circadian rhythm in methanotrophic bacteria: …circadian rhythm in methanotrophic bacteria:...

Circadian Rhythm in Methanotrophic Bacteria: Expression of pmoA Coding for Monooxygenase in Microcosms

Cycled with Methane

SES Independent Research Project December 16, 2013

Kayla Muirhead

Dickinson College Carlisle, PA 17013

Advisor: Dr. Julie Huber

Josephine Bay Paul Center, Marine Biological Laboratory Woods Hole, MA 02543

Advisor: Dr. Joe Vallino

Ecosystems Center, Marine Biological Laboratory Woods Hole, MA 02543

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Abstract:

Circadian rhythms can develop within the environment as organisms adapt to

environmental change. Since there is natural competition for resources developed

temporal strategies, such as circadian rhythms, allow organisms to obtain more useable

energy and therefore outcompete evolutionarily if they know when certain resources will

be available. Many recent studies have suggested circadian rhythm in cyanobacteria, but

in general little is known about bacterial circadian rhythm. The purpose of this

experiment is to test for circadian rhythm in an aerobic, methanotrophic microcosm

cycled with a mixture of methane and air every two days. In order to carry out this

experiment nutrient analysis of CH3OH, POC, PON, DOC, NH4, and NO3 as well as

molecular analysis for the expression and diversity of pmoA was measured in one

methane-cycled and one control (methane always on) microcosm. We hypothesized that

if the methanotrophs had a circadian rhythm there would be carbon storage, a pattern in

the expression of pmoA, and a similar pattern in regards to methanotrophic diversity in

the cycled microcosm. Our data suggests no carbon storage, and constant pmoA

expression. Although these two analyses do not directly suggest a circadian rhythm we

did find a pattern among the presence of pmoA OTUs at a distance of 0.11 that may

suggest a cyclical trend in relation to methane cycling. For future studies we suggest

testing a longer methane cycle to see if carbon storage and expression of pmoA would

change, indicating a circadian rhythm, as resource availability becomes scarcer. Studies

such as this are essential to understanding the overall energy and prosperity of an

ecosystem.

Key Words: Bacteria, carbon storage, circadian rhythm, energy, gene expression,

methane, methanotrophs, microcosms, monooxygenase, OTUs, pmoA, resource

availability

Introduction:

Methane is an important anthropogenic greenhouse gas that contributes to climate

change as it accumulates in the atmosphere. This gas is released into the environment

quickly and even though it is shorter lived than other greenhouse gases, such as CO2, its

affects are just as detrimental; currently, 60% of the global methane is due to

anthropogenic origins (EPA, 2013). The remaining input of methane comes from

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microbial mediated processes that can alter the magnitude of sources and sinks of

methane in the atmosphere. Naturally, this greenhouse gas can be taken up via microbial

processes, such as methane oxidation, that ultimately regulate methane flux (Conrad,

2009).

While investigating microbial processes in relation to the environment it is

important to keep in mind how organisms can adjust to environmental change within

their natural habitats. Most organisms can adapt to changes based on genetic or

evolutionary mechanisms that help them survive and prosper (Paranjpe, 2005). In order to

become more “fit” some organisms can develop circadian rhythms where they become

accustomed to a particular schedule, or time frame, based on environmental conditions;

one of which is resource availability (Vitaterna, 2013). Although evidence of circadian

rhythm has been tested in many organisms few studies have suggested the presence of

such rhythms in prokaryotes. With that being said, a couple researchers have suggested

evidence of circadian rhythm in cyanobacteria, but more research had been encouraged

(Johnson, 2007).

Methanotrophs, like cyanobacteria, are prokaryotic, but the two organisms differ

in their typical natural environments and available carbon sources. Methanotrophs are

gram-negative bacteria that reduce the amount of methane released to the atmosphere by

oxidizing it to CO2. In order to oxidize methane methanotrophs must first turn methane

into methanol with the aid of the enzyme monooxygenase. The reaction that occurs

produces short-term energy and carbon for the methanotrophs based on the following

aerobic equation; CH4 + ½ O2 � CH3OH + ATP. Monooxygenase is coded for by the

gene pmoA, which has been sequenced in many environmental studies (Johnson et al.,

2007). PmoA is an important gene of methanotrophic study because it codes for the first

step in the methane oxidation process and can be compared across studies in programs

such as GenBank (McDonald, 2008).

One research study suggests that when introduced to a 20 day cycle period of

methane (10 days of CH4 and air mixture, 10 days with only air) methanotrophic bacteria

will use the most of their available carbon resources to maximize available energy within

experimental microcosms. The mathematical and nutrient modeling suggests that a way

to make the most of available resources would be for methanotrophs to develop a

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circadian rhythm in relation to resource availability (Vallino et al., 2013). Part of this

experiment that remains to be tested, however, is both the expression of genes such as

pmoA, as well as the concentration of methanol that might suggest a developed circadian

rhythm within the methanotrophic population or within the microcosm as a whole.

