{"522451":{"#nid":"522451","#data":{"type":"news","title":"Dynamic Model Helps Understand Healthy Lakes to Heal Sick Ones","body":[{"value":"\u003Cp\u003EDevelopment of a dynamic model for microbial populations in healthy lakes could help scientists understand what\u2019s wrong with sick lakes, prescribe cures and predict what may happen as environmental conditions change. Those are among the benefits expected from an ambitious project to model the interactions of some 18,000 species in a well-studied Wisconsin lake.\u003C\/p\u003E\u003Cp\u003EThe research produced what may be the largest dynamic model of microbial species interactions ever created. Analyzing long-term data from Lake Mendota near Madison, Wisconsin, a Georgia Tech research team identified and modeled interactions among 14 sub-communities, that is, collections of different species that become dominant at specific times of the year. Key environmental factors affecting these sub-communities included water temperature and the levels of two nutrient classes: ammonia\/phosphorus and nitrates\/nitrites. The effects of these factors on the individual species were, in general, more pronounced than those of species-species interactions.\u003C\/p\u003E\u003Cp\u003EBeyond understanding what\u2019s happening in aquatic microbial environments, the model might also be used to study other microbial populations \u2013 perhaps even human microbiomes. The research was reported on March 24 in the journal \u003Cem\u003ESystems Biology and Applications\u003C\/em\u003E, a Nature partner journal. The work was sponsored by the National Science Foundation\u2019s Dimensions of Biodiversity program.\u003C\/p\u003E\u003Cp\u003E\u201cUltimately, we want to understand why some microbial populations are declining and why some are increasing at certain times of the year,\u201d said \u003Ca href=\u0022https:\/\/www.bme.gatech.edu\/bme\/faculty\/Eberhard-Voit\u0022\u003EEberhard Voit\u003C\/a\u003E, the paper\u2019s corresponding author and The David D. Flanagan Chair Professor in the \u003Ca href=\u0022http:\/\/www.bme.gatech.edu\/\u0022\u003EWallace H. Coulter Department of Biomedical Engineering\u003C\/a\u003E at Georgia Tech and Emory University. \u201cWe want to know why these populations are changing \u2013 whether it is because of environmental conditions alone, or interactions between the different species. Importantly, we also look at the temporal development: how interactions change over time.\u201d\u003C\/p\u003E\u003Cp\u003EBecause of the large number of different microorganisms involved, creating such a model was a monumental task. To make it more manageable, the researchers segmented the most abundant species into groups that had significant interactions at specific times of the year. Georgia Tech Research Scientist Phuongan Dam created 14 such categories or sub-communities \u2013 corresponding to roughly one per month \u2013 and mapped the relationships between them during different times of the year. Two of the 14 groups had two population peaks per year.\u003C\/p\u003E\u003Cp\u003E\u201cThe exciting part about this work is that we are now able to model hundreds of species,\u201d said \u003Ca href=\u0022http:\/\/www.cee.gatech.edu\/people\/Faculty\/711\/overview\u0022\u003EKostas Konstantinidis\u003C\/a\u003E, a co-author on the paper and the Carlton S. Wilder associate professor in Georgia Tech\u2019s \u003Ca href=\u0022http:\/\/www.cee.gatech.edu\/\u0022\u003ESchool of Civil and Environmental Engineering\u003C\/a\u003E. \u201cThe ability to dynamically model microbial communities containing hundreds or even thousands of species as those interactions change over time or after environmental perturbations will have numerous implications and applications for other research areas.\u201d\u003C\/p\u003E\u003Cp\u003EIn the past, researchers have created static models of interactions between large numbers of microorganisms, but those provided only snapshots in time and couldn\u2019t be used to model interactions as they change throughout the year. Scientists might want to know, for example, what would happen if a community lost one species, if a flood of nutrients hit the lake or if the temperature rose.