Gene expression profiles in neurological tissues during West Nile virus infection: a critical meta-analysis

Abstract

BackgroundInfections with the West Nile virus (WNV) can attack neurological tissues in the host and alter gene expression levels therein. Several individual studies have analyzed these changes in the transcriptome based on measurements with DNA microarrays. Individual microarray studies produce a high-dimensional data structure with the number of studied genes exceeding the available sample size by far. Therefore, the level of scientific evidence of these studies is rather low and results can remain uncertain. Furthermore, the individual studies concentrate on different types of tissues or different time points after infection. A general statement regarding the transcriptional changes through WNV infection in neurological tissues is therefore hard to make. We screened public databases for transcriptome expression studies related to WNV infections and used different analysis pipelines to perform meta-analyses of these data with the goal of obtaining more stable results and increasing the level of evidence.ResultsWe generated new lists of genes differentially expressed between WNV infected neurological tissues and control samples. A comparison with these genes to findings of a meta-analysis of immunological tissues is performed to figure out tissue-specific differences. While 5.879 genes were identified exclusively in the neurological tissues, 15 genes were found exclusively in the immunological tissues, and 44 genes were commonly detected in both tissues. Most findings of the original studies could be confirmed by the meta-analysis with a higher statistical power, but some genes and GO terms related to WNV were newly detected, too. In addition, we identified gene ontology terms related to certain infection processes, which are significantly enriched among the differentially expressed genes. In the neurological tissues, 17 gene ontology terms were found significantly different, and 2 terms in the immunological tissues.ConclusionsA critical discussion of our findings shows benefits but also limitations of the meta-analytic approach. In summary, the produced gene lists, identified gene ontology terms and network reconstructions appear to be more reliable than the results from the individual studies. Our meta-analysis provides a basis for further research on the transcriptional mechanisms by WNV infections in neurological tissues.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4914-4) contains supplementary material, which is available to authorized users.

Virus infection of the CNS disrupts the immune-neural-synaptic axis via induction of pleiotropic gene regulation of host responses

“…One unbiased approach to obtain a broad insight into complex pathophysiological changes occurring in the virus-infected CNS is to examine alterations in global gene expression. Although several studies have provided insights into changes in gene expression in the CNS after an experimental WNV infection, they mainly focused on the immune responses and apoptosis ( Lim et al, 2017 ; Kosch et al, 2018 ; Bourgeois et al, 2011 ; Clarke et al, 2014a ; Venter et al, 2005 ; Green et al, 2016 ; Kumar et al, 2016 ; Clarke et al, 2014b ; Vig et al, 2018 ). Our understanding of the molecular mechanisms underlying changes in the regulation of the CNS during WNV-ND is incomplete.…”

Meta-analysis of transcriptome datasets: An alternative method to study IL-6 regulation in coronavirus disease 2019

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Meta-Analysis of Peripheral Blood Transcriptome Datasets Reveals a Biomarker Panel for Tuberculosis in Patients Infected With HIV

“…Meta-analysis studies can integrate data from multiple individual studies and identify the candidate genes that are differentially expressed consistently across studies ( Adams et al., 2008 ). It was used to study infection with West Nile virus (WNV) by comparing new lists of genes that were differentially expressed between WNV-infected neurological tissues and control samples ( Kosch et al., 2018 ). Meta-analysis can also be very helpful for studying functional annotation among multi-gene families as shown with transcriptomic data for liver and heart samples from rats and mice ( Reyes et al., 2017 ).…”