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Peripheral Blood Gene Expression Profile of Infants with Atopic Dermatitis

  • Janna Nousbeck
    Affiliations
    National Children’s Research Centre, Crumlin, Dublin, Ireland

    Clinical Medicine, Trinity College Dublin, Dublin, Ireland
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  • Maeve A. McAleer
    Affiliations
    National Children’s Research Centre, Crumlin, Dublin, Ireland

    Paediatric Dermatology, Children’s Health Ireland at Crumlin, Dublin, Ireland
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  • Alan D. Irvine
    Correspondence
    Corresponding author: Alan D. Irvine, Paediatric Dermatology, Children’s Health Ireland at Crumlin, Dublin, Ireland. ,
    Affiliations
    National Children’s Research Centre, Crumlin, Dublin, Ireland

    Clinical Medicine, Trinity College Dublin, Dublin, Ireland

    Paediatric Dermatology, Children’s Health Ireland at Crumlin, Dublin, Ireland
    Search for articles by this author
Open AccessPublished:October 06, 2022DOI:https://doi.org/10.1016/j.xjidi.2022.100165

      Abstract

      To enhance understanding of molecular mechanisms and mine novel biomarkers of pediatric atopic dermatitis (AD), PBMC gene expression profiles were generated by RNA sequencing in infants with AD and age-matched controls. 178 significantly differentially expressed genes (DEG) (115 up-regulations and 63 down-regulations) were seen, compared with healthy controls. DEGs identified included IL-1β, TNF, TREM1, IL18R1 and IL18RAP. DEGs were validated by real-time RT- qPCR in a larger number of samples from PBMCs of infants with AD under 12 months old. Using the DAVID database, functional and pathway enrichment analyses of DEGs were performed. GO enrichment analysis demonstrated that DEGs were associated with immune responses, inflammatory responses, regulation of immune responses and platelet activation. Pathway analysis indicated that DEGs were enriched in cytokine-cytokine receptor interaction, immunoregulatory interactions between lymphoid and non-lymphoid cells, hematopoietic cell lineage, PI3K-Akt signalling pathway, natural killer cell mediated cytotoxicity and platelet activation. Furthermore, the protein-protein interaction (PPI) network was predicted using the STRING database and visualized with Cytoscape software. Finally, based on the PPI network, 18 hub genes were selected, and two significant modules were obtained. In conclusion, this study sheds light on the molecular mechanisms of pediatric AD and may provide diagnostic biomarkers and therapeutic targets.

      Key words

      Introduction

      Atopic Dermatitis (AD) is the most common chronic inflammatory skin disease of early childhood. It affects children with a prevalence of up to 20% and adults with prevalence rates of 7-10% (
      • Weidinger S.
      • Beck L.A.
      • Bieber T.
      • Kabashima K.
      • Irvine A.D.
      Atopic dermatitis.
      ). AD is a complex multifactorial disease, thought to result from interplay between environmental factors, an impaired skin barrier and immune dysfunctions; however, the overall pathologic mechanisms are still not fully understood (

      Langan SM, Irvine AD, Weidinger S. Atopic dermatitis. Lancet 2020;396(10247):345-360. Li S, Miao T, Sebastian M, Bhullar P, Ghaffari E, Liu M, et al. The transcription factors Egr2 and Egr3 are essential for the control of inflammation and antigen-induced proliferation of B and T cells. Immunity 2012;37(4):685-360.

      ). Although much has been learned about the molecular basis of AD, most investigations have focussed on adult AD with years of disease activity that is remarkably different from early onset AD in children. Few studies have been profiled skin tissue in infants with AD (
      • Esaki H.
      • Brunner P.M.
      • Renert-Yuval Y.
      • Czarnowicki T.
      • Huynh T.
      • Tran G.
      • et al.
      Early-onset pediatric atopic dermatitis is TH2 but also TH17 polarized in skin.
      ), (
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      ,
      • Brunner P.M.
      • Israel A.
      • Zhang N.
      • Leonard A.
      • Wen H.C.
      • Huynh T.
      • et al.
      Early-onset pediatric atopic dermatitis is characterized by TH2/TH17/TH22-centered inflammation and lipid alterations.
      ,
      • Cole C.
      • Kroboth K.
      • Schurch N.J.
      • Sandilands A.
      • Sherstnev A.
      • O'Regan G.M.
      • et al.
      Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic. dermatitis.
      ). Over the last decade RNA sequencing based transcriptome profiles have been implemented in identifying transcripts and pathways in many diseases, however limited studies using this method were performed on skin tissues in AD, particular in children with AD (
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      ,
      • Cole C.
      • Kroboth K.
      • Schurch N.J.
      • Sandilands A.
      • Sherstnev A.
      • O'Regan G.M.
      • et al.
      Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic. dermatitis.
      ). Only one study conducted has compared transcriptome profiles of both blood and skin tissue in AD children at various age up to 5 years old (
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      ) .
      Given that AD is an early childhood disease which generates a systemic immunological response and that 60% of all cases of atopic dermatitis begin during the first year of life (
      • Bieber T.
      Atopic dermatitis.
      ), we aimed to discover signature biomarkers of AD in infants that might help to identify new diagnostic biomarkers and molecular targets for treatment modalities in pediatric AD.
      For this purpose, we performed an integrative study comprised of RNA sequencing transcriptome profile of peripheral blood cells obtained from AD or healthy infants, quantitative RT-PCR and systems biology analysis.

