Name: Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands (Video) | JoVE Journal
Uploaded: 2025-01-31T14:30:00.000+00:00
Duration: 7 min 26 s
Description: Explore spatial mapping of methane-cycling genes in South Texas coastal wetlands using PCR and GIS visualization. This protocol reveals how environmental factors regulate microbial dynamics and gene abundance seasonally.

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5 minutes

We are thankful to C-REAL members for their assistance in field observation and laboratory analyses.

1. Sample collection
<ol>
	<li>Collect sediment samples using a sediment grab sampler or shovel. 
	NOTE: Samples were collected from two stations of three distinct coastal wetlands during cool (October-February, the average temperature is 20 &#176;C) and warm (April-June, average temperature is 27 &#176;C) seasons of 2023 and 2024. A sediment grab sampler was used when samples were collected from coastal freshwater wetlands (Figure 2) and a shovel was used for tidal-influenced coastal saltwater wetlands.</li>
	<li>Lower the sampler into the shallow waterbody first and allow it to sink to the sediment surface (within the top 50 cm) under its own weight, minimizing disturbance to the sediment structure as shown in Figure 2.</li>
	<li>Pull it out of the water column where the depth was typically 60 cm to 215 cm30, transfer the sediment samples to zip-lock bags, and store them in an ice box immediately. 
	NOTE: Clean and wash the grab sampler with deionized (DI) water before proceeding to the next stations.</li>
	<li>Store all samples at -20 &#176;C immediately in the laboratory.</li>
	<li>In each sampling site, measure surface water quality parameters such as salinity and temperature in situ using a multiparameter water quality meter. 
	NOTE: The probes were rinsed with deionized water (DI water) after use in each station.</li>
</ol>
2. Genomic DNA extraction
<ol>
	<li>Thaw the samples at room temperature before starting the procedure for genomic DNA extraction.</li>
	<li>Transfer approximately 500 mg of sediment samples to a 15 mL tube and centrifuge at 4,250 &#215; g for 3 min to remove all water.</li>
	<li>Extract genomic DNA using a DNA extraction kit for soil following the manufacturer&#39;s protocol31 with a little modification and store at -20 &#176;C immediately. 
	NOTE: The changes were made to reduce redundancy and efficient workflow.
	<ol>
		<li>Add up to 500 mg of soil sample to a glass bead/ceramic sphere-containing tube.</li>
		<li>Add 978 &#181;L of Sodium Phosphate Buffer to the sample in the bead/sphere-containing tube.</li>
		<li>Add 122 &#181;L of buffer lysis solution to the sample in the bead/sphere-containing tube to solubilize external contaminants.</li>
		<li>Homogenize using a bead mill homogenizer at 5x the speed level for 20 s and repeat 2x. 
		NOTE: A bead mill homogenizer was used in this study, which is why the speed was adjusted.</li>
		<li>Centrifuge the mixture for 10 min at 14,000 &#215; g.</li>
		<li>Transfer the supernatant to a clean 2.0 mL microcentrifuge tube.</li>
		<li>Add 250 &#181;L of protein precipitation solution (PPS) to separate the solubilized nucleic acids from the cellular debris and lysing matrix. Mix by inverting the tube 10x.</li>
		<li>Centrifuge at 14,000 &#215; g for 5 min to precipitate the pellet, removing the cellular debris and lysing matrix.</li>
		<li>Transfer the supernatant to a clean 15 mL microcentrifuge tube.</li>
		<li>Add 1.0 mL of the Binding Matrix suspension to the supernatant in the 15 mL tube. 
		NOTE: Shake the Binding Matrix suspension to resuspend before adding it.</li>
		<li>Allow the binding of DNA to the Binding Matrix by placing the tubes on a rotator for 2 min.</li>
