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High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome
BACKGROUND: Whole transcriptome analyses are an essential tool for understanding disease mechanisms. Approaches based on next-generation sequencing provide fast and affordable data but rely on the availability of annotated genomes. However, there are many areas in biomedical research that require no...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639954/ https://www.ncbi.nlm.nih.gov/pubmed/23575280 http://dx.doi.org/10.1186/1471-2164-14-237 |
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author | Schmucki, Roland Berrera, Marco Küng, Erich Lee, Serene Thasler, Wolfgang E Grüner, Sabine Ebeling, Martin Certa, Ulrich |
author_facet | Schmucki, Roland Berrera, Marco Küng, Erich Lee, Serene Thasler, Wolfgang E Grüner, Sabine Ebeling, Martin Certa, Ulrich |
author_sort | Schmucki, Roland |
collection | PubMed |
description | BACKGROUND: Whole transcriptome analyses are an essential tool for understanding disease mechanisms. Approaches based on next-generation sequencing provide fast and affordable data but rely on the availability of annotated genomes. However, there are many areas in biomedical research that require non-standard animal models for which genome information is not available. This includes the Syrian hamster Mesocricetus auratus as an important model for dyslipidaemia because it mirrors many aspects of human disease and pharmacological responses. We show that complementary use of two independent next generation sequencing technologies combined with mapping to multiple genome databases allows unambiguous transcript annotation and quantitative transcript imaging. We refer to this approach as “triple match sequencing” (TMS). RESULTS: Contigs assembled from a normalized Roche 454 hamster liver library comprising 1.2 million long reads were used to identify 10’800 unique transcripts based on homology to RefSeq database entries from human, mouse, and rat. For mRNA quantification we mapped 82 million SAGE tags (SOLiD) from the same RNA source to the annotated hamster liver transcriptome contigs. We compared the liver transcriptome of hamster with equivalent data from human, rat, minipig, and cynomolgus monkeys to highlight differential gene expression with focus on lipid metabolism. We identify a cluster of five genes functionally related to HDL metabolism that is expressed in human, cynomolgus, minipig, and hamster but lacking in rat as a non-responder species for lipid lowering drugs. CONCLUSIONS: The TMS approach is suited for fast and inexpensive transcript profiling in cells or tissues of species where a fully annotated genome is not available. The continuously growing number of well annotated reference genomes will further empower reliable transcript identification and thereby raise the utility of the method for any species of interest. |
format | Online Article Text |
id | pubmed-3639954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36399542013-05-01 High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome Schmucki, Roland Berrera, Marco Küng, Erich Lee, Serene Thasler, Wolfgang E Grüner, Sabine Ebeling, Martin Certa, Ulrich BMC Genomics Methodology Article BACKGROUND: Whole transcriptome analyses are an essential tool for understanding disease mechanisms. Approaches based on next-generation sequencing provide fast and affordable data but rely on the availability of annotated genomes. However, there are many areas in biomedical research that require non-standard animal models for which genome information is not available. This includes the Syrian hamster Mesocricetus auratus as an important model for dyslipidaemia because it mirrors many aspects of human disease and pharmacological responses. We show that complementary use of two independent next generation sequencing technologies combined with mapping to multiple genome databases allows unambiguous transcript annotation and quantitative transcript imaging. We refer to this approach as “triple match sequencing” (TMS). RESULTS: Contigs assembled from a normalized Roche 454 hamster liver library comprising 1.2 million long reads were used to identify 10’800 unique transcripts based on homology to RefSeq database entries from human, mouse, and rat. For mRNA quantification we mapped 82 million SAGE tags (SOLiD) from the same RNA source to the annotated hamster liver transcriptome contigs. We compared the liver transcriptome of hamster with equivalent data from human, rat, minipig, and cynomolgus monkeys to highlight differential gene expression with focus on lipid metabolism. We identify a cluster of five genes functionally related to HDL metabolism that is expressed in human, cynomolgus, minipig, and hamster but lacking in rat as a non-responder species for lipid lowering drugs. CONCLUSIONS: The TMS approach is suited for fast and inexpensive transcript profiling in cells or tissues of species where a fully annotated genome is not available. The continuously growing number of well annotated reference genomes will further empower reliable transcript identification and thereby raise the utility of the method for any species of interest. BioMed Central 2013-04-10 /pmc/articles/PMC3639954/ /pubmed/23575280 http://dx.doi.org/10.1186/1471-2164-14-237 Text en Copyright © 2013 Schmucki et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Schmucki, Roland Berrera, Marco Küng, Erich Lee, Serene Thasler, Wolfgang E Grüner, Sabine Ebeling, Martin Certa, Ulrich High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome |
title | High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome |
title_full | High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome |
title_fullStr | High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome |
title_full_unstemmed | High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome |
title_short | High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome |
title_sort | high throughput transcriptome analysis of lipid metabolism in syrian hamster liver in absence of an annotated genome |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639954/ https://www.ncbi.nlm.nih.gov/pubmed/23575280 http://dx.doi.org/10.1186/1471-2164-14-237 |
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