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

Combined Analysis of Multi-Study miRNA and mRNA Expression Data Shows Overlap of Selected miRNAs Involved in West Nile Virus Infections

The emerging zoonotic West Nile virus (WNV) has serious impact on public health. Thus, understanding the molecular basis of WNV infections in mammalian hosts is important to develop improved diagnostic and treatment strategies. In this context, the role of microRNAs (miRNAs) has been analyzed by several studies under different conditions and with different outcomes. A systematic comparison is therefore necessary. Furthermore, additional information from mRNA target expression data has rarely been taken into account to understand miRNA expression profiles under WNV infections. We conducted a meta-analysis of publicly available miRNA expression data from multiple independent studies, and analyzed them in a harmonized way to increase comparability. In addition, we used gene-set tests on mRNA target expression data to further gain evidence about differentially expressed miRNAs. For this purpose, we also studied the use of target information from different databases. We detected a substantial number of miRNA that emerged as differentially expressed from several miRNA datasets, and from the mRNA target data analysis as well. When using mRNA target data, we found that the targetscan databases provided the most useful information. We demonstrated improved miRNA detection through research synthesis of multiple independent miRNA datasets coupled with mRNA target set testing, leading to the discovery of multiple miRNAs which should be taken into account for further research on the molecular mechanism of WNV infections.

Interleukins, Chemokines, and Tumor Necrosis Factor Superfamily Ligands in the Pathogenesis of West Nile Virus Infection

West Nile virus (WNV) is a mosquito-borne pathogen that can lead to encephalitis and death in susceptible hosts. Cytokines play a critical role in inflammation and immunity in response to WNV infection. Murine models provide evidence that some cytokines offer protection against acute WNV infection and assist with viral clearance, while others play a multifaceted role WNV neuropathogenesis and immune-mediated tissue damage. This article aims to provide an up-to-date review of cytokine expression patterns in human and experimental animal models of WNV infections. Here, we outline the interleukins, chemokines, and tumor necrosis factor superfamily ligands associated with WNV infection and pathogenesis and describe the complex roles they play in mediating both protection and pathology of the central nervous system during or after virus clearance. By understanding of the role of these cytokines during WNV neuroinvasive infection, we can develop treatment options aimed at modulating these immune molecules in order to reduce neuroinflammation and improve patient outcomes.

Ocular Surface Infection Mediated Molecular Stress Responses: A Review

Infection mediated ocular surface stress responses are activated as early defense mechanisms in response to host cell damage. Integrated stress responses initiate the host response to different types of infections and modulate the transcription of key genes and translation of proteins. The crosstalk between host and pathogen results in profound alterations in cellular and molecular homeostasis triggering specific stress responses in the infected tissues. The amplitude and variations of such responses are partly responsible for the disease severity and clinical sequelae. Understanding the etiology and pathogenesis of ocular infections is important for early diagnosis and effective treatment. This review considers the molecular status of infection mediated ocular surface stress responses which may shed light on the importance of the host stress-signaling pathways. In this review, we collated literature on the molecular studies of all ocular surface infections and summarize the results from such studies systematically. Identification of important mediators involved in the crosstalk between the stress response and activation of diverse signaling molecules in host ocular surface infection may provide novel molecular targets for maintaining the cellular homeostasis during infection. These targets can be then explored and validated for diagnostic and therapeutic purposes.

Network meta-analysis of transcriptome expression changes in different manifestations of dengue virus infection

BACKGROUND

Several studies have been performed to study transcriptome profiles after dengue virus infections with partly different results. Due to slightly different settings of the individual studies, different genes and enriched gene sets are reported in these studies. The main aim of this network meta-analysis was to aggregate a selection of these studies to identify genes and gene sets that are more generally associated with dengue virus infection, i.e. with less dependence on the individual study settings.

METHODS

We performed network meta-analysis by different approaches using publicly available gene expression data of five selected studies from the Gene Expression Omnibus database. The study network includes dengue fever (DF), hemorrhagic fever (DHF), shock syndrome (DSS) patients as well as convalescent and healthy control individuals. After data merging and missing value imputation, study-specific batch effects were removed. Pairwise differential expression analysis and subsequent gene-set enrichment analysis were performed between the five study groups. Furthermore, mutual information networks were derived from the top genes of each group comparison, and the separability between the three patient groups was studied by machine learning models.