		<li>Place all the tubes on a rack and incubate for 3 min to allow the settling of the Binding Matrix.</li>
		<li>After 3 min, discard 750 &#181;L and gently mix the remaining supernatant with the pellet using a pipette.</li>
		<li>Transfer 750 &#181;L of the mixture to a SPIN filter and centrifuge at 14,000 &#215; g for 1 min. Empty the catch tube and reuse it. Repeat with the remaining mixture.</li>
		<li>Add 500 &#181;L of the prepared wash solution (with the appropriate amount of ethanol added) to further solubilize impurities. Gently resuspend the pellet using the force of the liquid from the pipet tip.</li>
		<li>Centrifuge at 14,000 &#215; g for 1 min to remove impurities. Empty the catch tube and reuse it.</li>
		<li>Centrifuge again at 14,000 &#215; g for 2 min without adding anything.</li>
		<li>Replace the catch tube with a new, clean catch tube and air dry the SPIN Filter for 5 min at room temperature.</li>
		<li>Add 50 &#181;L DNAse free water (DES) and centrifuge at 14,000 &#215; g for 1 min. 
		NOTE: In this protocol, 50 &#181;L of DES was used for DNA elution to ensure optimal recovery and stability of the extracted environmental DNA (eDNA).</li>
	</ol>
	</li>
</ol>
3. DNA quantification
<ol>
	<li>Add 1 &#181;L of extracted DNA with 200 &#181;L of fluorescent dye in a 0.5 mL tube and mix thoroughly by pipetting.</li>
	<li>Wrap the tube immediately with aluminum foil so light cannot penetrate and incubate at room temperature for 5 min.</li>
	<li>Measure DNA concentration by using a fluorometer at ONE DNA concentration mode following the manufacturer&#39;s protocol.</li>
</ol>
4. Detection of 16S rRNA, pmoA1 , pmoA2 , mmoX , and mcrA by conventional PCR
<ol>
	<li>Before running conventional PCR (cPCR), thaw all samples and reagents in an ice bucket.</li>
	<li>Dilute all extracted eDNA samples to 10 ng/&#181;L. 
	NOTE: A list of primers is given in Table 1.</li>
	<li>Prepare a 25 &#181;L cPCR reaction mixture for each sample, including 12.5 &#181;L of 2x PCR Master mix , 0.5 &#181;L of forward and reverse primers (10 &#181;M) (see Table 1 for primer list), 1 &#181;L of 10 ng/&#181;L eDNA, and 10.5 &#181;L of nuclease-free water. 
	NOTE: Prepare the master mix with enough volume for one extra sample to minimize pipetting errors.</li>
	<li>Perform cPCR reaction following the protocol consisting of an initial denaturation at 95 &#176;C for 2 min, followed by 40 cycles of denaturation at 95 &#176;C for 45 s, extension at 72 &#176;C for 30 s, and final extension at 72 &#176;C for 5 m, with varying annealing temperature for different primers (see Table 1 for annealing temperatures of different primers used for different genes). 
	NOTE: For mcrA gene,&#160;ML primers showed the desired band using a slow ramp rate of 0.1 &#176;C/s between the annealing and extension steps for the first five cycles32.</li>
	<li>Visualize cPCR products in an agarose gel prestained with ethidium bromide (EtBr). 
	NOTE: Use 2.5% agarose gel for a 50 bp ladder and 0.9% gel for a 1 kb ladder. Use 1x TAE buffer for a 2.5% agarose gel and 0.5x TAE buffer for a 0.9% agarose gel.</li>
</ol>
5. Detection of pmoA1 , pmoA2 , mmoX, and mcrA by quantitative real-time PCR
NOTE: Methanogen- and methanotroph-targeted genes such as pmoA1, pmoA2, mmoX, and mcrA abundance were observed by qPCR using a real-Time PCR system.
<ol>
	<li>Prepare the standards for each gene separately to obtain the standard curve for each gene.
	<ol>
		<li>Amplify the targeted gene with cPCR, using the samples that produced the brightest band during the gel electrophoresis of each gene. Follow the method and primers described in section 4.</li>
		<li>Purify the amplified products using a gel extraction kit and measure the DNA concentration following the method described in section 3 and store at -20 &#176;C. 