      Results

      Analysis of gene expression by RNA-Sequencing

      100 infants with moderate or severe AD at first year of life and 20 age-matched healthy control infants were initially recruited (
      • McAleer M.A.
      • Jakasa I.
      • Hurault G.
      • Sarvari P.
      • McLean W.H.I.
      • Tanaka R.J.
      • et al.
      Systemic and stratum corneum biomarkers of severity in infant atopic dermatitis include markers of innate and T helper cell-related immunity and angiogenesis.
      ). Peripheral blood mononuclear cells (PBMCs) were isolated from 42 patients and 19 controls. RNA samples were extracted and only 27 samples from patients with AD and 17 controls passed QC and were used in this study. The use of samples is presented in schematic flow chart (Figure 1). We performed RNA sequencing profiles on PBMCs from randomly selected infants with AD (n = 8) and controls (n = 5) using the Illumina platform. Differential expression analysis was conducted to identify differentially expressed genes (DEGs) between AD and controls based on the following criteria: false discovery rate/FDR < 0.05 and fold change ≥ 1.5. We identified a total of 178 significantly differentially expressed genes with 115 up-regulations and 63 down-regulations in AD PBMCs, when compared with control PBMCs. Among highly upregulated genes we identified IL-1β, previously shown to be upregulated in serum of adult AD patients (
      • Thijs J.L.
      • Strickland I.
      • Bruijnzeel-Koomen C.
      • Nierkens S.
      • Giovannone B.
      • Knol E.F.
      • et al.
      Serum biomarker profiles suggest that atopic dermatitis is a systemic disease.
      ), TNF, a pro-inflammatory cytokine which role in the pathogenesis of AD is well known (
      • Jacobi A.
      • Antoni C.
      • Manger B.
      • Schuler G.
      • Hertl M.
      Infliximab in the treatment of moderate to severe atopic dermatitis.
      ,
      • Sumimoto S.
      • Kawai M.
      • Kasajima Y.
      • Hamamoto T.
      Increased plasma tumour necrosis factor-alpha concentration in atopic dermatitis.
      ), and Early Growth Response genes EGR2 and EGR3, known to have a crucial role in regulation of the immune system (Li et al., 2012). Other upregulated genes included TREM1 previously shown to be elevated in lesional skin and serum in AD patients (
      • Suarez-Farinas M.
      • Ungar B.
      • Correa da Rosa J.
      • Ewald D.A.
      • Rozenblit M.
      • Gonzalez J.
      • et al.
      RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications.
      ) and CXCL5, an inflammatory chemokine found to be at elevated levels in bloods of AD patients (
      • Brunner P.M.
      • Suarez-Farinas M.
      • He H.
      • Malik K.
      • Wen H.C.
      • Gonzalez J.
      • et al.
      The atopic dermatitis blood signature is characterized by increases in inflammatory and cardiovascular risk proteins.
      ). Among downregulated genes we identified IL18R1 and IL18RAP found to be associated with AD (
      • Hirota T.
      • Takahashi A.
      • Kubo M.
      • Tsunoda T.
      • Tomita K.
      • Sakashita M.
      • et al.
      Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population.
      ). We summarized the ten DEGs randomly selected from our study and compared their expression to published data (Table 1). A complete list of differentially expressed genes in blood cells is shown in Table 2.
      Table 1A list of 10 differentially expressed genes randomly selected from our study and their changes identified in older children and adults with AD based on literature. DEG, differentially expressed genes; NA, non-available; AD, Atopic Dermatits; LS, lesional skin; NS, nonlesional skin; SC, stratum corneum; HC, healthy controls; Down, downregulated gene; Up, upregulated gene.
      DEGChildren at age 0-1 yoTissue, Down/UpOlder childrenTissue, Down/UpAdultsTissue, Down/Up
      IL18RAPCurrent studyPBMCs; DownBrunner PM et al. 2019(
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      ) 4 months-5 years old
      AD LS vs HC skin; UpHirota T et al. 2012(
      • Hirota T.
      • Takahashi A.
      • Kubo M.
      • Tsunoda T.
      • Tomita K.
      • Sakashita M.
      • et al.
      Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population.
      ); Ewald D et al. 2015(
      • Ewald D.A.
      • Malajian D.
      • Krueger J.G.
      • Workman C.T.
      • Wang T.
      • Tian S.
      • et al.
      Meta-analysis derived atopic dermatitis (MADAD) transcriptome defines a robust AD signature highlighting the involvement of atherosclerosis and lipid metabolism pathways.
      )
      Susceptibility loci for AD; AD LS vs NS, Up
      IL18R1Current studyPBMCs; DownNAHirota T et al. 2012(
      • Hirota T.
      • Takahashi A.
      • Kubo M.
      • Tsunoda T.
      • Tomita K.
      • Sakashita M.
      • et al.
      Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population.
      )
      Susceptibility loci for AD
      IL-1βCurrent studyPBMCs; UpCole C et al. 2014(
      • Cole C.
      • Kroboth K.
      • Schurch N.J.
      • Sandilands A.
      • Sherstnev A.
      • O'Regan G.M.
      • et al.
      Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic. dermatitis.
      ) 6-16 years old
      AD NS vs HC skin; DownThijs JL et al. 2018(
      • Thijs J.L.
      • Strickland I.
      • Bruijnzeel-Koomen C.
      • Nierkens S.
      • Giovannone B.
      • Knol E.F.
      • et al.
      Serum biomarker profiles suggest that atopic dermatitis is a systemic disease.
      ); McAleer M et al. 2019(
      • McAleer M.A.
      • Jakasa I.
      • Hurault G.
      • Sarvari P.
      • McLean W.H.I.
      • Tanaka R.J.
      • et al.
      Systemic and stratum corneum biomarkers of severity in infant atopic dermatitis include markers of innate and T helper cell-related immunity and angiogenesis.
      )
      AD vs HC Serum, Up; AD vs HC SC, Down
      TNFCurrent studyPBMCs; UpBrunner PM et al. 2019(
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      ) Sumimoto S et al. 1992(
      • Sumimoto S.
      • Kawai M.
      • Kasajima Y.
      • Hamamoto T.
      Increased plasma tumour necrosis factor-alpha concentration in atopic dermatitis.
      ) 1-15 years old
      AD LS vs HC skin, Up; AD NS vs HC skin, Up; AD vs HC Plasma, UpSuarez-Farinas M et al. 2015(
      • Suarez-Farinas M.
      • Ungar B.
      • Correa da Rosa J.
      • Ewald D.A.
      • Rozenblit M.
      • Gonzalez J.
      • et al.
      RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications.
      ); Thijs JL et al. 2018(
      • Thijs J.L.
      • Strickland I.
      • Bruijnzeel-Koomen C.
      • Nierkens S.
      • Giovannone B.
      • Knol E.F.
      • et al.
      Serum biomarker profiles suggest that atopic dermatitis is a systemic disease.
      )
      AD LS vs NS, Up AD vs HC Serum, Up
      TREM1Current studyPBMCs; UpNAThijs JL et al. 2018(
      • Thijs J.L.
      • Strickland I.
      • Bruijnzeel-Koomen C.
      • Nierkens S.
      • Giovannone B.
      • Knol E.F.
      • et al.
      Serum biomarker profiles suggest that atopic dermatitis is a systemic disease.
      ); Suarez-Farinas M et al. 2015(
      • Suarez-Farinas M.
      • Ungar B.
      • Correa da Rosa J.
      • Ewald D.A.
      • Rozenblit M.
      • Gonzalez J.
      • et al.
      RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications.
      )
      AD vs HC Serum, Up AD LS vs NS, Up
      EGR3Current studyPBMCs; UpNASuarez-Farinas M et al.2011(
      • Suarez-Farinas M.
      • Tintle S.J.
      • Shemer A.
      • Chiricozzi A.
      • Nograles K.
      • Cardinale I.
      • et al.
      Nonlesional atopic dermatitis skin is characterized by broad terminal differentiation defects and variable immune abnormalities.
      )
      AD LS vs HC skin, Down; AD NS vs HC skin, Down
      EGR2Current studyPBMCs; UpBrunner PM et al. 2019(
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      ) 4 months-5 years old
      AD LS vs HC skin, Down; AD NS vs HC skin, DownHirota T et al. 2012(
      • Hirota T.
      • Takahashi A.
      • Kubo M.
      • Tsunoda T.
      • Tomita K.
      • Sakashita M.
      • et al.
      Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population.
      )
      Susceptibility loci for AD
      EGR1Current studyPBMCs; UpBrunner PM et al. 2019(
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      ); 4 months-5 years old Cole C et al. 2014(
      • Cole C.
      • Kroboth K.
      • Schurch N.J.
      • Sandilands A.
      • Sherstnev A.
      • O'Regan G.M.
      • et al.
      Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic. dermatitis.
      ) 6-16 years old
      AD LS vs HC skin, Down; AD NS vs HC skin, Down; AD NS vs HC skin; UpSuarez-Farinas M et al.2011(
      • Suarez-Farinas M.
      • Tintle S.J.
      • Shemer A.
      • Chiricozzi A.
      • Nograles K.
      • Cardinale I.
      • et al.
      Nonlesional atopic dermatitis skin is characterized by broad terminal differentiation defects and variable immune abnormalities.
      )
      AD LS vs HC skin, Up; AD NS vs HC skin, Up
      NLRP3Current studyPBMCs; UpBrunner PM et al. 2019(
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      )
      AD LS vs HC skin, Down; AD NS vs HC skin, DownNiebuhr M et al.2014(
      • Niebuhr M.
      • Baumert K.
      • Heratizadeh A.
      • Satzger I.
      • Werfel T.
      Impaired NLRP3 inflammasome expression and function in atopic dermatitis due to Th2 milieu.
      )
      AD LS vs HC skin, Down
      FOSL1Current studyPBMCs; UpBrunner PM et al. 2019(
      • Brunner P.M.
      • Israel A.
      • Leonard A.
      • Pavel A.B.
      • Kim H.J.
      • Zhang N.
      • et al.
      Distinct transcriptomic profiles of early-onset atopic dermatitis in blood and skin of pediatric patients.
      ); 6-16 years old Cole C et al. 2014(
      • Cole C.
      • Kroboth K.
      • Schurch N.J.
      • Sandilands A.
      • Sherstnev A.
      • O'Regan G.M.
      • et al.
      Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic. dermatitis.
      ) 6-16 years old
      AD LS vs HC skin, Up AD NS vs HC skin; UpSuarez-Farinas M et al.2011(
      • Suarez-Farinas M.
      • Tintle S.J.
      • Shemer A.
      • Chiricozzi A.
      • Nograles K.
      • Cardinale I.
      • et al.
      Nonlesional atopic dermatitis skin is characterized by broad terminal differentiation defects and variable immune abnormalities.
      ); Ewald D et al. 2015(
      • Ewald D.A.
      • Malajian D.
      • Krueger J.G.
      • Workman C.T.
      • Wang T.
      • Tian S.
      • et al.
      Meta-analysis derived atopic dermatitis (MADAD) transcriptome defines a robust AD signature highlighting the involvement of atherosclerosis and lipid metabolism pathways.
      )
      AD LS vs HC skin, Up; AD LS vs AD NL skin, Up; AD LS vs NS, Up
      Table 2Differentially expressed genes in PBMCs from infants with AD. List of genes differentially expressed in blood of AD children versus age-matched healthy control, meeting criteria of fold change/FC ≥1.5 and false discovery rate/FDR<0.05.
      Genelog2FCFCP-valueFDR
      GLDC-2.2650.2083.34E-115.29E-07
      HRASLS2-2.0860.2362.18E-050.00576
      SDC1-1.8090.2850.000440.03940
      CDC20-1.7490.2976.14E-050.01085
      IL18RAP-1.7020.3071.56E-070.00031
      INTU-1.6640.3162.70E-050.00658
      IGKV3-11-1.6550.3170.000310.03247
      KIR2DL3-1.5240.3486.36E-050.01095
      AL645929.1-1.4970.3544.41E-050.00906
      IGKV5-2-1.4870.3571.46E-070.00031
      PLEKHG7-1.4720.3601.29E-050.00474
      BHLHA15-1.4370.3690.000210.02593
      IGHV3-21-1.4120.3760.000380.03721
      GTSE1-1.4020.3781.17E-050.00465
      AC007278.2-1.3890.3820.000320.03341
      FASLG-1.3670.3887.97E-050.01202
      MCM10-1.3570.3904.54E-050.00923
      NCAPG-1.3500.3921.58E-050.00534
      IGJ-1.3490.3930.000150.02018
      SPC24-1.3280.3982.10E-060.00210
      UBE2C-1.3260.3990.000380.03721
      AL365475.1-1.3210.4007.85E-050.01196
      CCDC150-1.2760.4130.000160.02125
      KLRD1-1.2570.4187.68E-050.01192
      MELK-1.2550.4190.000100.01551
      SKA3-1.2520.4200.000130.01832
      AC007278.1-1.2260.4280.000630.04921
      IGLV1-44-1.2250.4283.86E-050.00849
      NCR1-1.2070.4330.000560.04636
      IGLV3-19-1.1920.4382.91E-050.00687
      KLRC4-1.1760.4430.000200.02444
      RNF165-1.1340.4560.000460.04029
      IGLV8-61-1.1110.4637.35E-050.01176
      MYBL1-1.1040.4652.63E-080.00011
      AC006480.2-1.0930.4690.000130.01832
      AURKB-1.0730.4750.000260.02917
      SLCO4A1-1.0570.4817.15E-050.01167
      IGLV2-8-1.0570.4810.000220.02608
      IGLV2-23-1.0370.4872.85E-050.00684
      IL18R1-1.0300.4905.41E-080.00014
      IGLC2-1.0220.4930.000190.02387
      AL683813.1-1.0200.4930.000580.04670
      AC010536.1-1.0130.4960.000560.04625
      STRIP2-1.0020.4990.000300.03156
      KLRF1-0.9930.5020.000230.02686
      XYLB-0.9710.5100.000390.03721
      SH2D2A-0.9640.5130.000180.02284
      IGKV1-5-0.9580.5151.51E-050.00519
      IGKV4-1-0.9580.5150.000110.01580
      IGLV2-11-0.9470.5194.07E-050.00859
      PIWIL2-0.9310.5250.000420.03910
      SLFN13-0.8900.5408.03E-060.00340
      IGKV1-12-0.8700.5470.000550.04604
      PDGFD-0.8340.5610.000180.02275
      ABCB9-0.8210.5660.000430.03940
      ISG20-0.7960.5760.000250.02887
      KIF2C-0.7820.5820.000370.03676
      IGKV3-15-0.7770.5840.000570.04636
      TTC22-0.7620.5900.000440.03940
      C5orf56-0.7050.6132.15E-050.00576
      DLG3-0.6960.6170.000540.04512
      COLQ-0.6090.6563.98E-050.00859
      SPATS2-0.6080.6565.20E-050.00992
      APP0.5981.5144.61E-080.00014
      MCL10.6611.5814.80E-060.00256
      MAML30.6611.5811.85E-050.00576
      KCNQ10.7321.6610.000400.03721
      RFX20.7541.6877.45E-050.01180
      PGRMC10.7791.7166.33E-050.01095
      PRXL2C0.7861.7240.000610.04852
      FRMD4B0.8421.7930.000360.03648
      ANXA10.8691.8275.64E-050.01051
      ESAM0.9121.8810.000490.04315
      HIST1H2AC0.9161.8870.000450.04004
      RAPH10.9231.8960.000280.03031
      ADAM90.9401.9180.000270.03031
      TUBA1A1.0212.0290.000600.04766
      LDLR1.0682.0971.29E-050.00474
      CPNE21.0982.1403.93E-060.00244
      ZNF1851.1832.2700.000550.04604
      MYADM1.1872.2786.10E-050.01085
      F51.3012.4642.45E-060.00210
      AHR1.3432.5360.000580.04670
      LMNA1.3822.6067.10E-050.01167
      HIST2H2BE1.4672.7654.13E-060.00244
      GNG111.4912.8100.000230.02700
      HIST1H2BJ1.5602.9480.000330.03358
      FAM129B1.5702.9680.000340.03455
      SH3BGRL21.6173.0670.000280.03031
      PADI41.6253.0850.000400.03721
      LTBP11.6353.1060.000570.04636
      ICAM11.6453.1280.000570.04636
      FAM20C1.6623.1650.000490.04315
      C2orf881.6753.1942.45E-050.00625
      NAMPT1.7013.2520.000290.03156
      CD141.7033.2560.000420.03898
      SCN1B1.7283.3125.16E-050.00992
      NRGN1.7403.3401.77E-050.00560
      LGALS121.7483.3590.000110.01596
      GGTA1P1.7513.3670.000260.02917
      PPP1R15A1.7623.3920.000380.03721
      SEPT51.7653.3993.99E-060.00244
      TAL11.7673.4022.23E-060.00210
      PDGFC1.7693.4086.82E-050.01149
      PRKAR2B1.7813.4361.96E-060.00210
      RAB201.8073.4990.000390.03721
      PLAUR1.8103.5050.000510.04372
      CTTN1.8133.5131.97E-050.00576
      GP61.8133.5151.72E-050.00557
      GAS2L11.8343.5650.000160.02125
      CLDN51.8643.6410.000180.02321
      PEAR11.8673.6491.71E-050.00557
      KLF41.8713.6570.000390.03721
      SOWAHC1.8723.6610.000510.04372
      TSPAN91.8793.6794.20E-060.00244
      MMP251.9033.7407.48E-060.00329
      LGALSL1.9423.8434.86E-060.00256
      ANPEP1.9433.8467.25E-050.01171
      SPARC1.9543.8750.000280.03031
      FAXDC21.9663.9072.52E-060.00210
      ATP2B1-AS11.9693.9140.000140.01999
      ITGA2B1.9793.9420.000150.02018
      NLRP31.9953.9874.40E-050.00906
      ITGB31.9993.9980.000160.02125
      ITGB52.0014.0045.57E-050.01051
      BEND22.0124.0340.000510.04372
      TRIB12.0154.0420.000300.03156
      PTX32.0394.1090.000240.02744
      VWF2.0544.1521.32E-050.00474
      DUSP62.0664.1860.000140.01973
      ELOVL72.1094.3142.10E-050.00576
      ALOX122.1344.3892.96E-050.00690
      HOMER32.1404.4063.76E-050.00839
      ADM2.1424.4140.000450.04004
      PDE5A2.1514.4420.000220.02608
      TMEM402.1664.4890.000390.03721
      MPIG6B2.1784.5261.16E-060.00147
      ENKUR2.1784.5270.000620.04886
      LRP32.1944.5755.93E-050.01085
      AC245128.32.1954.5792.51E-050.00630
      TUBB12.2124.6331.31E-050.00474
      TREML12.2584.7853.55E-060.00244
      CAVIN22.2774.8475.78E-060.00269
      IER32.2904.8916.17E-050.01085
      WLS2.3024.9302.16E-050.00576
      NRIP32.3305.0300.000210.02593
      EMP12.3405.0621.21E-060.00147
      GP1BA2.3425.0695.15E-060.00256
      PF42.3495.0952.92E-060.00231
      TNF2.3525.1072.47E-060.00210
      PPBP2.3795.2049.63E-070.00139
      SGK12.4435.4395.32E-060.00256
      CLU2.4645.5170.000520.04420
      TREM12.4645.5199.14E-070.00139
      FCAR2.4725.5491.91E-050.00576
      LUCAT12.5605.8980.000500.04362
      GP92.5775.9684.27E-060.00244
      CMTM52.5805.9812.02E-050.00576
      AL391903.12.6046.0794.05E-050.00859
      ABLIM32.6206.1462.44E-050.00625
      CALD12.6296.1852.17E-050.00576
      B3GNT52.6816.4156.12E-060.00277
      AC007032.12.6956.4740.000620.04876
      SPOCD12.7136.5565.34E-060.00256
      ZNF5032.8437.1778.15E-060.00340
      CXCL52.8487.2020.000160.02125
      SEC14L52.8567.2413.18E-050.00730
      AQP102.8797.3560.000440.03940
      PDZK1IP13.0148.0800.000430.03921
      IL1B3.1278.7367.60E-050.01192
      SPX3.1849.0903.38E-060.00244
      HRAT923.1929.1401.82E-091.44E-05
      CLEC1B3.35610.2388.70E-070.00139
      ID13.45810.9863.66E-050.00827
      EGR33.49611.2790.000300.03156
      EGR13.51711.4470.000330.03344
      FOSL13.55711.7671.48E-050.00519
      EGR23.76313.5720.000240.02800

      Validation of RNA-Seq Data by RT-qPCR

      To confirm the results of RNA-seq, real-time RT-qPCR was performed to detect the mRNA expression of five randomly selected differentially expressed genes in PBMCs from controls (n = 17) and infants with AD (n = 27). As shown in Figure 2 mRNA levels of all five genes: IL18RAP, IL-1β, TNF, TREM1 and EGR3 had significant differences between AD and control group in accordance with RNA sequencing results.
      Figure thumbnail gr2
      Figure 2Validation of RNA sequencing data by RT-qPCR. RT-qPCR analyses for five genes from the top 10 differentially expressed genes identified by high-throughput RNA sequencing: IL18RAP (a), IL-1β (b), TNF (c), TREM1 (d) and EGR3 (e) in AD children (n=27) and healthy controls (n=17). Fold change was calculated by 2-ΔΔCT method. The normalized expression data were log2-transformed and shown as the means ± standard deviation. Significant difference among groups was calculated by unpaired t-test with Welch’s correction for normal distribution or with Mann-Whitney rank-sum test for non-normal distribution data. * = p-value< 0.05, **= p-value <0.01, ***= p-value <0.001