RESULTS

From the 10 possible pairwise group comparisons in the study network, six genes (IFI27, TPX2, CDT1, DTL, KCTD14 and CDCA3) occur with a noticeable frequency among the top listed genes of each comparison. Thus, there is an increased evidence that these genes play a general role in dengue virus infections. IFI27 and TPX2 have also been highlighted in the context of dengue virus infection by other studies. A few of the identified gene sets from the network meta-analysis overlap with findings from the original studies. Mutual information networks yield additional genes for which the observed pairwise correlation is different between the patient groups. Machine learning analysis shows a moderate separability of samples from the DF, DHF and DSS groups (accuracy about 80%).

CONCLUSIONS

Due to an increased sample size, the network meta-analysis could reveal additional genes which are called differentially expressed between the studied groups and that may help to better understand the molecular basis of this disease.

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

Biomarkers are critical for rapid diagnosis of tuberculosis (TB) and could benefit patients with AIDS where diagnosis of TB co-infection is challenging. Meta-analysis is an approach to combine the results of the studies with standard statistical method by weighting each study with different sample size. This study aimed to use meta-analysis to integrate transcriptome datasets from different studies and screen for TB biomarkers in patients who were HIV-positive. Five datasets were subjected to meta-analysis on whole-blood transcriptomes from 640 patients infected with HIV. A total of 293 differentially expressed genes (DEGs) were identified as significant (P<0.0001) using the random effective model to integrate the statistical results from each study. DEGs were enriched in biological processes related to TB, such as “Type I interferon signaling” and “stimulatory C-type lectin receptor signaling”. Eighteen DEGs had at least a two-fold change in expression between patients infected with HIV who were TB-positive and those who were TB-negative. GBP4, SERPING1, ATF3 and CDKBN3 were selected as a biomarker panel to perform multivariable logistic regression analysis on TB status and relative gene expression levels. The biomarker panel showed excellent accuracy (AUC>0.90 for HIV+TB) in clinical trial and suggests that meta-analysis is an efficient method to integrate transcriptome datasets from different studies.

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

Treatment for many viral infections of the central nervous system (CNS) remains only supportive. Here we address a remaining gap in our knowledge regarding how the CNS and immune systems interact during viral infection. By examining the regulation of the immune and nervous system processes in a nonhuman primate model of West Nile virus neurological disease, we show that virus infection disrupts the homeostasis of the immune-neural-synaptic axis via induction of pleiotropic genes with distinct functions in each component of the axis. This pleiotropic gene regulation suggests an unintended off-target negative impact of virus-induced host immune responses on the neurotransmission, which may be a common feature of various viral infections of the CNS.

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

In coronavirus disease 2019 (COVID-19) patients, interleukin (IL)-6 is one of the leading factors causing death through cytokine release syndrome. Hence, identification of IL-6 downstream from clinical patients’ transcriptome is very valid for analyses of its mechanism. However, clinical study is conditional and time consuming to collect optional size of samples, as patients have the clinical heterogeneity. A possible solution is to deeply mine the relative existing data. Several transcriptome-based studies on other diseases or treatments have revealed different genes to be regulated by IL-6. Through our meta-analysis of these transcriptome datasets, 352 genes were suggested to be regulated by IL-6 in different biological conditions, some of which were related to virus infection and cardiovascular disease. Among them, 232 genes were not identified by current transcriptome studies from clinical research. ICAM1 and PFKFB3 were the most significantly upregulated genes in our meta-analysis and could be employed as biomarkers in patients with severe COVID-19. In general, a meta-analysis of transcriptome datasets could be an alternative way to analyze the immune response and complications of patients suffering from severe COVID-19 and other emergency diseases.