		NOTE: Aliquot the purified standards into separate tubes for further use.</li>
		<li>Calculate gene copies from the measured DNA concentration using the copy number calculating website for qPCR.</li>
		<li>Dilute the calculated copy number of the standard with nuclease-free water to prepare each standard, ranging from 108 to 102 copies/&#181;L, before running the qPCR.</li>
		<li>Prepare the standard curve using three replicates of each copy number, including a negative control (NTC, no template DNA). 
		NOTE: The R2 value of each standard curve was greater than 0.99.</li>
	</ol>
	</li>
	<li>Prepare a 20 &#181;L qPCR reaction mixture for all samples, standards, and NTC. Perform the qPCR analysis in triplicates for all samples.
	<ol>
		<li>Place all reaction components, including qPCR master mix, primers, nuclease-free water, standards, and samples, on an ice rack before beginning.</li>
		<li>Prepare a 20 &#181;L qPCR reaction mixture for each sample, standard, and NTC, containing 10 &#181;L of SYBR Green master mix, 0.5 &#181;L of each 10 &#181;M forward and reverse primers, 8 &#181;L of nuclease-free water, and 1 &#181;L of either 10 ng/&#181;L template DNA, or standard, or DI water respectively. 
		NOTE: Use the primer sets shown to yield optimal results for pmoA1 and mcrA genes in conventional PCR for qPCR (see Table 1). To improve accuracy, run each sample in triplicate. The following steps provide an efficient method for preparing triplicate samples with a combined volume of 60 &#181;L per sample.
		<ol>
			<li>Prepare the reaction mixture to combine the master mix, forward and reverse primers for the specific gene, and nuclease-free water in a 2 mL tube, except the template DNA. 
			NOTE: Prepare the reaction mixture volume considering pipetting error. For example, if there are 24 samples and 8 standards, calculate the total volume for 33 reactions instead of 32 reactions to minimize pipetting errors. In this case, the total volume required for triplicate reactions would be as follows: 990 &#181;L of master mix (33 samples x 3 replicates x 10 &#181;L), 49.5 &#181;L of forward primers (33 samples x 3 replicates x 0.5 &#181;L), 49.5 &#181;L of reverse primers (33 samples x 3 replicates x 0.5 &#181;L), and 792 &#181;L of nuclease-free water (33 samples x 3 replicates x 8 &#181;L).</li>
			<li>Prepare PCR tubes according to the number of samples and standards.</li>
			<li>Dispense 57 &#181;L of the prepared reaction mixture into each PCR tube. 
			NOTE: To perform qPCR for each sample in triplicate, prepare a total reaction mixture volume of 57 &#181;L per sample (excluding template DNA, standard, or water). This volume will be divided equally into three wells for one sample, with 19 &#181;L allocated to each well.</li>
			<li>Add 3 &#181;L of template DNA, standard, or nuclease-free water to each tube and mix by gently tapping the tube bottom. 
			NOTE: The total volume of reaction mixture prepared for one sample would be 60 &#181;L now in each tube.</li>
		</ol>
		</li>
		<li>Aliquot 20 &#181;L of the prepared reaction mixture from each tube into the designated wells of a 96-well qPCR plate. Seal the PCR plate with adhesive PCR Sealing Film using an applicator.</li>
		<li>Centrifuge the sealed plate at 1,000 &#215; g for 1 min to ensure proper mixing to eliminate any bubbles within the wells.</li>
	</ol>
	</li>
	<li>Place the PCR plate in the thermal cycler. Turn on the qPCR machine and then open the related software to set up the protocol.
	<ol>
		<li>Set up the protocol according to the qPCR master mix guidelines. Use the following protocol: 95 &#176;C for 10 min, followed by 95 &#176;C for 15 s, and an extension step at 72 &#176;C for 30 s. Perform the annealing step at the annealing temperature specified for the relevant primers in Table 1 for 45 s. Conduct all qPCR runs for 35 cycles.</li>
