Shahida Ferdousee, Mohammad Sadman Alam, Myung Hwangbo, Jongsun Kim
{"title":"Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands.","authors":"Shahida Ferdousee, Mohammad Sadman Alam, Myung Hwangbo, Jongsun Kim","doi":"10.3791/67715","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48787,"journal":{"name":"Jove-Journal of Visualized Experiments","volume":" 215","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jove-Journal of Visualized Experiments","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.3791/67715","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
JoVE, the Journal of Visualized Experiments, is the world''s first peer reviewed scientific video journal. Established in 2006, JoVE is devoted to publishing scientific research in a visual format to help researchers overcome two of the biggest challenges facing the scientific research community today; poor reproducibility and the time and labor intensive nature of learning new experimental techniques.