Deep RNA Sequencing Reveals a Repertoire of Human Fibroblast Circular RNAs Associated with Cellular Responses to Herpes Simplex Virus 1 Infection

Shi, Jiandong; Hu, Ningzhu; Mo, Ling; Zeng, Zong Yuan; Sun, Jing; Hu, Yunzhang

doi:10.1159/000491471

Cited by 39 publications

(36 citation statements)

References 61 publications

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“…Nevertheless, the expression profiles, functions, and mechanisms of action of circRNAs in viral infection remain largely unknown. Several studies have addressed the identification, characterization, and function of circRNAs during viral infection (Shi et al, 2018;Wang S. et al, 2018;Zhang et al, 2018), but the properties and potential roles of circRNAs during HTNV infection have not been explored.…”

Section: Discussionmentioning

confidence: 99%

“…The unmapped reads were performed fusion gene alignment using Tophat-Fusion (v2.0.13) to identify candidate circRNAs (Zheng et al, 2016). The criteria were as follows: GT/AG splicing signals, mismatch ≤2, back-spliced junction reads ≥1, and distance between two splice sites is no more than 100 kb as previous reported (Shi et al, 2018). The circRNA expression level was described as RPM (Reads Per Million mapped reads).…”

Section: Identification and Quantification Of Circrnasmentioning

confidence: 99%

“…For the integrative analysis of circRNAs, miRNAs and genes, it was found that the differentially expressed circRNAs were involved in cellular responses to Herpes Simplex Virus 1, human cytomegalovirus, and HIV infection through the circRNA-miRNA-gene regulatory axis. Moreover, these genes regulated by circRNAs are enriched in inflammatory response, defense response to virus, and pathways of apoptosis, which may account for viral pathogenesis and cellular immunity (Shi et al, 2018;Zhang et al, 2018;Lou et al, 2019). Artificial circRNAs inhibit the production of HCV viral proteins by effectively adsorbing cellular miR-122 (Jost et al, 2018), and the delivery of purified circRNAs can activate RIG-I-mediated immune responses and provides effective protection against viral infection (Chen Y. G. et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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RNA-Seq Revealed a Circular RNA-microRNA-mRNA Regulatory Network in Hantaan Virus Infection

Zhu

Guo

et al. 2020

Front. Cell. Infect. Microbiol.

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In this work, HUVECs were mock infected or infected with HTNV for 3 days. Total RNA of the cells were analyzed by RNA-seq and obtained circRNA, miRNA, mRNA library. Differentially expressed (DE) RNAs were identified and subjected to GO analysis, KEGG analysis and ceRNA network construction. Then 8 DE circRNAs, 8 DE mRNAs, 6 DE miRNAs were verified by RT-qPCR. Besides, mRNAs (CMPK2, PARP10, GBP1, and RIG-I), circRNAs (circ_0000479), miRNAs (miR-149-5p, miR-411-3p, and miR-330-5p) in the ceRNA network were found effective to inhibit or promote virus replication. And the circ_0000479-miR-149-5p-RIG-I ceRNA axis was verified in HTNV infection.

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Section: Discussionmentioning

confidence: 99%

Section: Identification and Quantification Of Circrnasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RNA-Seq Revealed a Circular RNA-microRNA-mRNA Regulatory Network in Hantaan Virus Infection

Zhu

Guo

et al. 2020

Front. Cell. Infect. Microbiol.

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“…The first dataset is comprised of human ALS motor neurons and studies the effect of the SOD1A4V mutation (2 patient-derived samples) versus (3) isogenic controls (PRJNA236453 [34]). The second dataset contained 3 replicates each of uninfected and herpesvirus (HSV-1)infected samples (PRJNA406943 [35]). The third is a dataset that has recently been used to study the effect of the zika virus on the transcriptome in human neural progenitor cells (PRJNA313294 [36]).…”

Section: Quantification Differences Can Affect Differential Gene Exprmentioning

confidence: 99%

Alignment and mapping methodology influence transcript abundance estimation

et al. 2020

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Background The accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy. Results We investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment. Conclusion We observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly, and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification.

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“…In this direction, we wanted to test whether this tRNA reprogramming is a general adaptive mechanism among viral species. Using three previously published small RNA-sequencing datasets of human cell lines upon viral infection [40][41][42] , we quantified the tRNA of mock and productive infections at different time points (Sup. Table 6).…”

Section: Early Viral Proteins Are Better Adapted and Translationally mentioning

confidence: 99%

Translational adaptation of human viruses to the tissues they infect

Hernandez‐Alias

Schaefer

Serrano

2020

Preprint

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Viruses need to hijack the translational machinery of the host cell for a productive infection to happen. However, given the dynamic landscape of tRNA pools among tissues, it is unclear whether different viruses infecting different tissues have adapted their codon usage toward their tropism. Here, we collect the coding sequences of over 500 human-infecting viruses and determine that tropism explains changes in codon usage. Using an in silico model of translational efficiency, we validate the correspondence of the viral codon usage with the translational machinery of their tropism. In particular, we propose that the improved translational adaptation to the upper respiratory airways of the pandemic agent SARS-CoV-2 coronavirus could enhance its transmissibility. Furthermore, this correspondence is specifically defined in early viral proteins, as upon infection cells undergo reprogramming of tRNA pools that favors the translation of late counterparts.

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Deep RNA Sequencing Reveals a Repertoire of Human Fibroblast Circular RNAs Associated with Cellular Responses to Herpes Simplex Virus 1 Infection

Cited by 39 publications

References 61 publications

RNA-Seq Revealed a Circular RNA-microRNA-mRNA Regulatory Network in Hantaan Virus Infection

RNA-Seq Revealed a Circular RNA-microRNA-mRNA Regulatory Network in Hantaan Virus Infection

Alignment and mapping methodology influence transcript abundance estimation

Translational adaptation of human viruses to the tissues they infect

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