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J Zhejiang Univ (Med Sci)  2020, Vol. 49 Issue (6): 732-742    DOI: 10.3785/j.issn.1008-9292.2020.12.08
    
Master genes and co-expression network analysis in peripheral blood mononuclear cells of patients with gram-positive and gram-negative sepsis
LI Lu1(),FANG Junjun2,LI Zhitao2,SHEN Leixing3,WANG Guobin2,*(),FU Shuiqiao2,*()
1. Department of Clinical Pharmacy, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
2. Surgical Intensive Care Unit, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
3. School of Pharmaceutical Sciences, Zhejiang University City College, Hangzhou 310015, China
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Abstract  

Objective: To investigate the functional pathways enriched and differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMCs) of patients with gram-positive and gram-negative sepsis. Methods: Dataset GSE9960 obtained from NCBI GEO database containing PBMC samples from 16 non-infectious systematic inflammatory response syndrome (SIRS) patients, 17 gram-positive septic patients and 18 gram-negative septic patients were included in the study. Functional pathway annotations were conducted by gene set enrichment analysis and weighted gene co-expression network analysis. DEGs were filtered and master DEGs were then validated in PBMCs of gram-positive septic, gram-negative septic and non-infectious SIRS patients. Results: The enriched gene sets in gram-positive sepsis and gram-negative sepsis were significantly different. The results indicated the opposite co-expression networks in SIRS and gram-negative sepsis, and the entirely different co-expression networks in gram-positive and gram-negative sepsis. Furthermore, we validated that TYMS was up-regulated in gram-positive sepsis (P < 0.05), CD3D was down-regulated in gram-negative sepsis (P < 0.01), while IRAK3 was up-regulated in gram-negative sepsis (P < 0.05). Conclusion: The results indicate that there are differences in the mechanism and pathogenesis of gram-positive and gram-negative sepsis, which may provide potential markers for sepsis diagnosis and empirical antimicrobial therapy.



Key wordsSepsis      Systemic inflammatory response syndrome      Gram-positive bacterium      Gram-negative bacterium      Peripheral blood mononuclear cell     
Received: 13 August 2020      Published: 14 January 2021
CLC:  R447  
  R631  
Corresponding Authors: WANG Guobin,FU Shuiqiao     E-mail: lucille@zju.edu.cn;wgb8@163.com;2200048@zju.edu.cn
Cite this article:

LI Lu,FANG Junjun,LI Zhitao,SHEN Leixing,WANG Guobin,FU Shuiqiao. Master genes and co-expression network analysis in peripheral blood mononuclear cells of patients with gram-positive and gram-negative sepsis. J Zhejiang Univ (Med Sci), 2020, 49(6): 732-742.

URL:

http://www.zjujournals.com/med/10.3785/j.issn.1008-9292.2020.12.08     OR     http://www.zjujournals.com/med/Y2020/V49/I6/732


革兰阳性菌及革兰阴性菌感染脓毒症患者外周血单个核细胞关键基因鉴定及共表达网络分析

目的: 探讨革兰阳性菌与革兰阴性菌感染脓毒症患者相关的生物标志物。方法: 在美国国立生物技术信息中心平台获取GSE9960表达谱芯片,包含16例非感染全身炎症反应综合征(SIRS)患者外周血单个核细胞(PBMC)样本,17例革兰阳性菌感染脓毒症患者PBMC样本,以及18例革兰阴性菌感染脓毒症患者PBMC样本。利用基因集富集分析和加权基因共表达网络分析探索GSE9960基因表达谱芯片中革兰阳性菌感染与革兰阴性菌感染脓毒症富集功能通路与共表达网络模块,并对高度相关模块进行通路注释。利用R语言分析革兰阳性菌与革兰阴性菌感染脓毒症中差异表达基因,探索差异表达基因蛋白-蛋白相互作用网络。将高度相关模块中的枢纽基因与差异表达基因重叠,确定为主要差异表达基因,并进一步针对主要差异表达基因在临床样本中进行验证。结果: 革兰阳性菌感染脓毒症与革兰阴性菌感染脓毒症间富集通路存在显著差异,非感染SIRS和革兰阴性菌感染脓毒症存在相反的共表达网络;而革兰阳性菌感染和革兰阴性菌感染脓毒症存在完全不同的共表达网络。进一步在临床样本中验证显示,主要差异表达基因TYMS在革兰阳性菌感染脓毒症患者PBMC中显著上调(P < 0.05);CD3D在革兰阴性菌感染脓毒症中表达下调(P < 0.01),而IRAK3则显著上调(P < 0.05)。结论: 本研究结果有利于加深对不同细菌感染脓毒症发病机制的理解,并为脓毒症诊断、分型及经验性抗菌药物治疗提供潜在生物标志物。


关键词: 脓毒症,  全身炎症反应综合征,  革兰阳性菌,  革兰阴性菌,  外周血单个核细胞 
基因 引物序列(5′→3′)
TYMS GAGCTGTCTTCCAAGGGAGT
CAACTCCCTGTCCTGAATAATCTGA
DLGAP5 GTCGTCCAGACCGAGTGTTC
ATGAAGACATCCTGAGCCACC
CDKN3 ATGAAGCCGCCCAGTTCAAT
CCTGGAAGAGCACATAAACCG
CD3D CATGGGTAGAGGGAACGGTG
ACAGCTCTGGCACATTCGAT
TRAC ACAGGAAAAGCACAGCTCCC
ACAGCACAGATGTAGACGGC
LCK GGAGCTGGGACCCCCTATTT
AGCCCATGGTCCCTGAGATT
KIF1B CACCTCGATGCGGTGCC
ACAGCCAAGTTTCCTCAGCC
LIN7A ACTACGAGCGGTTGCTGATG
TCAATTGCTCTTGCAACATCTCTG
IRAK3 TTGGTCCTGGGCACAGAAAA
GGACTCAACACTGCTCCATAGT
GAPDH GCACCGTCAAGGCTGAGAAC
TGGTGAAGACGCCAGTGGA
Tab 1 Primer sequences of real-time RT-PCR
Fig 1 Enrichment plots of gene set enrichment analysis in gram-positive and gram-negative sepsis
Fig 2 Gram-positive and gram-negative sepsis co-expression network identification by weighted gene co-expression network analysis
Fig 3 GO and KEGG pathway enrichment analysis on highly correlated modules related to gram-positive and gram-negative sepsis
模块 枢纽基因
棕色模块 SAMD8TLR6IFRD1IL17RAPCYT1APRO2852FCER1GRNF149SLC12A6FAM160A2CAMKK2GALNT3COL4A3BPSTAT5BRNASELMYO1FALPK1C1RLFAM126BVPS37BTSPODENND5ALIMK2SLC2A14PPP1R10PGK1DNTTIP1MKNK1RNF24ARL8AMSL1ERI1SDF2TLR8GYG1RP11-295G20.2ZNF467LOC101928676ZBTB34UBXN2BOSBPL2TUBA1AFAM53CRBMS1NBEAL2HIP1ARL11XPO6CEBPBTMCO3CTBSSELPLGIL10RBDYSFMTF1GNAQGAS7SLC2A3KIF1BCCDC126IQGAP1RHOGIFNAR1PGM2STAT3RNF13PRKCDPECRRGS3SNX11SLMAPNDUFB3RRAGCNUPL1NFIL3NCF4MAP3K5APOBRSTX3FLOT2SKAP2TSEN34PPP4R1SLCO3A1FPR2CSNK1DUPP1CHSY1MEF2AHK3IRAK3SIPA1PHF20L1PLIN3SLC25A44RTFDC1CKLFATP6V1C1EIF4E3PIK3CGTLE3CHMP2AFPR1PTBP3LINC01000RGS19NRBF2ZNF281TLR1CHMP2BATXN1GLT1D1CFLARNCF2LYNKLF7THOC5LIN7ARIT1JMJD6FAM160B1ARID3AARPC2FAM129AC17orf62FLOT1BNIP2EXOC6PGS1PXKSVILPFKFB4WDFY3RALBH3F3AHCKC4orf3BIN2CANT1GNAI3LPCAT2LILRB3LPGAT1RFWD2FNDC3BLCP1PRR13TMEM260BTBD10GNG5CD58SHOC2CSF3RGMFGCAP1BAZ1AAGO4AGPAT9S100A11PJA2RLIMALOX5ZNF438RARA-AS1RPS6KA1OSTF1TMEM120AHSDL2PPP1R12AHN1C20orf24PORDENND3CKLFFRAT2NINCAB39YIPF1
黄色模块 ZBTB25GSPT2NDUFAF4LINC00263ZNF84IL32CD3GMAGEH1ITM2ASLC4A7CLEC2DJMYTRAF5NMT2TRIB2KPNA5PLEKHA1IFFO2TRMT13RASGRP1LPIN1PTPRCAPPJA1CD3DPRKCQTHEM4LYRM7SIDT1ZNF32CD247DIS3L2NFATC2BCL11BPPP1R16BZNF420TRBC1NR1D2ARL4CEVLLY9TRACKIF3ATRACZNF286ASKAP1ZAP70LCKDYRK2
粉色模块 OAS3DDX60IFIT5HERC5OASLXAF1OAS2IFI44ISG15HERC6PARP14DTX3LIFIT1APOL6
绿色模块 CDC6RMI2WDHD1WDR34KIFC1UBE2CAUNIPIQGAP3KIF18ABRCA2CDKN3KIAA0101STILNCAPG2GINS3TRIP13SKA1CEBPECRNDECASC5ANKRD18ACEP55MCM10KIAA1524STMN1GTSE1ZWINTLOC81691UBE2TPLS1BUB1NEK2AURKAASPMORC1ESPL1AURKBSUV39H2HMMRTYMSCDC20CENPEZNF788CCNB1GINS1PRC1BRIP1SLC27A2MELKCDT1CDCA8RAD51AP1TCF19SHCBP1UHRF1MND1CDC45DLGAP5KIF20AKIF4AKIF14POLE2FANCIKIF2CCCNA2POLQCENPAFOXM1DTLCHEK1TMEM52BDEPDC1SPAG5NUF2NUSAP1CDCA2HJURPBIRC5NCAPHTPX2NEIL3E2F8TTKTOP2AKIF11DEPDC1BNCAPGANLNMKI67PLK4CDCA5BUB1BCCNE2CDK1SPC25RAD51TK1KIF15KIF18BOIP5CDCA3
Tab 2 Hub genes in highly correlated modules related to gram-positive and gram-negative sepsis
差异表达基因 Log2差异表达倍数 P
RP3-525N10.2 -1.048 38 0.006 96
LOC102723918 -0.919 58 0.013 90
TPPP3 -0.747 88 0.002 80
LILRA3 -0.653 51 0.026 59
RRM2 1.173 583 0.029 91
CAMP 1.197 064 0.003 51
PRTN3 1.268 915 0.047 43
MS4A3 1.438 127 0.023 52
DEFA4 1.610 488 0.019 22
Tab 3 Four downregulated genes and top 5 upregulated genes in gram-positive sepsis
差异表达基因 Log2差异表达倍数 P
 MME -0.995 18 0.027 26
 TRAT1 -0.965 96 0.024 00
 GZMK -0.959 53 0.017 14
 LOC389834 -0.948 3 0.014 10
 EPSTI1 -0.868 45 0.035 92
 EMR1 1.107 529 0.000 66
 CA4 1.108 643 0.043 22
 C1QA 1.108 892 0.007 54
 GPR84 1.206 834 0.019 91
 FGF13 1.539 863 0.022 74
Tab 4 Top 5 upregulated genes and downregulated genes in gram-negative sepsis
Fig 4 PPI network and functional pathway annotation of differentially expressed genes in gram-positive and gram-negative sepsis
Fig 5 Validation of master differentially expressed genes in human peripheral blood mononuclear cells
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