東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Identification of Transcription Fact...

Lane, Jacqueline M.

FindBook

Google Book

Amazon

博客來

Identification of Transcription Factors involved in Obesity.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Identification of Transcription Factors involved in Obesity./
作者:	Lane, Jacqueline M.
面頁冊數:	144 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .
Contained By:	Dissertation Abstracts International72-08B.
標題:	Biology, Genetics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3457281
ISBN:	9781124675428

Identification of Transcription Factors involved in Obesity.
Lane, Jacqueline M.

Identification of Transcription Factors involved in Obesity. - 144 p.

Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .

Thesis (Ph.D.)--Sackler School of Graduate Biomedical Sciences (Tufts University), 2011.

Obesity is a leading cause of preventable death in the United States. With the prevalence of obesity >30% in the United States, to effectively treat this complex disorder we must identify the underlying genetic components. To understand complex disorders, understanding complex regulatory networks where many genes are affected is critical. Transcription factors lie at the heart of regulatory networks, small changes in the binding affinity or concentration can perturb entire networks of genes. Therefore we seek to identify transcription factors that are involved in obesity. In order to do this we developed a novel computational methodology to extract information about transcriptional gene networks in genome-scale data, yielding a list of 53 transcription factors potentially involved in obesity. We validated SPI1, our top candidate, by examining the effects of gene knock down in adipogenesis. First we generated and characterized a rapidly differentiating clonal OP9 preadipocyte cell population. We conclude that our cells differentiate in 72 hours, are readily transfectable, and differentiate through gene expression changes comparable to established preadipocyte lines. Gene expression profiles of differentiating OP9 cells express the classical adipogenic marker genes C/EBPbeta, C/EBPalpha, Gata2, and Plin1. Depletion of SPI1 increases lipid accumulation OP9 adipogenesis. In addition, variation in the human SPI1 gene modulates obesity risk. Individuals with the AA genotype of SNP rs4752829 have increased body mass index (BMI) compared to carriers of the G allele. SNP rs3740689 modulates the effect of dietary saturated fatty acid and omega-3 polyunsaturated (N3-PUFA) on BMI. These approaches demonstrate a strong role for SPI1 in obesity, with the potential to target SPI1 with pharmacological or dietary modifications. In addition, by describing the transcriptome of OP9 preadipocytes, we have validated an adipogenesis model for potential high-throughput screening of the effects of gene depletion on fat cell generation. For the first time the transcriptome of OP9 adipogenesis has been described and a gene-diet interaction influencing BMI has been found in SPI1. Our computational approach directed us to SPI1 and was validated in this study. This study demonstrates that our computational approach can be used to investigate the role of transcription factors in many other disorders.

ISBN: 9781124675428Subjects--Topical Terms:

1017730
Biology, Genetics.

Identification of Transcription Factors involved in Obesity.
LDR:03628nam 2200349 4500 001 1405572
005 20111219143106.5
008 130515s2011 ||||||||||||||||| ||eng d
020 $a 9781124675428
035 $a (UMI)AAI3457281
035 $a AAI3457281
040 $a UMI $c UMI
100 1 $a Lane, Jacqueline M. $3 1684964
245 1 0 $a Identification of Transcription Factors involved in Obesity.
300 $a 144 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-08, Section: B, page: .
500 $a Adviser: Jose M. Ordovas.
502 $a Thesis (Ph.D.)--Sackler School of Graduate Biomedical Sciences (Tufts University), 2011.
520 $a Obesity is a leading cause of preventable death in the United States. With the prevalence of obesity >30% in the United States, to effectively treat this complex disorder we must identify the underlying genetic components. To understand complex disorders, understanding complex regulatory networks where many genes are affected is critical. Transcription factors lie at the heart of regulatory networks, small changes in the binding affinity or concentration can perturb entire networks of genes. Therefore we seek to identify transcription factors that are involved in obesity. In order to do this we developed a novel computational methodology to extract information about transcriptional gene networks in genome-scale data, yielding a list of 53 transcription factors potentially involved in obesity. We validated SPI1, our top candidate, by examining the effects of gene knock down in adipogenesis. First we generated and characterized a rapidly differentiating clonal OP9 preadipocyte cell population. We conclude that our cells differentiate in 72 hours, are readily transfectable, and differentiate through gene expression changes comparable to established preadipocyte lines. Gene expression profiles of differentiating OP9 cells express the classical adipogenic marker genes C/EBPbeta, C/EBPalpha, Gata2, and Plin1. Depletion of SPI1 increases lipid accumulation OP9 adipogenesis. In addition, variation in the human SPI1 gene modulates obesity risk. Individuals with the AA genotype of SNP rs4752829 have increased body mass index (BMI) compared to carriers of the G allele. SNP rs3740689 modulates the effect of dietary saturated fatty acid and omega-3 polyunsaturated (N3-PUFA) on BMI. These approaches demonstrate a strong role for SPI1 in obesity, with the potential to target SPI1 with pharmacological or dietary modifications. In addition, by describing the transcriptome of OP9 preadipocytes, we have validated an adipogenesis model for potential high-throughput screening of the effects of gene depletion on fat cell generation. For the first time the transcriptome of OP9 adipogenesis has been described and a gene-diet interaction influencing BMI has been found in SPI1. Our computational approach directed us to SPI1 and was validated in this study. This study demonstrates that our computational approach can be used to investigate the role of transcription factors in many other disorders.
590 $a School code: 0845.
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Health Sciences, Nutrition. $3 1017801
690 $a 0369
690 $a 0570
710 2 $a Sackler School of Graduate Biomedical Sciences (Tufts University). $b Genetics. $3 1684965
773 0 $t Dissertation Abstracts International $g 72-08B.
790 1 0 $a Ordovas, Jose M., $e advisor
790 1 0 $a Slonim, Donna $e committee member
790 1 0 $a Kopin, Alan $e committee member
790 1 0 $a Alcivar Warren, Acacia $e committee member
790 1 0 $a Peter, Inga $e committee member
790 1 0 $a Bianchi, Diana $e committee member
790 1 0 $a Qi, Lu $e committee member
790 $a 0845
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3457281