Identification of Key Mrnas and Lncrnas in Neonatal Sepsis by Gene Expression Profiling

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Hindawi Computational and Mathematical Methods in Medicine Volume 2020, Article ID 8741739, 13 pages https://doi.org/10.1155/2020/8741739

Research Article Identification of Key mRNAs and lncRNAs in Neonatal Sepsis by Gene Expression Profiling

Lin Bu,1,2 Zi-wen Wang,2 Shu-qun Hu ,2 Wen-jing Zhao,2 Xiao-juan Geng,2 Ting Zhou,1 Luo Zhuo ,1 Xiao-bing Chen,1 Yan Sun,1 Yan-li Wang,1 and Xiao-min Li 1

1Department of Emergency Medicine, First People’s Hospital of Lianyungang, Hospital of the Clinical Medical School of Nanjing Medical University, Lianyungang 222002, China 2Department of Intensive Care Unit, Aﬃliated Hospital of Xuzhou Medical University, Xuzhou 221000, China

Correspondence should be addressed to Xiao-min Li; [email protected]

Received 2 February 2020; Revised 2 April 2020; Accepted 8 April 2020; Published 25 August 2020

Guest Editor: Tao Huang

Copyright © 2020 Lin Bu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Neonatal sepsis is one of the most prevalent causes of death of the neonates. However, the mechanisms underlying neonatal sepsis remained unclear. The present study identiﬁed a total of 1128 upregulated mRNAs and 1008 downregulated mRNAs, 28 upregulated lncRNAs, and 61 downregulated lncRNAs in neonatal sepsis. Then, we constructed PPI networks to identify key regulators in neonatal sepsis, including ITGAM, ITGAX, TLR4, ITGB2, SRC, ELANE, RPLP0, RPS28, RPL26, and RPL27. lncRNA coexpression analysis showed HS.294603, LOC391811, C12ORF47, LOC729021, HS.546375, HNRPA1L-2, LOC158345, and HS.495041 played important roles in the progression of neonatal sepsis. Bioinformatics analysis showed DEGs were involved in the regulation cellular extravasation, acute inﬂammatory response, macrophage activation of NF-kappa B signaling pathway, TNF signaling pathway, HIF-1 signaling pathway, Toll-like receptor signaling pathway, and ribosome, RNA transport, and spliceosome. lncRNAs were involved in regulating ribosome, T cell receptor signaling pathway, RNA degradation, insulin resistance, ribosome biogenesis in eukaryotes, and hematopoietic cell lineage. We thought this study provided useful information for identifying novel therapeutic markers for neonatal sepsis.

1. Introduction ated with the recognition of the bacteria in neonates [4]. PIK3CA, TGFBR2, CDKN1B, KRAS, E2F3, TRAF6, and Neonatal sepsis was a severe systematic infectious disease CHUK were reported to be key regulators in neonatal sepsis in neonates induced by bacteria, fungi, and viruses [1]. Neo- [5]. However, the detailed mechanisms underlying these pro- natal sepsis is one of the most prevalent causes of death of cesses remained elusive. neonates [2]. Adult sepsis has been studied in depth, but Long noncoding RNAs (lncRNAs) were a class of ncRNAs many abundant studies stated that the neonatal immune longer than 200 bps. Emerging studies showed lncRNAs were response to sepsis is different from adults; comparable important regulators in multiple human diseases such as dia- research on neonatal vascular endothelium is not enough. betes, cancers, and neonatal sepsis. lncRNAs regulate target Neonatal endothelial cells expressing lower amounts of adhe- expression in different levels, including transcriptional and sion molecules show a reduced capacity to reactive oxygen posttranscriptional levels. lncRNAs could bind to RNA, pro- species [3]. In the past decades, emerging studies showed tein, and DNA molecules in cells. Very few reports are aimed activation of lymphocytes, neutrophils, and mononuclear at elucidating the functions and roles of lncRNAs in neonatal macrophages played crucial roles in the progression of neo- sepsis. Until now, only one report showed lncRNA SNHG16 natal sepsis. A few genes were identified to be associated with reverses the effects of miR-15a/16 on the LPS-induced neonatal sepsis. For example, TLR2 and TLR4 were associ- inflammatory pathway in neonatal sepsis [6]. Exploring the 2 Computational and Mathematical Methods in Medicine roles of lncRNAs in neonatal sepsis could provide novel ing differently expressed mRNAs and lncRNAs in neonatal clues for us to understand the mechanisms underlying this sepsis by analyzing whole blood mRNA expression profiling, disease progression. GSE25504, from the NCBI GEO dataset (https://www.ncbi The previous study is aimed at identifying differently .nlm.nih.gov/geo/query/acc.cgi?acc=GSE25504). A total of expressed mRNAs and lncRNAs in neonatal sepsis by ana- 38 negative blood culture result samples and 25 positive lyzing GSE25504 [7]. Protein-protein interaction network blood culture result samples were included in this dataset. and coexpression network analysis were used to identify key As shown in Figures 1(a) and 1(b), 1128 upregulated mRNAs and lncRNAs. Bioinformatics analysis was also con- mRNAs and 1008 downregulated mRNAs with log2 fold ducted to predict the potential roles of these genes in neonatal change ðFCÞ∣≥1:0 and false discovery rate ðFDRÞ ≤ 0:01 sepsis. This study could provide novel clues to understand the were identified as differently expressed genes (DEGs). Mean- mechanisms of underlying neonatal sepsis progression. while, this study identified 28 upregulated lncRNAs and 61 downregulated lncRNAs in positive samples compared to 2. Materials and Methods negative samples as differently expressed lncRNAs (DElncs).

2.1. Microarray Data. Three gene expression profile 3.2. PPI Network Analysis of DEGs in Neonatal Sepsis. The GSE25504 [8] was downloaded from the GEO database. above analysis revealed multiple differently expressed genes GSE25504, which was based on the GPL6947 platform, in neonatal sepsis. However, the interactions of these DEGs was submitted by Dickinson et al. The GSE25504 dataset remained largely unclear. To obtain the interactions among contained 38 negative blood culture result samples and 25 the 22 upregulated mRNAs and 863 downregulated mRNAs positive blood culture result samples. The analysis for differen- in the neonatal sepsis, the present study constructed and pre- tial gene expression between tumor and normal tissue was sented PPI networks using the STRING database and Cytos- performed using GeneSpring software version 11.5 (Agilent cape software. The combined score > 0:4 was used as the cut- Technologies, Inc., Santa Clara, CA, USA). Student’s t-test off criterion. Following the construction of PPI network, a was used to identify DEGs with an alteration of ≥2-fold. MCODE plug-in analysis was performed to identify hub net- p <0:05 was considered to indicate a statistically significant works (degree cut-off ≥ 2 and the nodes with edges ≥ 2-core) difference. We applied Limma package to identify DEGs with in the PPI network using Cytoscape software (Figure 2). As R software [9]. shown in Figure 2, upregulated hub network 1 included 71 nodes and 1187 edges, upregulated hub network 2 included 2.2. Coexpression Network Construction and Analysis. In this ’ ffi ff 66 nodes and 611 edges, and upregulated hub network 3 study, Pearson s correlation coe cient of di erently expressed included 62 nodes and 529 edges. As shown in Figure 3, gene- (DEG-) lncRNA pairs was calculated according to the downregulated hub network 1 included 94 nodes and 4048 expression value of them. The coexpressed DEG-lncRNA edges, downregulated hub network 2 included 30 nodes pairs with the absolute value of Pearson’s correlation ≥ : and 247 edges, and downregulated hub network 3 included coefficient 0 8 were selected, and the coexpression network 26 nodes and 199 edges. Blue nodes indicate upregulated was established by using Cytoscape software. genes, and pink nodes indicate downregulated genes in the neonatal sepsis. 2.3. Pathway Enrichment Analysis. Pathway analysis was fi used to find the significant pathways according to Kyoto Also, we identi ed several key regulators in these PPI Encyclopedia of Genes and Genomes (KEGG). Fisher’s exact networks. The key regulators in upregulated PPI networks test was adopted to select the significant pathways, and the included ITGAM (degree = 131), ITGAX (degree = 101), threshold of significance was defined by FDR and p value. TLR4 (degree = 100), ITGB2 (degree = 92), SRC (degree = Significant pathways were extracted according to the thresh- 87), and ELANE (degree = 81). The key regulators in down- olds of p <0:05 and intersection gene count > 1. regulated PPI networks included RPLP0 (degree = 128), RPS28 (degree = 128), RPL26 (degree = 124), RPL27 (degree 2.4. Integration of the Protein-Protein Interaction (PPI) = 123), NSA2 (degree = 122), RPS15 (degree = 120), RPS10 Network. The Search Tool for the Retrieval of Interacting (degree = 117), RPS13 (degree = 117), RPS20 (degree = Genes version 10.0 (STRING: http://string-db.org) [10] was 117), RPL36 (degree = 110), FAU (degree = 108), NHP2L1 used for the exploration of potential DEG interactions at (degree = 106), RPL23 (degree = 106), RPS25 (degree = 105), the protein level. The PPI networks of DEGs by STRING RPL9 (degree = 101), RPL30 (degree = 100), and RPL35A were derived from validated experiments. A PPI score of (degree = 100). >0.4 was considered significant. The PPI networks were visu- alized using Cytoscape software [11] (http://www.cytoscape 3.3. Bioinformatics Analysis of DEGs in Neonatal Sepsis. .org). p <0:05 was considered to indicate a statistically signif- Furthermore, we explored the potential functions of DEGSs icant difference. in neonatal sepsis. We next performed bioinformatics analysis of upregulated and downregulated hub PPI networks in 3. Results thyroid cancer using Cytoscape’s ClueGo plug-in. Only significant biological processes and pathways (p ≤ 0:05) were 3.1. Identification of Differently Expressed mRNAs and shown. Our results (Figure 4) showed upregulated hub net- lncRNAs in Neonatal Sepsis by Analyzing Whole Blood work 1 was involved in regulation of myeloid cell apoptotic Expression Profiling. The present study is aimed at identify- process, cellular extravasation, acute inflammatory response, Computational and Mathematical Methods in Medicine 3

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Figure 1: Identification of differently expressed mRNAs and lncRNAs in neonatal sepsis by analyzing whole blood mRNA expression profiling. (a) Hierarchical clustering analysis showed differential mRNAs expression in negative blood culture result samples and positive blood culture result samples. (b) Hierarchical clustering analysis showed differential lncRNA expression in negative blood culture result samples and positive blood culture result samples. neutrophil degranulation, macrophage activation, antimi- tion. Downregulated hub network 3 was involved in regulat- crobial humoral response, and collagen metabolic process. ing spliceosome. Upregulated hub network 2 was involved in regulating NF- kappa B signaling pathway, TNF signaling pathway, HIF- 3.4. Coexpression Network Analysis of DElncs in Neonatal 1 signaling pathway, Toll-like receptor signaling pathway, Sepsis. We next explored the interactions between mRNAs tuberculosis, legionellosis, and complement and coagulation and lncRNAs. We performed Pearson’s correlation calcula- cascades. Upregulated hub network 3 was involved in reg- tion of lncRNA-mRNA pair in neonatal sepsis. Based on ulating ubiquitin-mediated proteolysis, Toll-like receptor the correlation analysis results, we constructed an mRNA- signaling pathway, chemokine signaling pathway, and circa- lncRNA coexpression network, including 62 lncRNAs, 726 dian entrainment. mRNAs, and 2041 interactions between lncRNAs and Meanwhile, our results (Figure 5) showed downregulated mRNAs (p value < 0.05 and absolute value of correlation hub network 1 was involved in regulating ribosome, and coefficient > 0:85). Eight lncRNAs are significantly associ- RNA transport. Downregulated hub network 2 was involved ated with more than 100 genes, suggesting their key roles in regulating Parkinson’s disease and oxidative phosphoryla- in this network (Figure 6), including HS.294603 (degree = 4 Computational and Mathematical Methods in Medicine

CTSD BPI SLCO4C1 MMP25 CANT1 P2RX1 PLAUR RETN DEFA4 ALDH3B1 GPR97 LTF LRG1 CKAP4 MS4A3 BST1 TIMP2 A RG1 QSOX1 FOLR3 CEACAM3TMBIM1 C3AR1 ORM2 MCEMP1 ELANE OLFM4 AGPAT2 CEACAM8 PGLYRP1 TOM1 CYBB QPCT OSCAR FCAR TCN1 LAMTOR1 FPR2 DNASE1L1 CLEC5A GPR84 ITGAM CFP SLC2A3 RAB27A CD177 MMP8 VCL LAIR1 STOM ADAM8 CEACAM1 LILRB2 PTPN6 ORM1 CD93 ITGB2 HP ATP8B4 CLEC4D LCN2

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Figure 2: Continued. Computational and Mathematical Methods in Medicine 5

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Figure 2: Construction of upregulated PPI networks in neonatal sepsis. PPI network analysis showed upregulated hub PPI network 1 (a), hub PPI network 1 (b), and hub PPI network 3 (c). 6 Computational and Mathematical Methods in Medicine

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Figure 3: Continued. Computational and Mathematical Methods in Medicine 7

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Figure 3: Construction of downregulated PPI networks in neonatal sepsis. PPI network analysis showed downregulated hub PPI network 1 (a), hub PPI network 1 (b), and hub PPI network 3 (c).

195), LOC391811 (degree = 179), C12ORF47 (degree = 168), the potential mechanisms underlying the progression of neo- LOC729021 (degree = 155), HS.546375 (degree = 151), natal sepsis. For example, Medzhitov et al. reported that HNRPA1L-2 (degree = 127), LOC158345 (degree = 100), and TLR2 and TLR4 were associated with the recognition of bac- HS.495041 (degree = 100). teria in neonates [12]. Meng et al. identified core regulators involved in the regulation of neonatal sepsis using bioinfor- 3.5. Bioinformatics Analysis of DElncs in Neonatal Sepsis. Bio- matics analysis [13]. Wynn et al. used gene microarray to informatics analysis for DElncs in neonatal sepsis was also identify whole genome gene expression change in very low conducted. GO analysis (Figure 7) showed that differentially birth weight with neonatal sepsis [14]. The present study is expressed lncRNAs were associated with translation, cyto- aimed at identifying differently expressed mRNAs and plasmic translation, rRNA processing, ribosomal large sub- lncRNAs in neonatal sepsis by analyzing GSE25504. A total unit biogenesis, regulation of translational initiation, tRNA of 1128 upregulated mRNAs, 1008 downregulated mRNAs, processing, response to peptidoglycan, positive regulation 28 upregulated lncRNAs, and 61 downregulated lncRNAs of natural killer cell mediated cytotoxicity, T cell receptor sig- were identified. Of note, several DEGs identified by this study naling pathway, and negative regulation of apoptotic process. had also been reported to be associated with neonatal sepsis. KEGG pathway analysis indicated these lncRNAs were asso- For example, IL1R2 and SOCS3 were reported to drive the ciated with ribosome, T cell receptor signaling pathway, RNA neonatal innate immune response to sepsis [15]. In order to degradation, insulin resistance, ribosome biogenesis in elucidate the interactions among these DEGs, we constructed eukaryotes, and hematopoietic cell lineage. upregulated and downregulated genes regulating PPI networks in neonatal sepsis. 4. Discussion Several key genes were identified in neonatal sepsis, including ITGAM, ITGAX, TLR4, ITGB2, SRC, ELANE, Neonatal sepsis is the most common cause of death of new RPLP0, RPS28, RPL26, and RPL27. ITGAM (CD11b) was born children with few certainly reported biomarkers. Infec- reported as an early diagnostic marker of neonatal sepsis. tions remained to be the main risk factor that causes the neo- TLR4 had been reported to be a key regulator in neonatal natal death. In the past decades, only few reports indicated sepsis [16]. TLR4 was associated with the recognition of the 8 Computational and Mathematical Methods in Medicine

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Negative regulation of antigenCEACAM1 Receptor-mediated signaling pathway ive regulation of regulated PTPN6 Secretory pathway RAB27ARegulationof myeloid leukocyte Mediated immu Regulation of regulated secretory Negative regulation of T cell pathwayPositive regulation of interleukin-6 ctivation Production Positive regulation of tumorosis ne Factor superfamilyregulation ofcytokine acuacute inﬂammatory Negative regulation of lymphocyte productionresponse proliferation ALDH3B1LILRB2 PTAFR NBEAL2 DNASE1L1 FCER1G FPR2 C3AR1 anulocyte migrationLRLRG1R CFP CLEC4D CYBB CEACAM8 ADAM8 ITGAM RegulationRegulation of of phagocytosis CLEC5A Regulation of neutrophilOLFM4 migration LAIR1 Myeloid cell Apoptotic process BST TOM1 Platelet degranulation PLAUR FCAR ITGB2 VCL nterleukin-6 AGPAT2 LAMTOR1 Acutee inﬂammatory QSOX1 ORM2 cellular P2RX1 Regulation ofCEACAM3 tumor TMBIM1 extravasation necrosis factor ORM1 superfamilyCKAP4 TCN1 HP SLC11A1 cytokine production Acute-phase response Neutrophil CANT1 degranulation ATP8B4 ELANE BPI SIRPA MGAM

MCEMP1 MS4A3 LTF Negative regulation of tumor MMP25 necrosis factor superfamilcytokine ADGRG3 production CYSTM1 CTSD RETN FOLR3 TIMP2 Activation Antimicrobial QPCT SLC2A3 humoral respo ARG1 SLCO4C1 Defense response to fungus CD177 LCN2 Modiﬁcation by host of symbiontAntibacterial humoral response GPR84 ATP6V0CITGAX Collagen catabolic process morphology or physiology Collagen metabolicMMP8 PGLYRP1 CD93 process DEFA4 STOM (a)

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Figure 4: Continued. Computational and Mathematical Methods in Medicine 9

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Figure 4: Bioinformatics analysis of hub upregulated PPI networks in neonatal sepsis. Bioinformatics analysis of up-regulated hub PPI network 1 (a), hub PPI network 1 (b), and hub PPI network 3 (c).

bacteria in neonates [17]. The single-nucleotide polymor- on the LPS-induced inflammatory pathway in neonatal sepsis phisms (SNPs) in TLR4 were regarded as genetic modulators [6]. However, the molecular functions of lncRNAs in neonatal of infection in neonatal sepsis [18]. Bioinformatics analysis sepsis remained unclear. This study identified 28 upregulated revealed these DEGs were significantly associated with multi- lncRNAs and 61 downregulated lncRNAs in neonatal sepsis. ple biological processes, including myeloid cell apoptotic Coexpression analysis were used to identify key lncRNAs, process, cellular extravasation, acute inflammatory response, including HS.294603, LOC391811, C12ORF47, LOC729021, neutrophil degranulation, macrophage activation, antimicro- HS.546375, HNRPA1L-2, LOC158345, and HS.495041. Bio- bial humoral response, collagen metabolic process, NF-kappa informatics analysis showed these lncRNAs were involved in B signaling pathway, TNF signaling pathway, HIF-1 signal- regulating ribosome, T cell receptor signaling pathway, RNA ing pathway, Toll-like receptor signaling pathway, tuberculo- degradation, insulin resistance, ribosome biogenesis in sis, legionellosis, complement and coagulation cascades, eukaryotes, and hematopoietic cell lineage. chemokine signaling pathway, circadian entrainment, ribo- In this study, there also existed some limitations. Firstly, some, RNA transport, and spliceosome. This signaling had more samples were needed considering the small sample size been demonstrated to play crucial roles in neonatal sepsis. in the present study. Secondly, further experimental valida- For example, altered neonatal Toll-like receptor (TLR) func- tion would be required for future verification. Moreover, spe- tion is hypothesized to contribute to the heightened suscepti- cific functions of those dysregulated circRNAs had not been bility to infection and perpetuated inflammation in term and further excavated in this study. Therefore, the further preterm neonates, clinically evident in neonatal sepsis and researches with a larger samples group should be performed increased rates of inflammatory disorders [19]. and more experimental validation and much deeper analysis Emerging studies had demonstrated noncoding RNAs, were still needed in the near future. such as lncRNAs and miRNAs, were involved in regulating the progression of human diseases. In neonatal sepsis, 5. Conclusion multiple miRNAs were reported. For example, microRNA- 300/NAMPT regulates inflammatory responses through acti- In conclusion, the present study identified a total of 1128 vation of the AMPK/mTOR signaling pathway in neonatal upregulated mRNAs, 1008 downregulated mRNAs, 28 upreg- sepsis [20]. miR-15a/16 are upregulated in the serum of neo- ulated lncRNAs, and 61 downregulated lncRNAs in neonatal natal sepsis patients and inhibit the LPS-induced inflamma- sepsis. Then, we constructed PPI networks to identify key tory pathway [21]. lncRNAs were a type of ncRNAs longer regulators in neonatal sepsis, including ITGAM, ITGAX, than 200 bps. Emerging evidences showed lncRNAs played TLR4, ITGB2, SRC, ELANE, RPLP0, RPS28, RPL26, and important roles in human diseases, such as diabetes, multiple RPL27. lncRNA coexpression analysis showed HS.294603, cancers, and neurodegenerative diseases. A recent study LOC391811, C12ORF47, LOC729021, HS.546375, HNRPA1L- showed lncRNA SNHG16 reverses the effects of miR-15a/16 2, LOC158345, and HS.495041 played important roles in 10 Computational and Mathematical Methods in Medicine

RPL31 RPL34RPL36ALRPL38 RPL30 RPLP0 RPL26 MRPS12 RPL23A RPS3 RPL21 RPS4X RPL17 RPS9 RPL13 RPS11

RPL11 RPS14

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RPL5 RPS17 EIF3B EIF3E RPL7 RPS15 EIF3F RPS13 EIF4B RPL9 EIF3H RPL12 RPS10 EIF4A2 RPL15 RPS6 RNA transport EIF3D RPL18A RPS3A NCBP2 EIF3A RPL22 RPS2 RPL24 RPLP1 RPL27 RPL39 RPL27ARPL32RPL35ARPL37A (a)

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NDUFS5 SF3A3 ATP5L Spliceosome ATP5G2 SNRPB NDUFB8 NDUFS3 LSM3 ATP5G1 Oxidative SRSF6 phosphorylation LSM2 (b) (c)

Figure 5: Bioinformatics analysis of hub downregulated PPI networks in neonatal sepsis. Bioinformatics analysis of down-regulated hub PPI network 1 (a), hub PPI network 1 (b), and hub PPI network 3 (c). Computational and Mathematical Methods in Medicine 11

Figure 6: Construction of diﬀerently expressed lncRNAs regulating coexpression networks in neonatal sepsis. the progression of neonatal sepsis. Bioinformatics analysis of NF-kappa B signaling pathway, TNF signaling path- showed DEGs were involved in the regulation cellular extrav- way, HIF-1 signaling pathway, Toll-like receptor signaling asation, acute inﬂammatory response, macrophage activation pathway, and ribosome, RNA transport, and spliceosome. 12 Computational and Mathematical Methods in Medicine

lncRNA related biological processes

Negative regulation of apoptotic process T cell receptor signaling pathway Positive regulation of natural killer cell mediated cytotoxicity lncRNA related KEGG pathways Response to peptidoglycan tRNA processing Hematopoietic cell lineage Regulation of translational initiation Ribosome biogenesis in eukaryotes Ribosomal large subunit biogenesis Insulin resistance rRNA processing RNA degradation Cytoplasmic translation T cell receptor signaling pathway Translation Ribosome 0246810 0 5 10 15 20 -Log10 (FDR) -Log10 (FDR) (a) (b)

Figure 7: Bioinformatics analysis of differently expressed lncRNAs regulating coexpression networks in neonatal sepsis. (a, b) GO and KEGG analysis of differently expressed lncRNAs in neonatal sepsis. lncRNAs were involved in regulating ribosome, T cell recep- [3] C. Pietrasanta, L. Pugni, A. Ronchi et al., “Vascular Endothe- tor signaling pathway, RNA degradation, insulin resistance, lium in Neonatal Sepsis: Basic Mechanisms and Translational ribosome biogenesis in eukaryotes, and hematopoietic cell Opportunities,” Frontiers in pediatrics, vol. 7, 2019. lineage. We thought this study provided useful information [4] B. Schaub, A. Bellou, F. K. Gibbons et al., “TLR2 and TLR4 for identifying novel therapeutic markers for neonatal sepsis. stimulation differentially induce cytokine secretion in human neonatal, adult, and murine mononuclear cells,” Journal of Interferon and Cytokine Research, vol. 24, no. 9, pp. 543–552, Data Availability 2004. “ All the data were reserved and can be accessed in GSE25504 [5] Y. X. Meng, X. H. Cai, and L. P. Wang, Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= ” GSE25504). Enrichment Analyses, Computational and Mathematical Methods in Medicine, vol. 2018, 10 pages, 2018. [6] X. Wang, X. Wang, X. Liu et al., “miR-15a/16 are upreuglated Conflicts of Interest in the serum of neonatal sepsis patients and inhibit the LPS- fl ” fl induced in ammatory pathway, International Journal of The authors declare that they have no con ict of interest. Clinical and Experimental Medicine, vol. 8, no. 4, pp. 5683– 5690, 2015. Authors’ Contributions [7] C. L. Smith, P. Dickinson, T. Forster et al., “Identification of a human neonatal immune-metabolic network associated with Lin Bu and Xiao-min Li guaranteed the integrity of the entire bacterial infection,” Nature Communications, vol. 5, no. 1, 2014. study and reviewed the manuscript. Xiao-min Li is assigned [8] P. Dickinson, C. L. Smith, T. Forster et al., “Whole blood gene to the study concepts and definition of intellectual content. expression profiling of neonates with confirmed bacterial sep- Lin Bu is responsible for the study design, literature research, sis,” Genomics data, vol. 3, pp. 41–48, 2015. manuscript preparation, and manuscript editing. Lin Bu and [9] G. K. Smyth, “Limma: linear models for microarray data,” in Shu-qun Hu performed the data acquisition, data analysis, Bioinformatics and computational biology solutions using R and statistical analysis. Lin Bu, Wen-jing Zhao, Xiao-juan and Bioconductor, Springer, 2005. Geng, and Yan-li Wang participated in the clinical studies. [10] D. Szklarczyk, A. L. Gable, D. Lyon et al., “STRING v11: pro- Lin Bu, Ting Zhou, Luo Zhuo, Xiao-bing Chen, and Yan tein–protein association networks with increased coverage, Sun participated in the experimental studies. supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Research, vol. 47, no. D1, pp. D607– References D613, 2019. [11] M. Kohl, S. Wiese, and B. Warscheid, “Cytoscape: software for ” [1] N. Chauhan, S. Tiwari, and U. Jain, “Potential biomarkers for visualization and analysis of biological networks, Methods in – effective screening of neonatal sepsis infections: An overview,” Molecular Biology, vol. 696, pp. 291 303, 2011. Microbial Pathogenesis, vol. 107, pp. 234–242, 2017. [12] S. A. Spector, M. Qin, J. Lujan-Zilbermann et al., “Genetic var- [2] S. ULLAH, K. RAHMAN, and M. HEDAYATI, “Hyperbiliru- iants in toll-like receptor 2 (TLR2), TLR4, TLR9, and FCγ binemia in neonates: types, causes, clinical examinations, pre- receptor II are associated with antibody response to quadriva- ventive measures and treatments: a narrative review article,” lent meningococcal conjugate vaccine in HIV-infected youth,” Iranian Journal of Public Health, vol. 45, no. 5, pp. 558–568, Clinical and Vaccine Immunology, vol. 20, no. 6, pp. 900–906, 2016. 2013. Computational and Mathematical Methods in Medicine 13

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