		<li>Set up the 96-well qPCR plate with standards and NTCs in the same configuration as the sample-containing plate.</li>
	</ol>
	</li>
	<li>Use the absolute quantification standard curve method to quantify the amplified product and the gene copy number in each sample33.</li>
</ol>
6. Visualizing methane-cycling genes in the map of South Texas Coastal wetlands
<ol>
	<li>Open geographic information system (GIS) software ArcGIS Pro and save the project file with the name Study Area in the specified folder on the computer.</li>
	<li>Click on Map in the top left | Basemap and select Terrain with Labels as the basemap.</li>
	<li>Click on Locate | Search and when the search bar opens up, locate the study area by typing the area&#39;s name; the area will show up.</li>
	<li>Draw the specific area using georeferencing.
	<ol>
		<li>Click on View | Catalog Pane from the top layer.</li>
		<li>Double-click on Folder from the catalog | File Name.</li>
		<li>Right-click on geodatabase (.gdb) file and then click on New | Feature Class and Create Feature Class will show up.</li>
		<li>Type Name and Alias box and click Finish at the bottom.</li>
		<li>Click on View | Contents. The Alias name will show up in the Contents Pane.</li>
		<li>Click on Edit from the top layer | Create. The Create Features pane will open. Double-click on the Alias in Create Features pane, and the Configure Tool Feedback Options will appear.</li>
		<li>Select Lines, then sketch lines on the map to create an outside boundary of the study area. Double-click on the map when finished.</li>
	</ol>
	</li>
	<li>Minimize GIS software, then open a spreadsheet. Type the sample name in the first column, enter the latitude in the second column, and the longitude in the third column. Use the next four columns for the qPCR data of pmoA1, pmoA2, mmoX, and mcrA.</li>
	<li>Save the file in CSV format in any specific folder of the computer.</li>
	<li>Open the GIS software again and click Add Data | XY Point Data.</li>
	<li>Select the CSV file from the folder on the computer in the Input Table box. Rename the file name in the Output Feature Class, then click Run to display the sampling points on the map.</li>
	<li>Click on thevsearch bar at the top and search Kriging.</li>
	<li>Select the sampling station file and then select pmoA1.</li>
	<li>Click the Environment | select the sampling station in layer and mask | click Run.</li>
	<li>Follow the protocols mentioned in steps 6.9, 6.10, and 6.11 to create Kriging for pmoA2, mmoX, and mcrA for all the study area.</li>
	<li>Create a layout of the map.
	<ol>
		<li>Click on Insert from the top layer | New Layout, and select ANSI - Landscape.</li>
		<li>Click on Map Frame, select the map with the Kriging, and place all the maps in the layout by drawing a rectangle. This will make the map visible in the layout.</li>
		<li>Select the North Arrow and place it in the layout to indicate the North direction.</li>
		<li>Select Scale Bar to display the scale of the area on the map.</li>
		<li>Click on Legend to display the legends, then place it in the layout.</li>
		<li>Click Grid and select any of the black graticule options. This will create the grid with latitude and longitude and display it in the Contents Pane with the label Black Horizontal Label Graticule.</li>
		<li>Double-click on Black Horizontal Label Graticule, then select Components. Click on Ticks 1 and Grid, and remove these components by clicking on the cross sign to their right.</li>
	</ol>
	</li>
	<li>Click on Share from the top layer, then click Export Layout. Select the file type as PDF, save the file on the computer using the Name Box, set the vertical resolution to 500 DPI, and click Export to create the PDF file of the map.</li>
</ol>

