東華大學圖書館 |

Metagenomic Characterization of Freshwater Microbial Communities to Evaluate Recreational Coastal Water Quality.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Metagenomic Characterization of Freshwater Microbial Communities to Evaluate Recreational Coastal Water Quality./
作者:	Durant, Brandon.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	125 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Water resources management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28964641
ISBN:	9798790655098

Metagenomic Characterization of Freshwater Microbial Communities to Evaluate Recreational Coastal Water Quality.
Durant, Brandon.

Metagenomic Characterization of Freshwater Microbial Communities to Evaluate Recreational Coastal Water Quality. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 125 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2022.

This item must not be sold to any third party vendors.

Microbial pollution in recreational freshwaters has critical economic, social, and public health implications. Coastal waters are closed to recreation during the formation of harmful (cytotoxic) algal blooms (HAB) or if fecal indicator bacteria (FIB) concentrations, specifically E. coli and Enterococcus spp., exceed set recreational water quality criteria. Methods for measuring HABs and FIB have limitations that can make it challenging to assess and manage human and ecosystem health risks. Characterization of bacterial and algal communities using genomic sequencing technology has the potential to identify public health risks and sources of microbial pollution. However, more research is needed on the potential of different sequencing technologies to give representative data that can inform risk assessments and mitigation efforts. The research presented in this dissertation applies genomic sequencing technologies to characterize and investigate the dynamics of bacterial and algal communities in surface waters and groundwater along an urban coastline. First, we investigate the spatiotemporal bacterial community dynamics in surface and groundwaters, and the influence of sewage pollution at an impaired beach over a four-week period during the swimming season. The microbial community at the beach is more similar to surface waters flowing to the beach than to groundwater, consistent with higher culture-based E. coli concentrations in surface waters. Variable and high E. coli concentrations at the beach, however, could not be explained by spatiotemporal variability in bacterial communities. This study demonstrates that microbial community analysis can be used to investigate spatiotemporal variability and how surface waters and groundwaters influence coastal waters, providing a microbial context for the water quality. We then characterize groundwater microbial communities at varying depths over a 6-month time period to investigate how bacterial community diversity and composition change in relation to geochemical variability and groundwater contamination. Results show surface and groundwater communities are distinct, significantly shaped by local environmental parameters, and exhibited temporal stability and spatial variability (on the order of meters). Bacterial communities in surface water and shallow groundwater were more similar to each other than to communities in deep groundwater. This study shows how microbial community analysis can be used to investigate surface water and groundwater interactions, identify legacy contamination, and illuminate understudied transport routes for microbes into coastal waters. Finally, we evaluate the potential of a new, portable, near real-time (hours) genomic sequencing technology, ONT's MinION nanopore sequencer, to characterize prokaryotic and eukaryotic algal communities. As part of this process, new computational methods to analyze sequence data were developed and tested. Using nanopore sequencing data, we were able to successfully detect and classify all algae, even at low abundances (<1%). Furthermore, we were able to improve upon the leading analytical methods of mean relative abundance estimation using our new improved computational method (LoRAE) that accounts for genome length. This was especially true for the understudied eukaryotic algal genomes having more complex genomic structures. Finally, we were able to detect and classify mock community members within an environmental matrix of closely related organisms. These findings show the potential for the MinION to be used to assess microbial water quality and public health risks in recreational waters and highlight the importance of continued bioinformatic development, especially when using new sequencing platforms to study more complex microbial communities.

ISBN: 9798790655098Subjects--Topical Terms:

794747
Water resources management.
Subjects--Index Terms:

16S rRNA

Metagenomic Characterization of Freshwater Microbial Communities to Evaluate Recreational Coastal Water Quality.
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520 $a Microbial pollution in recreational freshwaters has critical economic, social, and public health implications. Coastal waters are closed to recreation during the formation of harmful (cytotoxic) algal blooms (HAB) or if fecal indicator bacteria (FIB) concentrations, specifically E. coli and Enterococcus spp., exceed set recreational water quality criteria. Methods for measuring HABs and FIB have limitations that can make it challenging to assess and manage human and ecosystem health risks. Characterization of bacterial and algal communities using genomic sequencing technology has the potential to identify public health risks and sources of microbial pollution. However, more research is needed on the potential of different sequencing technologies to give representative data that can inform risk assessments and mitigation efforts. The research presented in this dissertation applies genomic sequencing technologies to characterize and investigate the dynamics of bacterial and algal communities in surface waters and groundwater along an urban coastline. First, we investigate the spatiotemporal bacterial community dynamics in surface and groundwaters, and the influence of sewage pollution at an impaired beach over a four-week period during the swimming season. The microbial community at the beach is more similar to surface waters flowing to the beach than to groundwater, consistent with higher culture-based E. coli concentrations in surface waters. Variable and high E. coli concentrations at the beach, however, could not be explained by spatiotemporal variability in bacterial communities. This study demonstrates that microbial community analysis can be used to investigate spatiotemporal variability and how surface waters and groundwaters influence coastal waters, providing a microbial context for the water quality. We then characterize groundwater microbial communities at varying depths over a 6-month time period to investigate how bacterial community diversity and composition change in relation to geochemical variability and groundwater contamination. Results show surface and groundwater communities are distinct, significantly shaped by local environmental parameters, and exhibited temporal stability and spatial variability (on the order of meters). Bacterial communities in surface water and shallow groundwater were more similar to each other than to communities in deep groundwater. This study shows how microbial community analysis can be used to investigate surface water and groundwater interactions, identify legacy contamination, and illuminate understudied transport routes for microbes into coastal waters. Finally, we evaluate the potential of a new, portable, near real-time (hours) genomic sequencing technology, ONT's MinION nanopore sequencer, to characterize prokaryotic and eukaryotic algal communities. As part of this process, new computational methods to analyze sequence data were developed and tested. Using nanopore sequencing data, we were able to successfully detect and classify all algae, even at low abundances (<1%). Furthermore, we were able to improve upon the leading analytical methods of mean relative abundance estimation using our new improved computational method (LoRAE) that accounts for genome length. This was especially true for the understudied eukaryotic algal genomes having more complex genomic structures. Finally, we were able to detect and classify mock community members within an environmental matrix of closely related organisms. These findings show the potential for the MinION to be used to assess microbial water quality and public health risks in recreational waters and highlight the importance of continued bioinformatic development, especially when using new sequencing platforms to study more complex microbial communities.
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653 $a Water quality
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