The goal of this study is to suggest that methanotrophs within an experimental

microcosm have developed a circadian rhythm based on methane cycling. The target of

this study will be the expression of the pmoA gene found in methanotrophs because its

expression suggests that methanotrophs are oxidizing methane to obtain energy. In other

words, when pmoA is expressed the methanotrophs are carrying out a favorable redox

reaction. This experiment will address three principal questions based on knowledge of

previous studies and the significance of circadian rhythm mentioned above. The first

question to be addressed; does the expression of pmoA change at different time points

during the methane cycle, suggesting development of a circadian rhythm? The second

question; does the concentration of methanol at each time point correspond to the

expression of pmoA and do the methanotrophs seem to be storing methanol/ carbon prior

to methane shut-off? Finally; is the pmoA being expressed in some pattern of diverse

methanotrophs? If experimental data does suggest the development of a circadian rhythm

this will add to our knowledge of the energy use and evolution of methanotrophs as well

as the biogeochemical processes that are occurring within the microcosms, which could

further suggest adaptive trends within prokaryotic populations (Johnson, 2007). This

study will hopefully provide researchers with a better understanding of energy use as well

as organismal adaptation and ways in which this information could be applied to

environmental change.

Methods:

Experimental Microcosms

The experimental setup consists of two 18 L microcosms. The microcosms were

originally set up following the procedures and intended experimental design described in

an experiment by Dr. Joe Vallino and Dr. Julie Huber (Vallino et al., 2013). The two

experimental microcosms were previously assembled about four years ago with one liter

of water from both a coastal pond and a cedar bog each. One microcosm (MC 1) is cycled

with a methane-air mixture (20.95% O2, 0.033% CO2, N2 to balance) for two days,

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followed by just air for two days. It is important to note that the systems always stay

aerobic. The second microcosm (MC 2) is constantly cycled with a methane mixture

(4.9% CH4, 19.6% O2, 0.03% CO2, N2 to balance). Water is added to the chemostat at a

rate of 0.1L d-1, and gas is diffused at a rate of 20 mL min-1. In addition to the supply of

water a mineral salt medium is added (10 mM K2HPO4, 50 uM KNO3, 100 uM MgSO4,

100 uM CaCl2, 100 uM NaCl, and trace elements). Data collected from the chemostats is

posted online every day (http://ecosystems.mbl.edu/ MEP).

Before sampling the microcosms gas diffusers were cleaned and homogenized as

tubes and ports were checked for extensive biofilm. Ports were opened and tubes were

carefully rinsed with DI water. Water (about 60 mL) was drawn from the sampling tubes

prior to each sampling time to dispose of any daily biomass buildup.

RNA Sampling

For this particular experimental time frame MC1 began being pumped with

methane on November 7th, 2013 at 15:30. Samples were slowly taken from the

microcosm using a sterile 60 mL syringe at each of the six time points in the methane

cycle; about 24, 47, 50, 72, 95, and 98 hours since the start of the cycle indicated. 120 mL

of sample was filtered using a sterile Sterivex and excess water was pushed out of the

filter by pulling air into the syringe. Then the filter was capped with Medex caps and

immediately placed in liquid nitrogen. When both filters (one from each microcosm)

were collected, they were taken to a -80oC freezer where they were stored in a plastic bag

in sterile 50 mL falcon tubes.

Nutrient Sampling

In addition to collecting samples for molecular analysis at each time point

indicated above samples were also taken for nutrient analysis. The nutrients include NO3,

NH4, POC/PON, DOC, and Methanol. The same sterile 60mL syringe used for RNA

sampling was used for each nutrient at each time point (one syringe for each microcosm).

For NO3 60 mL of sample was collected and filtered through an ashed GF/F. 10 mL was

used to rinse the filter, 30 mL was used to rinse the vial (two rinses), and 20 mL was

collected into an acid washed scintillation vial and stored at -20oC. The remaining water

was filtered into a liquid waste container. The same procedure was followed for NH4

using the same GF/F and filter, except 10 uL of 5 N HCl was added to each sample and

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the samples were stored at 4oC. After the NO3 and NH4 samples were filtered using the

same GF/F filter (a total of 120 mL) the filter was placed into a petri dish and stored at -

20oC for later POC/PON analysis.

Samples for DOC were collected using a new ashed GF/F filter. 60 mL was

collected; 10 mL was used to rinse the filter, and 20mL was used to rinse the DOC vial.

About 30 mL was filtered into an acid washed, ashed glass vial and 100 uL of H2PO4 was

added. Samples were stored at 4oC.

The last nutrient sample was for methanol analysis. 60 mL of water was collected

in the syringe, and a new Acro disk filter was rinsed with 10 mL of sample water. The

sterile 50 mL falcon tube was rinsed with 10 mL sample, and the remaining 40 mL was

placed in the tube. Samples were stored at -20oC.

Nutrient Analysis

Methanol samples were taken to WHOI where there were analyzed on an Aligent

Technologies 6850 Gas Chromotography System in Dr. Tracy Mincer’s lab (Tracy

Mincer General lab Protocol). Standards were graphed and compared to sample readings

in order to determine methanol concentrations in the samples at the various time points.

Ammonium samples were analyzed using a modification of Strickland and

Parsons ammonia methods (Strickland and Parsons, 1972). The following standards were

made using 10.000 uM NH4Cl stock; 0 uM, 0.5 uM, 1 uM, 5 uM, 10 uM, 50 uM, and 100

uM. Samples were mixed with 0.12 mL of phenol, 0.12 mL of sodium nitroprusside

solution, and 0.3 mL of oxidizing solution. Samples were incubated for an hour and read

on the Shimadzu 1601 Spectrophotometer at 640nm and then graphed and compared to

sample absorbance in order to determine NH4 concentrations at each time point.

