Dataset Information

Improved microarray gene expression profiling of virus-infected cells after removal of viral RNA.

ABSTRACT:

Background

Sensitivity and accuracy are key points when using microarrays to detect alterations in gene expression under different conditions. Critical to the acquisition of reliable results is the preparation of the RNA. In the field of virology, when analyzing the host cell's reaction to infection, the often high representation of viral RNA (vRNA) within total RNA preparations from infected cells is likely to interfere with microarray analysis. Yet, this effect has not been investigated despite the many reports that describe gene expression profiling of virus-infected cells using microarrays.

Results

In this study we used coronaviruses as a model to show that vRNA indeed interferes with microarray analysis, decreasing both sensitivity and accuracy. We also demonstrate that the removal of vRNA from total RNA samples, by means of virus-specific oligonucleotide capturing, significantly reduced the number of false-positive hits and increased the sensitivity of the method as tested on different array platforms.

Conclusion

We therefore recommend the specific removal of vRNA, or of any other abundant 'contaminating' RNAs, from total RNA samples to improve the quality and reliability of microarray analyses.

SUBMITTER: Raaben M

PROVIDER: S-EPMC2397413 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

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Publications

Improved microarray gene expression profiling of virus-infected cells after removal of viral RNA.

Raaben Matthijs M Whitley Penn P Bouwmeester Diane D Setterquist Robert A RA Rottier Peter J M PJ de Haan Cornelis A M CA

BMC genomics 20080514

<h4>Background</h4>Sensitivity and accuracy are key points when using microarrays to detect alterations in gene expression under different conditions. Critical to the acquisition of reliable results is the preparation of the RNA. In the field of virology, when analyzing the host cell's reaction to infection, the often high representation of viral RNA (vRNA) within total RNA preparations from infected cells is likely to interfere with microarray analysis. Yet, this effect has not been investigate ...[more]

PMID: 18479515

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Project description:The current outbreak of coronavirus disease (COVID-19) has been affecting millions of people and has caused devastating mortality worldwide. Moreover, it is to be noted that cytokine storm has become an important cause for the rising mortality. However, the efforts for the development of drugs, vaccines and treatment has also been intervened due to poor understanding of host's defense mechanism and also due to the development of cytokine storm against this viral infection. Thus, a deeper understanding of the mechanism behind the immune dysregulation and cytokine storm development might give us clues for the clinical management of the severe cases. Hence, we have implemented differential gene expression analysis together with protein-protein interaction and Gene Ontology (GO) studies with the help of Severe Acute respiratory syndrome coronavirus (SARS-CoV) data sets such as GSE1739 and GSE33267 to give us more knowledge on the host immune response for the pathogenic coronavirus which in turn reduces the mortality. A total of 79 differentially-expressed genes (DEGs) were identified in our data set using the filters such as P-value and log2 fold change values of less than 0.05 and 1.5 respectively. Further, network analysis and GO studies showed that differential expression of two hub genes namely ELANE and LTF which could induce higher levels of pro-inflammatory cytokines in the lungs. We are certain that differential expression of ELANE and LTF results in an excessive inflammatory reaction known as the cytokine storm and ultimately leading to death. Therefore, targeting these key drivers of cytokine storm genes appears to be the potential therapeutic targets for combating the Severe Acute respiratory syndrome coronavirus - 2 (SARS-CoV-2) infection ultimately resulting in reduced mortality. Indeed, this predictive view may open new insights for designing an immune intervention for COVID-19 in the near future resulting in the mitigation of mortality rate.

Dataset Information

Improved microarray gene expression profiling of virus-infected cells after removal of viral RNA.

Background

Results

Conclusion

Publications

Improved microarray gene expression profiling of virus-infected cells after removal of viral RNA.

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