Tandem 3' UTR Patterns and Gene Expression Profiles of Marc-145 Cells During PRRSV Infection

  • Ying Wei ,

    Ying Wei and Jie Li have contributed equally to this work

    Affiliation State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China

  • Jie Li ,

    Ying Wei and Jie Li have contributed equally to this work

    Affiliation State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China,
    School of Biology and Food Engineering, Changshu Institute of Technology, Changshu 215500, China

  • Yun Zhang,

    Affiliation State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China

  • Chunyi Xue,

    Affiliation State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China

  • Yongchang Cao

    caoych@mail.sysu.edu.cn

    Affiliation State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China

Tandem 3' UTR Patterns and Gene Expression Profiles of Marc-145 Cells During PRRSV Infection

  • Ying Wei, 
  • Jie Li, 
  • Yun Zhang, 
  • Chunyi Xue, 
  • Yongchang Cao
x

Abstract

Porcine reproductive and respiratory syndrome virus (PRRSV) causes substantial economic losses to the global pig industry. Alternative polyadenylation (APA) is a mechanism that diversifies gene expression, which is important for tumorigenesis, development, and cell differentiation. However, it is unclear whether APA plays a role in the course of PRRSV infection. To address this issue, in this study we carried out a whole-genome transcriptome analysis of PRRSV-infected Marc-145 African green monkey kidney cells and identified 185 APA switching genes and 393 differentially expressed genes (DEGs). Most of these genes were involved in cellular process, metabolism, and biological regulation, and there was some overlap between the two gene sets. DEGs were found to be more directly involved in the antiviral response than APA genes. These findings provide insight into the dynamics of host gene regulation during PRRSV infection and a basis for elucidating the pathogenesis of PRRSV.

Introduction

Porcine reproductive and respiratory syndrome (PRRS), also known as blue ear disease, is one of the most important porcine infectious diseases affecting the global pig industry. PRRS is characterized by symptoms in infected pregnant sows including anaphase abortion, stillbirth, mummified or weak fetus, respiratory distress (interstitial pneumonia), and high morality of pigs at various ages, especially piglets (Hopper et al. 1992). The disease was first reported in the United States in 1987 and is now widely transmitted in many swine-producing countries (Collins et al. 1992). The causative agent of the disease, PRRS virus (PRRSV), is an enveloped, positive-sense, single-stranded RNA virus belonging to the order Nidovirales, family Arteriviridae (Cavanagh 1997). The viral genome is approximately 15 kb with a 5' cap, 3' polyadenylation (poly [A]) sequence, and at least 11 overlapping open reading frames (Kappes and Faaberg 2015). PRRSV is classified into two genotypes: European type 1 (prototype Lelystad-LV) and American type 2 (prototype VR2332) (Allende et al. 1999).

PRRSV causes persistent infection with a long period of viremia, macrophage tropism, and antibody-dependent enhancement (Butler et al. 2014; Cancel-Tirado et al. 2004). Vaccination is typically used to prevent and control the disease (Chia et al. 2010). However, constant mutations in the PRRSV genome lead to antigen shift and drift, making traditional inactivated and attenuated vaccines ineffective. Identifying host factors that enable PRRSV infection may provide new approaches to disease prevention.

Different mRNA variants of genes are produced through selective transcription initiation, splicing, and poly(A). The protein isoforms thus generated play an important role in the precise spatial and temporal control of gene expression networks. Alternative poly(A) (APA) is a regulatory mechanism for generating mRNAs with distinct 3' untranslated regions (3' UTRs) or code sequences of distinct isoforms via recognition of different poly(A) signals (PAS). 3' UTRs of varying length containing diverse cis-regulatory elements including microRNA- and RNA binding protein-binding sites thus influence mRNA stability, transport, and translation efficiency (Fabian et al. 2010; Elkon et al. 2013; Tian and Manley 2013). APA sites are present in over half of human genes and are also abundant in mouse and other model animals (Shi 2012; Sun et al. 2012). There is increasing evidence suggesting that APA is a mechanism for diversifying gene expression in tumorigenesis, development, and cell differentiation (Sandberg et al. 2008; Fu et al. 2011; Hilgers et al. 2011).

With the development of 3' end sequencing technology, research has focused on the switching of APA sites during viral infection and the search for novel infection biomarkers. In this study, we used the sequencing alternative poly(A) sites (SAPAS) method combined with in vitro transcription (IVT) to profile APA sites switching events and differentially expressed genes (DEGs) in PRRSV-infected Marc-145 cells in order to clarify the dynamics of host–pathogen interactions. We identified 185 APA sites switching genes and 393 DEGs in infected Marc-145 African green monkey kidney cells, and found that these two gene sets reflect distinct but complementary mechanisms in the host response to PRRSV infection.