DOC samples were run using an Aurora 1030 TOC Analyzer. Standards including

0 uM, 20 uM, 50 uM, and 100 uM were made using KH stock solution. The machine

calculated outputs in ppm, which were then modified to uM C. Methods were based on a

modification of the SES instructions (Strebel, 2011).

The concentration of nitrate in each sample was calculated using a QuikChem

8500 Series 2 FIA Automated Ion Analyzer. Samples were loaded into the machine and a

modification of QuikChem methods were utilized (Latchat Applications Group, 2007).

Data was collected in terms of uM.

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The concentration of POC and PON was measured on a Thermo Scientific CN

Analyzer (Model Flash 2000). Filters were packed and loaded into the machine along

with multiple standards and blanks. Readings were produced as ug, but were modified to

uM using the amount of water filtered per sample (120 mL). Methods were based on a

modification of a previous protocol (R. Rubin and J. Laundre, 2012).

Molecular Methods

RNA samples were taken out of the -80oC freezer and thawed at room

temperature. When thawed, samples were disrupted and vortexed using a lysis/binding

solution. Ethanol (64%) was added to the solution and then the mixture was drawn

through a Filter Cartridge. The mixture was washed with three wash solutions to isolate

RNA. RNA was eluted from the filter using a heated elution solution. After elution,

samples were treated with DNase in order to get rid of any leftover DNA in the sample.

They were then distributed into working and archive stocks and stored at -80oC (Ambion

RNAqueous- 4 PCR Kit Extraction Method and DNase Treatment).

Ribogreen was used to run a high range assay in order to quantify RNA. A 200-

fold dilution of Ribogreen was prepared. Six standards were made using a 2 ng/ul stock

of RNA. Samples were plated with a mixture of sample RNA, 1 x TE, and Ribogreen and

concentrations were calculated based on a standard graph of concentration versus assay

reading (Julie Huber Lab General Protocol).

RNA was made into cDNA after Ribogreen data was used to normalize the

concentration of RNA (30 ng/ul) going into each reaction. A positive and negative

reaction was made for each sample as well as a negative control for the reaction. The

positive and negative master mixes (consisting of 2X RT Buffer, 20X RT enzyme mix,

and nuclease-free H2O) were made separately and aliqouted. The sample was added

depending on the volume determined to normalize the reaction, and the remaining

volume (of the 2 uL total) was supplemented with DEPC H2O. The 20 uL reaction was

loaded into the thermal cycler at the following profile; 37oC for 60 minutes, and 95oC for

5 minutes. Samples were stored at -20oC (Applied Biosystems High Capacity RNA-to-

cDNA Kit).

In order to test for the presence of bacterial DNA a 16 S rRNA bacterial PCR

reaction was made. A master mix with DEPC H2O, 5X Buffer, dNTP mix, GoTaq, and

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8F (10 uM) and 1492R (10uM) primers were first used. Due to suggested contamination,

however, the primers were changed to 27F (10 uM) and 1492R (10uM). The 50 uL

reaction consisted of 49 uL master mix, and 1 uL sample cDNA. A positive control was

made using a 1:10 dilution of local Eel Pond DNA obtained from the Julie Huber Lab. A

negative control was also run for the PCR reaction. The reactions were run on the

following profile; 94oC for 3 minutes, and then 35 cycles of 94oC for 40 seconds, 55oC

for 1 minute and 30 seconds, 72oC for 2 minutes, and then a final 72oC for 10 minutes

(Julie Huber General Lab Protocol). Samples were run on a gel, and when visible bands

were seen in both samples and the positive control a pmoA PCR reaction was then made

using the same synthesized cDNA to amplify the desired gene of interest.

The pmoA PCR was made using the following master mix; DEPC H2O, 5X

Buffer, dNTP mix, GoTaq, and A189F and A682R primers. The 50 uL reaction consisted

of 48 ul master mix, and 2 uL cDNA in order to amplify more visible gel bands. Only the

positive cDNA reactions and a positive and negative PCR control were run. The

following profile was modified for pmoA; 35 cycles of 94oC for one minute, 56oC for one

minute, and 72oC for one minute, and then a five-minute 72oC extension (Julie Huber

General Lab Protocol). Samples were run on a gel to compare relative band intensity.

Once bands were observed in the pmoA gel four samples were selected for

cloning and sequencing based on relative band intensity. These samples were samples #1,

7, 8, and 11, which will be defined in the results section below. Before running an

additional gel to isolate the bands the original pmoA PCR reaction was cleaned up and

purified using the MinElute PCR Purification Kit. Five volumes of Buffer PB and 750 uL

of Buffer PE were used to wash the solution and then 10 uL of Buffer EB was used to

elute the cDNA (MinElute PCR Purification Kit Protocol). Since the cDNA was intended

for a gel 4 uL of loading dye from a separate Qiagen MinElute PCR Purification Kit was

added to each of the four reactions. The entire volume of solution (~11-14 uL) was

loaded onto a gel (Julie Huber General Lab Protocol).

The pmoA bands (cDNA fragments) for the gel detailed above were excised (and

weighed), isolated, and purified based on the MinElute Gel Extraction Kit. The cDNA

fragments were dissolved in a volume of Buffer QG determined by gel weight and then

bound to a MinElute column where they were washed with Buffer PE and eluted with

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Buffer EB (MinElute Gel Extraction Kit Protocol). The samples were then stored at -

20oC.