The authors have no conflicts of interest to declare.

Coastal wetlands are recognized as significant contributors to atmospheric methane, an important greenhouse gas40. Although there have been studies on methane flux and methanogens in wetlands41,42,43, little is known about how methanotrophs operate across different environments or under various management practices, especially in wetlands with fluctuating water levels44. Moreover, ...

Coastal wetlands are vital ecosystems that contribute to climate regulation, biodiversity conservation, and water management through processes such as carbon sequestration, evapotranspiration, and methane (CH4) emissions1. These ecosystems, including both freshwater and saltwater wetlands2, are highly productive and act as critical zones for uptake of carbon dioxide (CO2) and capture organic matter from terrestrial and marine environments3,4. The dynamic interactions within these wetlands stimulate microbial CH4 production and consumption5, positioning them as one of the largest natural sources of CH46. As the second most important greenhouse gas, CH4 has a global warming potential approximately 27-30x greater than that of CO24,7,8,9, making the study of CH4 emissions from coastal wetlands essential in the era of climate change. The emission of CH4 is influenced by various environmental factors, particularly salinity, playing a crucial role in microbial processes10. Freshwater wetlands contribute significantly to atmospheric methane due to their lower sulfate levels, which facilitates greater microbial CH4 production, whereas saltwater wetlands generally tend to emit less CH4 due to higher sulfate concentrations11,12,13.
CH4 emissions from coastal wetlands are generally controlled by two groups of microorganisms, known as methanogens and methanotrophs14. Methanogens produce CH4 in anoxic sediments by breaking down substrates like formate, acetate, hydrogen, or methylated compounds through a process known as methanogenesis15. The important enzyme in this pathway is methyl-coenzyme M reductase (MCR), as it catalyzes the final and rate-limiting step of methanogenesis15,16,17. The mcrA gene, which encodes the alpha subunit of MCR, is a functional marker that can be found in all methanogenic archaea18. Moreover, in coastal wetlands, the sulfate-methane transition zone (SMTZ) forms above the methanogenic zone, where methane diffusing upward and sulfate moving downward converge and are depleted19. Within this zone, anaerobic methanotrophic archaea (ANME) oxidize methane to carbon dioxide using the MCR enzyme, while sulfate-reducing bacteria (SRB) reduce sulfate to sulfide. SRB outcompete methanogens for hydrogen and acetate, limiting methane production until sulfate is depleted16,17.
In contrast, aerobic methanotrophic bacteria oxidize CH4 in aerobic environments20, utilizing different forms of methane monooxygenase (MMO). These include particulate methane monooxygenase (pMMO), a copper-containing enzyme embedded in the intracytoplasmic membrane, and soluble methane monooxygenase (sMMO), an iron-containing enzyme found in the cytoplasm. However, for pMMO, there are three gene operons pmoCAB21; among them, pmoA gene is the most conservative for all the methanotrophs. There are two different biomarker genes for pmoA: pmoA1 and pmoA222. Moreover, for a comprehensive understanding of methanotrophs, mmoX gene is used as a tool in molecular biology to identify sMMO-containing methanotrophs23. This distinction in metabolic pathways and environmental requirements of methanogens and aerobic methanotrophs highlights the complex microbial interactions regulating methane cycling in coastal wetland ecosystems.
The Boca Chica (BC) wetland, a productive saltwater environment in South Texas, experiences tidal influences from the Gulf of Mexico (GOM), leading to variable surface salinity levels, especially due to its proximity to the hypersaline Laguna Madre24. This tidal action, alternating between high and low tides, causes oxygen levels to fluctuate25 that might alter methanogen and methanotroph activity in sediments26. In contrast, coastal freshwater wetlands are considered to be a significant hotspot for CH4 fluxes27. The coastal freshwater wetlands in South Texas, including Resaca Del Rancho Viejo (RV) and Lozano Banco (LB), distant from the GOM&#39;s tidal effects, have distinct hydrological management. RV experiences pulse flows supplemented by river water during low water levels, whereas LB operates as an offline flow system without such supplementation. Moreover, RV and LB maintain lower salinity levels due to a high discharge of artificially pumped freshwater and being an oxbow lake, respectively. The different environmental factors can significantly influence methane cycling across South Texas coastal wetlands. However, methane cycling in South Texas coastal wetlands remains an area that has yet to be thoroughly investigated.
Polymerase chain reaction (PCR) and real-time PCR (also called quantitative PCR [qPCR]) represent fundamental and widely utilized techniques for detecting and quantifying the relative abundance of specific genes in environmental samples. These techniques specifically amplify targeted regions of DNA to indicate the presence and relative quantity of CH4 cycling-related genes, providing indicators of potential methane cycling. Nevertheless, the availability and efficacy of PCR primer sets might be limited by various inhibitory factors in the extracted environmental DNA, being impacted by the types of environments28,29. Thus, this study mainly established an optimal PCR method for detecting the presence of CH4 cycling-related genes in South Texas coastal wetlands (Figure 1) and then visualized their quantified relative abundance in these ecosystems. The results from this study can be applied to other coastal regions to enhance the understanding of CH4 cycling and microbial dynamics in diverse coastal ecosystems.