Materials and Methods

Cell Culture and PRRSV Infection

Marc-145 cells were cultured in Dulbecco's Modified Eagle's Medium (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (Gibco) in an incubator at 37 ℃ and 5% CO2. PRRSV was provided by Professor Yaosheng Chen (Sun Yat-sen University, Guangzhou China) and were prepared in Marc-145 cells. The cells were infected at a multiplicity of infection of 5, with mock-infected cells serving as a control. Cells were collected at 0, 6, 12, 24, and 36 h post-infection (hpi). A total of nine samples were used for library preparation.

IVT-SAPAS Library Preparation and Sequencing

Sequencing libraries were prepared as previously described (Fu et al. 2015). Briefly, total RNA was extracted from Marc-145 cells using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Approximately 2 μg total RNA were fragmented by heating, and specific primers were used for reverse transcription and PCR amplification. Fragments 250–400 bp in size were excised and purified with Agencourt Ampure magnetic beads (Beckman Coulter, Brea, CA, USA). The average size was determined with a Model 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The final pooled fragments were sequenced from the 3' end with Genome Analyzer Ⅱx (Illumina, San Diego, CA, USA).

Profiling of APA Sites and DEGs

Sequencing was performed as previously described (Fu et al. 2011). Briefly, raw reads were filtered and trimmed for quality control utilizing a Perl script and then mapped to the Chlorocebus sabaeus genome. The genomic location of poly(A) sites were defined based on gene annotations. The poly(A) signal of each site was selected based on its genomic sequences. Genes with significant P values corresponding to a false discovery rate < 0.05 and fold change > 1.5 were identified as significant DEGs. A combined model to test tandem APA switching events was generated (Fu et al. 2011) by identifying genes with significant APA switching between infected and normal cells with the linear trend and independence tests. Gene Ontology (biological process) analysis was performed with PANTHER (http://www.pantherdb.org/). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using Database for Annotation, Visualization, and Integrated Discovery (https://david.ncifcrf.gov/).

Quantitative Real-Time (qRT)-PCR Validation

Poly(A) sites were divided into two supersites (proximal and distal). Primers were designed to target the upstream region of the two supersites based on published sequences. cDNA was synthesized from total RNA using the PrimeScript RT reagent kit (Takara Bio, Otsu, Japan), and qRTPCR was performed on a LightCycler 480 real-time PCR system (Roche Diagnostics, Indianapolis, IN, USA) using the SYBR Pre-mix ExTaq Ⅱ kit (Takara Bio) in a 10 μL reaction volume. Each cDNA was analyzed in triplicate. A similar process was used to analyze DEGs.

Raw Data Availability

Raw poly(A)-sequencing data are available from https://figshare.com/s/d17f00575d6d4c3c662e.

Results

Poly(A) Site Profiling in Marc-145 Cells

To investigate the dynamics of host–pathogen interactions based on poly(A) pattern and gene expression profile during PRRSV infection, we used the SAPAS method combined with IVT to generate nine IVT-SAPAS libraries (C0, C6, T6, C12, T12, C24, T24, C36, and T36, where C and T denote control and treatment groups, respectively, and numbers indicate hpi).

A total of 332 million raw reads were generated by Illumina sequencing. After mapping to the monkey genome and internal priming filtering, about 156 million reads were obtained for poly(A) sites analysis (Table 1). Only 39% of reads used University of California at Santa Cruz annotated poly(A) sites, indicating that IVT-SAPAS was effective in detecting novel poly(A) sites, especially in samples with low mRNA expression levels (Fig. 1A). Additionally, 22% and 12% of reads were located in the intergenic region and 1 kb downstream of the gene's 3' UTR. About 2 × 105 variable poly(A) sites—only 13% (Fig. 1B) of which are known—accounted for 58% of reads (Fig. 1A).

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Table 1. Summary of IVT-SAPAS data from Illumina sequencing.

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Fig 1. Sequencing reads and poly(A) sites in Marc-145 cells. A Genomic location of sequencing reads. B Distribution of poly(A) sites in the genome. C Genes with different numbers of tandem poly(A) sites. D Histogram of the median distance between stop codon and poly(A) sites in genes with single poly(A) sites, and distance between stop codon and closest or longest poly(A) sites in genes with multiple poly(A) sites.

We arranged the annotated 3' UTR APA sites in tandem based on the stop codon and combined these with poly(A) site profiling as previously described (Fu et al. 2011), and found that 6536 genes had tandem 3' UTRs, while 34.33% had at least two tandem 3' UTR poly(A) sites (Fig. 1C). Poly(A) sites switching may regulate a variety of biological functions. The average distribution of distances between poly(A) site and stop codon was 426 bp (median: 270 bp). Median distances between the stop codon and proximal and distal poly(A) sites were 237 and 720 bp, respectively (Fig. 1D). These results reveal the detailed landscape of poly(A) site usage in Marc-145 cells and suggest an important regulatory role for APA in the monkey genome.