      Effect of infant’s age on gene expression in blood of AD infants

      We wondered if the infant’s age has an effect on gene expression in blood of AD patients in the first year of life. Previous studies have been shown that differences in skin microbiome depend on infant’s age in healthy (
      • Capone K.A.
      • Dowd S.E.
      • Stamatas G.N.
      • Nikolovski J.
      Diversity of the human skin microbiome early in life.
      ) and AD infants (
      • Nakamura Y.
      • Takahashi H.
      • Takaya A.
      • Inoue Y.
      • Katayama Y.
      • Kusuya Y.
      • et al.
      Staphylococcus Agr virulence is critical for epidermal colonization and associates with atopic dermatitis development.
      ). To elucidate the effect of age, we stratified AD patients and healthy controls accordingly: 0-6 months and 7-12 months and performed differential expression analysis on RNA seq data. Table 3 summarizes DEG between four groups: AD patients greater than 6 months old (n=5) vs age-matched healthy controls (n=2); AD patients less than 6 months old (n=3) vs age-matched healthy controls (n=3); AD patients greater than 6 months old vs AD patients less than 6 months old and healthy controls greater than 6 months old vs healthy controls less than 6 months old. Interestingly, four DEGs that have been identified between AD infants and healthy controls and validated by RT-qPCR in this study (IL1B, TNF, TREM1 and EGR3), were differentially expressed in AD patients less than 6 months old compared to age-matched healthy controls suggesting its unique differential expression in first six months of life in AD patients. IL18RAP has been shown to be differentially expressed in both age groups between AD patients and healthy controls suggesting no effect of age stratification on this DEG in AD patients (Table 4). Data was further validated by RT-qPCR (Figure 3). Four DEG (IL1B, TNF, TREM1 and IL18RAP) showed significant differential expression affected by age in accordance with RNA seq results. Expression of EGR3 showed a trend to be affected by age, however, it was not significant as shown by RNA seq analysis. Altogether, these data indicate that identified differentially expressed genes in AD patients in first year of life could be affected by age, however, more samples are required to approve this effect.
      Table 3Effect of infant’s age on gene expression in AD infants and healthy controls. Age above half year AD patients (n=5) vs healthy controls (n=2). Age under half year AD patients (n=3) vs healthy controls (n=3). Healthy controls, age above half year (n=2) vs under half year (n=3)AD patients, age above half year (n=5) vs under half year (n=3). List of DEG in AD patients and healthy controls stratified by age. AD patients and healthy controls were stratified by age (0-6 months and 7-12 months) and differential expression analysis on RNA seq data was performed between four groups: AD children above half year age versus age-matched healthy controls; AD children under half year age vs age-matched healthy controls; AD patients above half year age vs AD patients under half year age and healthy controls above half year age vs healthy controls under half year age. Meeting criteria of fold change/FC ≥1.5 and false discovery rate/FDR<0.05. DEG, differentially expressed genes; AD, Atopic Dermatits; HC, healthy controls; FC, fold change; FDR, False discovery rate.
      GeneLog2FCFCP-valueFDR
      MTCO3P12-8.8230.0025.674E-099.209E-05
      IGKV1-16-3.5840.0834.496E-066.483E-03
      GLDC-2.2440.2111.331E-063.086E-03
      IL18RAP-1.9680.2561.241E-063.086E-03
      MIF-AS1-1.9160.2651.954E-051.885E-02
      WASHC1-1.7390.3001.974E-051.885E-02
      SNHG22-1.5000.3547.604E-054.255E-02
      ENSG00000273295-1.2940.4085.821E-072.362E-03
      ENSG00000260404-1.0450.4854.011E-066.483E-03
      KLRF1-1.0110.4962.685E-052.294E-02
      ITGB10.8831.8448.051E-054.355E-02
      H2AC61.5382.9049.485E-054.966E-02
      PRKAR2B2.1574.4605.766E-053.743E-02
      GNG112.2784.8493.485E-052.507E-02
      PEAR12.4245.3666.523E-054.072E-02
      MPIG6B2.4995.6511.643E-051.885E-02
      PF42.5205.7387.597E-054.255E-02
      FAXDC22.5405.8165.347E-053.616E-02
      CAVIN22.5856.0008.349E-061.042E-02
      PPBP2.5896.0183.512E-066.483E-03
      ITGB32.5956.0433.553E-052.507E-02
      TUBB12.6126.1123.955E-066.483E-03
      TREML12.7106.5422.979E-052.417E-02
      FPR13.0118.0602.393E-052.158E-02
      CLEC4F3.2929.7981.938E-071.048E-03
      ABLIM33.37210.3553.510E-052.507E-02
      FPR23.43810.8367.061E-054.244E-02
      LIN7A4.01016.1134.794E-066.483E-03
      SEC14L54.34820.3678.718E-072.830E-03
      DDX11L106.851115.4081.897E-051.885E-02
      IGHV5-10-19.633793.9812.177E-081.767E-04
      GeneLog2FCFCP-valueFDR
      ENSG00000271993-6.4510.0111.429E-046.675E-03
      LOC107987373-6.1020.0159.330E-042.534E-02
      ENSG00000254851-4.1360.0577.154E-042.125E-02
      ENSG00000266302-4.0210.0623.886E-041.392E-02
      GPR82-3.7360.0753.149E-041.211E-02
      ENSG00000244167-3.3260.1001.761E-051.365E-03
      GAPDHP1-3.2070.1087.216E-042.133E-02
      CHL1-3.0460.1217.483E-042.173E-02
      SDC1-2.9500.1295.232E-041.712E-02
      RAVER2-2.9430.1304.456E-052.817E-03
      FCGR3B-2.9200.1326.584E-053.805E-03
      SLC4A10-2.9040.1342.548E-062.678E-04
      ZBED2-2.8880.1352.211E-034.666E-02
      SSPN-2.6330.1614.028E-041.426E-02
      LOC730101-2.5810.1672.129E-034.537E-02
      GLDC-2.4640.1812.327E-051.709E-03
      IGLV2-18-2.4200.1873.579E-052.360E-03
      IGHV3-13-2.3940.1905.888E-041.865E-02
      IGHV3-21-2.3900.1911.176E-127.064E-10
      CHAC2-2.3890.1912.231E-051.660E-03
      IGKV3D-20-2.3080.2023.317E-041.251E-02
      TNFRSF17-2.2990.2031.029E-061.254E-04
      CDC20-2.2400.2124.534E-091.230E-06
      IGHV3-15-2.2020.2173.022E-085.775E-06
      MIR3142HG-2.1840.2208.995E-054.727E-03
      LINC01355-2.1810.2203.300E-041.249E-02
      ENSG00000230521-2.1720.2221.578E-047.192E-03
      BHLHE41-2.1210.2301.844E-034.116E-02
      BHLHA15-2.1120.2318.746E-042.427E-02
      PPP1R17-2.1030.2332.441E-041.004E-02
      OR2A9P-2.0840.2362.116E-034.522E-02
      ENSG00000275481-2.0660.2391.494E-046.923E-03
      PARS2-2.0450.2422.013E-034.369E-02
      EOMES-2.0110.2481.357E-046.399E-03
      IGHGP-1.9830.2538.716E-042.426E-02
      JCHAIN-1.9820.2531.711E-061.918E-04
      IGHV3-72-1.9810.2537.772E-054.244E-03
      CISH-1.9300.2627.131E-081.187E-05
      CD180-1.9200.2641.204E-033.039E-02
      MCM10-1.9190.2646.847E-053.890E-03
      LINC02273-1.9180.2651.005E-045.058E-03
      KLRC4-1.9110.2668.811E-081.398E-05
      IGKV1-6-1.8910.2707.272E-042.138E-02
      TRGV8-1.8600.2761.861E-034.134E-02
      IGLV3-19-1.8570.2768.468E-081.356E-05
      SLC23A3-1.8460.2781.713E-033.930E-02
      C1orf220-1.8440.2798.631E-042.407E-02
      IGKV1-17-1.8410.2799.117E-071.136E-04
      IGHG1-1.8350.2809.853E-042.625E-02
      ENSG00000258810-1.8260.2821.423E-033.409E-02
      TRGV10-1.7850.2901.548E-033.615E-02
      IGHV3-11-1.7800.2912.487E-041.016E-02
      FASLG-1.7790.2911.466E-051.174E-03
      NCAPG-1.7720.2932.036E-048.800E-03
      LINC01560-1.7680.2946.509E-041.979E-02
      ZNF781-1.7550.2964.745E-041.622E-02
      IGHV1-18-1.7540.2973.218E-052.164E-03
      IGHJ3-1.7400.2995.973E-041.886E-02
      TRAV4-1.7270.3021.912E-034.220E-02
      IGKV1D-8-1.7220.3031.633E-047.322E-03
      TRGC2-1.7080.3067.025E-091.773E-06
      CUTALP-1.7000.3082.636E-041.060E-02
      IGLV1-44-1.6990.3088.700E-102.703E-07
      AKAP6-1.6880.3109.866E-042.625E-02
      IGLV1-51-1.6690.3148.440E-091.999E-06
      ZNF181-1.6670.3151.396E-051.123E-03
      GIMAP4-1.6630.3161.074E-061.281E-04
      KLRK1-1.6540.3181.775E-072.608E-05
      SAMD9-1.6370.3211.956E-062.156E-04
      MMACHC-1.6240.3247.667E-054.227E-03
      IGKV5-2-1.6170.3267.368E-042.155E-02
      THNSL1-1.6000.3301.009E-032.665E-02
      PDE4DIPP6-1.5990.3303.369E-074.644E-05
      ZNF658-1.5950.3317.522E-054.176E-03
      GIMAP7-1.5860.3331.304E-033.220E-02
      ZNF816-1.5710.3371.156E-071.817E-05
      ZNF772-1.5710.3377.636E-091.834E-06
      IGLV3-25-1.5630.3381.939E-034.269E-02
      ZNF501-1.5610.3393.145E-041.211E-02
      ENSG00000272913-1.5540.3403.354E-041.260E-02
      MOCS2-DT-1.5520.3412.297E-034.756E-02
      SAMD9L-1.5450.3439.236E-042.521E-02
      ZNF404-1.5430.3434.938E-041.657E-02
      ZNF470-1.5390.3448.973E-054.727E-03
      TAS2R4-1.5230.3488.426E-042.381E-02
      ENSG00000279696-1.5190.3491.312E-051.061E-03
      PAQR8-1.5160.3501.029E-061.254E-04
      ZNF594-1.5020.3535.013E-041.666E-02
      LINC02397-1.5000.3542.194E-051.654E-03
      IGLV8-61-1.4960.3551.777E-072.608E-05
      TIGD7-1.4940.3552.569E-062.683E-04
      IGKV1-39-1.4900.3568.397E-067.511E-04
      MOXD1-1.4900.3568.500E-042.386E-02
      CEP19-1.4800.3582.381E-034.866E-02
      KLRD1-1.4750.3604.971E-041.662E-02
      FRMPD3-1.4650.3621.696E-051.326E-03
      IGKV1-9-1.4570.3641.158E-045.679E-03
      ZNF780A-1.4430.3681.961E-062.156E-04
      RTP4-1.4340.3707.186E-042.128E-02
      IGLV2-8-1.4330.3705.396E-053.288E-03
      BTN3A2-1.4310.3711.402E-061.626E-04
      TMEM60-1.4140.3757.592E-042.192E-02
      FAM111A-DT-1.3890.3823.412E-041.272E-02
      ENSG00000279267-1.3870.3822.038E-034.405E-02
      SLAMF7-1.3840.3832.446E-034.961E-02
      GEMIN6-1.3750.3861.182E-061.399E-04
      IGKV2-30-1.3740.3861.834E-048.071E-03
      CASP4LP-1.3700.3872.150E-034.565E-02
      JRKL-1.3700.3874.453E-041.535E-02
      BTLA-1.3660.3883.303E-063.306E-04
      ZNNT1-1.3640.3881.516E-061.734E-04
      ENSG00000246596-1.3630.3892.840E-041.121E-02
      UBE2T-1.3610.3891.501E-046.935E-03
      ZBTB32-1.3560.3912.469E-034.990E-02
      EEF1AKNMT-1.3560.3912.746E-051.938E-03
      IGHV3-33-1.3550.3911.341E-046.372E-03
      ENSG00000232611-1.3530.3922.330E-034.784E-02
      CXCR3-1.3500.3923.158E-041.211E-02
      CD200-1.3490.3931.689E-047.535E-03
      IGKV4-1-1.3470.3939.343E-042.534E-02
      DTX3L-1.3420.3945.186E-041.703E-02
      IGHV1-46-1.3390.3956.659E-053.835E-03
      GCSAM-1.3370.3963.056E-052.096E-03
      ZNF66-1.3250.3995.140E-041.691E-02
      GIMAP1-1.3240.3997.225E-091.773E-06
      AURKA-1.3240.4006.992E-042.096E-02
      CENPBD1-1.3220.4002.913E-041.144E-02
      IL18RAP-1.3090.4041.963E-034.305E-02
      NA-1.3040.4052.527E-097.081E-07
      ZNF737-1.3020.4065.127E-041.690E-02
      IGLV2-23-1.3000.4063.615E-041.324E-02
      IGLV3-27-1.2980.4071.261E-033.136E-02
      PGBD2-1.2960.4078.355E-067.511E-04
      IGKV1-12-1.2920.4095.242E-076.996E-05
      IGKV1-16-1.2910.4091.932E-051.477E-03
      DENND2D-1.2830.4113.314E-052.202E-03
      IGKV3-11-1.2810.4123.948E-075.354E-05
      NA-1.2790.4129.526E-042.571E-02
      CMTR2-1.2730.4147.274E-081.199E-05
      BBS10-1.2660.4161.207E-033.041E-02
      FIGNL1-1.2650.4161.068E-061.281E-04
      IGLV3-1-1.2640.4169.491E-042.566E-02
      ARSK-1.2630.4174.091E-052.626E-03
      IGLV1-47-1.2580.4188.770E-042.430E-02
      ZKSCAN7-1.2420.4238.287E-042.354E-02
      IGKV3-15-1.2400.4234.808E-052.994E-03
      HCP5-1.2380.4242.807E-051.966E-03
      TMED2-DT-1.2310.4261.984E-034.328E-02
      C14orf119-1.2270.4276.280E-041.941E-02
      ZFP3-1.2180.4305.653E-053.383E-03
      ZNF583-1.2160.4305.982E-041.886E-02
      IGLC2-1.2120.4321.360E-033.330E-02
      TMEM140-1.2070.4331.390E-033.373E-02
      ZNF613-1.2020.4351.454E-033.459E-02
      ACKR3-1.2020.4351.373E-046.451E-03
      ZNF780B-1.2010.4353.300E-041.249E-02
      IGKV3-20-1.1980.4361.620E-047.318E-03
      ZNF626-1.1910.4381.015E-032.674E-02
      ZNF607-1.1840.4401.295E-051.052E-03
      ZNF175-1.1810.4412.765E-062.853E-04
      LRIF1-1.1780.4422.817E-041.117E-02
      STAT1-1.1750.4431.256E-033.134E-02
      TRGC1-1.1750.4437.599E-054.203E-03
      RBM12B-1.1740.4437.139E-066.560E-04
      IGLV2-11-1.1660.4461.560E-033.629E-02
      GIMAP6-1.1630.4472.190E-049.323E-03
      IGKV1-5-1.1590.4481.131E-059.558E-04
      MRPL35-1.1590.4481.822E-051.399E-03
      ZNF721-1.1570.4492.130E-073.088E-05
      ZNF799-1.1550.4491.154E-032.937E-02
      CARD8-AS1-1.1540.4496.959E-042.090E-02
      ZNF665-1.1520.4503.676E-041.338E-02
      ZNF226-1.1500.4505.285E-091.411E-06
      ZNF616-1.1470.4511.652E-033.821E-02
      LXN-1.1450.4526.716E-042.028E-02
      IGLV4-69-1.1380.4551.009E-032.665E-02
      C5orf51-1.1320.4561.835E-034.104E-02
      FCRL3-1.1270.4582.841E-041.121E-02
      MYBL1-1.1270.4589.251E-042.521E-02
      TIGD2-1.1180.4614.773E-041.627E-02
      IL18R1-1.1170.4611.725E-033.947E-02
      LBH-1.1170.4614.585E-052.866E-03
      ENSG00000279059-1.1140.4621.704E-033.916E-02
      ENSG00000259877-1.1130.4628.156E-042.324E-02
      ZNF189-1.1000.4676.862E-042.068E-02
      C17orf80-1.0960.4689.718E-068.380E-04
      PREPL-1.0950.4685.271E-041.721E-02
      ZKSCAN3-1.0940.4682.583E-041.047E-02
      OGFOD1-1.0940.4681.696E-047.547E-03
      ZBTB38-1.0900.4701.106E-032.849E-02
      N6AMT1-1.0870.4718.993E-042.471E-02
      NAPEPLD-1.0840.4721.109E-032.853E-02
      INTS5-1.0800.4734.717E-064.533E-04
      IGKC-1.0790.4737.877E-042.253E-02
      GIMAP8-1.0760.4749.667E-068.379E-04
      ABCB1-1.0730.4757.390E-042.158E-02
      TRDC-1.0730.4752.020E-034.378E-02
      ZNF230-1.0720.4769.037E-054.734E-03