In order to begin cloning cells were first ligated. The TOPO cloning reaction

included the following; 4 uL PCR template, 1 uL salt solution, and 1 uL TOPO vector 4.0

(Julie Huber General Lab Protocol). Next, the samples were transformed using competent

cells. 18 uL of water was added to the 6 uL ligation reaction, and then 2 uL of the diluted

ligation reaction was added to a vial of competent cells and flicked to mix. The remaining

ligation reaction was stored at -20oC. For each sample about 50 uL of the competent cell

mixture was added to a cuvette, shocked using an eletroporator at 1600V, and then mixed

with 250 uL of SOC. The transformed cells were incubated at 37oC for one hour (~225

rpm). Two agar plates containing Kanamycin were made for each sample. On one plate

25 uL was spread, and on the other 50 uL. Plates were then incubated for 18 hours at

37oC (Julie Huber General Lab Protocol for Cloning).

Individual colonies from the four 25 uL agar plates were picked for template

preparation. 48 colonies from each sample 25 uL plate were picked and placed in a

growth block containing Superbroth and Kanamycin. The two 96-well blocks were then

incubated for about 18 hours at 37oC (~250 rpm). When finished with incubation the

plates were centrifuged to form a pellet, and all supernatant was disposed of. Blocks were

then frozen and submitted at the JBPC for further Plasmid Preparation (JBPC Protocol

for Automated Template Preparations Using BiomekFX).

In order to check for correct plasmid insert length in the prepped plasmids a PCR

was run using a master mix containing DEPC H2O, 5x buffer, dNTP mix, M13F (10uM),

M13R (10uM), and GoTaq. In addition 1 uL of the prepped plasmid was added to the 50

uL reaction (49 uL of master mix). The following thermal profile was utilized; 94oC for 5

minutes, 30 cycles of 94oC for 5 minutes, 94oC for 30 seconds, 55oC for 45 seconds, and

72oC for one minute, and then 72oC for 10 minutes (Julie Huber General Lab Protocol).

Four random colonies from each of the four samples were run on a gel. When the insert

length was affirmed sequencing reactions were made.

In order to make a 1/16 X sequencing reaction two master mixes (one for T3, one

for T7) were made containing BDT, primer (T3 or T7), DMSO, 5X reaction buffer, and

DEPC H2O. 3 uL of the master mix was added to each well in the two new plates. Then 3

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uL of template was added to each well; 24 templates from each of the original four

samples were added to both the T3 and the T7 plates. The plates were then run at the

following thermal profile; 60 cycles of 96oC for 10 seconds, 50oC for 5 seconds, and

60oC for 4 minutes (JBPC Protocol for 1/16X Sequencing Reactions).

The last step in the sequencing process was to complete the final preparation for

sequencing. The plates were washed with both 75% Isopropanol and 70% Isopropanol

and then re-suspended in 7 uL of HiDi Formamide (JBPC General Sequencing Protocol).

They were then left at the JBPC and sequenced. Julie Huber aligned and analyzed the

returning sequences.

Results:

Nutrient Analysis

There is a visible trend between methanol concentrations in microcosm one

(MC1) and microcosm two (MC2). In MC1 methanol is present at about 0.1 uM when

methane is turned on (24 hours), but decreases to about 0.02 uM when the methane is

first turned off (50 hours) and then continues to decrease to about zero when it has been

off for almost two days (95 hours). Methanol begins to increase again as soon as methane

is turned back on (98 hours) (Fig.1). MC2 generally stays at the same concentration (0.1

uM) for each temporal point (Fig.1). Overall, when methane is on methanol is present,

but as soon as methane turns off methanol decreases to about zero.

Ammonium concentrations are low in both microcosms (Fig. 2). There is no

general cyclical trend seen in either microcosm and it is important to note that some low

measurements are at the detection limit of the analyzer. Ammonium in MC1 ranges from

7.38 to 0.8 uM, while ammonium in MC2 ranges from 1.1 to -0.1 uM (Fig.2). The

difference between the microcosms is that generally, at each temporal point there is a

greater concentration of ammonium in MC1 compared to MC2, until about 72 hours

(methane off) to 98 hours (methane on) when it is slightly greater in MC2 (Fig.2). Similar

to ammonium concentrations, nitrate concentrations were relatively low for both

microcosms, suggesting no visible cyclical trend. The concentration of nitrate in MC1

ranged from 0 to 2.2 uM, while the concentration of nitrate in MC2 ranged from 0 to 2.8

uM (Fig.3).

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The concentration of dissolved organic carbon (DOC) in MC1increases from 90

uM at 24 hours when methane was on to 117 uM at 50 hours when methane was off and

then decreases to about 104 uM at 94 hours when methane was still off (Fig.4). This

change in concentration of DOC shows no cycle relative to the methane cycling in MC1.

The concentration of DOC in MC2 ranges from 172 uM to 178 uM which means DOC is

relatively the same for all six time points. The concentration of DOC in MC1 is about

half of that measured in MC2 (Fig.4).

