Biochips & Tissue Chips · as antibiotics. For example, for Hepatitis C Virus (HCV) infection, upto...
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Open Access Editorial Biochips & Tissue Chips Volume 2 • Issue 4 • 1000e118 J Biochip Tissue Chip ISSN: 2153-0777 JBTC, an open access journal Chen et al., Biochip Tissue Chip 2012, 2:4 http://dx.doi.org/10.4172/2153-0777.1000e118 Virus infection is one of the major issues threatening our health, leading to chronic infections and viral persistence in many cases. Unlike bacteria infection, anti-viral drugs are not as much effective as antibiotics. For example, for Hepatitis C Virus (HCV) infection, upto 70% of those infected will develop into chronic infections and almost half of them will not respond to current standard treatment with pegylated interferon and ribavirin [1,2]. Although most recently the addition of the HCV specific protease inhibitors (triple-therapy) increased the overall response rate upto 70%, there is still a significant portion of the patients will not respond, not to mention the rapid development of viral resistance to these Direct-acting Agents (DAAs) widely observed in clinic [3]. One of the major reasons for lacking the effective antiviral drugs is that the molecular mechanisms of the viral resistance to these drugs are poorly understood. Virus-host interaction provides much information for us to understand how a specific virus infects the host cells and how the host cells respond to the infection. Previously, a lot of data have been gathered as to how the virus responds to host immune attack, especially to counteract the innate and adaptive immune responses. e virus can utilize many mechanisms to evade host immune surveillance, such as by rapid mutation, by interfering with the recognition step of the virus invasion and induction of the innate immunity as the first-line defense, and also by dampening the antiviral effect of the antiviral molecules [4]. However, how the host cells respond to the virus infection at the whole genomic level is not well defined. Microarray gene-expression profiling, a high throughput method that allows simultaneously examine gene expression changes at the transcript (mRNA) level, makes it possible to look at the host response to any given virus infection at the whole genomic scale. Combined with data analysis tools, these large gene expression data sets provided insights into the molecular mechanisms of viral resistance. Novel anti- viral drug targets can be identified and further developed. By comparing the pretreatment hepatic gene expression levels between treatment responders and non-responders of patients chronically infected with HCV, Chen et al. [5] identified 18 genes (out of 19,000 host genes or transcripts) whose expression levels are significantly and statistically different between the two groups. Most of these 18 genes are Interferon-stimulated (-sensitive) Genes (ISGs) and they are more highly expressed in the pretreatment liver tissues of treatment non-responders. erefore, they proposed that the pre-activation of the interferon signaling pathway leading to the increased expression of a subset of ISGs is involved in HCV resistance to interferon therapy [5]. is hypothesis has now been confirmed by many other groups and was generally accepted in the file [6,7]. More interestingly, based on the gene expression data and the 18 differentially-expressed gene set, they identified a novel ubiquitin-like pathway (ISG15/USP18) whose activation is related to HCV resistance [5]. Silencing USP18, a specific protease to strip ISG15 from its conjugation proteins, potentiated the anti-HCV effect of Interferon Alpha (IFNa) by 40-100 fold [8]. Furthermore, they demonstrated that the cell-type specific protein expression of some ISGs, such as ISG15 and MxA, in the pretreatment liver tissues of HCV patients correlated well with treatment outcomes [9]: predominant expression in hepatocytes predicts non-response while in macrophages predicts treatment response, with prediction accuracy higher than that predicted by the polymorphism of IL28B [10], which was identified by large-scale Genome-wide Association Studies (GWAS) [11-14]. is is an excellent example on how genome-wide gene expression study can be used to delineate virus-host interactions, especially to understand how the host responds to virus infection at the whole genomic level. Not only a novel pathway (ISG15/USP18 ubiquitin-like pathway) but also new drug targets have been identified [15]. ese studies and novel data opens a new avenue of research into HCV-host interactions, especially into the molecular mechanisms of the host role in HCV resistance to interferon therapy, leading the hope to develop novel anti-HCV drugs. Although the microarray gene-expression profiling method is robust, with enormous data being generated from a single experiment, the key to arrive at any significant conclusion depends on the successful use of various data analysis soſtware [5]. We believe more ready-to- use data mining tools will be readily available in the future, which will definitely facilitate the application of this high throughput gene expression tool to study how host responds to virus infections at the whole genomic scale. Data from these studies will shed light on the detailed molecular mechanisms of viral resistance, leading to new drug target identification and development, and eventually wipe out the virus infection. References 1. National Institutes of Health (2002) National Institutes of Health Consensus Development Conference Statement: Management of Hepatitis C 2002--June 10-12, 2002. Hepatology 36: S3-S20. 2. Thimme R, Lohmann V, Weber F (2006) A target on the move: Innate and adaptive immune escape strategies of hepatitis C virus. Antiviral Res 69: 129- 141. 3. Selzner N, Chen L, Borozan I, Edwards A, Heathcote EJ, et al. (2008) Hepatic gene expression and prediction of therapy response in chronic hepatitis C patients. J Hepatol 48: 708-713. 4. Gale M jr, Foy EM (2005) Evasion of intracellular host defence by hepatitis C virus. Nature 436: 939-945. *Corresponding author: Dr. Limin Chen, Toronto General Research Institute, University of Toronto, Toronto, ON, Canada, Tel: 1-416-946-3435, 011-86-28- 61648530; E-mail: [email protected], [email protected] Received November 19, 2012; Accepted November 21, 2012; Published November 23, 2012 Citation: Chen L, Li S, Li Y, Duan X, McGilvray I (2012) Genome-wide Gene Expression Profiling: An Excellent Tool to Study Virus-Host Interactions. J Biochips Tiss Chips 2:e118. doi: 10.4172/2153-0777.1000e118 Copyright: © 2012 Chen L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Genome-wide Gene Expression Profiling: An Excellent Tool to Study Virus- Host Interactions Limin Chen 1,2 *, Shilin Li 1 , Yujia Li 1 , Xiaoqiong Duan 1 and Ian McGilvray 1,2 1 Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical College, Chengdu, Sichuan, China 2 Toronto General Research Institute, University of Toronto, Toronto, ON, Canada
Transcript of Biochips & Tissue Chips · as antibiotics. For example, for Hepatitis C Virus (HCV) infection, upto...
Open AccessEditorial
Biochips & Tissue Chips
Volume 2 • Issue 4 • 1000e118J Biochip Tissue ChipISSN: 2153-0777 JBTC, an open access journal
Chen et al., Biochip Tissue Chip 2012, 2:4http://dx.doi.org/10.4172/2153-0777.1000e118
Virus infection is one of the major issues threatening our health, leading to chronic infections and viral persistence in many cases. Unlike bacteria infection, anti-viral drugs are not as much effective as antibiotics. For example, for Hepatitis C Virus (HCV) infection, upto 70% of those infected will develop into chronic infections and almost half of them will not respond to current standard treatment with pegylated interferon and ribavirin [1,2]. Although most recently the addition of the HCV specific protease inhibitors (triple-therapy) increased the overall response rate upto 70%, there is still a significant portion of the patients will not respond, not to mention the rapid development of viral resistance to these Direct-acting Agents (DAAs) widely observed in clinic [3]. One of the major reasons for lacking the effective antiviral drugs is that the molecular mechanisms of the viral resistance to these drugs are poorly understood.
Virus-host interaction provides much information for us to understand how a specific virus infects the host cells and how the host cells respond to the infection. Previously, a lot of data have been gathered as to how the virus responds to host immune attack, especially to counteract the innate and adaptive immune responses. The virus can utilize many mechanisms to evade host immune surveillance, such as by rapid mutation, by interfering with the recognition step of the virus invasion and induction of the innate immunity as the first-line defense, and also by dampening the antiviral effect of the antiviral molecules [4]. However, how the host cells respond to the virus infection at the whole genomic level is not well defined.
Microarray gene-expression profiling, a high throughput method that allows simultaneously examine gene expression changes at the transcript (mRNA) level, makes it possible to look at the host response to any given virus infection at the whole genomic scale. Combined with data analysis tools, these large gene expression data sets provided insights into the molecular mechanisms of viral resistance. Novel anti-viral drug targets can be identified and further developed.
By comparing the pretreatment hepatic gene expression levels between treatment responders and non-responders of patients chronically infected with HCV, Chen et al. [5] identified 18 genes (out of 19,000 host genes or transcripts) whose expression levels are significantly and statistically different between the two groups. Most of these 18 genes are Interferon-stimulated (-sensitive) Genes (ISGs) and they are more highly expressed in the pretreatment liver tissues of treatment non-responders. Therefore, they proposed that the pre-activation of the interferon signaling pathway leading to the increased expression of a subset of ISGs is involved in HCV resistance to interferon therapy [5]. This hypothesis has now been confirmed by many other groups and was generally accepted in the file [6,7]. More interestingly, based on the gene expression data and the 18 differentially-expressed gene set, they identified a novel ubiquitin-like pathway (ISG15/USP18) whose activation is related to HCV resistance [5]. Silencing USP18, a specific protease to strip ISG15 from its conjugation proteins, potentiated the anti-HCV effect of Interferon Alpha (IFNa) by 40-100 fold [8]. Furthermore, they demonstrated that the cell-type specific protein expression of some ISGs, such as ISG15 and MxA, in the pretreatment
liver tissues of HCV patients correlated well with treatment outcomes [9]: predominant expression in hepatocytes predicts non-response while in macrophages predicts treatment response, with prediction accuracy higher than that predicted by the polymorphism of IL28B [10], which was identified by large-scale Genome-wide Association Studies (GWAS) [11-14].
This is an excellent example on how genome-wide gene expression study can be used to delineate virus-host interactions, especially to understand how the host responds to virus infection at the whole genomic level. Not only a novel pathway (ISG15/USP18 ubiquitin-like pathway) but also new drug targets have been identified [15]. These studies and novel data opens a new avenue of research into HCV-host interactions, especially into the molecular mechanisms of the host role in HCV resistance to interferon therapy, leading the hope to develop novel anti-HCV drugs.