<table><tbody><tr><td>0.2 mL PCR tubes</td><td>ThermoFisher Scientific</td><td>AB0620</td><td>https://www.thermofisher.com/order/catalog/product/AB0620?SID=srch-srp-AB0620</td></tr><tr><td>0.5 mL PCR Tubes</td><td>Promega</td><td>E4941</td><td>https://www.promega.com/products/biochemicals-and-labware/tips-and-accessories/0_5ml-pcr-tubes/?catNum=E4941</td></tr><tr><td>10 &amp;mu;L tips</td><td>ThermoFisher Scientific</td><td>05-408-187</td><td>Fisherbrand SureGrip Pipet Tip Racked or Reload System Tips Natural; 10&amp;mu;L; | Fisher Scientific</td></tr><tr><td>15 mL centrifuge tube</td><td>ThermoFisher Scientific</td><td>14-959-53A</td><td>https://www.fishersci.com/shop/products/falcon-15ml-conical-centrifuge-tubes-5/p-193301</td></tr><tr><td>200 &amp;mu;L tips</td><td>ThermoFisher Scientific</td><td>05-408-190</td><td>Fisherbrand SureGrip Pipet Tip Racked or Reload System Tips Natural; 200&amp;mu;L; | Fisher Scientific</td></tr><tr><td>1000 &amp;mu;L tips</td><td>ThermoFisher Scientific</td><td>02-707-402</td><td>https://www.fishersci.com/shop/products/sureone-micropoint-pipette-tips-specific-standard-fit/02707402?gclid=Cj0KCQiAp&lt;br/&gt; NW6BhD5ARIsACmEb&lt;br/&gt; kUsQ9Lu0YIq5i4vWege&lt;br/&gt; 17qPdtxIYZyvmJH1cDo&lt;br/&gt; ARuwereO1V4GLz9UaA&lt;br/&gt; lDREALw_wcB&amp;amp;ef_id=C&lt;br/&gt; j0KCQiApNW6BhD5ARI&lt;br/&gt; sACmEbkUsQ9Lu0YIq5i&lt;br/&gt; 4vWege17qPdtxIYZyvmJ&lt;br/&gt; H1cDoARuwereO1V4GLz&lt;br/&gt; 9UaAlDREALw_wcB:G:s&lt;br/&gt; &amp;amp;ppc_id=PLA_goog_2175&lt;br/&gt; 7693617_171052169911_02&lt;br/&gt; 707402__715434303113_1555&lt;br/&gt; 377385658230343&amp;amp;ev_chn=sh&lt;br/&gt; op&amp;amp;s_kwcid=AL!4428!3!71543430&lt;br/&gt; 3113!!!g!2366517300713!&amp;amp;gad_source=1</td></tr><tr><td>Applied Biosystem Power SYBR Green Master Mix&amp;nbsp;</td><td>ThermoFisher Scientific</td><td>4368577</td><td>https://www.thermofisher.com/order/catalog/product/4368577</td></tr><tr><td>ArcGIS Pro&amp;nbsp;</td><td>esri</td><td></td><td>https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview?srsltid=AfmBOopatJ4&lt;br/&gt; JvHJfscHRcAaDx0Jz5_Jrl8l5&lt;br/&gt; vYkkBvfOqE-uNSsMghN1</td></tr><tr><td>CFX Duet Real-Time PCR system&amp;nbsp;</td><td>Bio-Rad</td><td>12016265</td><td>https://www.bio-rad.com/en-us/product/cfx-duet-real-time-pcr-system?ID=97722926-9ed9-16a4-1d83-c92f587e427a</td></tr><tr><td>Corning Lambda plus single channel pipettor&lt;br/&gt; volume 0.5-10 &amp;mu;L</td><td>Sigma-Aldrich</td><td>CLS4071-1EA</td><td>https://www.sigmaaldrich.com/US/en/product/sigma/cls4071</td></tr><tr><td>Corning&amp;nbsp;Lambda&amp;nbsp;plus single channel pipettor volume 100-1000&amp;nbsp;&amp;mu;L</td><td>Sigma-Aldrich</td><td>CLS4075-1EA</td><td>https://www.sigmaaldrich.com/US/en/product/sigma/cls4075</td></tr><tr><td>Corning&amp;nbsp;Lambda&amp;nbsp;plus single channel pipettor volume 