The concentration of particulate organic carbon (POC) is greater in MC1. When

methane is first turned off in MC1 POC increases from 793 uM (24 hours) to 2192 uM

(72 hours), but the in the middle of the two-day off cycle it begins to decrease again to

1494 uM (95 hours) (Fig.5). In MC2 the POC ranges from 23 uM to 37 uM, which is

much lower than MC1 (Fig.5). There is no visible cyclical trend for POC in either

microcosm. In both microcosms the concentration of particulate organic nitrogen (PON)

is much lower than that of POC, but there is a similar trend between lower levels detected

in MC2 compared to MC1. In MC1 PON increases from 86 uM when the methane is on

(24 hours) to 274 uM whe the methane is off (72 hours) and then decreases to 196 uM

when methane is still off (95 hours) (Fig.6). In MC2 PON is between 26.7 and 38 uM,

with little variation at each temporal point (Fig.6). Similar to POC, there is no visible

cyclical trend for PON.

Carbon to Nitrogen ratios based on the measured POC and PON data show that

the C:N ratios in MC1 are overall slightly higher than those in MC2. In general the C:N

ratios in MC1 decrease over time with a range from 10.7 to 8.7 (Fig.8). In general MC2

has similar C:N ratios at each point. MC2 starts and ends with a C:N ratio of 7.5 with a

little variation in-between those points of time (Fig.8).

Molecular Methods

Based on the ribogreen quantification assay the concentration of RNA in MC1

starts at about 46 ng/ul 24 hours after the methane turned on in the microcosm. The

concentration of RNA peaks (80 ng/ul) at 49 hours just one hour after the methane turned

off, and then decreases to 34 ng/ul at 72 hours when the methane had been off for about

one day. The concentration of RNA then increases to about 66 ng/ul just before methane

turned on again at 95 hours (Fig.8). The concentration of RNA in MC2 starts at 185 ng/ul

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at 24 hours, decreases to 74 ng/ul at 47 hours, and then increases again and peaks to 312

ng/ul at 95 hours (Fig.8). Overall, the general trend between the two microcosms is that

MC1 has an increase in the concentration of RNA when methane is turned off while MC2

has a decrease at the same time point. The second trend is that MC2 has a higher

concentration of RNA for all points compared to MC1.

The 16S rRNA bacterial PCR, run to ensure that there was bacterial cDNA within

all of the samples, is positive for all sample reactions. The gel that was run to test for the

expression and relative band intensity of pmoA is also positive for all microcosm samples

(Fig.9). There is therefore pmoA expression in both microcosms at all six time points.

Keeping in mind that band intensity is not quantitative the band intensity is relatively the

same except for slightly fainter bands seen in samples 1 and 7 for all 12 samples (Fig.9)

(Appendix B).

Sequencing for pmoA suggests two main OTUs for samples 1, 7, and 11 based on

a distance of 0.11 (similarity of 89% between sequences) (Appendix B). In samples 1 and

11 methane is on in MC1 and OTU 2 is about 40% of the total OTUs, but when methane

is off in MC1 (sample 7) OTU2 is only about 5% of the total OTUs (Fig.10). This shows

a pattern of increasing presence of OTU 2 when methane is on in MC1. In the control

sample (8) there is only presence of OTU 1 (Fig.10). An OTU cluster was also created at

a distance of 0.03 (97% similarity between sequences), which can be used for

comparison. The only difference between the graph with a 0.11 distance and the one with

a 0.03 distance is that there was a third OTU found in sample 1 that is not present in any

other sample on the graph (Fig. 11). It is important to note that before clustering

sequences into OTUs there was an initial blast that showed unique sequences which are

depicted in a Venn diagram of comparison suggesting that the three samples (1,7, and 11)

only had two OTUs in common prior to further clustering (Fig.12).

Discussion:

The purpose of this study was to see if there was any indication of circadian

rhythm within an experimental microcosm cycled with a mixture of methane and air

every two days (MC1). Based on chemical analysis of nitrate, ammonium, POC, DOC,

and methanol there is no indication of a cyclical pattern within either system (Fig.1-7).

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If there were to be a circadian rhythm we assumed that there would be carbon storage in

the form of methanol. Methanol is essential to methanotrophic bacteria because it is their

main source of energy and carbon. As they oxidize methane to methanol they create

short-term energy, but for more usable energy they oxidize methanol to carbon dioxide

(Brock et al., 2003)(Appendix A). All organisms need energy and carbon to survive.

Since methane is the main resource for methanotrophs we guessed that right before

methane was turned off in MC1, at about 48 hours in the four-day cycle, there would be

methanol storage because they would need more usable energy during the time of

resource depletion. A previous study testing energy storage in sulfur bacteria suggests a

developed strategy much like the one we were hypothesizing where bacteria save

compounds when they are readily available in order to later use them to create energy in

an environment where resource availability fluctuates (J. Mas and H.V. Germerden,

1995). If the methanotrophs anticipated the change in resource availability, they should

store carbon compounds as a precaution for later energy use. The results, however,

suggest that as soon as methane is turned off in MC1 methanol is depleted and there is no

observed carbon storage (Fig.1).

It could be that there is some form of unobserved carbon storage, but

methylotrophs in the microcosms are taking up methanol and consuming it before it

could be measured. Methylotrophs are one of only a few organisms that can obtain

carbon from organic compounds with methyl groups attached to non-carbon atoms (A.J.