Transcriptome analysis of duck embryo fibroblasts for the dynamic response to duck tembusu virus infection and dual regulation of apoptosis genes

Duck Tembusu virus (DTMUV) is an emerging pathogenic flavivirus that has caused enormous economic losses in Southeast Asia. However, the pathogenic mechanism and host's responses after DTMUV infection remain poorly understood. During this study, total mRNA sequencing (RNA-Seq) analysis was used to detect the global gene expression in DEFs at various time points after DTMUV infection. We identified 326 genes altered significantly at all time points, and these genes were dynamically enriched in multifarious biological processes, including apoptosis, innate immune response, DNA replication, cell cycle arrest and DNA repair. Next, the results showed that apoptosis was induced and the proportion of apoptosis increased with time, and pro-apoptotic molecules caspases were activated. The RNA-seq data analysis further revealed that most pro-apoptosis and anti-apoptosis genes were early continually responsive, and the genes involved in both intrinsic and extrinsic apoptotic pathways were initiated. Further, the considerably enriched immune-relevant pathways were involved in apoptosis process, and protein-protein interactions (PPIs) analysis showed that IL6, STAT1, TNFAIP3, CFLAR and PTGS2 may be key regulators of DEFs apoptosis. In conclusion, this study not only contributes to understanding the underlying mechanism of DEFs infection with DTMUV, but also provides new insights into targets screening for antiviral therapy.

Nuclear localization of Newcastle disease virus matrix protein promotes virus replication by affecting viral RNA synthesis and transcription and inhibiting host cell transcription

Nuclear localization of paramyxovirus proteins is crucial for virus life cycle, including the regulation of viral replication and the evasion of host immunity. We previously showed that a recombinant Newcastle disease virus (NDV) with nuclear localization signal mutation in the matrix (M) protein results in a pathotype change and attenuates viral pathogenicity in chickens. However, little is known about the nuclear localization functions of NDV M protein. In this study, the potential functions of the M protein in the nucleus were investigated. We first demonstrate that nuclear localization of the M protein could not only promote the cytopathogenicity of NDV but also increase viral RNA synthesis and transcription efficiency in DF-1 cells. Using microarray analysis, we found that nuclear localization of the M protein might inhibit host cell transcription, represented by numerous up-regulating genes associated with transcriptional repressor activity and down-regulating genes associated with transcriptional activator activity. The role of representative up-regulated gene prospero homeobox 1 (PROX1) and down-regulated gene aryl hydrocarbon receptor (AHR) in the replication of NDV was then evaluated. The results show that siRNA-mediated knockdown of PROX1 or AHR significantly reduced or increased the viral RNA synthesis and viral replication, respectively, demonstrating the important roles of the expression changes of these genes in NDV replication. Together, our findings demonstrate for the first time that nuclear localization of NDV M protein promotes virus replication by affecting viral RNA synthesis and transcription and inhibiting host cell transcription, improving our understanding of the molecular mechanism of NDV replication and pathogenesis.

Network meta-analysis correlates with analysis of merged independent transcriptome expression data

BACKGROUND

Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results.

RESULTS

We demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research.

CONCLUSIONS

Network meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.

Conducting gene set tests in meta-analyses of transcriptome expression data

Research synthesis, eg, by meta-analysis, is more and more considered in the area of high-dimensional data from molecular research such as gene and protein expression data, especially because most studies and experiments are performed with very small sample sizes. In contrast to most clinical and epidemiological trials, raw data are often available for high-dimensional expression data. Therefore, direct data merging followed by a joint analysis of selected studies can be an alternative to meta-analysis by P value or effect-size merging or, more generally spoken, the merging of results. While several methods for meta-analysis of differential expression studies have been proposed, meta-analysis of gene set tests has very rarely been considered, although gene set tests are standard in the analysis of individual gene expression studies. We compare in this work the different strategies of research synthesis of gene set tests, in particularly the "early merging" of data cleaned from batch effects versus the "late merging" of individual results. In simulation studies and in examples of manipulated real-world data, we found that in most scenarios, the early merging has a higher sensitivity of detecting a gene set enrichment than the late merging. However, in scenarios with few studies, large batch effect, moderate and large sample sizes of late merging are more sensitive than early merging.