20-200&amp;nbsp;&amp;mu;L</td><td>Sigma-Aldrich</td><td>CLS4074-1EA</td><td>https://www.sigmaaldrich.com/US/en/product/sigma/cls4074</td></tr><tr><td>FastDNA spin kit for soil</td><td>MP Biomedical</td><td>116560200-CF</td><td>https://www.mpbio.com/us/116560000-fastdna-spin-kit-for-soil-samp-cf?srsltid=AfmBOoqOxxGilzY3IHNIZR&lt;br/&gt; ajegGTr9MoX1oMZUh&lt;br/&gt; 3dcbJqe0UvvukY128</td></tr><tr><td>Gene copy&amp;nbsp; calculator</td><td>Science Primer</td><td></td><td>https://scienceprimer.com/copy-number-calculator-for-realtime-pcr&amp;nbsp;.</td></tr><tr><td>High speed benchtop centrifuge</td><td>ThermoFisher Scientific</td><td>75004241</td><td>https://newlifescientific.com/products/thermo-scientific-sorvall-st16-high-speed-benchtop-centrifuge-75004241?gad_source=1&amp;amp;gclid=Cj0KCQiApN&lt;br/&gt; W6BhD5ARIsACmEbkVC_-cCIN9j&lt;br/&gt; 20TvYq8iDsBlUR5cPK_1_wN&lt;br/&gt; OBEcjMdv-CYVoGCfeOLYaAv&lt;br/&gt; enEALw_wcB</td></tr><tr><td>High speed microcentrifuge</td><td>VWR</td><td>75838-336</td><td>https://us.vwr.com/store/product/20546590/null</td></tr><tr><td>Lysing Matrix E tube&amp;nbsp;</td><td></td><td></td><td>glass bead/ceramic sphere-containing tube</td></tr><tr><td>Microcentrifuge tube</td><td>ThermoFisher Scientific</td><td>02-681-320</td><td>https://www.fishersci.com/shop/products/fisherbrand-low-retention-microcentrifuge-tubes-8/02681320?gclid=Cj0KCQiAp&lt;br/&gt; NW6BhD5ARIsACm&lt;br/&gt; EbkWbG4_o3oUiGk&lt;br/&gt; HJPU-_31-CuexDwQ&lt;br/&gt; fmWPnfyhBOf2BHXsy&lt;br/&gt; K3fFW1toaAgJbEALw_&lt;br/&gt; wcB&amp;amp;ef_id=Cj0KCQiAp&lt;br/&gt; NW6BhD5ARIsACmEb&lt;br/&gt; kWbG4_o3oUiGkHJPU-&lt;br/&gt; _31-CuexDwQfmWPnfy&lt;br/&gt; hBOf2BHXsyK3fFW1toa&lt;br/&gt; AgJbEALw_wcB:G:s&amp;amp;ppc&lt;br/&gt; _id=PLA_goog_21757693&lt;br/&gt; 617_171052169911_0268&lt;br/&gt; 1320__715434303113_10&lt;br/&gt; 349826094968484711&amp;amp;ev&lt;br/&gt; _chn=shop&amp;amp;s_kwcid=AL!4&lt;br/&gt; 428!3!715434303113!!!g!23&lt;br/&gt; 66517300713!&amp;amp;gad_source=1</td></tr><tr><td>PCR Master mix&amp;nbsp;</td><td>Promega</td><td>M7502</td><td>https://www.promega.com/products/pcr/taq-polymerase/master-mix-pcr/?catNum=M7502</td></tr><tr><td>Quantiflour ONE dsDNA system&amp;nbsp;</td><td>Promega</td><td>E4871</td><td>https://www.promega.com/products/rna-analysis/dna-and-rna-quantitation/quantifluor-one-dsdna-system/?gad_source=1&amp;amp;gbraid=0AAAAAD&lt;br/&gt; _rg189yJTY3cxeVqMdu8RPx10&lt;br/&gt; Ma&amp;amp;gclid=CjwKCAjwxNW2BhAk&lt;br/&gt; EiwA24Cm9FUgViPNyWq7UfZL&lt;br/&gt; VeeoroLAZ5JIP6w07RGK_4D0w&lt;br/&gt; oZgAqf-G1XTmxoCxm8QAvD_B&lt;br/&gt; wE&amp;amp;catNum=E4871</td></tr><tr><td>Quantus Fluorometer&amp;nbsp;</td><td>Promega</td><td>E6150</td><td>https://www.promega.com/products/microplate-readers-fluorometers-luminometers/fluorometers/quantus-fluorometer/?catNum=E6150</td></tr><tr><td>YSI Pro 2030</td><td>YSI a xylem brand</td><td>603174</td><td>https://www.ysi.com/product/id-p2030/pro2030-kits</td></tr></tbody></table>