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      BNIP11.4482.7295.154E-064.869E-04
      ETS21.4492.7295.042E-041.672E-02
      HIF1A1.4572.7459.755E-092.242E-06
      TOE11.4682.7664.288E-052.731E-03
      CLP11.4682.7668.330E-081.347E-05
      ENSG000002607081.4772.7836.458E-041.967E-02
      DUSP51.4772.7835.683E-112.209E-08
      C17orf491.4792.7875.141E-101.729E-07
      MYADM1.4912.8103.961E-063.941E-04
      SERTAD11.4912.8115.429E-065.072E-04
      SERTAD31.4982.8257.674E-081.253E-05
      TTC341.4992.8263.359E-041.260E-02
      NFKBID1.4992.8261.296E-046.227E-03
      ENSG000002796021.4992.8274.282E-064.168E-04
      LDLRAD31.5162.8612.247E-034.711E-02
      CRABP21.5242.8771.358E-033.330E-02
      ZP31.5252.8782.169E-049.279E-03
      SMARCD31.5252.8781.459E-033.466E-02
      MMP141.5272.8822.038E-034.405E-02
      SDE21.5332.8934.053E-087.328E-06
      GLT1D11.5362.9001.483E-033.506E-02
      F51.5392.9071.117E-032.863E-02
      H2BC211.5472.9214.282E-041.491E-02
      CEACAM191.5472.9232.489E-041.016E-02
      ENSG000002778791.5512.9301.364E-033.334E-02
      DUSP11.5542.9363.121E-041.211E-02
      ARRDC41.5552.9381.240E-082.707E-06
      OTUD11.5612.9509.380E-054.868E-03
      STX111.5672.9639.889E-055.044E-03
      RNVU1-301.5682.9668.028E-042.292E-02
      VENTX1.5762.9812.028E-048.796E-03
      JDP21.5782.9865.716E-077.451E-05
      SOX41.5913.0134.929E-041.657E-02
      RBM38-AS11.5943.0181.567E-047.159E-03
      HIC11.5953.0204.478E-064.328E-04
      BTG31.5963.0247.075E-042.106E-02
      LINC009631.5973.0251.758E-034.012E-02
      NRGN1.5993.0291.192E-045.809E-03
      CXCR41.6003.0325.429E-089.511E-06
      UBE2FP11.6073.0478.372E-054.469E-03
      DUSP101.6123.0565.420E-053.290E-03
      MTSS21.6153.0648.435E-102.676E-07
      STAB11.6173.0681.627E-033.768E-02
      LONRF31.6193.0711.771E-047.835E-03
      IER5L1.6253.0844.887E-064.669E-04
      SH3RF11.6303.0963.304E-041.249E-02
      NIBAN21.6533.1441.078E-045.384E-03
      SBDS1.6533.1465.381E-077.069E-05
      ZC3H12A1.6543.1481.620E-131.048E-10
      LOC1027239961.6673.1751.768E-034.018E-02
      TNRC18P11.6753.1947.768E-042.229E-02
      FAM20C1.6923.2323.151E-041.211E-02
      JUND1.7053.2614.856E-101.701E-07
      CSF3R1.7183.2911.421E-033.409E-02
      BTG21.7273.3101.556E-047.131E-03
      AHR1.7353.3285.842E-053.483E-03
      GGTA11.7403.3412.319E-034.767E-02
      SKOR11.7423.3461.901E-034.202E-02
      GADD45A1.7433.3471.157E-059.729E-04
      MIR222HG1.7473.3569.930E-055.045E-03
      MIDN1.7493.3614.288E-052.731E-03
      ZBTB101.7503.3631.332E-046.346E-03
      KLF101.7533.3712.696E-041.079E-02
      RAB3A1.7583.3837.505E-042.176E-02
      HES11.7593.3844.456E-041.535E-02
      SNORA751.7603.3881.416E-033.406E-02
      FSCN11.7603.3884.913E-041.656E-02
      ENSG000002750561.7613.3902.029E-048.796E-03
      JAG11.7683.4058.961E-054.727E-03
      GNA151.7853.4462.928E-062.966E-04
      ADM1.7913.4616.120E-041.909E-02
      MGP1.7923.4632.357E-034.828E-02
      JUNB1.8003.4821.334E-082.841E-06
      NINJ11.8043.4923.508E-086.485E-06
      CCR11.8103.5073.162E-041.211E-02
      COL9A31.8123.5101.799E-034.050E-02
      ENSG000002800671.8143.5169.222E-042.521E-02
      RPL32P11.8203.5328.565E-042.400E-02
      GAS2L11.8323.5603.706E-041.343E-02
      NFKBIE1.8503.6041.032E-082.283E-06
      NOCT1.8513.6072.854E-051.991E-03
      SMOX1.8523.6105.454E-041.764E-02
      SLC6A81.8593.6281.896E-034.200E-02
      RGS21.8603.6301.027E-058.808E-04
      ENSG000002722561.8763.6708.199E-042.333E-02
      S100A81.8803.6813.006E-052.080E-03
      LMNA1.8913.7104.578E-088.190E-06
      KLF41.9063.7483.816E-052.459E-03
      JUN1.9093.7541.266E-082.729E-06
      PDGFC1.9123.7641.164E-032.952E-02
      EREG1.9143.7693.635E-041.328E-02
      SLC22A41.9163.7751.061E-032.763E-02
      ITPRIP1.9183.7784.358E-143.490E-11
      BHLHE401.9183.7801.442E-072.204E-05
      CDKN1A1.9203.7842.221E-062.395E-04
      GADD45B1.9253.7971.663E-083.410E-06
      IER21.9263.8011.071E-059.093E-04
      RETN1.9373.8284.254E-064.168E-04
      P2RY21.9433.8461.003E-032.657E-02
      CD141.9533.8722.367E-049.780E-03
      TNFAIP31.9563.8814.405E-052.795E-03
      RASGEF1B1.9583.8856.554E-053.801E-03
      VEGFA1.9673.9092.005E-083.966E-06
      DDIT31.9733.9253.717E-154.167E-12
      ENSG000002328111.9783.9402.881E-062.937E-04
      LRG11.9793.9431.737E-047.707E-03
      ARHGAP221.9793.9436.293E-041.942E-02
      IGHEP21.9883.9665.389E-041.756E-02
      ENSG000002795201.9943.9844.254E-165.503E-13
      RHBDL12.0024.0057.004E-042.096E-02
      BTBD192.0144.0402.229E-049.444E-03
      HOMER32.0174.0479.268E-054.825E-03
      ENC12.0224.0632.181E-062.366E-04
      ENSG000002739722.0304.0841.666E-033.837E-02
      RGS17P12.0484.1351.199E-059.982E-04
      S100A92.0494.1378.698E-067.739E-04
      FOS2.0564.1597.651E-079.674E-05
      ENSG000002706402.0674.1901.267E-033.137E-02
      SNAI12.0704.2001.332E-033.280E-02
      LRP32.0874.2496.814E-053.890E-03
      KCNK72.0894.2533.744E-052.421E-03
      IFITM102.0954.2733.236E-052.168E-03
      NECTIN22.1184.3423.293E-041.249E-02
      SCN1B2.1294.3747.524E-054.176E-03
      S100A122.1314.3807.126E-053.994E-03
      NRARP2.1524.4432.014E-034.369E-02
      IRS22.1544.4516.444E-102.125E-07
      DUSP22.1584.4629.808E-159.461E-12
      MAFB2.1674.4913.263E-074.535E-05
      ZFP362.1684.4932.525E-097.081E-07
      NFKBIA2.1704.5001.211E-115.817E-09
      NR6A12.1764.5188.261E-054.424E-03
      ENSG000002075252.1824.5396.065E-041.906E-02
      FOSL22.1874.5521.416E-116.582E-09
      AREG2.1894.5615.583E-041.789E-02
      PPP1R15A2.1964.5838.779E-145.905E-11
      ICAM12.1984.5873.686E-122.066E-09
      SOCS32.1984.5872.672E-062.774E-04
      MIR6162.2084.6191.664E-033.837E-02
      SLC2A32.2094.6231.570E-182.934E-15
      PLAU2.2094.6242.676E-051.907E-03
      DIP2A-IT12.2094.6254.566E-052.865E-03
      ENSG000002669932.2154.6422.278E-034.736E-02
      PPIF2.2274.6831.967E-121.141E-09
      NA2.2484.7492.364E-084.622E-06
      NAMPT2.2594.7864.009E-087.328E-06
      DUSP62.2594.7881.487E-072.253E-05
      ENSG000002569132.2614.7931.039E-032.712E-02
      ENSG000002752102.2724.8309.994E-042.651E-02
      IFI302.2824.8639.844E-055.044E-03
      PIM32.2854.8741.380E-255.800E-22
      LGALS122.2864.8763.171E-052.141E-03
      ASGR22.2904.8902.306E-049.642E-03
      ANPEP2.2914.8921.736E-072.606E-05
      FFAR12.2924.8961.448E-116.582E-09
      CSRNP12.2994.9235.836E-144.267E-11
      NFIL32.3044.9397.050E-081.185E-05
      TCF3P12.3074.9489.014E-042.473E-02
      HLX2.3445.0771.339E-033.291E-02
      PLK32.3465.0852.505E-258.425E-22
      PDE2A2.3515.1032.590E-041.047E-02
      PFKFB32.3565.1211.178E-171.981E-14
      SOWAHC2.3635.1436.411E-041.960E-02
      TNFRSF12A2.3745.1836.012E-053.547E-03
      MIR22HG2.3785.1982.038E-095.908E-07
      XK2.3875.2301.413E-033.403E-02
      BCL32.3935.2546.803E-123.690E-09
      ENSG000002687342.4025.2843.668E-075.015E-05
      GADD45G2.4155.3321.857E-034.131E-02
      ENSG000002740082.4235.3636.394E-041.959E-02
      ASTL2.4355.4091.479E-046.872E-03
      ELOVL72.4695.5372.200E-034.652E-02
      MATN12.4695.5374.546E-041.563E-02
      TRIB12.4765.5622.206E-051.656E-03
      CHRM42.5075.6837.352E-042.154E-02
      ENSG000002501382.5145.7118.650E-054.603E-03
      ATP2B1-AS12.5205.7352.634E-051.896E-03
      PLAUR2.5555.8755.065E-101.729E-07
      TSPEAR-AS12.5825.9891.314E-033.239E-02
      GP92.5896.0182.218E-034.674E-02
      AVPI12.5916.0235.787E-041.839E-02
      RND12.6016.0688.633E-042.407E-02
      PTX32.6186.1407.139E-123.752E-09
      NLRP32.6256.1681.345E-072.075E-05
      MMP252.6306.1912.027E-141.794E-11
      IER32.6596.3158.094E-135.041E-10
      FAM238A2.6696.3591.596E-116.884E-09
      RBKS2.6776.3952.392E-073.409E-05
      EMP12.6946.4715.779E-112.209E-08
      DUSP82.7006.5002.755E-051.938E-03
      RRAD2.7186.5815.245E-053.207E-03
      MIR23AHG2.7336.6466.241E-156.559E-12
      RAB202.7396.6758.601E-145.905E-11
      NA2.7416.6847.517E-171.149E-13
      MAFF2.7426.6901.013E-149.461E-12
      TMEM882.7446.7001.217E-051.008E-03
      MMP92.7666.8027.058E-042.104E-02
      PHLDA12.8026.9732.759E-073.895E-05
      CHMP4BP12.8066.9922.993E-052.080E-03
      NR4A12.8437.1762.237E-062.396E-04
      IGFBP22.8537.2261.002E-045.058E-03
      ENSG000002344362.8937.4265.720E-041.825E-02
      ENSG000002746772.8937.4261.663E-047.437E-03
      PER12.8987.4521.029E-255.765E-22
      ENSG000002780222.9407.6779.969E-055.050E-03
      ATF32.9437.6914.281E-111.714E-08
      NTSR12.9547.7477.847E-067.094E-04
      SLC22A162.9847.9131.026E-032.696E-02
      TP53INP22.9907.9431.099E-269.240E-23
      TMEM1193.0028.0081.083E-032.809E-02
      CCL23.0208.1102.218E-049.420E-03
      THNSL23.0218.1162.647E-051.896E-03
      RNVU1-33.0278.1487.264E-042.138E-02
      ENSG000002706813.0388.2127.719E-042.219E-02
      PANX23.0428.2356.241E-053.645E-03
      TAMALIN3.0448.2491.567E-162.196E-13
      TNFSF93.0788.4461.045E-115.287E-09
      NRIP33.0828.4674.518E-052.845E-03
      TNF3.0928.5268.842E-102.703E-07
      MT1XP13.1148.6551.089E-045.419E-03
      FCAR3.1258.7268.222E-071.032E-04
      ENSG000002740513.1368.7916.113E-041.909E-02
      PDGFA-DT3.1378.7971.983E-034.328E-02
      KRT863.1488.8661.644E-051.291E-03
      LOC3999003.1498.8733.641E-041.328E-02
      TREM13.1648.9637.190E-102.325E-07
      MIR44203.1989.1765.616E-144.267E-11
      ENSG000002243563.2029.2027.259E-066.634E-04
      FOSB3.2119.2601.335E-094.009E-07
      OSM3.2609.5821.623E-083.370E-06
      C17orf1073.3129.9318.022E-054.324E-03
      HP3.33310.0789.749E-042.615E-02
      RNU5D-13.34210.1401.076E-032.797E-02
      CD833.38510.4481.852E-333.114E-29
      CXCL23.40210.5731.213E-061.427E-04
      HBEGF3.50511.3501.069E-115.287E-09
      SGK13.51611.4431.492E-116.602E-09
      B3GNT53.53611.6007.350E-079.363E-05
      NR4A33.58612.0111.649E-116.932E-09
      NR4A23.62212.3136.717E-231.883E-19
      FAM71A3.65712.6185.086E-041.680E-02
      ZNF5033.70713.0556.415E-091.659E-06
      LRRC323.72113.1907.182E-091.773E-06
      ENSG000002804073.75613.5142.566E-051.860E-03
      GJB63.76413.5852.250E-049.505E-03
      PHLDA23.83414.2635.363E-077.069E-05
      THBS13.89714.8962.312E-034.767E-02
      IL1B3.90114.9422.202E-152.645E-12
      SEMA6B3.92215.1611.147E-045.658E-03
      RNVU1-63.98115.7941.348E-046.387E-03
      RNF1524.01016.1152.091E-034.490E-02
      SPX4.02816.3151.538E-033.597E-02
      SNORD3B-24.09217.0482.855E-041.124E-02
      ENSG000002188094.10417.1921.987E-083.966E-06
      RN7SL368P4.60924.4047.879E-054.282E-03
      TEX454.75126.9196.112E-041.909E-02
      CCL3L34.76827.2415.399E-041.756E-02
      EGR14.84028.6322.278E-049.579E-03
      G0S24.98131.5785.628E-201.352E-16
      CXCL84.99531.8879.194E-054.801E-03
      EGR35.07633.7324.196E-041.470E-02
      ADRA2B5.11034.5272.412E-049.943E-03
      LINC012205.21637.1686.991E-053.955E-03
      SLED15.24637.9581.372E-061.603E-04
      ID15.48544.7864.129E-091.138E-06
      ENSG000002240295.50145.3024.341E-041.508E-02
      EGR25.78855.2663.139E-052.137E-03
      MMP2-AS16.12069.5451.158E-032.943E-02
      ENSG000002584136.19273.1131.445E-033.448E-02
      FOSL16.35781.9417.520E-191.581E-15
      CXCL16.43986.7427.316E-042.147E-02
      LERFS6.59796.8234.893E-041.652E-02
      ENSG000002610266.791110.7696.213E-053.645E-03
      CLLU1-AS18.387334.8311.475E-094.350E-07
      GeneLog2FCFCP-valueFDR
      WASHC1-2.3280.1993.255E-105.279E-06
      MYO18B-4.9470.0322.603E-061.477E-02
      IFI27-3.7950.0722.732E-061.477E-02
      NRIR-2.2060.2178.777E-063.558E-02
      Table 4Effect of infant’s age on the expression of selected genes that were differentially expressed in AD infants and validated by RT-qPCR. Differential expression analysis between AD patients and healthy controls was performed using DESeq2 on RNA seq data after stratifying the dataset by age (0-6 months and 7-12 months). DEG, differentially expressed genes; AD, Atopic Dermatits; HC, healthy controls; LFC, log2 fold change; FCH, fold change; FDR, False discovery rate.
      DEGGroupLFCFCHFDR
      IL18RAP> 6months old AD patients vs HC-1.9680.2560.003
      <6 months old AD patients vs HC-1.3090.4040.043
      IL1B<6 months old AD patients vs HC3.90114.9420.000
      TNF<6 months old AD patients vs HC3.0928.5260.000
      TREM1<6 months old AD patients vs HC3.1648.9630.000
      EGR3<6 months old AD patients vs HC5.07633.7320.015
      Figure thumbnail gr3
      Figure 3Effect of infant’s age on the expression of differentially expressed genes in AD infants. RT-qPCR analyses for five genes from the top 10 differentially expressed genes identified by high-throughput RNA sequencing: IL18RAP (a), IL-1β (b), TNF (c), TREM1 (d) and EGR3 (e) in AD children (n=27) and healthy controls (n=17) after stratifying the dataset by age (0-6 months and 7-12 months). Fold change was calculated by 2-ΔΔCT method. The normalized expression data were log2-transformed and shown as the means ± standard deviation. Significant difference among groups was calculated by unpaired t-test with Welch’s correction for normal distribution or with Mann-Whitney rank-sum test for non-normal distribution data. * = p-value< 0.05, **= p-value <0.01, ***= p-value <0.001

