
appeared clearly at 60 DAP. It can be seen that the diversity was increased from
vegetative phase to early of generative phase. It was consistent with Das and
Adhya (2012) who stated that the methanogenic archaea population will decline
during the vegetative to generative phase then the methanotrophic bacterial
population will be increased. The increased methanotrophic bacterial population
was also due to the wetland drainage activities in the generative phase so the
wetland conditions become more aerobic and unsuitable habitat for methanogenic
archaea growth.
The blast result of band no 1, 4, and 12 showed the consistency for both
forward and reverse sequence which analyzed in www.ncbi.nlm.nih.gov. The
phylogenetic tree showed the clustering view of samples analyzed and
methanotrophic bacteria. The band no 1 was closely related into type II
methanotroph in genera of Methylocystis while the band no 4 and 12 were closely
related to the type I methanotroph in genera of Methylococcus. These results were
correlated to Hoffman et al. (2002) ; Mohanty et al. (2007) ; Zheng et al. (2008)
who reported that the dominance of Methylobacter, Methylomicrobium,
Methylococcus, Methylocaldium, Methylocystis, and Methylosinus in the rice
fields soil. Horz et al. (2001) also stated the different pattern of methanotroph
bacteria diversity between rice field soil and rice plant roots. Genera of
Methylomonas, Methylobacter, Methylococcus were found in the rice plant roots
while genera of Methylocystis and Methylosinus can be found in both rice fields
soil and rice plant roots. Different diversity of methanotrophic bacteria between
flooded rice fields and drained rice fields also reported by Mayumi et al. (2010)
that flooded rice fields were dominated by Methylocystis and Methylosinus while
drained rice fields were dominated by Methylomonas, Methylosarcina, and
Methylomicrobium.
The observation result of methane gas emission measurement seen to be
higher in the vegetative phase, while the gas emission began to decline in
generative phase. This was similar to Das and Adhya (2012) who stated that
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methane gas emission increased in the vegetative phase and decrease towards the
generative phase. The soil which begins to dry in the generative phase decreases
the population of methanogenic archaea. This effect will cause the rate of methane
production and emissions go down. Setyanto and Susilawati (2007) also reported
the high methane was produced during the vegetative phase, especially at
maximum tiller, and tended to go down in the generative phase. The decrease was
caused by use of plant photosynthate at the process leading to formation of
flowers womb and also root exudates in soil were low in the generative phase. The
lower content of root exudates was the higher inhibition of methanogenesis
process so that flux of methane was down. Root exudates are organic compounds
consisting of sugars, amino acids, and organic acids as constituent materials
immediately available for methanogenic archaea. In the flooded condition,
methane emission was higher than that in dry condition (Kimura et al. 1991;
Wihardjaka 2005).
The comparison of actual and estimation emission of methane showed the
same trend from 30 DAP to 90 DAP. The methane gas measurement system used
in this research was be able to represent the methane gas emission simulated by
DNDC 9.5 software. Cai et al. (2003) and Li et al. (2003) also stated that the
estimation of methane gas emissions in China and eastern Asia were able to be
represented using DNDC 9.5 software. The decreasing percentage comparison of
methane emission from vegetative phase to generative phase was not slightly
different at all treatments. The methane gas oxidation at the soaking treatment
showed the better percentage than spread treatment compared than control
treatment. It can be seen from the methane gas emission which produced from all
treatments. The soaking treatment showed 12.29% lower emission of methane
while the spread treatment only showed 2.29% lower emission of methane
compared than control treatment. This result could be correlated to the treatment
used. When the methanotrophic bacterial isolates were soaked to the rice clumps
before planting step, the methane oxidation in rice fields will be more effective
than the soil which only given by inorganic fertilizer or spread by bacterial
isolates. It was because there was association between rice plants root and
methanotrophic bacterial isolates after the soaking step. The methane gas which
resulted from methanogenic archaea will be oxidized by the methanotrophic
bacteria who lived near the rice plants root before being transferred into
aerenchyme and atmosphere. The estimation result of total methane emmision at
soaking and spread treatment were 2256 kg C ha-1 yr-1 and 2469 kg C ha-1 yr-1
respectively. It was lower than total methane emission reported by Khalil et al.
(2008) which around 3494 kg C ha-1 yr-1. This result could be correlated to the
methane-oxidizer bacterial diversity on DGGE gel. It was described before that
microbial community at soaking treatment from vegetative phase to generative
phase always clearly showed the highest diversity compared than spread and
control treatment. That higher number of methane-oxidizer microbial community
gave the positive correlation to its methane emission. The higher of diversity
made the lower of methane emission. This result could be also related to the
methanotrophic bacterial isolates which given in this research then the total
methane emission became lower due to the methane oxidation performed by
methanotrophic bacterial isolates. The role of given methanotrophic bacterial
isolates (BGM 1, BGM 5, BGM 9, and SKM 14) showed clear effect in methane
17
gas emission reduction. Pingak et al. (2014) also reported that the application of
methanotrophic bacterial isolates in rice fields were known to decrease the rate of
gas emission compared with soil which only given by the inorganic fertilizer.
Methane gas emission can be reduced by methanotrophic bacterial isolates
because the combination of these bacteria (BGM 1, BGM 5, BGM 9, and SKM
14) were confirmed to have the methane monooxygenase (MMO) enzyme
acitivity which play roles in the methane gas oxidation (Maharani 2011). MMO is
an enzyme that plays role in the oxidation of methane to methanol (Hanson and
Hanson 1996). The control treatment was the highest among others because the
use of inorganic fertilizer with excessive dosing while the lowest emission
obtained at the soaking treatment result can be related to the better methane
oxidation activity performed by methanotrophic bacteria isolates of BGM 1, BGM
5, BGM 9, and SKM 14 which soaked to the rice clumps at the soaking treatment
compared to other treatments.
CONCLUSION AND RECOMMENDATION
Conclusion
The DGGE showed methane-oxidizing microbial community succession
during the vegetative phase to generative phase. The treatment which rice clumps
was soaked by biofertilizer showed both the highest diversity and the lowest
emission than the control treatment as well as the treatment which only spread by
biofertilizer. The blast result of DGGE bands were closely related to uncultured
bacterium pmoA gene clone 18f_9H, uncultured bacterium pmoA gene clone 162000yo-B, and uncultured bacterium pmoA gene clone 32-2000yo-B. The actual
result of methane gas emission measurement in this study had similar trend to the
estimation result analyzed by DNDC 9.5 software. Methane gas emission rate was
decreased from the vegetative phase to the generative phase.
Recommendation
The recommendation from this research are the test in lowland rice fields
to see the effectivity and stability of methanotrophic bacterial isolates used in
reducing methane emission and the methanotrophic bacteria community
succession can also be obtained with RNA analysis.
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