Although the microarray gene-expression profiling method is robust, with enormous data being generated from a single experiment, the key to arrive at any significant conclusion depends on the successful use of various data analysis software [5]. We believe more ready-to-use data mining tools will be readily available in the future, which will definitely facilitate the application of this high throughput gene expression tool to study how host responds to virus infections at the whole genomic scale. Data from these studies will shed light on the detailed molecular mechanisms of viral resistance, leading to new drug target identification and development, and eventually wipe out the virus infection.
References
1. National Institutes of Health (2002) National Institutes of Health Consensus Development Conference Statement: Management of Hepatitis C 2002--June 10-12, 2002. Hepatology 36: S3-S20.
2. Thimme R, Lohmann V, Weber F (2006) A target on the move: Innate and adaptive immune escape strategies of hepatitis C virus. Antiviral Res 69: 129-141.
3. Selzner N, Chen L, Borozan I, Edwards A, Heathcote EJ, et al. (2008) Hepatic gene expression and prediction of therapy response in chronic hepatitis C patients. J Hepatol 48: 708-713.
4. Gale M jr, Foy EM (2005) Evasion of intracellular host defence by hepatitis C virus. Nature 436: 939-945.
*Corresponding author: Dr. Limin Chen, Toronto General Research Institute, University of Toronto, Toronto, ON, Canada, Tel: 1-416-946-3435, 011-86-28-61648530; E-mail: [email protected], [email protected]
Received November 19, 2012; Accepted November 21, 2012; Published November 23, 2012
Citation: Chen L, Li S, Li Y, Duan X, McGilvray I (2012) Genome-wide Gene Expression Profiling: An Excellent Tool to Study Virus-Host Interactions. J Biochips Tiss Chips 2:e118. doi: 10.4172/2153-0777.1000e118
Copyright: © 2012 Chen L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Genome-wide Gene Expression Profiling: An Excellent Tool to Study Virus-Host InteractionsLimin Chen1,2*, Shilin Li1, Yujia Li1, Xiaoqiong Duan1 and Ian McGilvray1,2
1Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical College, Chengdu, Sichuan, China2Toronto General Research Institute, University of Toronto, Toronto, ON, Canada
Citation: Chen L, Li S, Li Y, Duan X, McGilvray I (2012) Genome-wide Gene Expression Profiling: An Excellent Tool to Study Virus-Host Interactions. J Biochips Tiss Chips 2:e118. doi: 10.4172/2153-0777.1000e118
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Volume 2 • Issue 4 • 1000e118J Biochip Tissue chipISSN: 2153-0777 JBTC, an open access journal
5. Chen L, Borozan I, Feld J, Sun J, Tannis LL, et al. (2005) Hepatic gene expression discriminates responders and nonresponders in treatment of chronic hepatitis C viral infection. Gastroenterology128: 1437-1444.
6. Feld JJ, Nanda S, Huang Y, Chen W, Cam M, et al. (2007) Hepatic gene expression during treatment with peginterferon and ribavirin: identifying molecular pathways for treatment response. Hepatology 46: 1548-1563.
7. Asselah T, Bieche I, Narguet S, Sabbagh A, Laurendeau I, et al. (2007) Liver gene expression signature to predict response to pegylated interferon plus ribavirin combination therapy in patients with chronic hepatitis C. Gut 57: 516-524.
8. Randall G, Chen L, Panis M, Fischer AK, Lindenbach BD, et al. (2006) Silencing of USP18 potentiates the antiviral activity of interferon against hepatitis C virus infection. Gastroenterology 131: 1584-1591.
9. Chen L, Borozan I, Sun J, Guindi M, Fischer S, et al. (2010) Cell-type specific gene expression signature in liver underlies response to interferon therapy in chronic hepatitis C infection. Gastroenterology 138: 1123-1133.e1-e3.
10. McGilvray I, Feld JJ, Chen L, Pattullo V, Guindi M, et al. (2012) Hepatic cell-
type specific gene expression better predicts HCV treatment outcome than IL28B genotype. Gastroenterology 142: 1122-1131.e1.
11. Thomas DL, Thio CL, Martin MP, Qi Y, Ge D, et al. (2009) Genetic variation in IL28B and spontaneous clearance of hepatitis C virus. Nature 461: 798-801.
12. Suppiah V, Moldovan M, Ahlenstiel G, Berg T, Weltman M, et al. (2009) IL28B is associated with response to chronic hepatitis C interferon-alpha and ribavirin therapy. Nat Genet 41: 1100-1104.
13. Tanaka Y, Nishida N, Sugiyama M, Kurosaki M, Matsuura K, et al. (2009) Genome-wide association of IL28B with response to pegylated interferon-alpha and ribavirin therapy for chronic hepatitis C. Nat Genet 41: 1105-1109.
14. Ge D, Fellay J, Thompson AJ, Simon JS, Shianna KV,et al. (2009) Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 461: 399-401.
15. Chen L, Sun J, Meng L, Heathcote J, Edwards AM, et al. (2010) ISG15, a ubiquitin-like interferon stimulated gene, promotes Hepatitis C Virus production in vitro: Implications for chronic infection and response to treatment. J Gen Virol 91: 382-388.
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