      Gene Ontology and pathway analysis of differentially expressed genes

      To gain insight into the gene ontology (GO) categories of DEGs between the AD group and control group, all DEGs were uploaded to the DAVID database. GO analysis contained three categories: biological process (BP), cellular component (CC) and molecular function (MF). In total, 86 significant GO terms were enriched for DEGs identified in the AD group, of which 57 were within the BP category, 21 were within the CC category and 8 were within the MF category. The enriched BP categories included: immune response, inflammatory response, regulation of immune response, leukocyte migration, positive regulation of NF-kappaB transcription factor activity, cell adhesion, cell surface receptor signalling pathway and many others. In the category CC, the DEGs were significantly enriched in plasma membrane, extracellular region, extracellular exosome, cell surface and cytoplasm. Furthermore, in the category MF DEG were mainly enriched in antigen binding, receptor activity, cytokine activity, enzyme binding and protein binding. Selected pathways significantly enriched in AD group included hematopoietic cell lineage, natural killer cell-mediated cytotoxicity, PI3K-Akt signalling pathway, cytokine-cytokine receptor interaction, ECM-receptor interaction and immunoregulatory interactions between a lymphoid and a non-lymphoid cell (Figure 4). Further details of results of the GO enrichment and pathway analyses are provided in Table 5.
      Figure thumbnail gr4
      Figure 4GO enrichment and pathway analysis of differentially expressed genes. (a) The top 20 enriched GO terms; the x-axis represents gene counts, and the y-axis represents GO terms. (b) Selected KEGG pathways; the x-axis represents gene counts, and the y-axis represents KEGG pathway names.
      Table 5GO enrichment and pathway analyses of DEGs. DEGs with significant change between AD and healthy control children (cut-off of fold change FC ≥1.5 and FDR<0.05) were used for GO enrichment and pathway analyses using DAVID database. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway analyses were used to determine the pathways of DEGs between two groups.
      GO TermGene CountP-ValueGenes
      Biological process
      regulation of immune response173.76E-12ICAM1, IGLV1-44, IGKV5-2, NCR1, IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV2-23,

      IGKV4-1, KLRF1,TREM1, KIR2DL3, TREML1, IGLC2, KLRD1, IGKV3-15
      receptor-mediated endocytosis168.68E-11LDLR, IGLV1-44, IGKV5-2, JCHAIN, SPARC, IGLV2-11,

      IGLV2-8, IGLV3-19, CTTN, IGKV1-5, IGLV2-23, IGKV4-1,

      IGLC2, LRP3, CD14, IGKV3-15
      complement activation114.37E-09IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44, IGLV2-23, IGKV5-2, CLU, IGKV4-1, IGLC2, IGKV3-15
      immune response204.97E-09IL18R1, TNF, IL18RAP, CXCL5, IGLV1-44, IGKV5-2, JCHAIN, FASLG, PF4, IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, PPBP, FCAR, IGLV2-23, IGKV4-1, IL1B,

      KIR2DL3, IGKV3-15
      complement activation, classical pathway111.56E-08IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44, IGLV2-23, IGKV5-2, CLU, IGKV4-1, IGLC2, IGKV3-15
      platelet activation107.55E-07VWF, GP6, F5, C6ORF25, PF4, GP1BA, ITGB3, TREML1,

      CLEC1B, GP9
      Fc-gamma receptor signaling pathway involved in phagocytosis101.74E-06IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44, IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      platelet degranulation93.36E-06VWF, APP, F5, PPBP, CLU, PF4, SPARC, ITGB3, ITGA2B
      blood coagulation115.08E-06PRKAR2B, VWF, GP6, F5, C6ORF25, PDGFC, GP1BA,

      PDGFD, ITGB3, GP9, PLAUR
      Fc-epsilon receptor signaling pathway102.71E-05IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44, IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      positive regulation of nitric oxide biosynthetic process63.54E-05ICAM1, TNF, CLU, IL1B, PTX3, KLF4
      proteolysis140.000NRIP3, IGLV1-44, IGKV5-2, ANPEP, MMP25, IGLV2-11,

      IGKV1-5, IGLV3-19, IGLV2-8, F5,

      IGLV2-23, IGKV4-1, IGLC2, IGKV3-15
      inflammatory response120.001SDC1, TNF, IL18RAP, PPBP, CXCL5, ANXA1, IL1B, PF4,

      PTX3, NLRP3, CD14, MMP25
      antibacterial humoral response50.001APP, ADM, HIST2H2BE, HIST1H2BJ, JCHAIN
      positive regulation of cell division50.001TAL1, PPBP, IL1B, PDGFC, PDGFD
      innate immune response120.001APP, CLU, ANXA1, JCHAIN, PADI4, TREM1, PTX3,

      NLRP3, IGLC2, TREML1, KLRD1, CD14
      extracellular matrix organization80.002ICAM1, VWF, APP, TNF, ITGB5, SPARC, ITGB3, ITGA2B
      response to glucocorticoid50.003SDC1, TNF, ADM, SPARC, ADAM9
      response to lipopolysaccharide70.003PPBP, ADM, CXCL5, FASLG, PF4, SPARC, TRIB1
      positive regulation of chemokine biosynthetic process30.003EGR1, TNF, IL1B
      negative regulation of extrinsic apoptotic signaling pathway in absence of ligand40.004TNF, MCL1, IL1B, PF4
      viral entry into host cell50.005ICAM1, LDLR, ITGB5, ANPEP, ITGB3
      response to yeast30.006APP, ADM, PTX3
      defense response to Gram-positive bacterium50.007APP, TNF, ADM, HIST2H2BE, HIST1H2BJ
      negative regulation of gene expression60.007TNF, LDLR, GAS2L1, ZNF503, MYADM, KLF4
      cell adhesion mediated by integrin30.007ICAM1, ITGB3, ADAM9
      positive regulation of membrane protein ectodomain proteolysis30.007TNF, IL1B, ADAM9
      positive regulation of interferon-gamma production40.008IL18R1, TNF, IL1B, CD14
      cell surface receptor signaling pathway80.01IL18RAP, ANXA1, GP1BA, TSPAN9, KLRF1, KLRD1,

      CD14, CLEC1B
      blood coagulation, intrinsic pathway30.011VWF, GP1BA, GP9
      positive regulation of leukocyte chemotaxis30.011PPBP, CXCL5, PF4
      integrin-mediated signaling pathway50.011C6ORF25, ITGB5, ITGB3, ADAM9, ITGA2B
      platelet formation30.012TAL1, C6ORF25, CLEC1B
      positive regulation of transcription from RNA polymerase II promoter170.012EGR1, NAMPT, TNF, EGR2, ABLIM3, PF4, MYBL1, NLRP3, AHR, TAL1, APP, SPX, BHLHA15, IL1B, MAML3, FOSL1, KLF4
      cellular response to hydrogen peroxide40.014IL18RAP, ANXA1, PDGFD, KLF4
      positive regulation of NF-kappaB import into nucleus30.014IL18R1, TNF, IL1B
      positive regulation of MAP kinase activity40.015TNF, PDE5A, PDGFC, PDGFD
      positive regulation of smooth muscle cell proliferation40.016NAMPT, TNF, PDGFD, ALOX12
      cellular response to lipopolysaccharide50.017ICAM1, TNF, NLRP3, CD14, ADAM9
      regulation of gastric acid secretion20.017SGK1, KCNQ1
      cell adhesion100.02ICAM1, VWF, APP, SCN1B, ITGB5, GP1BA, ITGB3, ADAM9, GP9, ITGA2B
      leukocyte migration50.022ICAM1, GP6, ESAM, TREM1, ITGB3
      regulation of cell proliferation60.024TAL1, SGK1, TNF, PPBP, ANXA1, PF4
      establishment or maintenance of microtubule cytoskeleton polarity20.026KIF2C, LMNA
      positive regulation of calcidiol 1-monooxygenase activity20.026TNF, IL1B
      positive regulation of phagocytosis30.026TNF, IL1B, PTX3
      positive regulation of gene expression70.028TNF, LDLR, ID1, IL1B, PF4, KLF4, ALOX12
      positive regulation of NF-kappaB transcription factor activity50.03ICAM1, TNF, CLU, IL1B, NLRP3
      lipopolysaccharide-mediated signaling pathway30.032TNF, IL1B, CD14
      sequestering of triglyceride20.035TNF, IL1B
      interleukin-1 beta production20.043IL1B, NLRP3
      positive regulation of fever generation20.043TNF, IL1B
      cell-matrix adhesion40.044ITGB5, ITGB3, ADAM9, ITGA2B
      transforming growth factor beta receptor signaling pathway40.047ID1, CLDN5, ITGB5, ADAM9
      positive regulation of cysteine-type endopeptidase activity involved in apoptotic process30.048TNF, NLRP3, ALOX12
      positive regulation of apoptotic process70.049TNF, ADM, CLU, FASLG, FOSL1, MELK, DUSP6
      platelet aggregation30.049GP1BA, ITGB3, ITGA2B
      Cellular Component
      plasma membrane684.45E-09SEPT5, IGLV1-44, LDLR, C6ORF25, CLDN5, FASLG,