Smith and D.S. Hoare, 1977). Considering the diversity of the microcosms, it makes

sense that the methylotrophs would prefer to compete for a carbon resource that not many

other organisms utilize. On the other hand, the depletion of methanol could also just

suggest that there is no methanol storage to begin with considering the fact that other

nutrients similarly suggest no sign of circadian rhythm.

Concentrations of DOC and POC were taken to describe the chemical

composition of the microcosms and potentially suggest further carbon storage. The data

shows that there is twice as much DOC in MC2, which makes sense because twice as

much methane is available due to the fact that it is not on a four-day cycle like MC1 (Fig.

4). The relatively low levels of DOC in both microcosms suggest that carbon is being

taken up frequently within the system. This suggests a dynamic environment with

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organismal diversity, which was previously suggested in a prior study but does not

suggest storage, and therefore does not support the hypothesis for a circadian rhythm

(Vallino et al., 2013). POC was measured in both microcosms, and the data suggests that

MC1 has more POC than MC2 (Fig.5). While observing both microcosms, however, it is

evident that MC1 has more POC in the water column while MC2 has more POC attached

to the microcosm walls, so this reading may have been due to sampling bias (Fig.13). The

concentration of POC suggests some type of biodiversity between the two systems, but

again, there is no evidence of cyclical carbon storage or circadian rhythm.

There were very low levels of ammonium and nitrate detected in both microcosms

at all six time points (Figs. 2-3). It is important to keep in mind that nitrate is pumped into

the microcosms so that there is 50 μΜ available to be taken up. Since low levels of

nitrate were measured, this suggests that the available 50 μΜ of nitrate in the system is

almost all being consumed. Although there is a dilution rate of 0.1 d-1 due to the addition

of water to each microcosm every day this does not account for the loss of 50 μΜ of

nitrate. We did however expect to see low levels of ammonium because microbial

oxidation of ammonium creates energy, and ammonium is produced when nitrate is

consumed (Brock et al., 2003).

All of the nutrient analysis discussed above was used in conjunction with

molecular analysis in order to look for a pattern that could suggest some circadian rhythm

within MC1. As stated in the results, we found that pmoA is expressed at all of the six

time points in MC1 and MC2 (Fig.9). We expected to see expression always on in the

control microcosm (MC2) because methane is always available, and therefore the

methanotrophs need to express pmoA in order to oxidize the methane and create energy.

We did not, however, expect pmoA to always be expressed in the cycled microcosm

(MC1) because methane turns off every two days. We assumed that the methanotrophs

would turn off pmoA at some point in the two-day period without methane to oxidize.

Since pmoA is always expressed in MC1 we hypothesize that it might cost the

methanotrophs more energy to turn the gene off than to just keep it on for the entire four-

day cycle. A study assessing the energy cost of bacterial production of amino acids

suggests that organisms have evolutionarily preferred certain functional genes with lower

overall energy costs over time, which might suggest why pmoA is not turned off (Heizer

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et al., 2006). Although these results do not directly indicate a circadian rhythm they do

suggest some kind of adjustment to the methane cycling, where the organisms might

know that the methane will come back on in two days, so they keep expressing the gene.

The sequencing of pmoA shed some light on the methanotrophic diversity within

the cycled microcosm (MC1). Although pmoA was only sequenced at three time points

during the cycle (samples 1, 7, and 11) and at one control point (sample 8), the analysis

was indicative of some type of developed pattern in relation to methane cycling. Based

on a similarity of 89% sequences were clustered into two OTUs which were present at all

three points in the cycled microcosm (MC1), but different in their amount of presence at

each point in time (Fig. 10). The data suggests that OTU 2 may be favored when methane

is turned on in the cycled microcosm, but when it is turned off OTU 1 is favored. The

significance of having two OTUs in MC1 is that different niches are created with the

cycling of methane, so increased methanotrophic diversity allows the organisms to obtain

the most possible energy from their environment (Brock et al., 2003). In MC2 we saw

OTU 1 only, which suggests it is not beneficial to have methanotrophic diversity within

the controlled system (Fig.10). This data can be compared to a previous study looking at

the relationship of pmoA sequences within experimental, methanotrophic methane-

enriched cultures. Researchers found that sequences from the enriched cultures were very

closely related on a phylogenetic tree, suggesting that similar type II methanotrophs were

favored in methane-enriched cultures (I.R. McDonald and J.C. Murrell, 1997). To

continue this study of circadian rhythm it would be useful to make a phylogenetic tree to

further compare sequence differentiation and specific methanotrophic diversity.

In order to further research the possibility of a circadian rhythm in MC1 a longer

cycle could be tested, such as 20 days of methane on and 20 days of methane off. It has

been suggested that when bacteria are introduced to extreme environmental conditions

their selection of amino acid can be altered, costing the organism more overall energy

(Heizer et al., 2006). The methanotrophs, therefore, might turn off pmoA during a time of

more severe resource deprivation to save energy. Previous research by Dr. Joe Vallino

has been done on the microcosms during a 10-day cycle, which focused on models of

nutrients and storage as opposed to specific gene expression (Vallino et al., 2013).

