The house dust microbiota in the Drakenstein Child Health Study

 

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dc.contributor.advisor Nicol, Mark en_ZA
dc.contributor.advisor Ah Tow, Lemese en_ZA
dc.contributor.author Duyver, Menna en_ZA
dc.date.accessioned 2015-12-02T12:05:23Z
dc.date.available 2015-12-02T12:05:23Z
dc.date.issued 2015 en_ZA
dc.identifier.citation Duyver, M. 2015. The house dust microbiota in the Drakenstein Child Health Study. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/15516
dc.description.abstract Introduction: The indoor home environment comprises many niches that are occupied by bacterial communities. The composition of these bacterial communities may be influenced by numerous factors such as number of occupants, pets, season and location. Understanding the house dust microbial community is vital to understanding its' influence on human respiratory health. Aims: The aims of the studies described in this MSc dissertation were to: 1) evaluate the performance of ten commercial nucleic acid extraction kits on dust samples; 2) optimise dust removal from electrostatic dustfall collectors (EDC); 3) determine the bacterial composition of house dust using 16S rRNA gene sequencing and 4) determine those factors influencing the bacterial composition of house dust by performing bioinformatic and data analysis on the sequenced dust samples. Methods: In order to study the microbial content of house dust, an efficient DNA extraction protocol was required. Ten commercial nucleic acid purification protocols were evaluated on their ability to efficiently extract good quality DNA from very low quantities (20 mg) of wet bulk house dust. For the purpose of this study, EDCs were used to collect settled dust from homes of participants in the Drakenstein Child Health Study (DCHS). Electrostatic Dustfall Collectors were placed twice within the same household, approximately 6 months apart, spanning two seasons. The Z/R Fungal/Bacterial DNA MicroprepTM (ZMC) protocol was used to extract DNA from dust removed from EDCs. The V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq platform to determine the bacterial taxonomic composition of the house dust samples. A custom python wrapper that meshes a set of tools integrated into a computationally efficient workflow, known as the YAP pipeline was used to classify 16S rRNA sequences into bacterial taxonomies. Based on 97% sequence similarity, the pre-processed sequences were assigned to Operational Taxonomic Units (OTU). R software together with RStudio software was used for all statistical analysis and graphical representations of the data. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Medical Microbiology en_ZA
dc.title The house dust microbiota in the Drakenstein Child Health Study en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Health Sciences en_ZA
dc.publisher.department Department of Clinical Laboratory Sciences en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc (Med) en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Duyver, M. (2015). <i>The house dust microbiota in the Drakenstein Child Health Study</i>. (Thesis). University of Cape Town ,Faculty of Health Sciences ,Department of Clinical Laboratory Sciences. Retrieved from http://hdl.handle.net/11427/15516 en_ZA
dc.identifier.chicagocitation Duyver, Menna. <i>"The house dust microbiota in the Drakenstein Child Health Study."</i> Thesis., University of Cape Town ,Faculty of Health Sciences ,Department of Clinical Laboratory Sciences, 2015. http://hdl.handle.net/11427/15516 en_ZA
dc.identifier.vancouvercitation Duyver M. The house dust microbiota in the Drakenstein Child Health Study. [Thesis]. University of Cape Town ,Faculty of Health Sciences ,Department of Clinical Laboratory Sciences, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/15516 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Duyver, Menna AB - Introduction: The indoor home environment comprises many niches that are occupied by bacterial communities. The composition of these bacterial communities may be influenced by numerous factors such as number of occupants, pets, season and location. Understanding the house dust microbial community is vital to understanding its' influence on human respiratory health. Aims: The aims of the studies described in this MSc dissertation were to: 1) evaluate the performance of ten commercial nucleic acid extraction kits on dust samples; 2) optimise dust removal from electrostatic dustfall collectors (EDC); 3) determine the bacterial composition of house dust using 16S rRNA gene sequencing and 4) determine those factors influencing the bacterial composition of house dust by performing bioinformatic and data analysis on the sequenced dust samples. Methods: In order to study the microbial content of house dust, an efficient DNA extraction protocol was required. Ten commercial nucleic acid purification protocols were evaluated on their ability to efficiently extract good quality DNA from very low quantities (20 mg) of wet bulk house dust. For the purpose of this study, EDCs were used to collect settled dust from homes of participants in the Drakenstein Child Health Study (DCHS). Electrostatic Dustfall Collectors were placed twice within the same household, approximately 6 months apart, spanning two seasons. The Z/R Fungal/Bacterial DNA MicroprepTM (ZMC) protocol was used to extract DNA from dust removed from EDCs. The V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq platform to determine the bacterial taxonomic composition of the house dust samples. A custom python wrapper that meshes a set of tools integrated into a computationally efficient workflow, known as the YAP pipeline was used to classify 16S rRNA sequences into bacterial taxonomies. Based on 97% sequence similarity, the pre-processed sequences were assigned to Operational Taxonomic Units (OTU). R software together with RStudio software was used for all statistical analysis and graphical representations of the data. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - The house dust microbiota in the Drakenstein Child Health Study TI - The house dust microbiota in the Drakenstein Child Health Study UR - http://hdl.handle.net/11427/15516 ER - en_ZA


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