Signaling via Poly(A) Sites in Marc-145 Cells

The poly(A) signal (PAS) is an important cis-acting element in 3' end processing that is located 10–30 nt upstream of the cleavage site. The six-nucleotide base sequence is recognized by cleavage poly(A) specificity factor (Elkon et al. 2013). To date, 12 types of PAS have been identified, with AATAAA and ATTAAA being the most common (Proudfoot 2011), which was confirmed by our finding that the usage frequency was close to 50%. Our data suggest that poly(A) is dynamic; moreover, 26.6% of the six PAS dimers were not observed (Fig. 2), suggesting that there are other regulatory mechanisms.

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Fig 2. Signaling via poly(A) sites in Marc-145 cells.

Tandem 3' UTR Switching and Regulation of Gene Expression in PRRSV-Infected Marc-145 Cells

The IVT-SAPAS method can be used not only to analyze APA, but also to quantify the expression of a gene based on an analysis of the 3' UTR of its transcripts (Jia et al. 2017). Genes with tandem 3' UTR lengths that differed significantly between libraries were defined as APA switching genes. We identified 19, 17, 52, and 142 such genes at 6, 12, 24, and 36 hpi, respectively (Fig. 3A). In total, there were 185 APA switching genes (false discovery rate [FDR] < 0.05, Rcut ≤ 0.05) after PRRSV infection, of which 141 used shorter 3' UTRs, 32 used longer 3' UTRs, and 12 were dynamically regulated at different time points (Fig. 3B). Genes exhibiting a greater than 1.5-fold difference in expression and had an FDR < 0.05 were considered as differentially expressed. We identified 15, 14, 71 and 361 DEGs at 6, 12, 24, and 36 hpi, respectively (Fig. 3C). In total, there were 393 genes that were differentially regulated between mock- and PRRSV-infected samples, of which 234 were upregulated, 152 were downregulated, and 7 were dynamically regulated at different time points (Fig. 3B).

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Fig 3. Analysis of tandem 3' UTR switching and regulation of gene expression in PRRSV-infected Marc-145 cells. A Summary of APA genes between samples at different time points. B Summary of APA genes and DEGs. C Summary of DEGs between samples at different time points. D Wayne chart of APA genes and DEGs. E, F Functional classification of APA genes (E) and DEGs (F).

To clarify the functional relationship between APA sites and differential gene expression in antiviral immunity, we compared APA genes and DEGs and identified 35 genes involved in the simultaneous regulation of the two mechanisms, indicating that the two mechanisms are independent (Fig. 3D). Furthermore, a classification of gene function revealed that there was little difference in gene function between the two gene sets, which covered cellular and metabolic processes, biological regulation, and response to stimulus, among other categories (Fig. 3E, 3F). These results indicate that these two mechanisms are actively involved in the response to viral infection.

Dynamic Gene Regulation in PRRSV-Infected Marc-145 Cells

To clarify the changes in gene expression over the course of viral infection, we generated a growth curve. Viral titer increased slightly within 12 hpi and then rapidly thereafter (black curves, left Y axis; red curve, right Y axis) unlike mock-infected cells (blue curve, right Y axis), which is consistent with the sequencing data (Fig. 4A). We then analyzed the number of APA genes (Fig. 4B) and DEGs (Fig. 4C) at two sequential time points and found that their total numbers increased during the infection cycle, especially at 12–24 and 24–36 hpi, respectively, when viral replication was in the logarithmic phase, suggesting a robust antiviral response. It is possible that this was due to the induction of host interferon (IFN)-stimulated gene expression after viral infection.

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Fig 4. Dynamic regulation in PRRSV-infected Marc-145 cells. A PRRSV growth curve and number of reads. B, C Regulation of APA genes (B) and DEGs (C) at two sequential time points. D, E KEGG analysis of APA genes (D) and DEGs (E).

The KEGG analysis (P ≤ 0.05) showed that APA genes (Fig. 4D) and DEGs (Fig. 4E) were upregulated during the immune response to PRRSV infection. The DEGs were also associated with the adaptive immune response, including B and T cell receptor signaling pathways. Additionally, DEGs were more directly involved in the antiviral innate immune response than APA genes, including apoptosis and activation of IFN-stimulated pathways. These results suggest that the two mechanisms act cooperatively in the host response to PRRSV infection.