      IGKV1-12, TSPAN9, MMP25, GLDC, GP9, ATP2B1, PRKAR2B, CTTN, APP, GP6, HOMER3, IGLV2-23, ZNF185, DLG3, PDGFC, ESAM, FAM129B, KLRD1, KCNQ1, ICAM1, SGK1, IL18RAP, SLCO4A1, WLS, NCR1,

      MYADM, PLAUR, IGLV2-11, IGKV1-5, SDC1, F5, COLQ,

      IGKV4-1, TREM1, KIR2DL3, EMP1, MELK, CLEC1B,

      ITGA2B, TNF, SCN1B, CALD1, IGKV5-2, ITGB5, GNG11,

      ITGB3, C2ORF88, IGLV2-8, IGLV3-19, GP1BA, KLRF1,

      IGKV3-15, IL18R1, ANXA1, SPARC, RAPH1, AQP10, FCAR, CPNE2, IGLC2, TREML1, CD14
      extracellular region341.16E-06LTBP1, SCN1B, TNF, IGLV1-44, CXCL5, IGKV5-2, CLU, JCHAIN, FASLG, PF4, IGKV1-12, IGLV2-8, APP, IGLV3-19, IGLV2-23, IL1B, PDGFC, PDGFD, PTX3, IGKV3-15, ANXA1, SPARC, NLRP3, IGLV2-11, VWF, IGKV1-5, F5, ADM, FCAR, PPBP, IGKV4-1, TREM1, IGLC2, CD14
      extracellular space302.25E-06NAMPT, TNF, CXCL5, FAM20C, CLU, JCHAIN, FASLG, PF4, ANPEP, APP, SPX, HIST1H2BJ, IL1B, DLG3, PDGFC, PDGFD, PTX3, ADAM9, ICAM1, ANXA1, SPARC, F5, PPBP, ADM, COLQ, HIST2H2BE, FRMD4B, IGLC2, CD14, CMTM5
      platelet alpha granule lumen76.55E-06VWF, APP, F5, PPBP, CLU, PF4, SPARC
      cell surface171.34E-05ICAM1, TNF, LDLR, CLU, ANXA1, ITGB5, SPARC, ITGB3,

      APP, SDC1, GP6, GP1BA, PDGFC, TREML1, INTU,

      ITGA2B, ADAM9
      extracellular exosome444.28E-05HIST1H2AC, NAMPT, CLU, FAM20C, CLDN5, JCHAIN,

      FASLG, ITGB5, ANPEP, ITGB3, ATP2B1, PRKAR2B,

      CTTN, APP, IGLV3-19, GP6, PGRMC1, IL1B, PDGFC,

      ESAM, GP1BA, FAM129B, PDGFD, TUBA1A, TUBB1,

      ADAM9, ICAM1, ANXA1, WLS, MYADM, PLAUR,

      IGLV2-11, PDZK1IP1, VWF, IGKV1-5, SDC1, HIST2H2BE,

      SH3BGRL2, CPNE2, IGLC2, CD14, XYLB, ALOX12,

      ITGA2B
      membrane raft93.55E-04ATP2B1, PRKAR2B, ICAM1, APP, TNF, SDPR, KCNQ1,

      MYADM, CD14
      external side of plasma membrane94.43E-04ICAM1, SDC1, TNF, LDLR, FASLG, ANPEP, IGLC2,

      KLRD1, ITGA2B
      focal adhesion110.002ICAM1, SDC1, CTTN, ZNF185, ANXA1, ITGB5, TSPAN9,

      ITGB3, ADAM9, PLAUR, ITGA2B
      blood microparticle70.002IGKV1-5, CLU, JCHAIN, IGKV4-1, IGLC2, IGKV3-15,

      ITGA2B
      platelet alpha granule membrane30.005SPARC, ITGB3, ITGA2B
      platelet alpha granule30.006VWF, SPARC, TREML1
      integral component of plasma membrane220.008ICAM1, TNF, LDLR, SLCO4A1, FASLG, ANPEP, TSPAN9,

      ITGB3, NCR1, AQP10, PLAUR, GP9, ATP2B1, APP, SDC1,

      GP6, FCAR, GP1BA, KLRF1, KIR2DL3, CLEC1B, ITGA2B
      phagocytic cup30.011TNF, PEAR1, ANXA1
      clathrin-coated pit40.011APP, CTTN, LDLR, LRP3
      anchored component of external side of plasma membrane30.014GGTA1P, GP1BA, CD14
      basolateral plasma membrane60.018ATP2B1, LDLR, ANXA1, DLG3, KCNQ1, ADAM9
      integrin complex30.021ITGB5, ITGB3, ITGA2B
      receptor complex50.022APP, LDLR, ITGB5, ITGB3, KLRD1
      dendritic shaft30.029PRKAR2B, APP, DLG3
      cytoplasm550.043NAMPT, MCM10, ISG20, PRKAR2B, CTTN, APP,

      HOMER3, SDPR, ZNF185, HIST1H2BJ, DLG3, PIWIL2,

      PDGFC, FAM129B, TUBB1, KCNQ1, EGR1, SGK1, EGR2,

      PADI4, UBE2C, NLRP3, AHR, SH2D2A, SDC1, ADM,

      HIST2H2BE, FRMD4B, PPP1R15A, INTU, XYLB,

      ALOX12, MCL1, ABLIM3, CLU, TRIB1, SPATS2, NCAPG,

      RNF165, STRIP2, SKA3, GP1BA, HRASLS2, ANXA1,

      LMNA, CDC20, SPARC, RAPH1, SH3BGRL2, GAS2L1,

      CPNE2, RFX2, TREML1, KLF4, DUSP6
      Molecular Function
      antigen binding121.30E-09IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, JCHAIN, IGKV4-1, KIR2DL3, IGLC2,

      IGKV3-15
      receptor activity111.82E-05ICAM1, IL18R1, GP6, IL18RAP, LDLR, ITGB5, ANPEP,

      TREM1, ITGB3, KIR2DL3, PLAUR
      serine-type endopeptidase activity117.17E-05IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, F5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      virus receptor activity50.003ICAM1, LDLR, ITGB5, ANPEP, ITGB3
      platelet-derived growth factor receptor binding30.007PDGFC, PDGFD, ITGB3
      collagen binding40.015VWF, GP6, SPARC, ADAM9
      extracellular matrix binding30.021SPARC, ITGB3, ITGA2B
      IgA binding20.026FCAR, JCHAIN
      Pathway
      KEGG pathway
      ECM-receptor interaction84.38E-05VWF, SDC1, GP6, ITGB5, GP1BA, ITGB3, GP9, ITGA2B
      Hematopoietic cell lineage84.38E-05TNF, IL1B, ANPEP, GP1BA, ITGB3, CD14, GP9, ITGA2B
      African trypanosomiasis40.006ICAM1, TNF, IL1B, FASLG
      Pertussis50.009TNF, CXCL5, IL1B, NLRP3, CD14
      Hypertrophic cardiomyopathy (HCM)50.011TNF, LMNA, ITGB5, ITGB3, ITGA2B
      Natural killer cell mediated cytotoxicity60.011ICAM1, TNF, FASLG, KIR2DL3, NCR1, KLRD1
      PI3K-Akt signaling pathway100.013VWF, SGK1, MCL1, ITGB5, FASLG, GNG11, PDGFC,

      PDGFD, ITGB3, ITGA2B
      Dilated cardiomyopathy50.014TNF, LMNA, ITGB5, ITGB3, ITGA2B
      Platelet activation60.014VWF, GP6, GP1BA, ITGB3, GP9, ITGA2B
      Malaria40.017ICAM1, SDC1, TNF, IL1B
      Cytokine-cytokine receptor interaction80.017IL18R1, TNF, IL18RAP, PPBP, CXCL5, IL1B, FASLG, PF4
      Pathogenic Escherichia coli infection40.018CTTN, TUBB1, TUBA1A, CD14
      Proteoglycans in cancer70.023SDC1, CTTN, TNF, ITGB5, FASLG, ITGB3, PLAUR
      Phagosome60.025FCAR, ITGB5, ITGB3, TUBB1, TUBA1A, CD14
      Inflammatory bowel disease (IBD)40.033IL18R1, TNF, IL18RAP, IL1B
      Arrhythmogenic right ventricular cardiomyopathy (ARVC)40.037LMNA, ITGB5, ITGB3, ITGA2B
      REACTOME pathway
      Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell172.07E-10ICAM1, IGLV1-44, IGKV5-2, NCR1, IGLV2-11, IGLV2-8,

      IGLV3-19, IGKV1-5, IGLV2-23, IGKV4-1, KLRF1, TREM1,

      KIR2DL3, TREML1, IGLC2, KLRD1, IGKV3-15
      Scavenging of heme from plasma114.03E-09IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, JCHAIN, IGKV4-1, IGLC2, IGKV3-15
      CD22 mediated BCR regulation102.14E-08IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      Fc epsilon receptor (FCERI) signaling102.14E-08IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      Classical antibody-mediated complement activation103.31E-08IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      Role of LAT2/NTAL/LAB on calcium mobilization108.38E-08IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      FCGR activation107.38E-08IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      Initial triggering of complement101.36E-07IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      Role of phospholipids in phagocytosis103.31E-07IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      FCERI mediated Ca+2 mobilization105.49E-07IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      FCERI mediated MAPK activation104.97E-07IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      Antigen activates B Cell Receptor (BCR) leading to generation of second messengers108.82E-07IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      Regulation of actin dynamics for phagocytic cup formation106.50E-06IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      CERI mediated NF-kB activation102.18E-05IGLV2-11, IGLV2-8, IGLV3-19, IGKV1-5, IGLV1-44,

      IGLV2-23, IGKV5-2, IGKV4-1, IGLC2, IGKV3-15
      Platelet degranulation91.01E-04VWF, APP, F5, PPBP, CLU, PF4, SPARC, ITGB3, ITGA2B
      Platelet Adhesion to exposed collagen45.69E-04VWF, GP6, GP1BA, GP9
      GP1b-IX-V activation signalling30.005VWF, GP1BA, GP9
      Mitotic Prometaphase60.007SPC24, KIF2C, CDC20, AURKB, TUBB1, TUBA1A
      ECM proteoglycans50.01APP, ITGB5, SPARC, ITGB3, ITGA2B
      GRB2:SOS provides linkage to MAPK signaling for Integrins30.012SPC24, KIF2C, CDC20, AURKB, TUBB1, TUBA1A
      p130Cas linkage to MAPK signaling for integrins30.012VWF, ITGB3, ITGA2B
      Resolution of Sister Chromatid Cohesion60.012VWF, ITGB3, ITGA2B
      Integrin cell surface interactions50.016ICAM1, VWF, ITGB5, ITGB3, ITGA2B
      RHO GTPases Activate Formins60.018SPC24, KIF2C, CDC20, AURKB, UBE2C, TUBB1, TUBA1A
      Separation of Sister Chromatids70.018SPC24, KIF2C, CDC20, AURKB, TUBB1, TUBA1A
      Cell surface interactions at the vascular wall40.023GP6, ESAM, PF4, TREM1
      Intrinsic Pathway of Fibrin Clot Formation30.025PRKAR2B, TUBB1, TUBA1A, INTU
      Hedgehog 'off' state40.025VWF, GP1BA, GP9
      Integrin alphaIIb beta3 signaling30.027VWF, ITGB3, ITGA2B
      Syndecan interactions30.037SDC1, ITGB5, ITGB3

      Construction of PPI network and module analysis

      To explore interactions among the DEG genes, STRING analysis was applied, and the most important modules were then screened and visualized using Cytoscape software. A PPI network containing 82 connected nodes (proteins) and 194 interaction edges (interactions of proteins), where the average degree of connectivity (i.e., average number of neighbours) was 4.732, is presented at Figure 5a. The hub nodes with the greatest number of neighbours (≥8), such as TNF, IL-1β, VWF and ITGB3 were identified (labelled in red in Figure 5a) and analysed by GO enrichment and pathway analyses (Table 6). The KEGG pathway analysis revealed that the hub genes were involved in the cytokine-cytokine receptor interaction, hematopoietic cell lineage, ECM-receptor interaction, platelet activation, cell division and other pathways. In addition, two significant modules with 10 nodes were obtained from the PPI network of DEGs using MCODE (Figure 5b and 5c). Enrichment analysis suggested that the genes in the first significant module (Figure 5b) were mainly associated with functional terms in the category BP, including cell division, cell proliferation and mitotic nuclear division. In the category CC, the genes in this module were significantly enriched in cytosol and nucleus, and in the category MF, the genes were mainly enriched in protein and ATP binding. The genes in the second module (Figure 5c) were significantly enriched in inflammatory response, chemokine-mediated signalling, platelet degranulation and activation, immune response and signal transduction in the category BP. In the category CC, the genes were significantly enriched in extracellular region, extracellular space and platelet alpha granule lumen and in the category MF, the genes were mainly enriched in chemokine activity and CXCR chemokine receptor binding. Furthermore, results from KEGG analysis demonstrated that the genes in this significant module were associated with chemokine signalling pathway and cytokine-cytokine receptor interaction (Table 7).
      Figure thumbnail gr5
      Figure 5PPI networks. (a) PPI network with 82 nodes. In the network, nodes represent proteins and lines (edges) represent the interactions between proteins. Red nodes represent the hub nodes with a large number of neighbours (≥8). (b) A first significant module with 12 nodes identified by MCODE. (c) A second significant module with 12 nodes identified by MCODE.
      Table 6GO enrichment and pathway analyses of hub genes. Hub genes identified in PPI network were used for GO enrichment and pathway analyses using DAVID database.
      GO TermGene_CountP-Value
      Biological process
      platelet degranulation75.39E-10
      platelet activation54.64E-06
      extracellular matrix organization53.80E-05
      cell division53.56E-04
      negative regulation of extrinsic apoptotic signaling pathway in absence of ligand36.29E-04
      platelet aggregation37.73E-04
      blood coagulation47.86E-04
      cell adhesion59.88E-04
      mitotic nuclear division40.002
      anaphase-promoting complex-dependent catabolic process30.003
      positive regulation of calcidiol 1-monooxygenase activity20.003
      sequestering of triglyceride20.004
      sister chromatid cohesion30.005
      positive regulation of fever generation20.005
      inflammatory response40.006
      regulation of establishment of endothelial barrier20.007
      positive regulation of protein phosphorylation30.007
      cytokine-mediated signaling pathway30.008
      regulation of chromosome segregation20.008
      negative regulation of lipid storage20.008
      immune response40.008
      positive regulation of ubiquitin protein ligase activity20.009
      positive regulation of chemokine biosynthetic process20.010
      protein ubiquitination involved in ubiquitin-dependent protein catabolic process30.010
      positive regulation of heterotypic cell-cell adhesion20.011
      regulation of cell proliferation30.015
      regulation of I-kappaB kinase/NF-kappaB signaling20.015
      positive regulation of membrane protein ectodomain proteolysis20.015
      positive regulation of vascular endothelial growth factor receptor signaling pathway20.016
      negative regulation of lipid catabolic process20.016
      cell-substrate adhesion20.017
      proteasome-mediated ubiquitin-dependent protein catabolic process30.018
      blood coagulation, intrinsic pathway20.018
      positive regulation of leukocyte chemotaxis20.018
      positive regulation of protein export from nucleus20.019
      positive regulation of NF-kappaB import into nucleus20.021
      regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle20.023
      positive regulation of interleukin-8 production20.026
      positive regulation of gene expression30.028
      positive regulation of phagocytosis20.029
      lipopolysaccharide-mediated signaling pathway20.032
      protein kinase B signaling20.033
      positive regulation of nitric oxide biosynthetic process20.043
      positive regulation of interleukin-6 production20.045
      positive regulation of interferon-gamma production20.046
      positive regulation of cell division20.047
      Cellular component
      platelet alpha granule lumen51.72E-07
      cell surface50.001
      extracellular region70.002
      kinetochore30.003
      extracellular space60.006
      membrane70.012
      platelet alpha granule membrane20.012
      spindle midzone20.018
      anaphase-promoting complex20.021
      cytosol80.023
      ER to Golgi transport vesicle20.024
      integrin complex20.025
      integral component of plasma membrane50.038
      spindle microtubule20.040
      cytoplasmic microtubule20.047
      Molecular function
      protein binding160.002
      protease binding30.005
      identical protein binding50.006
      extracellular matrix binding20.026
      enzyme binding30.043
      chemokine activity20.048
      KEGG pathway
      Hematopoietic cell lineage51.09E-05
      ECM-receptor interaction43.96E-04
      Platelet activation40.001
      Cytokine-cytokine receptor interaction40.008
      Hypertrophic cardiomyopathy (HCM)30.008
      Dilated cardiomyopathy30.009
      Osteoclast differentiation30.021
      Alzheimer's disease30.033
      Focal adhesion30.048
      Table 7GO enrichment and pathway analyses of significant modules. Two significant modules identified in PPI network were used for GO enrichment and pathway analyses using DAVID database.
      GO TermGene_CountP-Value
      Module 1
      Biological process
      cell division64.50E-07
      mitotic nuclear division55.52E-06
      sister chromatid cohesion41.83E-05
      anaphase-promoting complex-dependent catabolic process37.70E-04
      cell proliferation47.82E-04
      protein ubiquitination involved in ubiquitin-dependent protein catabolic process30.003
      regulation of chromosome segregation20.004
      positive regulation of ubiquitin protein ligase activity20.005
      proteasome-mediated ubiquitin-dependent protein catabolic process30.005
      regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle20.012
      negative regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle20.037
      positive regulation of ubiquitin-protein ligase activity involved in regulation of mitotic cell cycle transition20.040
      Cellular Component
      kinetochore36.88E-04
      cytosol70.002
      anaphase-promoting complex20.011
      spindle microtubule20.022
      nucleus70.024
      cytoplasmic microtubule20.025
      nucleoplasm50.036
      condensed chromosome kinetochore20.042
      Molecular Function
      protein binding100.003
      ATP binding40.039
      KEGG pathway
      Ubiquitin mediated proteolysis20.039
      Module 2
      Biological process
      platelet degranulation73.80E-12
      positive regulation of leukocyte chemotaxis33.89E-05
      response to lipopolysaccharide47.36E-05
      chemokine-mediated signaling pathway36.23E-04
      inflammatory response48.66E-04
      platelet activation30.002
      regulation of cell proliferation30.004
      extracellular matrix organization30.005
      response to lead ion20.007
      G-protein coupled receptor signaling pathway40.010
      immune response30.020
      signal transduction40.020
      innate immune response30.021
      response to peptide hormone20.023
      positive regulation of tumor necrosis factor production20.025
      negative regulation of angiogenesis20.033
      Cellular Component
      platelet alpha granule lumen74.75E-14
      extracellular region93.03E-08
      extracellular space83.74E-07
      cell surface40.002
      platelet alpha granule20.007
      ER to Golgi transport vesicle20.013
      mitochondrial membrane20.045
      Molecular Function
      chemokine activity32.93E-04
      CXCR chemokine receptor binding20.005
      collagen binding20.032
      chaperone binding20.042
      KEGG pathway
      Chemokine signaling pathway43.66E-04
      Cytokine-cytokine receptor interaction30.017

      Discussion

      AD is a complex disease associated with immunological and epidermal barrier dysfunctions. Most of our knowledge in the field of AD is based on studies performed in adult AD patients although remarkable differences between pediatric and adult AD have been shown recently. Therefore, it is of great importance to identify molecular basis of pediatric AD and elucidate biomarkers that could help to identify young patients at risk at an earlier stage of life and to explore new therapies in pediatric AD. Given that more than half of all cases of atopic dermatitis begin during the first year of life we aimed to discover signature biomarkers of AD in infants. Considering that skin biopsies are very difficult to obtain at such a young age and that AD generates a systemic immunological response and blood is non-invasive source of biological tissue, we analysed blood profiles of pediatric AD patients at first year of life.
      Using RNA sequencing transcriptome profile of peripheral blood cells obtained from AD or healthy infants we identified 178 genes differentially expressed in pediatric AD patients: 115 were up-regulated and 63 were down-regulated. To further investigate the functions of the DEGs, GO functional annotation and pathway enrichment analysis were used based on the DAVID database. The GO analysis demonstrated that DEGs were associated with immune responses, inflammatory responses, regulation of immune responses and platelet activation, which are all known to be AD related. The results of the pathway analysis indicated that the DEGs were enriched in immunoregulatory interactions between a lymphoid and a non-lymphoid cell, hematopoietic cell lineage, PI3K-Akt signalling pathway, cytokine-cytokine receptor interaction, Natural killer cell mediated cytotoxicity and platelet activation.
      Randomly selected differentially expressed genes were further validated in larger number of samples collected from AD patients at first year of life using RT-qPCR. Among highly upregulated genes we identified IL-1β, previously shown to be upregulated in serum of adult AD (
      • Thijs J.L.
      • Strickland I.
      • Bruijnzeel-Koomen C.
      • Nierkens S.
      • Giovannone B.
      • Knol E.F.
      • et al.
      Serum biomarker profiles suggest that atopic dermatitis is a systemic disease.
      ) and stratum corneum of our pediatric AD collection as reported previously (
      • McAleer M.A.
      • Jakasa I.
      • Hurault G.
      • Sarvari P.
      • McLean W.H.I.
      • Tanaka R.J.
      • et al.
      Systemic and stratum corneum biomarkers of severity in infant atopic dermatitis include markers of innate and T helper cell-related immunity and angiogenesis.
      ). It has been shown to be involved in AD development (
      • Bernard M.
      • Carrasco C.
      • Laoubi L.
      • Guiraud B.
      • Rozieres A.
      • Goujon C.
      • et al.
      IL-1beta induces thymic stromal lymphopoietin and an atopic dermatitis-like phenotype in reconstructed healthy human epidermis.
      ). IL-1β is a potent proinflammatory cytokine that can mediate inflammatory responses by supporting T-cell survival, upregulation of the IL-2 receptor on lymphocytes, enhancing antibody production of B cells and by promoting B-cell proliferation and T-helper 17 cell differentiation (
      • Lamkanfi M.
      • Vande Walle L.
      • Kanneganti T.D.
      Deregulated inflammasome signaling in disease.
      ). IL-1β activity is regulated at multiple levels, one of which is controlled by inflammasomes (Schroder and Tschopp, 2010). Recent findings suggest that inflammasome-dependent IL-1β activation plays a role in a variety of disorders including AD. Of note, among upregulated differentially expressed genes we identified NLRP3 - one of the important inflammasome proteins.
      Another interesting upregulated DEG in our pediatric patients was TREM1 (Triggering receptor expressed on myeloid cells 1). Recently, it has been reported to be highly expressed in lesional skin and serum of adult AD (
      • Suarez-Farinas M.
      • Ungar B.
      • Correa da Rosa J.
      • Ewald D.A.
      • Rozenblit M.
      • Gonzalez J.
      • et al.
      RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications.
      ). It has also been reported to be expressed in psoriasis and has been suggested to be a therapeutic target to modify the effects of inflammatory myeloid DCs in psoriasis (
      • Hyder L.A.
      • Gonzalez J.
      • Harden J.L.
      • Johnson-Huang L.M.
      • Zaba L.C.
      • Pierson K.C.
      • et al.
      TREM-1 as a potential therapeutic target in psoriasis.
      ). TREM-1 (CD354) is a cell-surface receptor that is expressed on various types of cells: monocytes, neutrophils NK cells, dendritic cells, B and T cells, has been implicated in innate and adaptive immune responses. Activation of TREM-1 was shown to result in the production of a variety of inflammatory cytokines, including TNF, IL6, MCP1, IL-1β and amplification of TLR-initiated inflammation (
      • Roe K.
      • Gibot S.
      • Verma S.
      Triggering receptor expressed on myeloid cells-1 (TREM-1): a new player in antiviral immunity?.
      ). Of interest, TNF was highly expressed in blood cells of our pediatric AD collection. It has been shown to be involved in inflammatory processes in atopic dermatitis (
      • Jacobi A.
      • Antoni C.
      • Manger B.
      • Schuler G.
      • Hertl M.
      Infliximab in the treatment of moderate to severe atopic dermatitis.
      ,
      • Sumimoto S.
      • Kawai M.
      • Kasajima Y.
      • Hamamoto T.
      Increased plasma tumour necrosis factor-alpha concentration in atopic dermatitis.
      ). Furthermore, TNF together with the Th2 cytokines induced AD-like features in a skin model (
      • Danso M.O.
      • van Drongelen V.
      • Mulder A.
      • van Esch J.
      • Scott H.
      • van Smeden J.
      • et al.
      TNF-alpha and Th2 cytokines induce atopic dermatitis-like features on epidermal differentiation proteins and stratum corneum lipids in human skin equivalents.
      ). In addition, TNF together with TNF-like weak inducer of apoptosis induced keratinocyte apoptosis in AD skin (
      • Zimmermann M.
      • Koreck A.
      • Meyer N.
      • Basinski T.
      • Meiler F.
      • Simone B.
      • et al.
      TNF-like weak inducer of apoptosis (TWEAK) and TNF-alpha cooperate in the induction of keratinocyte apoptosis.
      ).
      Another interesting group of genes found to be upregulated in bloods of pediatric AD patients included early growth response genes (EGR1, EGR2 and EGR3), a family of zinc-finger transcription factors. EGR1, an important player in the regulation of cell growth, differentiation, cell survival and immune responses, has been reported to be upregulated in psoriatic skin lesions (
      • Jeong S.H.
      • Kim H.J.
      • Jang Y.
      • Ryu W.I.
      • Lee H.
      • Kim J.H.
      • et al.
      Egr-1 is a key regulator of IL-17A-induced psoriasin upregulation in psoriasis.
      ). EGR 2/3 are known to play a crucial role in regulation of the immune system. They control inflammation, regulate B and T cell function in adaptive immune responses and have been suggested to be involved in preventing the development of autoimmune disease (
      • Morita K.
      • Okamura T.
      • Sumitomo S.
      • Iwasaki Y.
      • Fujio K.
      • Yamamoto K.
      Emerging roles of Egr2 and Egr3 in the control of systemic autoimmunity.
      ) and limiting immunopathology during productive adaptive immune responses (Li et al., 2012). Notably, EGR2 is located in a susceptibility locus for AD identified by genome-wide association study in the Japanese population (
      • Hirota T.
      • Takahashi A.
      • Kubo M.
      • Tsunoda T.
      • Tomita K.
      • Sakashita M.
      • et al.
      Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population.
      ).
      Among downregulated genes we identified IL18R1 and IL18RAP, also previously found to be associated with AD (
      • Hirota T.
      • Takahashi A.
      • Kubo M.
      • Tsunoda T.
      • Tomita K.
      • Sakashita M.
      • et al.
      Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population.
      ). IL18RAP enhances the IL18-binding activity of the IL18 receptor (IL18R1) and plays a role in signalling by Interleukin (IL)-18 (IL18), a pleiotropic immune regulator. IL-18 plays a strong proinflammatory role by inducing interferon (IFN)-γ. Previous studies have implicated IL-18 in the pathogenesis of AD. It has been shown to contribute to the spontaneous development of atopic dermatitis-like skin lesions in a transgenic mouse model (
      • Konishi H.
      • Tsutsui H.
      • Murakami T.
      • Yumikura-Futatsugi S.
      • Yamanaka K.
      • Tanaka M.
      • et al.
      IL-18 contributes to the spontaneous development of atopic dermatitis-like inflammatory skin lesion independently of IgE/stat6 under specific pathogen-free conditions.
      ). It has been reported to be elevated in skin lesions of adults with AD. In our previous study we analysed plasma and stratum corneum biomarkers in this collection of patients and showed that IL18 was observed in very high levels in the stratum corneum of pediatric patients however no difference was observed in IL18 plasma levels (
      • McAleer M.A.
      • Jakasa I.
      • Hurault G.
      • Sarvari P.
      • McLean W.H.I.
      • Tanaka R.J.
      • et al.
      Systemic and stratum corneum biomarkers of severity in infant atopic dermatitis include markers of innate and T helper cell-related immunity and angiogenesis.
      ). Another study showed that peripheral blood mononuclear cells from patients with atopic dermatitis have a decreased IL18 expression and capacity to produce IFN-γ which is inversely correlated with serum IgE concentrations (
      • Higashi N.
      • Gesser B.
      • Kawana S.
      • Thestrup-Pedersen K.
      Expression of IL-18 mRNA and secretion of IL-18 are reduced in monocytes from patients with atopic dermatitis.
      ) suggesting an IL18 role in the skewing of the immune system in patients with AD.
      Another gene significantly downregulated in AD infants was GLDC. GLDC, glycine metabolism and the metabolic enzyme glycine decarboxylase, is a key enzyme of the mitochondrial glycine cleavage system (
      • Hiraga K.
      • Kikuchi G.
      The mitochondrial glycine cleavage system. Functional association of glycine decarboxylase and aminomethyl carrier protein.
      ). GLDC plays important role in many human cancers (
      • Zhang W.C.
      • Shyh-Chang N.
      • Yang H.
      • Rai A.
      • Umashankar S.
      • Ma S.
      • et al.
      Glycine decarboxylase activity drives non-small cell lung cancer tumor-initiating cells and tumorigenesis.
      ). It has been shown to be differentially expressed in psoriatic skin (
      • Rittie L.
      • Tejasvi T.
      • Harms P.W.
      • Xing X.
      • Nair R.P.
      • Gudjonsson J.E.
      • et al.
      Sebaceous Gland Atrophy in Psoriasis: An Explanation for Psoriatic Alopecia?.
      ). Interestingly, GLDC has been reported to be differentially expressed in AD-like reconstructed human epidermis (
      • Céline E.
      • Emilie F.
      • Evelyne D.V.
      • Olivier S.
      • Vale´rie D.G.
      • David B.
      • et al.
      Deletion of TNFAIP6 Gene in Human Keratinocytes Demonstrates a Role for TSG-6 to Retain Hyaluronan Inside Epidermis.
      ) suggesting its involvement in AD development.
      A protein-protein interaction network among the screened DEGs was predicted. The PPI analysis allowed us to determine significant modules and hub genes. In the resulting PPI network, 18 hub genes with the highest degree of connectivity were selected, which included IL-1β, VWF, PF4, ITGB3, ITGA2B, APP, F5, AURKB, SKA3, MELK, CDC20, PPBP, NCAPG, GTSE1, KIF2C, GP1BA, UBE2C and TNF. Pathway analysis revealed that the hub genes were involved in the cytokine-cytokine receptor interaction, hematopoietic cell lineage, ECM-receptor interaction, cell division, platelet activation and other pathways. In addition, two significant modules were identified. The genes in the first significant module were mainly associated with cell division, cell proliferation and mitotic nuclear division. The genes in the second module were significantly enriched in inflammatory response, chemokine-mediated signalling, platelet degranulation and activation, immune response and signal transduction and were associated with chemokine signalling pathway and cytokine-cytokine receptor interaction.
      We wondered if the hub genes could be linked to AD or other skin inflammatory diseases. The important role of IL-1β and TNF in AD has been shown earlier in this report. Von Willebrand factor (VWF), a key player in hemostasis, has been reported in relation to cutaneous inflammation (
      • Hillgruber C.
      • Steingraber A.K.
      • Poppelmann B.
      • Denis C.V.
      • Ware J.
      • Vestweber D.
      • et al.
      Blocking von Willebrand factor for treatment of cutaneous inflammation.
      ). Increased expression of Platelet Factor 4 (PF4) has been proposed to play an important role in the etiology of atopic dermatitis (
      • Watanabe O.
      • Natori K.
      • Tamari M.
      • Shiomoto Y.
      • Kubo S.
      • Nakamura Y.
      Significantly elevated expression of PF4 (platelet factor 4) and eotaxin in the NOA mouse, a model for atopic dermatitis.
      ). Increased Integrin β3 (ITGB3) expression has been reported in Th17-associated skin inflammatory diseases such as psoriasis (
      • Goedkoop A.Y.
      • Kraan M.C.
      • Picavet D.I.
      • de Rie M.A.
      • Teunissen M.B.
      • Bos J.D.
      • et al.
      Deactivation of endothelium and reduction in angiogenesis in psoriatic skin and synovium by low dose infliximab therapy in combination with stable methotrexate therapy: a prospective single-centre study.
      ) and psoriatic arthritis (
      • Canete J.D.
      • Pablos J.L.
      • Sanmarti R.
      • Mallofre C.
      • Marsal S.
      • Maymo J.
      • et al.
      Antiangiogenic effects of anti- tumor necrosis factor alpha therapy with infliximab in psoriatic arthritis.
      ). A significant increase in platelet-leukocyte aggregates expressing ITGA2B were found in bloods of mice with chronic hapten-induced allergic dermatitis (
      • Tamagawa-Mineoka R.
      • Katoh N.
      • Ueda E.
      • Takenaka H.
      • Kita M.
      • Kishimoto S.
      The role of platelets in leukocyte recruitment in chronic contact hypersensitivity induced by repeated elicitation.
      ). Pro-platelet basic protein (PPBP) has been found to be important for regulating excessive inflammation in psoriasis (
      • Oka T.
      • Sugaya M.
      • Takahashi N.
      • Takahashi T.
      • Shibata S.
      • Miyagaki T.
      • et al.
      CXCL17 Attenuates Imiquimod-Induced Psoriasis-like Skin Inflammation by Recruiting Myeloid-Derived Suppressor Cells and Regulatory T Cells.
      ). Elevated AURKB expression in lesional psoriatic tissues has been suggested to contribute to the development of psoriasis (
      • Liu Y.
      • Luo W.
      • Chen S.
      Comparison of gene expression profiles reveals aberrant expression of FOXO1, Aurora A/B and EZH2 in lesional psoriatic skins.
      ). Actively proliferating UBE2C+TOP2A+ type 2/type 22 T cells were expanded in lesional AD skin and were either absent or less abundant in nonlesional and healthy samples (
      • He H.
      • Suryawanshi H.
      • Morozov P.
      • Gay-Mimbrera J.
      • Del Duca E.
      • Kim H.J.
      • et al.
      Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis.
      ). These finding suggest that identified hub genes could be considered as important candidates for prognostic and therapeutic targets of pediatric AD.
      Taken together, this study demonstrated that blood gene expression profile identified distinct key genes and pathways of early onset pediatric AD. Observed dramatic changes in the PBMC transcriptome were predominantly related to immune responses in AD. New data assessed from the current study may help better understand processes leading to AD in infants and may serve for the development of novel treatment possibilities. However, to decipher a full mechanism involved in paediatric AD pathogenesis, skin RNA profile should be further investigated in infants with AD. Blood profile along with skin profile in infants with AD could provide us with a larger number of potential biomarkers which may contribute to AD prediction, risk of comorbidity development and responses to AD treatment in infants.

      Materials and Methods

      Patients

      We recruited infants under 12 months of age with moderate-to-severe atopic dermatitis who were treatment naive (apart from the use of emollients and hydrocortisone 1% cream or ointment) along with age-matched healthy controls. The study was approved by the Research Ethics Committee of Children's Health Ireland at Crumlin (CHI), Dublin and was conducted in compliance with the Helsinki Declaration. Written informed consent was given by parents or legal guardians for all study subjects. The age of onset of AD was recorded. Severity was assessed by the SCORing Atopic Dermatitis index (SCORAD). Clinical and demographic features are summarized in Table 8. Analysis of cytokine and miRNA biomarkers in this collection has previously been reported (
      • McAleer M.A.
      • Jakasa I.
      • Hurault G.
      • Sarvari P.
      • McLean W.H.I.
      • Tanaka R.J.
      • et al.
      Systemic and stratum corneum biomarkers of severity in infant atopic dermatitis include markers of innate and T helper cell-related immunity and angiogenesis.
      ,
      • Nousbeck J.
      • McAleer M.A.
      • Hurault G.
      • Kenny E.
      • Harte K.
      • Kezic S.
      • et al.
      MicroRNA analysis of childhood atopic dermatitis reveals a role for miR-451a.
      ).
      Table 8Clinical and demographical characteristics of the study participants
      Patients with ADHealthy controls
      Total2717
      Sex
      Male1811
      Female96
      Age (months)
      Average6.97.94
      Range3-103-12
      Age of AD onset (weeks)
      Average9
      Range4-20
      SCORAD
      Average49.4
      Range23.4-91.3
      AD, Atopic Dermatitis; SCORAD, Scoring Atopic Dermatitis

      PBMC preparation and RNA isolation

      PBMCs were isolated from whole blood, as previously described (
      • Nousbeck J.
      • McAleer M.A.
      • Hurault G.
      • Kenny E.
      • Harte K.
      • Kezic S.
      • et al.
      MicroRNA analysis of childhood atopic dermatitis reveals a role for miR-451a.
      ) using histopaque double-gradient density centrifugation (Sigma-Aldrich, MO, USA) and cryopreserved for further analysis. Total RNA was isolated from PBMCs according to RNeasy Mini Kit protocol (Qiagen, Germany). RNA concentrations, integrity and quality of RNA were evaluated using Qubit fluorometer (Thermo Fisher Scientific, MA, USA) and RNA 6000 Nano Lab Chips on an Agilent 2100 Bioanalyzer (Agilent technologies, CA, US). RNA samples with optimal RNA integrity number (RIN) values (≥8) were considered to construct libraries for sequencing.

      RNA sequencing, Data Processing and Differential expression analysis

      Library preparation (using Illumina TruSeq stranded mRNAseq library kit) and sequencing was conducted by Edinburgh Genomics, The University of Edinburgh. The sequencing of libraries was performed with Illumina NovaSeq 6000 (100 cycles, 50 bp paired-end sequencing). Sequencing reads showed excellent quality with the overall Q30 above 94%. After sequencing, reads were trimmed using Cutadapt (
      • Martin M.
      Cutadapt removes adapter sequences from high-throughput sequencing reads.
      ) and clean paired end reads were mapped to human reference genome GRCh38 using STAR software (
      • Dobin A.
      • Davis C.A.
      • Schlesinger F.
      • Drenkow J.
      • Zaleski C.
      • Jha S.
      • et al.
      STAR: ultrafast universal RNA-seq aligner.
      ). Number of reads for each gene was counted using featureCounts (
      • Liao Y.
      • Smyth G.K.
      • Shi W.
      featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.
      ) and the count matrix was used for differential expression analysis. Differential expression was performed using package DESeq2 in R software (version 3.5.2), considering an expression of greater than 20 read counts in at least 25% of the samples, a cut-off of at least 1.5-fold change in expression and a Benjamini-Hochberg corrected false discovery rate (FDR) < 0.05.

      Reverse transcription quantitative real-time PCR

      The differentially expressed genes were further verified using reverse transcription quantitative real-time PCR (RT-qPCR). Briefly, total RNA was reverse transcribed using SensiFAST cDNA synthesis kit (Bioline, UK). cDNA PCR amplification were carried out using the SensiFAST SYBR® Hi-ROX Kit (Bioline) on 7900HT Fast Real-Time PCR System with gene specific intron-crossing oligonucleotide pairs. Primers are available in Table 9. Results were normalized to GAPDH mRNA levels. Triplicates of each reaction were performed as the mean ± standard deviation. Relative quantification of target mRNA expression was performed using the 2-ΔΔCT method (
      • Livak K.J.
      • Schmittgen T.D.
      Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.
      ). The normalized expression data were log2-transformed before data analysis.
      Table 9Primers sequences for RT-qPCR
      GeneForward primerReverse primer
      IL18RAPCCAGGGGTGAATAATTCTGGGTCATTTGTCTGGGGCTTAACTTCT
      IL1BTTCGACACATGGGATAACGAGGTTTTGCTGTGAGTCCCGGAG
      TNFCCTCTCTCTAATCAGCCCTCTGGAGGACCTGGGAGTAGATGAG
      TREM1GAACTCCGAGCTGCAACTAAATCTAGCGTGTAGTCACATTTCAC
      EGR3CCAACGACATGGGCTCCATTGGTCTCCAGAGGGGTAATAGG
      GAPDHGAGTCAACGGATTTGGTCGTGACAAGCTTCCCGTTCTCAGCC

      Gene ontology enrichment and pathway analysis

      Differentially expressed genes (DEGs) were submitted to Visualisation and Integrated Discovery analysis (DAVID; ver. 6.8)(Huang da et al., 2009) for gene ontology (GO) term enrichment and pathway analysis using default parameters. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway analyses were used to determine the pathways of DEGs between two groups. Any GO terms and pathways with P-values less than 0.05 were considered significantly enriched.

      Construction of protein-protein interaction (PPI) network and module analysis

      Association between DEGs were investigated by using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (
      • Szklarczyk D.
      • Morris J.H.
      • Cook H.
      • Kuhn M.
      • Wyder S.
      • Simonovic M.
      • et al.
      The STRING database in 2017: quality-ontrolled protein-protein association networks, made broadly accessible.
      ) STRING10.5 (http://string-db.org/) and a confidence score of >0.6 was considered to indicate significance. Cytoscape software (version 3.6.1) was then used to visualize the PPI network (
      • Shannon P.
      • Markiel A.
      • Ozier O.
      • Baliga N.S.
      • Wang J.T.
      • Ramage D.
      • et al.
      Cytoscape: a software environment for integrated models of biomolecular interaction networks.
      ). In the network, nodes represented proteins and lines (edges) represented the interactions. In addition, the most significant modules were identified with the plug-in Molecular Complex Detection (MCODE; version 1.5.1) with the following settings: Degree cutoff, 2; node score cutoff, 0.2; k-core, 2; and maximum depth, 100, and the following criteria: MCODE score >5; number of nodes >5. Finally, the hub genes in the PPI network were determined, defined as those with a degree of connectivity of ≥8.

      Data availability statement

      Datasets related to this article can be found at: https://osf.io/hfwyt/?view_only=ececc20afd5d42e2806b11483edb9d0d , hosted at The Open Science Framework

      Uncited reference

      • Huang da W.
      • Sherman B.T.
      • Lempicki R.A.
      Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.
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      .

      Acknowledgements

      We are especially grateful to all members of the family for their participation in this study. We also thank the Edinburgh Genomics, The University of Edinburgh that conducted RNA sequencing. Our work was funded by the National Children’s Research Centre through grants to JN and ADI.

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