Although no circadian rhythm was suggested during the 10-day cycle, a longer cycle

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might exhibit cyclical behavior, because the organisms would become even more

dependent on resource availability. It might be more beneficial for them to develop

temporal strategies to know exactly when methane will turn back on so that they can

make the most energy out of the available methane when it is present. A very early study

of evolution highlights the importance of resource allocation as it suggests both energy

and time as the main factors creating competition among species (Cody, 1966). The

energy cost for them to keep the gene turned on would be higher, so they would turn it

off in times when methane is not present. In addition to the qualitative PCR that was run

we could run a quantitative PCR to get a better idea of the relative amount of pmoA

expressed at each point in time. It could be that some pmoA is always expressed within

the microcosms, but it is expressed more or less at certain times. In addition, we could

test for carbon storage in additional compounds within the microcosms.

The importance of studying circadian rhythm in bacteria is that once you

understand temporal strategies of the organisms you can make estimates of how energy is

transferred within a system as well as which organisms are most fit. Since energy is

essential to all life and ecosystems understanding its role in the environment opens doors

to studies in relation to environmental change. One application is that organisms that are

better at taking up methane will reduce more anthropogenic methane in the environment.

Circadian rhythm is the basis of temporal strategy, which can determine the success of an

organism as it adapts to environmental change.

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Appendix:

A. The General Oxidation of Methane Depicting Energy Created

CH4 + ½ O2 � CH3OH + short-term energy� CO2 + long-term energy

B. Molecular Methods: Sample Identification

Sample # Microcosm #

(1= cycled)

(2= control)

Date

Taken

Time Since Start

of Cycle (Hrs)

Methane

on/off

1 1 11/8/13 24 On

2 2 11/8/13 24 On

3 1 11/9/13 47 On

4 2 11/9/13 47 On

5 1 11/9/13 49 Off

6 2 11/9/13 49 On

7 1 11/10/13 72 Off

8 2 11/10/13 72 On

9 1 11/11/13 95 Off

10 2 11/11/13 95 On

11 1 11/11/13 98 On

12 2 11/11/13 98 On

Acknowledgements:

I would like to thank both of my advisors Dr. Julie Huber and Dr. Joe Vallino for all of

their help and wonderful advice throughout my project. I would also like to thank Emily

Reddington for teaching and helping me with molecular methods, and Dr.Tracy Mincer at

WHOI for his help with methanol sample analysis. In addition I appreciate the help of all

of our TAs; Rich McHorney, Fiona Jevon, Sarah Nalven, and Alice Carter. Thank you to

Dr. Ken Foreman and SES for supporting and funding this independent project. It was

truly incredible.

Literature Cited:

Brock, T.D, and M.T. Madigan, 2003. The Biology of Microorganisms. 12th ed. San

Francisco, CA.

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Cody, M. 1966. A general theory of clutch size. Evolution 20:174-184.

Conrad, R., 2009. The Global Methane Cycle: Recent Advances in Understanding the

Microbial Processes Involved. Environmental Microbiology Reports. 1(5): 285-

292.

EPA. 2013. Overview of Greenhouse Gases: Methane Emissions.

http://epa.gov/climatechange/ghgemissions/gases/ch4.html.

Heizer E.M., D.W. Raiford, M.L. Raymer, T.E. Doom, R.V, Miller, and D.E. Krane,

2006. Amino Acid Cost and Codon-Usage Biases in 6 Prokaryotic Genomes: A

Whole- Genome Analysis. Mol. Biol. Evol. 23 (9): 1670-1680.

Johnson, C.H. 2007. Bacterial Circadian Programs. Cold Spring Harbor Symposia on

Quantitative Biology. 72:395-404.

Mas, J. and H.V. Germerden, 1995. Storage Products in Purple and Green Sulfur

Bacteria. Advances in Photosynthesis and Respiration. 2:973-990.

McDonald, I.R. and J.C. Murrell, 1997. The particulate methane monooxygenase gene

pmoA and its use as a functional gene probe for methanotrophs. FEMS

Microbiology Letters. 156: 205-210.

McDonald, I.R., L. Bodrossy, Y. Chen, and J.C. Murrell, 2008. Molecular Ecology

Techniques for the Study of Aerobic Methanotrophs. Appl. Environ. Microbiol.

74(5): 1305-1315.

Paranjpe, D.A., and V. K. Sharma. 2005. Evolution of Temporal Order in Living

Organisms. Journal of Circadian Rhythms. 3:7.

QuikChem Method: Determination of Nitrate/Nitrite in Surface and Wastewaters by Flow

Injection Analysis. Revised 29 Nov 2007. Latchat Applications Group.

Rubin, R. and J. Laundre. 2012. Thermo Scientific CN Analyzer Protocol: Model Flash

2000.

Smith, A.J. and D.S. Hoare, 1977. Specialist Phototrophs, Lithotrophs, and

Methylotrophs: A Unity Among a Diversity of Prokaryotes? Bacteriol Rev. 41(2):

419-448.

Strebel, S. 2011. OI Analytical Aurora 1030 TOC Analyzer: Detailed Instructions. SES

Protocol. Revised Fall 2011.

Strickland, J.D.H. and T.R. Parsons. 1972. A Practical Handbook of Seawater Analysis.

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Fisheries Research Board of Canada. 2ndEd.

Vallino, J.J., C.K. Algar, N.F. Gonzalez, J.A. Huber. 2013. (Accepted) Use of

receding horizon optimal control to solve MaxEP-based biogeochemistry

problems. Beyond the Second Law: Entropy Production and Non-Equilibrium

Systems.

Vitaterna, M.H., J.S. Takahashi, F.W. Turek. 2013. Overview of Circadian Rhythms.

