Center for Biofilm Engineering (CBE)

Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/9334

At the Center for Biofilm Engineering (CBE), multidisciplinary research teams develop beneficial uses for microbial biofilms and find solutions to industrially relevant biofilm problems. The CBE was established at Montana State University, Bozeman, in 1990 as a National Science Foundation Engineering Research Center. As part of the MSU College of Engineering, the CBE gives students a chance to get a head start on their careers by working on research teams led by world-recognized leaders in the biofilm field.

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Microbial Functional Gene Diversity Predicts Groundwater Contamination and Ecosystem Functioning
(2018-02) He, Zhili; Zhang, Ping; Wu, Linwei; Rocha, Andrea M.; Tu, Qichao; Shi, Zhou; Wu, Bo; Qin, Yujia; Wang, Jianjun; Yan, Qingyun; Curtis, Daniel; Ning, Daliang; Van Nostrand, Joy D.; Wu, Liyou; Yang, Yunfeng; Elias, Dwayne A.; Watson, David B.; Adams, Michael W. W.; Fields, Matthew W.; Alm, Eric J.; Hazen, Terry C.; Adams, Paul D.; Arkin, Adam P.; Zhou, Jizhong
Contamination from anthropogenic activities has significantly impacted Earth\'s biosphere. However, knowledge about how environmental contamination affects the biodiversity of groundwater microbiomes and ecosystem functioning remains very limited. Here, we used a comprehensive functional gene array to analyze groundwater microbiomes from 69 wells at the Oak Ridge Field Research Center (Oak Ridge, TN), representing a wide pH range and uranium, nitrate, and other contaminants. We hypothesized that the functional diversity of groundwater microbiomes would decrease as environmental contamination (e.g., uranium or nitrate) increased or at low or high pH, while some specific populations capable of utilizing or resistant to those contaminants would increase, and thus, such key microbial functional genes and/or populations could be used to predict groundwater contamination and ecosystem functioning. Our results indicated that functional richness/diversity decreased as uranium (but not nitrate) increased in groundwater. In addition, about 5.9% of specific key functional populations targeted by a comprehensive functional gene array (GeoChip 5) increased significantly (P < 0.05) as uranium or nitrate increased, and their changes could be used to successfully predict uranium and nitrate contamination and ecosystem functioning. This study indicates great potential for using microbial functional genes to predict environmental contamination and ecosystem functioning.IMPORTANCE Disentangling the relationships between biodiversity and ecosystem functioning is an important but poorly understood topic in ecology. Predicting ecosystem functioning on the basis of biodiversity is even more difficult, particularly with microbial biomarkers. As an exploratory effort, this study used key microbial functional genes as biomarkers to provide predictive understanding of environmental contamination and ecosystem functioning. The results indicated that the overall functional gene richness/diversity decreased as uranium increased in groundwater, while specific key microbial guilds increased significantly as uranium or nitrate increased. These key microbial functional genes could be used to successfully predict environmental contamination and ecosystem functioning. This study represents a significant advance in using functional gene markers to predict the spatial distribution of environmental contaminants and ecosystem functioning toward predictive microbial ecology, which is an ultimate goal of microbial ecology.
Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses
(2016-08) Montenegro-Burke, Jose R.; Phommavongsay, Thiery; Aisporna, Aries E.; Huan, Tao; Rinehart, Duane; Forsberg, Erica M.; Poole, Farris L.; Thorgersen, Michael P.; Adams, Michael W. W.; Krantz, Gregory; Fields, Matthew W.; Northen, Trent R.; Robbins, Paul D.; Niedernhofer, Laura J.; Lairson, Luke L.; Benton, H. Paul; Siuzdak, Gary
Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.
Systems biology guided by XCMS Online metabolomics
(2017-04) Huan, Tao; Forsberg, Erica M.; Rinehart, Duane; Johnson, Caroline H.; Ivanisevic, Julijana; Benton, H. Paul; Fang, Mingliang; Aisporna, Aries E.; Hilmers, Brian; Poole, Farris L.; Thorgersen, Michael P.; Adams, Michael W. W.; Krantz, Gregory; Fields, Matthew W.; Robbins, Paul D.; Niedernhofer, Laura J.; Ideker, Trey; Majumder, Erica L.; Wall, Judy D.; Rattray, Nicholas J. W.; Goodacre, Royston; Lairson, Luke L.; Siuzdak, Gary