\u003C\/p\u003E\u003Cp\u003EAs with many communities, the lake includes organisms from different species and families that are highly interconnected, playing a variety of interrelated roles, such as fixing nitrogen, carrying out photosynthesis, degrading pollutants and providing metabolic services used by other organisms. Information about the microbes came from a long-term data set compiled by other scientists who study the lake on a regular basis.\u003C\/p\u003E\u003Cp\u003EVoit, a bio-mathematician, said the model, although itself nonlinear, uses algorithms based on linear regression, which can be analyzed using standard computer clusters. Using their 14 sub-communities, the researchers found 196 interactions that could describe the species interactions \u2013 a far easier task than analyzing the 300 million potential interactions between the full 18,642 species in the lake. Reducing the number of potential interactions was possible only due to the strategy of defining sub-communities and a clever modeling approach.\u003C\/p\u003E\u003Cp\u003EThe researchers initially tried to organize the microbes into genetically related organisms, but that strategy failed.\u003C\/p\u003E\u003Cp\u003E\u201cAt any time of the year, the lake needs species that can do certain tasks,\u201d said Voit. \u201cClosely-related species tend to play essentially the same roles, so that putting them all together into the same group results in having many organisms doing the same things \u2013 but not executing other tasks that are needed at a specific time. By looking at the 14 sub-communities, we were able to get a smorgasbord of every task that needed to be done using different combinations of the microorganisms at each time.\u201d\u003C\/p\u003E\u003Cp\u003EBy looking at sub-communities present at specific times of the year, the research team was able to study interactions that occurred naturally \u2013 and avoided having to study interactions that rarely took place. The model examines interactions at two levels: among the 14 sub-communities, and between the sub-communities and individual species.\u003C\/p\u003E\u003Cp\u003EThe research depended heavily on metagenomics, the use of genomic analysis to identify the microorganisms present. Only 1 percent of microbial species can be cultured in the laboratory, but metagenomics allows scientists to obtain the complete inventory of species present by identifying specific sections of their DNA. Because they are not fully characterized species, the components of genomic data are termed \u201coperational taxonomic units\u201d (OTUs), which the team used as a \u0022proxy\u0022 for species.\u003C\/p\u003E\u003Cp\u003EThe next step in the research will be to complete a similar study of Lake Lanier, located north of Atlanta. In addition to the information studied for Lake Mendota, that study will gather data about the enzymatic and metabolic activities of the microorganism communities. Lake Lanier feeds the Chattahoochee River and a series of other lakes, and the researchers hope to study the entire river system to assess how different environments and human activities affect the microbial populations.\u003C\/p\u003E\u003Cp\u003EThe work could lead to a better understanding of what interactions are necessary for a healthy lake, which may help scientists determine what might be needed to address problems in sick lakes. The modeling technique might also help scientists with other complex microbial systems.\u003C\/p\u003E\u003Cp\u003E\u201cOur work right now is with the lake community, but the methods could be applicable to other microbial communities, including the human microbiome,\u201d said Konstantinidis. \u201cAs with sick lakes, understanding what is healthy might one day allow scientists to diagnose microbiome-related disease conditions and address them by adjusting the populations of different microorganism sub-communities.\u201d\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EThis material is based upon work supported by the National Science Foundation under Grant No. DEB-1241046. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECITATION\u003C\/strong\u003E: Phuongan Dam, Luis L. Fonseca, Konstantinos T. Konstantinidis and Eberhard O. Voit, \u201cDynamic models of the complex microbial metapopulation of Lake Mendota,\u201d (Nature Partner Journal Systems Biology and Applications, 2016). \u003Ca href=\u0022http:\/\/dx.doi.org\/10.1038\/npjsba.2016.7\u0022\u003Ehttp:\/\/dx.doi.org\/10.1038\/npjsba.2016.7\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EResearch News\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003EGeorgia Institute of Technology\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003E177 North Avenue\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003EAtlanta, Georgia 30332-0181 USA\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EMedia Relations Contacts\u003C\/strong\u003E: John Toon (\u003Ca href=\u0022mailto:jtoon@gatech.edu\u0022\u003Ejtoon@gatech.edu\u003C\/a\u003E) (404-894-6986) or Ben Brumfield (\u003Ca href=\u0022mailto:ben.brumfield@comm.gatech.edu\u0022\u003Eben.brumfield@comm.gatech.edu\u003C\/a\u003E) (404-385-1933).\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EWriter\u003C\/strong\u003E: John Toon\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EDevelopment of a dynamic model for microbial populations in healthy lakes could help scientists understand what\u2019s wrong with sick lakes, prescribe cures and predict what may happen as environmental conditions change. Those are among the benefits expected from an ambitious project to model the interactions of some 18,000 species in a well-studied Wisconsin lake.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A dynamic model for microbial populations in healthy lakes could help scientists understand what\u2019s wrong with sick lakes."}],"uid":"27303","created_gmt":"2016-04-07 11:33:15","changed_gmt":"2016-10-08 03:21:17","author":"John Toon","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2016-04-07T00:00:00-04:00","iso_date":"2016-04-07T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"522421":{"id":"522421","type":"image","title":"Lake Lanier, Georgia","body":null,"created":"1460134800","gmt_created":"2016-04-08 17:00:00","changed":"1475895291","gmt_changed":"2016-10-08 02:54:51","alt":"Lake Lanier, Georgia","file":{"fid":"205371","name":"lake-lanier_3652-sm.jpg","image_path":"\/sites\/default\/files\/images\/lake-lanier_3652-sm_0.jpg","image_full_path":"http:\/\/www.tlwarc.hg.gatech.edu\/\/sites\/default\/files\/images\/lake-lanier_3652-sm_0.jpg","mime":"image\/jpeg","size":188627,"path_740":"http:\/\/www.tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/lake-lanier_3652-sm_0.jpg?itok=MtrrYGE8"}},"522431":{"id":"522431","type":"image","title":"Lake Lanier, Georgia2","body":null,"created":"1460134800","gmt_created":"2016-04-08 17:00:00","changed":"1475895291","gmt_changed":"2016-10-08 02:54:51","alt":"Lake Lanier, Georgia2","file":{"fid":"205372","name":"lake-lanier_3673-sm.jpg","image_path":"\/sites\/default\/files\/images\/lake-lanier_3673-sm_1.jpg","image_full_path":"http:\/\/www.tlwarc.hg.gatech.edu\/\/sites\/default\/files\/images\/lake-lanier_3673-sm_1.jpg","mime":"image\/jpeg","size":101427,"path_740":"http:\/\/www.tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/lake-lanier_3673-sm_1.jpg?itok=KnO67FOj"}}},"media_ids":["522421","522431"],"groups":[{"id":"1188","name":"Research Horizons"}],"categories":[{"id":"145","name":"Engineering"},{"id":"154","name":"Environment"},{"id":"146","name":"Life Sciences and Biology"},{"id":"135","name":"Research"}],"keywords":[{"id":"251","name":"Eberhard Voit"},{"id":"12758","name":"Kostas Konstantinidis"},{"id":"9262","name":"lake"},{"id":"7078","name":"microbe"},{"id":"171898","name":"microbial modeling"},{"id":"171899","name":"microbial monitoring"}],"core_research_areas":[{"id":"39441","name":"Bioengineering and Bioscience"},{"id":"39531","name":"Energy and Sustainable Infrastructure"}],"news_room_topics":[{"id":"71911","name":"Earth and Environment"}],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EJohn Toon\u003C\/p\u003E\u003Cp\u003EResearch News\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:jtoon@gatech.edu\u0022\u003Ejtoon@gatech.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E(404) 894-6986\u003C\/p\u003E","format":"limited_html"}],"email":["jtoon@gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}