Summary and Validation of Mixed APA Genes and DEGs

To investigate the relationship between APA and DEGs, we identified genes common to the two gene sets at 36 hpi (Table 2). Compared to the negative control group, infected cells preferentially utilized shorter 3' UTRs. Accordingly, most genes involved in APA shortening were upregulated. Proteins associated with innate immunity including JUN and NFKB1 were at the center of the protein interaction map and did not show interactions with other proteins (Fig. 5A). Six mixed APA genes (black columns in Fig. 5B) and DEGs (white columns in Fig. 5B) were validated by qRT-PCR and the results were consistent with the sequencing data.

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Table 2. Summary of mixed APA genes and DEGs at 36 hpi.

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Fig 5. Validation of mixed APA genes and DEGs. Protein interaction diagram (A) and qRT-PCR validation (B) of mixed APA genes and DEGs.

Discussion

APA can affect the stability, transport, translation efficiency and subcellular location of mRNA by producing mRNA isomers with different 3' UTRs (Fabian et al. 2010; Elkon et al. 2013; Tian and Manley 2013). During the differentiation of mouse B cells, the APA site in the IgM gene was altered, resulting in the conversion of IgM protein from a membrane-bound to a secretory type (Takagaki et al. 1996). Since this initial report (Danckwardt et al. 2011), APA sites have been reported in thousands of genes in yeast, zebrafish, mouse, and human, most of which cannot be detected by microarray analysis (Tian et al. 2005; Ulitsky et al. 2012; Schlackow et al. 2013). Recent studies of PRRSV infection have focused on single antiviral immunomodulatory genes and changes in host gene expression at the whole-genome level (Ke et al. 2017; Li et al. 2017; Proll et al. 2017); however, there have been no reports of APA during PRRSV infection.

PRRSV is mainly propagated in porcine alveolar macrophages (Rossow et al. 1995). At present, there are no suitable porcine cell lines for in vitro PRRSV infection experiments, and Marc-145 cells are used instead since the virus can replicate and cause cytopathic changes in these cells (Zhao et al. 2016; Ji et al. 2017; Ma et al. 2018). In this study, we used Marc-145 cells for high-throughput transcriptome profiling of APA and changes in host gene expression during PRRSV infection by SAPAS combined with IVT.

APA is an important post-transcriptional mechanism for gene regulation. It was previously reported that 3' UTRs tend to shorten during cell proliferation in early embryonic development and tumor transformation (Mayr and Bartel 2009; Hoque et al. 2013). Shorter 3' UTRs have also been detected during T cell activation (Sandberg et al. 2008). On the contrary, the 3' UTR was lengthened during embryonic development in mice (Ji et al. 2009). However, genomewide poly(A) site switching and a gradual reduction in 3' UTR length was associated with the response to vesicular stomatitis virus (VSV) infection in macrophages (Jia et al. 2017). In our study, more genes had shorter as compared to longer 3' UTRs during the antiviral response to PRRSV infection. Given that this response requires the generation of large amounts of protein, 3' UTR shortening may simplify gene regulation and thereby accelerate protein synthesis to improve the body's antiviral response.

It was recently that there was no clear correlation between tandem 3' UTRs and mRNA abundance in the response to VSV infection in macrophages (Jia et al. 2017), which is consistent with our findings. During infection by Marek's disease virus, only 42 genes were simultaneously regulated by the two mechanisms (Li et al. 2017), which is close to the value observed here (35 genes). Our functional analysis showed that both APA genes and DEGs regulated variety of biological processes associated with the antiviral immune response, with the host not only modulating mRNA abundance but also switching poly(A) sites to combat virus invasion. The KEGG pathway analysis showed that DEGs were enriched in antiviral immune response-related pathways to a greater extent than APA genes. This may be because regulation of APA is a relatively slow process. Further analysis of mixed APA genes and DEGs revealed that most upregulated genes showed shortening of the APA sequence, although the mechanistic basis for this effect requires further investigation.

In conclusion, the results of our study reveal tandem 3' UTR patterns in Marc-145 cells during PRRSV infection. Our data suggest that APA alters mRNA abundance, which may be correlated with the antiviral response. These findings highlight the functional relationship between APA and gene expression during PRRSV infection and provide a set of genes that are potential therapeutic targets for the prevention or treatment of PRRS.

Acknowledgements

This work was supported by the Natural Science Foundation of Guangdong Province (2014A030312011) and Guangzhou Science and Technology Plan (201804020039).

Author Contributions

YC, YW, JL and CX designed the study; YW performed the experiments; JL and YW analyzed the data; YW prepared the figures and tables; YW and YZ wrote the main manuscript. YC checked and finalized the manuscript. All authors read and approved the final manuscript.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Animal and Human Rights Statement

This article does not contain any studies with human or animal subjects performed by any of the authors.

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