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Total particulate matter concentration skews cigarette smoke's gene expression profile

Anna Dvorkin-Gheva, Gilles Vanderstocken, Ali Önder Yildirim, Corry-Anke Brandsma, Ma'en Obeidat, Yohan Bossé, John A. Hassell, Martin R. Stampfli
ERJ Open Research 2016 2: 00029-2016; DOI: 10.1183/23120541.00029-2016
Anna Dvorkin-Gheva
1Dept of Pathology and Molecular Medicine, McMaster Immunology Research Centre, Hamilton, ON, Canada
2Centre for Functional Genomics, McMaster University, Hamilton, ON, Canada
9These authors contributed equally
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Gilles Vanderstocken
1Dept of Pathology and Molecular Medicine, McMaster Immunology Research Centre, Hamilton, ON, Canada
9These authors contributed equally
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Ali Önder Yildirim
3Institute of Lung Biology and Disease (iLBD), Helmholtz Zentrum München, Neuherberg, Germany, Member of the German Center for Lung Research (DZL)
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Corry-Anke Brandsma
4University of Groningen, University Medical Center Groningen, GRIAC research institute, Groningen, The Netherlands
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Ma'en Obeidat
5The University of British Columbia Center for Heart Lung Innovation, St. Paul's Hospital, Vancouver, BC, Canada
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Yohan Bossé
6Centre de Recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Quebec City, QC, Canada
7Dept of Molecular Medicine, Laval University, Quebec City, QC, Canada
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John A. Hassell
2Centre for Functional Genomics, McMaster University, Hamilton, ON, Canada
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Martin R. Stampfli
1Dept of Pathology and Molecular Medicine, McMaster Immunology Research Centre, Hamilton, ON, Canada
8Dept of Medicine, Firestone Institute of Respiratory Health at St. Joseph's Healthcare, McMaster University, Hamilton, ON, Canada
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  • For correspondence: stampfli@mcmaster.ca
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  • FIGURE 1
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    FIGURE 1

    Combining datasets. a) Clustering of samples before the batch effect removal procedure (Distance-Weighted Discrimination; DWD) was performed. b) Clustering of samples after DWD. c) Clustering of samples after removing GSE8790 and samples from two control mice that clustered with the samples obtained from smoke-exposed mice. