signaling pathway and - interaction affect the rehabilitation process after respiratory syncytial virus infection

Zuanhao Qian*, Zhenglei Zhang* and Yingying Wang Department of Pediatrics, Taikang Xianlin Drum Tower Hospital, Nanjing, China * These authors contributed equally to this work.

ABSTRACT Background. Respiratory syncytial virus (RSV) is the main cause of respiratory tract infection, which seriously threatens the health and life of children. This study is conducted to reveal the rehabilitation mechanisms of RSV infection. Methods. E-MTAB-5195 dataset was downloaded from EBI ArrayExpress database, including 39 acute phase samples in the acute phase of infection and 21 samples in the recovery period. Using the limma package, differentially expressed RNAs (DE- RNAs) were analyzed. The significant modules were identified using WGCNA package, and the mRNAs in them were conducted with enrichment analysis using DAVID tool. Afterwards, co-expression network for the RNAs involved in the significant modules was built by Cytoscape software. Additionally, RSV-correlated pathways were searched from Comparative Toxicogenomics Database, and then the pathway network was constructed. Results. There were 2,489 DE-RNAs between the two groups, including 2,386 DE- mRNAs and 103 DE-lncRNAs. The RNAs in the black, salmon, blue, tan and turquoise modules correlated with stage were taken as RNA set1. Meanwhile, the RNAs in brown, blue, magenta and pink modules related to disease severity were defined as RNA set2. In the pathway networks, CD40LG and RASGRP1 co-expressed Submitted 14 January 2019 with LINC00891/LINC00526/LINC01215 were involved in the T cell receptor sig- Accepted 6 May 2019 Published 12 June 2019 naling pathway, and IL1B, IL1R2, IL18, and IL18R1 co-expressed with BAIAP2- AS1/CRNDE/LINC01503/SMIM25 were implicated in cytokine-cytokine receptor Corresponding author Zuanhao Qian, interaction. [email protected] Conclusion. LINC00891/LINC00526/LINC01215 co-expressed with CD40LG and Academic editor RASGRP1 might affect the rehabilitation process of RSV infection through the T cell Howard Young receptor signaling pathway. Besides, BAIAP2-AS1/CRNDE/LINC01503/SMIM25 co- Additional Information and expressed with IL1 and IL18 families might function in the clearance process after RSV Declarations can be found on infection via cytokine-cytokine receptor interaction. page 14 DOI 10.7717/peerj.7089 Subjects Bioinformatics, Infectious Diseases, Pediatrics Copyright Keywords Pathway, Long non-coding RNAs, Weighed co-expression network analysis, 2019 Qian et al. Respiratory syncytial virus, Enrichment analysis Distributed under Creative Commons CC-BY 4.0

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How to cite this article Qian Z, Zhang Z, Wang Y. 2019. T cell receptor signaling pathway and cytokine-cytokine receptor interaction af- fect the rehabilitation process after respiratory syncytial virus infection. PeerJ 7:e7089 http://doi.org/10.7717/peerj.7089 INTRODUCTION Respiratory syncytial virus (RSV) belongs to the paramyxovirus family of pneumonia genus, which is the most common cause of respiratory tract infection in infants (Hall et al., 2009). RSV can induce interstitial pneumonia and bronchiolitis, and RSV infection is a disease that seriously endangers the health and life of children (Fauroux et al., 2017). At present, the main treatment methods of RSV infection include oxygen therapy, fogging machine antivirus therapy, and antibacterial drug treatment (Handforth, Sharland & Friedland, 2004). Approximately 60% of infants will be infected with RSV during their first season of the virus, and almost all children experience RSV infection at the age of 2–3 years old in the United States (Hama et al., 2015; Louis et al., 2016). Therefore, exploring the possible mechanisms of RSV infection is of great importance. Through specific antagonistic effects on host functions, induction of RNA stress particles and induction of changes in host patterns, RSV can change the of host and the translation of host transcripts (Tripp, Mejias & Ramilo, 2013). Long noncoding RNAs (lncRNAs) are a class of RNAs with a length greater than 200 nt and lacking the ability to encode (Andreia, Marc & George, 2015). At present, lncRNAs are found to have complex functions and can be involved in all stages of gene expression regulation (Wahlestedt, 2013; Wang et al., 2011). LncRNA maternally expressed 3 (MEG3) expression is decreased in RSV-infected nasopharyngeal samples, and MEG3 can suppress RSV infection via inhibiting toll-like receptor 4 (TLR4) signaling (Tao et al., 2018). Through mediating 3 () expression, lncRNA variant translocation 1 (PVT1) is deemed to be correlated with the effects of α-asarone in the treatment of RSV-induced asthma (Yu et al., 2017). Additionally, transcription elongation is involved in IFN-stimulated gene (ISG) expression induced by RSV, and cyclin-dependent kinase 9 (CDK9) activity may serve as a potential target for immunomodulation in RSV-associated lung disease (Tian et al., 2013). Nevertheless, the pathogenesis of RSV infection has not been fully understood. Jong et al. (2016) performed a blood transcriptome profiling analysis and identified an 84 gene signature that could predict the course of RSV disease. However, they had not performed in-depth bioinformatics analyses to explore the rehabilitation mechanisms of RSV infection. Using the dataset deposited by Jong et al. (2016), the lncRNA and mRNA expression profiles of RSV-infected blood samples from infants were compared to screen the molecules markers between acute phase and recovery period of RSV infection. This study might contribute to investigating the gene expression changes in blood from acute phase to recovery period of RSV infection and revealing the underlying mechanisms of disease rehabilitation.

MATERIALS AND METHODS Data source and data preprocessing The E-MTAB-5195 dataset was acquired from EBI ArrayExpress database (https: //www.ebi.ac.uk/arrayexpress/)(Brazma et al., 2003), which was detected on the platform of Affymetrix GeneChip Genome U133 Plus 2.0. E-MTAB-5195 contained 60

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 2/17 blood samples from infants, including 39 samples in the acute phase of infection (14 females and 25 males; mean age = 146.49 days) and 21 samples in the recovery period (11 females and 10 males; mean age = 137.95 days). This study analyzed the expression profile dataset obtained from public database, and no animal or human experiments were involved. Therefore, no ethical review or informed consents were needed. The raw expression profile data in .CEL format were conducted with format transformation, filling of missing data (median method), background correction (MicroArray Suite method), and data standardization (quantile method) using the R package oligo (version 1.41, http://www.bioconductor.org/packages/release/bioc/html/ oligo.html)(Parrish & Spenceriii, 2004). Differential expression analysis The platform information of E-MTAB-5195 was downloaded. Combined with the Transcript ID, RefSeq ID, and location information provided in platform information, the mRNAs and lncRNAs in the expression profile E-MTAB-5195 were annotated based on the 19,198 protein coding genes and 3909 lncRNAs included in HUGO Committee (HGNC, http://www.genenames.org/) database (Bruford et al., 2008). According to disease stage information, the samples were divided into samples in the acute phase of infection and samples in the recovery period. Using the R package limma (version 3.34.0, https://bioconductor.org/packages/release/bioc/html/limma.html)(Ritchie et al., 2015), differential expression analysis for the two groups were carried out. The false discovery rate (FDR) < 0.05 and |log fold change (FC)| > 0.263 were defined as the criteria for screening differentially expressed RNAs (DE-RNAs). Based on the expression levels of the screened RNAs, the R package pheatmap (version 1.0.8, Kolde, 2015)(Wang et al., 2014) was used to perform hierarchical clustering analysis (Szekely & Rizzo, 2005) and present the result in clustering heatmap. Weighed gene co-expression network analysis (WGCNA) and enrichment analysis The systems biology method WGCNA can be applied for integrating gene expression and identify disease-associated modules (Li et al., 2018). Using the R package WGCNA (version 1.61, Langfelder & Horvath, 2008)(Langfelder & Horvath, 2008), the DE-RNAs were analyzed to identify the modules significantly related to disease states and clinical factors. The analysis procedures included assumption of the scale-free network, definition of co-expression correlation matrix, definition of adjacency function, calculation of dissimilarity coefficients, and identification of disease-associated modules. The modules having significant correlation with disease symptoms (p-value < 0.05) and higher correlation coefficients compared with the control (grey module) were taken as the significant modules. Then, DAVID online tool (version 6.8, https://david.ncifcrf.gov/)(Lempicki, 2008) was utilized to perform (GO) (The Gene Ontology Consortium, 2015) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al., 2016) enrichment analyses for the mRNAs in the significant modules. The p-value < 0.05 was selected as the threshold of enrichment significance.

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 3/17 Co-expression network analysis For the lncRNAs and mRNAs in the significant modules, pearson correlation coefficients (PCCs) (Hauke & Kossowski, 2011) were calculated using the cor function in R (https://www.rdocumentation.org/packages/stats/versions/3.6.0/topics/cor). Afterwards, visualization of co-expression network was conducted using Cytoscape software (http://www.cytoscape.org/)(Kohl, Wiese & Warscheid, 2011). Moreover, KEGG pathways were enriched for the mRNAs in the co-expression network using DAVID tool (Lempicki, 2008). Network analysis for the RSV-correlated pathways Using ‘‘Respiratory Syncytial Virus’’ as keyword, the KEGG pathways directly related to RSV were searched form Comparative Toxicogenomics Database (2017 update, http://ctd.mdibl.org/)(Davis et al., 2013). By comparing the searched pathways and the pathways enriched for the mRNAs in the co-expression network, the overlapped pathways and the mRNAs involved in them were obtained. Furthermore, the clusters (including both lncRNAs and mRNAs) of the RNAs involved in the RSV-correlated pathways were extracted from the co-expression network to construct the network of RSV-correlated pathways.

RESULTS Differential expression analysis The raw data of expression profile dataset E-MTAB-5195 were preprocessed (Fig. 1). After annotation was performed based on platform information, a total of 16,984 protein coding RNAs and 939 lncRNAs were obtained. Then, the samples were grouped according to disease stage information. There were a total of 2,489 DE-RNAs between the two groups, including 2,386 DE-mRNAs (1,393 up-regulated and 993 down-regulated) and 103 DE- lncRNAs (62 up-regulated and 41 down-regulated) (Fig. 2). The clustering heatmap of the DE-RNAs indicated that the DE-RNAs could separate the two groups of samples and thus had sample characteristics (Fig. 3). WGCNA and enrichment analysis The 2,489 DE-RNAs were further analyzed and screened using WGCNA. In order to satisfy the premise of scale-free network distribution as far as possible, the value of the weight parameter ‘‘power’’ of adjacency matrix needed to be explored. The selection range of network construction parameters was set, and the scale-free topological matrix was calculated. The value of ‘‘power’’ when the square value of the correlation coefficient reached 0.9 for the first time was selected, namely, power = 18. At this time, the average node degree of the constructed co-expression network was 1, which fully conformed to the properties of small world network (Fig. 4A). Then, the dissimilarity coefficients between the gene points were calculated, and the system cluster tree was obtained. The minimum number of genes was set as 70 for each module, and the pruning height was set as cutHeight = 0.99. A total of 13 modules (except of grey module) (Fig. 4B), and the number and type of RNAs involved in each module were listed in Table 1. Finally, the correlation between each

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 4/17 Figure 1 The box plots before and after data normalization. Full-size DOI: 10.7717/peerj.7089/fig-1

● N = 1034 ● ● N = 1455

5 ● ● ● ●

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−1 0 1 2 3 Log 2 (FC)

Figure 2 The volcano plot of the differentially expressed RNAs (DE-RNAs). The green horizontal dashed line represents false discovery rate (FDR) < 0.05, the two green vertical dashed lines represent— log fold change (FC)—> 0.263. The blue and red dots separately represent the significantly down- regulated and up-regulated RNAs in the samples in the acute phase of infection. Full-size DOI: 10.7717/peerj.7089/fig-2

module and different disease symptoms were calculated. The black, salmon, blue, tan and turquoise modules were significantly correlated with stage (acute/recovery), meanwhile, brown, blue, magenta and pink modules were significantly related to disease severity (Mild/Moderate/Severe) (Fig. 4C). The RNAs in black, salmon, blue, tan and turquoise modules were taken as RNA set1 correlated with stage. Similarly, the RNAs in brown, blue, magenta and pink modules were considered as RNA set2 associated with disease severity. The mRNAs in RNA set1 and RNA set2 separately were conducted with enrichment analysis. For the mRNAs in RNA set1, 17 GO terms (such as regulation of transcription

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 5/17 Figure 3 The lustering heatmap based on the expression levels of the differentially expressed RNAs (DE-RNAs). Red and blue in the sample bar represent the samples in the acute phase of infection and the samples in the recovery period, respectively. Full-size DOI: 10.7717/peerj.7089/fig-3

and activation) and seven KEGG pathways (such as cell adhesion molecules (CAMs) and primary immunodeficiency) were enriched. For the mRNAs in RNA set2, 20 GO terms (including defense response and response to wounding) and eight KEGG pathways (including cytokine-cytokine receptor interaction and lysosome) were predicted (Table 2). Co-expression network analysis For the RNA set1 and RNA set2, the PCCs between the lncRNAs and mRNAs separately were calculated and the lncRNA-mRNA pairs with PCC >0.6 were remained. Subsequently,

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 6/17 Figure 4 The results of weighed gene co-expression network analysis (WGCNA). (A) The selection of the weight parameter ‘‘power’’ of adjacency matrix (left diagram; red line is the standard line when the square value of the correlation coefficient achieved 0.9) and the mean connectivity of RNAs (right dia- gram; red line indicates that the average node degree of the constructed co-expression network is 1 when power = 18); (B) The tree diagram for module division (different color represent different modules); (C) The correlation heatmap for each module and different disease symptoms including disease severity (mild, moderate and severe) and stage (acute and recovery). The numbers in the grids represent correlation coef- ficients, and the numbers in parentheses represent the significance p-values. Full-size DOI: 10.7717/peerj.7089/fig-4

the co-expression networks for RNA set1 (Fig. 5A) and RNA set2 (Fig. 5B) separately were constructed. In the co-expression network for RNA set1, there were 813 nodes (including 777 mRNAs and 36 lncRNAs) and 3773 edges. In the co-expression network for RNA set2, there were 529 nodes (including 521 mRNAs and eight lncRNAs) and 967 edges. Moreover, 10 (such as hematopoietic cell lineage and primary immunodeficiency) and 11 (such as glutathione metabolism and cytokine-cytokine receptor interaction) KEGG pathways separately were enriched for the mRNAs in the co-expression networks for RNA set1 and RNA set2 (Table 3).

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 7/17 Table 1 The number and type of RNAs involved in each module.

Module color Number of Number of Number of RNAs lncRNAs mRNAs black 124 11 113 blue 217 5 212 brown 215 1 214 green 206 3 203 greenyellow 88 6 82 grey 379 17 362 magenta 110 3 107 pink 122 1 121 purple 91 2 89 red 182 15 167 salmon 71 6 65 tan 80 6 74 turquoise 394 8 386 yellow 210 19 191 Notes. lncRNA, long non-coding RNA. Network analysis for the RSV-correlated pathways Based on Comparative Toxicogenomics Database, 44 KEGG pathways directly related to RSV were obtained. After comparing the searched pathways and the pathways enriched for the mRNAs in the co-expression networks, four (including hsa04660:T cell receptor signaling pathway, involving CD40 ligand (CD40LG) and RAS guanyl releasing protein 1 (RASGRP1); hsa04514:Cell adhesion molecules (CAMs); hsa04650: mediated cytotoxicity; and hsa04350:TGF-beta signaling pathway) and two (including hsa04060:Cytokine-cytokine receptor interaction, involving 1 beta (IL1B), interleukin 1 receptor type II (IL1R2), (IL18) and interleukin 18 receptor 1 (IL18R1); and hsa04621:NOD-like receptor signaling pathway) overlapped pathways separately were obtained for the mRNAs in the co-expression networks for RNA set1 and RNA set2. Furthermore, the networks of RSV-correlated pathways were built (Fig. 6). In the pathway network for RNA set1, long intergenic non-protein coding RNA 891 (LINC00891), long intergenic non-protein coding RNA 526 (LINC00526) and long intergenic non-protein coding RNA 1215 (LINC01215) were co-expressed with CD40LG, and LINC01215 was co-expressed with RASGRP1. Moreover, long intergenic non-protein coding RNA 1503 (LINC01503) and SMIM25 co-expressed with IL1B, colorectal neoplasia differentially expressed (CRNDE) co-expressed with IL1R2, BAIAP2 antisense RNA 1 (BAIAP2-AS1) and CRNDE co-expressed with IL18, and SMIM2 5 co-expressed with IL18R1 were involved in the pathway network for RNA set2.

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 8/17 Table 2 The Gene Ontology (GO) terms (A) and pathways (B) separately enriched for the mRNAs in RNA set1 and RNA set2.

(A) Category Term Count P-value FDR RNA set1 GO:0006350∼transcription 155 8.10E-12 1.81E-08 GO:0046649∼lymphocyte activation 34 2.86E-11 3.20E-08 GO:0045449∼regulation of transcription 178 7.42E-11 5.54E-08 GO:0045321∼leukocyte activation 34 5.22E-09 2.92E-06 GO:0030098∼lymphocyte differentiation 21 1.52E-08 6.81E-06 GO:0042110∼T cell activation 23 2.25E-08 8.41E-06 GO:0001775∼cell activation 35 1.07E-07 3.42E-05 GO:0030217∼T cell differentiation 15 5.89E-07 1.65E-04 GO:0002521∼leukocyte differentiation 21 9.30E-07 2.31E-04 GO:0030097∼hemopoiesis 27 1.27E-05 2.84E-03 GO:0006355∼regulation of transcription, DNA-dependent 113 2.25E-05 4.57E-03 GO:0048534∼hemopoietic or lymphoid organ development 28 2.54E-05 4.73E-03 GO:0002520∼ development 29 2.77E-05 4.76E-03 GO:0006955∼immune response 54 3.95E-05 5.89E-03 GO:0051252∼regulation of RNA metabolic process 114 3.70E-05 5.91E-03 GO:0042113∼ activation 13 1.00E-04 1.39E-02 GO:0002684∼positive regulation of immune system process 24 2.89E-04 3.73E-02 RNA set2 GO:0006952∼defense response 68 5.40E-17 1.32E-13 GO:0009611∼response to wounding 55 1.48E-12 1.81E-09 GO:0009617∼response to bacterium 29 1.73E-10 1.40E-07 GO:0006954∼inflammatory response 36 3.71E-09 2.26E-06 GO:0006955∼immune response 57 3.78E-09 1.84E-06 GO:0001817∼regulation of cytokine production 24 9.50E-08 3.86E-05 GO:0042742∼defense response to bacterium 18 3.59E-07 1.25E-04 GO:0006935∼chemotaxis 21 8.54E-07 2.60E-04 GO:0042330∼taxis 21 8.54E-07 2.60E-04 GO:0007599∼hemostasis 16 5.45E-06 1.47E-03 GO:0002237∼response to molecule of bacterial origin 14 8.97E-06 2.18E-03 GO:0050817∼coagulation 15 1.28E-05 2.84E-03 GO:0007596∼blood coagulation 15 1.28E-05 2.84E-03 GO:0042060∼wound healing 21 1.33E-05 2.69E-03 GO:0006690∼icosanoid metabolic process 10 3.17E-05 5.93E-03 GO:0050878∼regulation of body fluid levels 17 3.61E-05 6.26E-03 GO:0033559∼unsaturated fatty acid metabolic process 10 6.25E-05 1.01E-02 GO:0032496∼response to lipopolysaccharide 12 7.47E-05 1.13E-02 GO:0045087∼innate immune response 16 1.03E-04 1.47E-02 GO:0007626∼locomotory behavior 23 2.79E-04 3.51E-02

(B) Category Term Count P-value RNA set1 hsa05340:Primary immunodeficiency 11 6.61E-07 (continued on next page)

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 9/17 Table 2 (continued) (B) Category Term Count P-value hsa04662:B cell receptor signaling pathway 10 2.84E-03 hsa04514:Cell adhesion molecules (CAMs) 13 6.34E-03 hsa05330:Allograft rejection 6 1.35E-02 hsa03010:Ribosome 9 2.26E-02 hsa04650:Natural killer cell mediated cytotoxicity 11 4.01E-02 hsa04612: processing and presentation 8 4.78E-02 RNA set2 hsa04610:Complement and coagulation cascades 9 4.69E-03 hsa04142:Lysosome 12 5.12E-03 hsa04060:Cytokine-cytokine receptor interaction 20 5.45E-03 hsa04664:Fc epsilon RI signaling pathway 9 9.80E-03 hsa04620:Toll-like receptor signaling pathway 10 1.53E-02 hsa04666:Fc gamma R-mediated phagocytosis 9 2.92E-02 hsa04621:NOD-like receptor signaling pathway 7 3.11E-02 hsa04722:Neurotrophin signaling pathway 10 4.89E-02 Notes. FDR, false discovery rate. DISCUSSION In this study, 2,489 DE-RNAs between the two groups of samples were obtained, including 2386 DE-mRNAs (1393 up-regulated and 993 down-regulated) and 103 DE-lncRNAs (62 up-regulated and 41 down-regulated). The black, salmon, blue, tan and turquoise modules were significantly correlated with stage, and the RNAs in them were taken as RNA set1. Meanwhile, brown, blue, magenta and pink modules were significantly related to disease severity, and the RNAs in these modules were considered as RNA set2. In the pathway network for RNA set1, LINC00891, LINC00526 and LINC01215 were co-expressed with CD40LG, and LINC01215 was co-expressed with RASGRP1. In the pathway network for RNA set2, LINC01503 and SMIM25 co-expressed with IL1B, CRNDE co-expressed with IL1R2, BAIAP2-AS1 and CRNDE co-expressed with IL18, and SMIM25 co-expressed with IL18R1 were found. After RSV infection, a large number of , , reactive oxygen species and other active mediators are induced in cells (Hansdottir et al., 2010). Natural immunity and regulatory immune responses are activated by identifying the conserved structures of corresponding in , viruses and the environment (Shayakhmetov, 2010; Sigurs et al., 2000). Immune response in the body changes dynamically all the time during viral infection (Thompson et al., 2011). This study analyzed the blood samples from infants in acute phase or recovery period of RSV infection, which was the clearance process after virus infection. After removing the virus in vivo, of the effector T cells occurred and immune response of the T cells decreased (Jie et al., 2011). In the pathway network for RNA set1, CD40LG and RASGRP1 were involved in the T cell receptor signaling pathway in our present study. CD40LG promotes virus clearance ability of DNA vaccines encoding the F glycoprotein and enhances the immune response to RSV infection, therefore, CD40LG can strengthen the durability of DNA vaccines against RSV

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 10/17 GLCCI1 ABRAXAS1 TNFRSF21 ZNF831 RBM14 MBLAC2 CTDSPL2 UQCRC2 CDC42SE2 C1orf174 AP1AR ST6GAL1 HLA-DRA PLEKHF2 UBE2E2 SYNJ2BP MED21 VAV3 SPOCK2 BTN3A3 CXCR3 DNAJB1 SLC4A7 CCT2 ZFX FYTTD1 PPTC7 PRPF4B AMZ2 MAN2A1 OSGEPL1 PHTF2 CAMK2D KPNA5 TMC6 UTP23 PRKDC SRSF8 A RYK TMEM168 SCOC TCF7 DCTD RPP30 ZNF41 CRIP2 ABHD10 FCRL1 SLC7A6 TP53 TXK RASA3 MAML2 SGPP1 LPAR5 HAUS6 ABCD2 USP28 HS3ST1 BLNK ZNF317 ZNF146 C19orf12 CDK8 MCM3AP KCNH8 NEDD1 TAGAP C7orf31 PLD4 DEAF1 FCN3 CD200 ITK SYPL1 CREBL2 PABPC1L2B ACTR5 ITGA6 ADRB3 IRF8 HLA-DMA STAP1 SLC35C2 TCL1A EIF2AK3 QRSL1 LINC02270 CSNK1G2 GUCD1 IL7 LUC7L ZNF697 ERC2 CD1A NSMCE4A ZBTB14 MLANA FCRLA EBLN3P NR2C1 TACC3 FCRL3 NUDT13 QSOX2 CD79A FCER2 CDC42EP5 SESN1 CAPN14 PLEKHG1 LDLRAP1 CCDC50 NDFIP2 UBTF BCL2L10 MRNIP TRIM42 PNRC1 OSER1-AS1 RPL3 CD22 PIK3C2B ZIK1 CD200R1 KCNJ1 CD180 RAB39B WDR89 TFAM SSPO AMBP LCK DEFB121 CST2 UGT8 CTGF NEPRO YPEL1 FOXCUT PLPP5 TMEM133 MSANTD2 PAXX SUSD4 ZNF549 P2RX5 SLC25A53 DOPEY1 DEPDC7 ZNF416 APBB1 PNOC ABHD15 SLC25A6 ZNF211 FAM102A TWISTNB LINC01657 DACH2 TEX19 MBD4 BEND4 RSAD1 BTBD11 C1orf132 CD19 TADA1 ELP2 FBXO28 SLC44A5 BANK1 ZNF331 ZNF165 FANCF ZNF135 HSF2 S1PR1 LIG3 HDAC5 KRTAP3-1 RUBCNL PEBP1 MS4A1 SIDT1 SLC25A26 BACH2 PPP1R14A KDSR YY1 FUT8 ZNF793 GCSAM TM2D3 TBC1D22A LINC02016 CD28 ODF2 MTMR2 NKX2-5 ZNF296 CYSRT1 ITGB7 EPHX2 UBE2Q2 ZNF782 RABL2A SRSF1 LINC01301 BRMS1L LINC00926 C14orf28 RPL15 GALNT12 GPR174 CYP7B1 LIPF GRK7 FAM43A MPP6 CPNE5 TSPYL1 CARNS1 CYB561A3 KRBOX4 SHROOM2 TLR10 LY9 DNAAF2 FAM210A MMP28 VASH1-AS1 KBTBD8 PPP1R16BLINC02397 DTD2 CAMK2N2 RXFP3 TBC1D4 NT5E SLC5A12 UFSP2 PRPS1 AKAP11 DIP2C LPXN ZNF304 NARS2 ZNF227 MRPL50 SMIM10L1 UCKL1 PTPRK TPD52 TSHZ1 THNSL1 PTPN4 CDRT1 HIVEP2 PKIG SMURF2 EPB41L4A-AS1 HUS1 TMEM161B EID3 TMEM267 ZNF671 BRF2 ANKRD31 KIR3DX1 CCT4 RPS5 CEP57 KRT38 ZCCHC18 VPREB3 ARL2BP ZNF285 IPO5 PRKCQ MRFAP1L1 CASD1 PSMD11 MTERF2 ZNF649 COX7C DZIP3 UTP15 C3orf67 P2RY10 HVCN1 ZNF14 MRPS27 NAA40 PARP15 ZNF480 ZNF540 TMEM204 PEX1 NRG3 RORA SUCLG1 ZNF791 CD1C USP11 TBC1D10C BICD1 GNG7 LINC01013 CD81 NOP53 ZNF382 LINC01561 DLG3 HSF4 PARM1 ZHX2 ARHGAP5 RCN2 PRKCH DIDO1 DIMT1 RPA2 RHOH MARCKSL1 DPH5 CR2 LINC00954 NACAD MOS EBF1 ADAM23 CD72 CIRBP CYB5B KRT2 PRPSAP2 SCAI ZAP70 STRBP HMG20A ZNF571 ZNF607 DDX53 MMP15 AMOT KIAA0355 ZBTB25 HSF5 ZBTB12 RPS29 LINC01857 FAM69ABIRC3 C16orf74 EFCAB7 GAL3ST4 LINC00638 BCL11A ZNF582-AS1 SSBP2 ZNF84 BDH2 ZNF26 MIOS CA1 NGEF ZDHHC23 ID3 CELSR2 NEURL2 TSPAN13 CRY1 ZNF329 ELOVL4 ZNF275 LINC01345 ADRA1B SCN3A RFTN1 BCL7A CCL20 DGKD PDE9A SNRNP40 ZBED5 DDHD2 GSPT2 GIPC3 ZNF256 PHB2 SNRNP70 SMAD7 C1orf27 C9orf152 AASS ZNF266 GPR183 CD27 KLHDC2 TC2N G6PC3 DOCK9 KLHL26 ZC3H12D CA11 TMEM243 ZNF74 ZNF566 ZNF45 RPL14 TTC9 LRRC3B SSMEM1 SIRT4 LBH DMAC1 SEC22A CAAP1 CBR3 NUDT6 ATXN7L2 CRTAM ZNF419 ACSS1 GNPDA2 ZNF891 VPS51 PIGV TRAPPC6A CXXC5 RANGRF BAHD1 DIS3 EARS2 RABEP2 STMN1 MXRA7 ELAC2 BAG3 MOAP1 ATAD1 ASF1A C8orf33 LGALS4 SLC16A10 ZNF439 PPP3CC ZNF253 TMEM102 ANHX DNHD1 DGKK SIT1 ZNF786 CERK ZBTB3 FADS3 VSIG1 DSG3 L3MBTL3 TRMT11 CD40LG SMARCAD1 CD7 CLEC2D ANKRD46 SFI1 PKIA ZNF780B CD247 STK17A MGAT4A CHCHD6 BICDL1 ATF2 C2orf50 MINOS1 GPR18 CERNA1 TMEM225 PTPRZ1 ZNF569 RAD1 MARVELD2 LINC00445 EIF1AX LINC00526 ZNF404 LIPT1 CCDC106 PSMG3-AS1 GNG4 SHISA2 NCR3 CTSC AGPAT5 RPS20 GSTA3 DYRK2 ZBTB5 GTF2A2 ZNF223 CD2 ZNF383 APEX1 SCAF8 CCDC25 SLC5A4 ETS1 FHIT ZNF235 DHRS3 HMOX2 LRRD1 CCDC191 GPA33 NOSIP UBAC2 EPHA6 PCDHAC2 LYRM4 CD52 AES TOX2 PPP1R13B NFYC KLHL3 ZBTB24 IL21R PRAG1 LINC00891 B4GAT1 BOLA1 RNF113A CHRM1 CCDC122 TM9SF3 FCMR CDCA7L FBL ABHD17A ZNF25 LSM14A ZFP62 RLN2 TRIM68 ZNF823 LINC00635 AIF1 SUPT20H IL7R CDC25B CD96 FARS2 DUSP14 RASAL3 RLN1 USE1 LRP1B LINC00881 ZNF260 C5orf34 EIF3L PLPP4 GDF7 PLA2G5 CHRNA9 SERTAD1 BBS2 YAE1D1 AHSA2 CHGA MACC1 OPN3 NOG ZNF232 REXO4 PMP2 BOLL ATP6V0E2 BTLA POP5 C12orf10 TMEM231 SDR39U1 MRPS33 ABHD14B SKAP1 SH3GLB2 TRIML1 CC2D1B IL23A EIF2D DTX1 DNAJA3 TMEM220 ZNF420 RPL22 PLXDC1 CD3D PLEKHB1 ANXA2R PCBP2 NLRC3 KDM2B FAM111A-DT GRB7 BEST3 AKAP4 KBTBD6 BEX4 PHF10 AKR1B1 TOX INTS11 C6orf47 CASC17 CDR2 GCOM1 P2RY8 DIS3L UXT MAGED4 TP53TG1 PLEKHF1 NPM2 HOXC12 SLC39A10 ZNF239 C6orf48 C1orf109 CASC15 BEX2 IMPDH2 SNRPD2 RYR1 ARL14EP ACKR3 FUNDC1 TEF ZNF677 ZNF575 PPIL1 LINC01770 ICOS LINC01215 RASGRP1 PDCD2LTRIM59 ZNF57 MEN1 HSPA4 EXTL2 THEMIS POLR1E SIGIRR EPHB6 ITPRIPL1 DBF4 CRISPLD2 ECD OCIAD2 IMMP1L ALDOC LRRN3 LSM7 SNHG8 PPIL3 RNF125 QDPR MYOZ2 ATE1 CCT3 PJA1 CCR7 ZNHIT3 LYRM7 YARS2 ZBTB49 USB1 ANXA8L1 FAM200A GNPNAT1 ITGAD RPS23 ASTE1 CFAP36 FAIM CHMP7 HYLS1 SMAGP CDK9 RAB33A MRPL34 CCDC167 RNF167 RPL37 BEND5 NSA2 BSN TMEM117 FAM171A1 TTC27 DGUOK C5orf49 BTBD17 IGSF9 ITM2A KLHL34 ARL4C SH2D1A ADGRG5 MRPL45 ZNF195 CHI3L2 ZNF708 TIMM21 CBY1 EID1 TP53RK TMEM263 UBASH3A OXCT1 GTF3A MRPS6 CD3G PIGP GZMM RTN4R TCEAL1 TULP3 ZNF600 OOSP2 TOMM7 PITPNC1 FYN GATA3 FAM24B WDR54 ACYP2 TUBB2B SSX3 EEF1A1 AK5 ABCB1 AMIGO1 MRPL39 LRRC75B EEF1A2 SEC62 ZNF32 SMKR1 TRIM65 COA4 PIK3R3 ZFP1 EIF2A NPAT OXNAD1 CCDC107 CCNB1IP1 GPX7 THEM6 NBDY RTL6 HINT1 HIP1R ZNF22 CCDC102A IRAK1BP1 BNIP3 AGMAT ZBTB2 PRR30 HACD3 ADPRM TCEAL8 TMEM14A RNF138 TM4SF4 COA1 TRMU ECI2 LINC01579 MLLT11 BRD3OS KIF3A AVEN FCGBP ST6GALNAC6 STK39 SBK1 HDAC1 PVRIG CLSTN1 MRPL42 SLC2A4RG LINC00937 PLEKHA1 BTN3A2 SCML1 SYNC MAD1L1 LRFN3 SLC25A38 RGCC PHOSPHO2 ARHGEF3 MRPL9 NXNL2 HIVEP3

CHSY1 ACOX1 RP2 GPR141 ACSL1 SLC2A14 PROK2 AP5B1 IL18R1 IER3 ALPK1 LRRC4 IL17RA TMEM71 CPQ HSPA1A MS4A4A FCGR1A GPR107 SLC40A1 BCL2A1 LMNB1 PGLYRP1 LILRA3 KCNE1 POR DHCR24 ADORA2B CD58 SUSD1 GRINA GPAT3 B LILRA5 BCL6 WDFY3 GBA LOXL3 PKP2 CEACAM8 LGALSL HOMER3 CAPG CCNJL SOCS3 SLC39A5 SLC16A5 KIF1B FBXL5 TXN SLC2A3 HLX DACH1 ZNF106 TYROBP MS4A3 ORM1 FAM214B DRAM1 LMO2 ADAMTS3 FRMD4B BID QPCT LILRB2 OPLAH FPR1 AZU1 CDKN2D SIRPD DUSP18 CHST15 TIMP1 SLC51A RETN TP63 DDAH2 ADGRG3 FRAT2 THEMIS2 HK3 ACER3 CTSG IDI1 GAS6 IL18RAP ALOX5APCLEC11A FUT4 SAT1 LTBP1 ANKRD9 TANGO2 IL10RB GRAMD1A PILRA MTX1 C5orf30 CLGN CREG1 TLR2 SIRPB2 PROM1 IFI27L2 AOAH SLC37A3 S100A8 RAB3D ENTPD7 CLEC4D MICAL1 TCN1 WFDC1 PLAGL1 SLC26A8 SAP30L SLCO4C1 APMAP NME8 TMEM52B SERPINB2 S100A11 LGALS12 ALAS1 CSF2RB APTX NFIB SLC28A3 GCLM MACROD1 SIK3 RNF19B SLC22A4 SLC22A15 GP1BA C20orf24 MSRB1 RAB20 TNNI2 PLB1 C15orf41 CRYM SEMA4A DNAJC25-GNG10 AATK HP RIPOR3 KDM7A JPT1 PNKD PTX3 RNASEL C3AR1 GPR84 MSRB3 MMP8 MMRN1 AQP9 TFEB TBC1D30 FTH1 SLC16A6 MOSPD2 CEACAM4CLEC4E NLRP12 BMX ERMAP RXRA DHRS13PLOD1 ALOX12 ZNF521 NPR3 LINC00265 NFE2 KCNJ2 STX10 ALPL FKBP1A TTC8 TGFB1I1 MXD3 TLR5 RGL4 NCF2 NT5DC4 PIWIL4 CPNE2 PGM2 PTAFR TLR1 DGAT2 ROPN1L S100A9 PFKFB2 H1F0 FOXO4 APTR FRAT1 CCPG1 LIN7A TSPAN2 IFI27 VPS9D1 GCA LAMP2 RAB32 LCN2 C3orf79 CXCR2 GPR160 SUCNR1 IFNGR2 PPP4R1 HCK MGAM GBE1 GPC5 SMARCD3 SMIM25 TMEM165 CDA PNPLA1 MPO SLC7A5 PLIN3 SIGLEC9 GYG1 ROGDI XK TECPR2 SRGN TRIM71 PIP4P2 CYBRD1 FAM129A TMCC3 BAZ2BZBTB34 NFAM1 IL1B PTGR1 NEDD4 C17orf99 PRG3 EGF CTSD IDH1 S100A12 CYP51A1 FURIN CSF3R ITGAM CKAP4 AIPL1 C2orf88 LINC01270 SIRPB1 FCER1G KL ANXA3 PRTN3 IGSF10 PLEKHO2 PPP1R3D S100P FZD5 AGO4 TNFRSF1A ADM RRAGD INHBA FCGR3B APH1B ZNF788 FAM166B PLPPR2 SLCO3A1 B4GALT5 NEU1 PLA2G4A AGPAT2 NSD1 PPP1R26 NCF4 VNN2 PLBD1 SLC25A44 TALDO1 ANKRD33B ZGLP1 PLXDC2 CEACAM3 RMI1 PRDM5 SERPINB10 GADD45A SPOPL PRKCD C15orf65 SELP ATP2C2 NFE4 SNX11 C5AR1 BAZ1A FES IMPDH1 GNA15 RAB10 GAS7 LRRK2 SULT1B1 SERPINB1 CRNDE GLT1D1 IMPA2 G6PD UGCG OSBPL2 GAB2 ST6GALNAC3 LRRN2 RHD TBXAS1 LINC01503 IRAK3 FUCA2 RAB27A FNBP1L INSC KRT23 LRRC25 SIRPA GLA PELI2 DNASE1L1 SSH3 CYSTM1 CTBS CD300LF COQ2 CYYR1 PLP2 CRISP3 ITPRIP HK2 ZNF516 TBC1D8 NAPRT CD63 ARF3 RNASE3 DGKG PDGFA EHBP1L1 SLC9A8 TSEN34 DDIAS FAM47E-STBD1 MCU WIPI1 CRISPLD1 NFIL3 CSTA MAP3K3 MICU1 KCNJ15ALDH3B1 KIF3C MNDA CFP ADAP1 MAFG VWA5A TSHZ3 CTSA KIAA0930 TMEM88 FHOD1 TOR4A ACSS2 MGST1 CORO2A MAP3K5 KLHL2 TIMP2 FIG4 KBTBD11 ELANE FSTL3 PADI4 MCEMP1 DEFA1 FGR AP3S2 TMEM170B MGST2 VSTM1 DPY19L3 ANPEP P2RY13 LRG1 B3GNT8CPPED1 MAPK3 METRNL RAB31 ASGR1 STOM TSPO ERG CXCR1 SERINC2 IL18 RNASE2 BAMBI ATP9A IGFBP7 BASP1 PSRC1 SULT1A2 ASRGL1 RAB8B SDCBP ZYX ZNF467 CEBPB PSTPIP2 SHOX2 ANKS1A FAM129B BAIAP2-AS1 ANO10 NTSR1 ORM2 COL17A1 CLEC1B ARG1 PROS1 SLC2A5 IGSF6 TKT SPI1 METTL9 TBC1D2 PCOLCE2 LY96 DYSF BST1 CIDEB BAG4 DEFA4 SLC39A2 OSCAR PYCARD FAM228B HMGB3 PLOD2 FAM160A2 ASAH1 NINJ2 LTF SLPI GLIPR2 RCBTB2 RNF135 SLC39A11 CEBPE SOS2 CEBPD TLE3 LGALS1 IL1R2 MMP9 CLEC5A SQOR COL8A2 FAM110B CA4 CARD6 EFHD2 LRMDA CYTH4 MTF1 PLAUR CD33 OLFM4 ATP8B4 TCTEX1D1 CEBPA SLC31A2 ALDH2 RC3H1 PRAM1 S100A4 CEACAM6 RNF217 RHOG PFKFB3 DLK1 MCTP1 TMEM120A CARD9 TMEM45A UPP1 DHCR7 PADI2 CDC42EP3 ACPP LILRB1 RNF175 YIPF1ADGRE1 TST CPE FAM200B NFKBIZ EMILIN2 VSIR DSE MANSC1 PSTPIP1 F8 WSB1 F5 PPM1N FXYD6 CD177

Figure 5 Co-expression networks. (A) The co-expression network for RNA set1. (B) The co-expression network for RNA set2. Blue and green represent RNAs that are significantly down-regulated in the sam- ples in the acute phase of infection. Red and orange represent RNAs that are significantly up-regulated in the samples in the acute phase of infection. Squares and circles separately represent long non-coding RNAs (lncRNAs) and mRNAs. Full-size DOI: 10.7717/peerj.7089/fig-5

infection (Harcourt et al., 2003). RASGRP1 deficiency is related to reduced extracellular signal-regulated kinase (ERK ) in B cells and T cells, and can lead to defective proliferation, motility, and activation of the cells (Salzer et al., 2016). Therefore, the fact that LINC00891, LINC00526 and LINC01215 co-expressed with CD40LG, as well as LINC01215 co-expressed with RASGRP1 in our study might suggest the potential function of them in the rehabilitation mechanisms of RSV infection via the T cell receptor signaling pathway. In the pathway network for RNA set2, IL1B, IL1R2, IL18, and IL18R1 were implicated in cytokine-cytokine receptor interaction in our study. Proinflammatory cytokines are found to be associated with the progression of cerebral white matter injury (WMI) in preterm infants, and cytokine-receptor interaction may be critical in determining the effects of inflammation in the development of the disease (Bass et al., 2008). IL18 is a cytokine that

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 11/17 Table 3 The pathways enriched for the mRNAs in the co-expression networks for RNA set1 and RNA set2. Category Term Count P-value RNA set1 hsa04640:Hematopoietic cell lineage 15 2.25E-06 hsa05340:Primary immunodeficiency 9 2.89E-05 hsa04660:T cell receptor signaling pathway 15 3.41E-05 hsa04662:B cell receptor signaling pathway 10 1.60E-03 hsa03010:Ribosome 9 1.41E-02 hsa04514:Cell adhesion molecules (CAMs) 9 1.12E-02 hsa04650:Natural killer cell mediated cytotoxicity 9 1.15E-02 hsa05330:Allograft rejection 4 1.45E-02 hsa00600:Sphingolipid metabolism 4 1.72E-02 hsa04350:TGF-beta signaling pathway 6 2.15E-02 RNA set2 hsa00480:Glutathione metabolism 7 3.50E-03 hsa04060:Cytokine-cytokine receptor interaction 17 4.80E-03 hsa00600:Sphingolipid metabolism 6 5.70E-03 hsa00052:Galactose metabolism 5 6.90E-03 hsa04142:Lysosome 10 7.90E-03 hsa04621:NOD-like receptor signaling pathway 7 1.00E-02 hsa04640:Hematopoietic cell lineage 8 1.39E-02 hsa00010:Glycolysis/Gluconeogenesis 6 3.26E-02 hsa00500:Starch and sucrose metabolism 5 3.56E-02 hsa00030:Pentose phosphate pathway 4 3.72E-02 hsa00561:Glycerolipid metabolism 5 4.43E-01

can enhance antiviral immunity and decrease viral load, while the RSV/IL18 recombinant deteriorates pulmonary viral infection (Harker et al., 2010). RSV-induced bronchiolitis is correlated with the occurrence of atopic and allergy asthma, and IL12 and IL18 play critical roles in Th1 and/or Th2 immune responses to airway inflammation induced by RSV infection (Wang et al., 2004). IL1α can boost IL8 expression in RSV-infected epithelial cells, and IL1α inhibition may be applied for repressing the inflammation correlated with IL1α and IL8 (Patel et al., 2010). IL1α is the main cytokine secreted by RSV-infected epithelial cells, and the endothelial cell activation mediated by IL1α may contribute to the initiation of RSV-associated leukocyte inflammation (Chang, Huang & Anderson, 2003). Thus, the cytokines in IL1 and IL18 families might be correlated with clearance process of RSV infection through cytokine-cytokine receptor interaction, and BAIAP2-AS1, CRNDE, LINC01503 and SMIM25 co-expressed with the cytokines might be also involved in the rehabilitation process of RSV infection.

CONCLUSIONS In conclusion, a total of 2,489 DE-RNAs between the acute and recovery phase of RSV infection were screened. We identified that LINC00891, LINC00526 and LINC01215 co-expressed with CD40LG, as well as LINC01215 co-expressed with RASGRP1 might affect the rehabilitation process of RSV infection through the T cell receptor signaling

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 12/17 ITGB7 CD2 FAM111A-DT A HLA-DMA CD28 hsa04514:Cell ICOS LINC00926 adhesion molecules PPP3CC CD22 HLA-DRA (CAMs) NCR3 SMAD7 CD40LG GDF7 E2F5 LINC01013 LINC01215 CD247 ITGA6 NOG hsa04650:Natural hsa04350:TGF-beta CD3D killer signaling RASGRP1 hsa04660:T ID3 LINC00526 EBLN3P cell cell pathway VAV3 mediated ITK receptor LINC00891 signaling cytotoxicity PIK3R3 pathway SMURF2 EPB41L4A-AS1 PRKCQ

ZAP70 SH2D1A LCK CD3G

FYN

hsa04621:NOD-like PSTPIP1 receptor B signaling MAPK3 pathway CARD9

LINC01503 LINC01270 PYCARD CARD6

IL1B BAIAP2-AS1 CSF2RB SMIM25 PLEKHO2 IL18 CSF3R IL18RAP IFNGR2 IL1R2 CXCR2 CXCR1 CRNDE hsa04060:Cytokine-cytokine TNFRSF1A receptor IL17RA interaction INHBA

IL18R1 IL10RB PDGFA EGF

Figure 6 Networks of respiratory syncytial virus (RSV)-correlated pathways. (A) The network of RSV- correlated pathways for RNA set1. (B) The network of RSV-correlated pathways for RNA set2. Blue and green represent RNAs that are significantly down-regulated in the samples in the acute phase of infection. Red and orange represent RNAs that are significantly up-regulated in the samples in the acute phase of in- fection. Squares and circles separately represent long non-coding RNAs (lncRNAs) and mRNAs. Red dia- monds represent the overlapped pathways. Full-size DOI: 10.7717/peerj.7089/fig-6

Qian et al. (2019), PeerJ, DOI 10.7717/peerj.7089 13/17 pathway. Furthermore, BAIAP2-AS1, CRNDE, LINC01503 and SMIM25 co-expressed with the cytokines in IL1 and IL18 families might act in the rehabilitation mechanisms of RSV infection via cytokine-cytokine receptor interaction. However, these findings obtained from bioinformatics analyses should be further validated by experimental researches.

ADDITIONAL INFORMATION AND DECLARATIONS

Funding This work was supported by Correlation between vitamin D level in mother/newborn and neonatal respiratory distress syndrome of Jiangsu (No.TKKY0716). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant Disclosures The following grant information was disclosed by the authors: Correlation between vitamin D level in mother/newborn and neonatal respiratory distress syndrome of Jiangsu: TKKY0716. Competing Interests The authors declare there are no competing interests. Author Contributions • Zuanhao Qian conceived and designed the experiments, authored or reviewed drafts of the paper, approved the final draft. • Zhenglei Zhang and Yingying Wang performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft. Data Availability The following information was supplied regarding data availability: Raw data is available at EBI Array Express database under accession number E-MTAB- 5195 here: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5195/.

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