NIH. �http://pubs.niaaa.nih.gov/publications/arh25-2/85-93.htm.

Figures:

Figure 1. Methanol concentrations (μM) in MC1 (cycled) and MC2 (control).

Figure 2. Ammonium concentrations (μM) in MC1 (cycled) and MC2 (control).

Figure 3. Nitrate concentrations (μM) in MC1 (cycled) and MC2 (control).

Figure 4. DOC concentrations (μM) in MC1 (cycled) and MC2 (control).

Figure 5. POC concentrations (μM) in MC1 (cycled) and MC2 (control).

Figure 6. PON concentrations (μM) in MC1 (cycled) and MC2 (control).

Figure 7. Calculated C:N ratios of MC1 (cycled) and MC2 (control).

Figure 8. The concentration of RNA (ng/ul) in MC1 (cycled) and MC2 (control).

Figure 9. pmoA illustrating visible bands in all 12 samples (MC1 and MC2).

Figure 10. Clustered OTUs of pmoA sequences at a distance of 0.11.

Figure 11. Clustered OTUs of pmoA sequences at a distance of 0.03.

Figure 12. Venn Diagram suggesting unique OTUs of pmoA at a distance of 0.0.

Figure 13. An image of both experimental microcosms.

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Figure 1. The concentration of methanol (μM) in MC1 (cycled) and MC2 (control) at each time point in relation to the start of the four-day cycle where methane would be turned on at t=0.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 20 40 60 80 100 120

[CH

3OH

] (μ

M)

Time Since Start of Cycle (Hrs)

Microcosm 1

Microcosm 2

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Figure 2. The concentration of ammonium (μM) in MC1 (cycled) and MC2 (control) at each time point in relation to the start of the four-day cycle where methane would be turned on at t=0.

-1

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100 120

�NH

4��μ��

Time Since Start of Four-day Cycle (hrs)

Microcosm 1

Microcosm 2

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Figure 3. The concentration of nitrate (μM) in MC1 (cycled) and MC2 (control) at each time point in relation to the start of the four-day cycle where methane would be turned on at t=0.

0

0.5

1

1.5

2

2.5

3

0 20 40 60 80 100 120

[NO

3] (μ

M)

�� Start��

Microcosm 1

Microcosm 2

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Figure 4. The concentration of DOC (μM) in MC1 (cycled) and MC2 (control) at each time point in relation to the start of the four-day cycle where methane would be turned on at t=0.

0

20

40

60

80

100

120

140

160

180

200

0 20 40 60 80 100 120

[DO

C] (

μM

car

bon)


Microcosm 1

Microcosm 2

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Figure 5. The concentration of POC (μM) in MC1 (cycled) and MC2 (control) at each time point in relation to the start of the four-day cycle where methane would be turned on at t=0.

0.00

500.00

1000.00

1500.00

2000.00

2500.00

0 20 40 60 80 100 120

[PO

C] (

μM

)


Microcosm 1

Microcosm 2

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Figure 6. The concentration of PON (μM) in MC1 (cycled) and MC2 (control) at each time point in relation to the start of the four-day cycle where methane would be turned on at t=0.

0.00

50.00

100.00

150.00

200.00

250.00

300.00

0 20 40 60 80 100 120

[PO

N] (

μM

)


Micocosm 1

Microcosm 2

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Figure 7. Calculated C:N ratios of MC1 (cycled) and MC2 (control) based on the measurements of [POC] and [PON] taken using CHN analysis. Each time point is in relation to the start of the four-day cycle where methane would be turned on at t=0.

0

2

4

6

8

10

12

0 20 40 60 80 100 120

C:N

Rat

io


Microcosm 1

Microcosm 2

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Figure 8. The concentration of RNA (ng/ul) in MC1 (cycled) and MC2 (control) at each time point in relation to the start of the four-day cycle where methane would be turned on at t=0. Concentration was measured using a Ribogreen assay.

0

50

100

150

200

250

300

350

0 20 40 60 80 100 120

[RN

A] (

ng/u

l)

Microcosm 2

Microcosm 1


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Figure 9.The final gel for pmoA illustrating visible bands in all 12 samples (MC1 and MC2) and a positive control.

�

��

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��

��

��

��

��

��

��

��

��

��

��

��

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Figure 10. Clustered OTUs of pmoA sequences at a distance of 0.11 (89% similarity). Sanger-sequencing was utilized to obtain this data. . See appendix for specific sample information (Appendix B).

12

21

13

23

9

1

7

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% o

f Tot

al O

TUs

Sample

OTU 2

OTU 1

1 7 11 8 (control)

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Figure 11. Clustered OTUs of pmoA sequences at a distance of 0.03 (97% similarity). Sanger-sequencing was utilized to obtain this data. See appendix for specific sample information (Appendix B).

�

�

��

��

��

�

��

�

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 (on) 7 (off) 11 (on) 8 (control)

% o

f Tot

al O

TU

s

Sample

OTU 3

OTU 2

OTU 1

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Figure 12. Venn Diagram suggesting unique OTUs of pmoA at a distance of 0.0 based on Sanger Sequencing and clustering. See appendix for specific sample information (Appendix B).

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Figure 13. An image of both experimental microcosms taken about four weeks after sampling. To the left is MC1 (cycled) and to the right is MC2 (control). These were set up four years ago by Dr. Joe Vallino.

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