論文題目：Zika virus infection reprograms global transcription of host cells to allow sustained infection
scholar 引用：100
頁數：11
發表時間：15 Jan 2019
發表刊物：Emerging Microbes & Infections
作者：Shashi Kant Tiwari, Jason Dang, Yue Qin, Gianluigi Lichinchi, Vikas Bansal & Tariq M Rana
摘要：
Zika virus (ZIKV,寨卡病毒) is an emerging virus causally linked to neurological disorders, including congenital microcephaly(先天性小頭畸形) and Guillain–Barré syndrome. There are currently no targeted therapies for ZIKV infection. To identify novel antiviral targets and to elucidate the mechanisms by which ZIKV exploits the host cell machinery to support sustained replication, we analyzed the transcriptomic landscape of human microglia(小膠質細胞), fibroblast(成人纖維細胞), embryonic kidney(胚胎腎) and monocyte-derived macrophage cell lines before and after ZIKV infection. The four cell types differed in their susceptibility to ZIKV infection, consistent with differences in their expression of viral response genes before infection. Clustering and network analyses of genes differentially expressed after ZIKV infection revealed changes related to the adaptive immune system, angiogenesis and host metabolic processes that are conducive to sustained viral production. Genes related to the adaptive immune response were downregulated in microglia cells, suggesting that ZIKV effectively evades the immune response after reaching the central nervous system. Like other viruses, ZIKV diverts host cell resources and reprograms the metabolic machinery to support RNA metabolism, ATP production and glycolysis. Consistent with these transcriptomic analyses, nucleoside metabolic inhibitors abrogated ZIKV replication in microglia cells.
結論：
- Here we analyzed the transcriptomic changes associated with ZIKV infection across multiple cell types to identify novel therapeutic targets and understand the host–pathogen interaction for sustained ZIKV replication.
- The response to ZIKV is cell-type specific with the greatest replication found in microglia cells.
- ZIKV is highly expressed in microglia and downregulates immune response genes, whereas high expression of viral response genes in macrophages confers ZIKV resistance.
- In addition, ZIKV reprograms the host metabolic processes to enhance virus replication through the upregulation of glycolysis and RNA metabolism related genes.
- Antimetabolites floxuridine abrogated ZIKV replication through inhibition of host nucleoside metabolic pathways.
- These results reveal that thymidine synthesis pathway can be exploited to develop novel therapeutics to treat ZIKV infections.
Introduction：
- 介紹了ZIKV病毒，跟百度百科的內容差不多。跟DENV病毒都屬於黃熱病毒，通過伊蚊傳播。
- In this study, we analyzed transcriptomic changes induced by ZIKV infection in four human cell lines (microglia, fibroblast, macrophage and human embryonic kidney cells) to identify genes that could be developed as potential therapeutic targets and to provide insight into the interaction between ZIKV and the host cell.
正文組織架構：
1. Introduction
2. Materials and methods
2.1 Cell lines and culture conditions
2.2 ZIKV propagation and infection of cell lines
2.3 Immunofluorescence microscopy
2.4 RNA extraction, cDNA synthesis and qRT-PCR
2.5 RNA-Seq and data analysis
2.6 Drug treatment
3. Results
3.1 Cell-type-specific ZIKV replication and infection
3.2 Analysis of viral response genes identifies potential cell-type-specific regulators
3.3 ZIKV infection modulates the metabolic and transcriptional landscape
4. Discussion
5. Conclusions
正文部分內容摘錄：
- The single-end reads that passed Illumina filters were filtered for reads aligning to transfer RNA, ribosomal RNA, adapter sequences and spike-in controls.
- The reads were then aligned to UCSC hg19 reference genome using TopHat (v 1.4.1, Baltimore, MD, USA).
- DUST scores were calculated with PRINSEQ Lite (v 0.20.3, open source software), and low-complexity reads (DUST>4) were removed from the BAM files.
- The alignment results were parsed via the SAMtools to generate SAM files. Read counts to each genomic feature were obtained with the htseq-count program (v 0.6.0) using the ‘union’ option.
- After removing absent features (zero counts in all samples), the raw counts were imported into R/Bioconductor package DESeq2 to identify differentially expressed genes among the samples.
- DESeq2 normalizes counts by dividing each column of the count table (samples) by the size factor of this column.
- The size factor is calculated by dividing the samples by the geometric means of the genes.
- This brings the count values to a common scale suitable for comparison.
- P-values for differential expression were calculated using binomial test for differences between the base means of two conditions.
- The P-values were adjusted for multiple test correction using the Benjamini–Hochberg algorithm to control the false discovery rate. Cluster analyses, including principal component analysis and hierarchical clustering, were performed using standard algorithms and metrics.
- Gene ontology analyses on biological processes were performed using The Database for Annotation, Visualization and Integrated Discovery.20 Grouped functional pathway/gene ontology network analyses were performed using Cytoscape with the ClueGo add-on.
- Figure 2 Changes in the transcriptional landscape following ZIKV infection. 這些圖是怎麼畫出來的
- Figure 3 Cell-type-specific differences in steady-state expression of viral response genes reveal potential antiviral targets. 理解這些圖是如何畫出來的，以及這些圖的含義。
- Figure 4 Analysis of differentially expressed genes post ZIKV infection identifies key pathways exploited by ZIKV. 感覺畫出來這些圖就分析的差不多了嗎