visualizing methane-cycling microbial dynamics in coastal wetlands

Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

Coastal wetlands are the largest biotic source of methane, where methanogens convert organic matter into methane and methanotrophs oxidize methane, thus playing a critical role in regulating the methane cycle. The wetlands in South Texas, which are subject to frequent weather events, fluctuating salinity levels, and anthropogenic activities due to climate change, influence methane cycling. Despite the ecological importance of these processes, methane cycling in South Texas coastal wetlands remains insufficiently explored. To address this gap, we developed and optimized a method for detecting genes related to methanogens and methanotrophs, including mcrA as a biomarker for methanogens and pmoA1, pmoA2, and mmoX as biomarkers for methanotrophs. Additionally, this study aimed to visualize the spatial and temporal distribution patterns of methanogen and methanotroph abundance utilizing the geographic information system (GIS) software ArcGIS Pro. The integration of these molecular techniques with advanced geospatial visualization provided critical insights into the spatial and temporal distribution of methanogen and methanotroph communities across South Texas wetlands. Thus, the methodology established in this study offers a robust framework for mapping microbial dynamics in wetlands, enhancing our understanding of methane cycling under varying environmental conditions, and supporting broader ecological and environmental change studies.

To understand the distribution and abundance of CH4 cycling-related genes (mcrA, pmoA1, pmoA2, and mmoX) in the coastal wetlands of South Texas, the extracted eDNA from each sample was analyzed by cPCR and qPCR. Universal primers for each biomarker were selected to run cPCR from previous studies (Table 1)22,34,35,36,<sup...

The protocol detects key methane-cycling genes in South Texas coastal wetlands and visualizes their spatial distribution to enhance understanding of methane regulation and its environmental impacts in these dynamic ecosystems.

Coastal wetlands are the largest biotic source of methane, where methanogens convert organic matter into methane and methanotrophs oxidize methane, thus ...

visualizing-methane-cycling-microbial-dynamics-in-coastal-wetlands

Research

JoVE Journal

Environment

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

A protocol for the generation of dynamic chemical landscapes by photolysis within microfluidic and millifluidic setups is presented. This methodology is suitable to study diverse biological processes, including the motile behavior, nutrient uptake, or adaptation to chemicals of microorganisms, both at the single cell and population level.

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Here, we aim to visualize the zonation of biological productivity in Narragansett Bay, Rhode Island, based on the nitrogen mass balance model. The results will inform nutrient management in the coastal regions to reduce hypoxia and eutrophication.

Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island

This study demonstrates an approach to measure methane gas concentrations in aqueous samples using portable optical analyzers coupled to an injection chamber in a closed loop. The results are similar to conventional gas chromatography, presenting a practical and low-cost alternative particularly suitable for remote field studies.

Measuring Dissolved Methane in Aquatic Ecosystems Using An Optical Spectroscopy Gas Analyzer

A protocol is described for preparing a simple model ecosystem that recreates the methane-oxygen counter gradient found in the natural habitat of aerobic methane-oxidizing bacteria, enabling the study of their physiology in a spatially resolved context. Modifications to common biochemical assays for use with the agarose-based model ecosystem are also described.

Agarose-Based Model Ecosystem for Cultivating Methanotrophs in a Methane-Oxygen Counter Gradient

Here we present a protocol outlining how to sample wooden specimens for the overall assessment of their growth structures. Macro- and microscopic preparation and visualization techniques necessary to generate well-replicated and highly resolved wood anatomical and dendroecological dataset, are described are described.

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

This manuscript describes an automated gel size selection approach for purifying DNA fragments for next-generation sequencing. The Ranger Technology provides complete automation of the entire process of agarose gel loading, electrophoretic analysis, and recovery of targeted DNA fragments allowing for high-throughput and high quality next-generation sequencing libraries.

Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples

A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. The methodology includes the co-extraction of DNA and RNA; preparation of DNA-free RNA; and 16S rRNA gene and transcript quantification via RT-q-PCR, including standard curve construction.

Simultaneous DNA-RNA Extraction from Coastal Sediments and Quantification of 16S rRNA Genes and Transcripts by Real-time PCR

Phospholipid fatty acids provide information about the structure of soil microbial communities. We present methods for extraction from soil samples with a single-phase chloroform mixture, fractionation of extracted lipids using solid phase extraction columns, and methanolysis to produce fatty acid methyl esters, which are analyzed by capillary gas chromatography.

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils

Explore spatial mapping of methane-cycling genes in South Texas coastal wetlands using PCR and GIS visualization. This protocol reveals how environmental factors regulate microbial dynamics and gene abundance seasonally.

Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

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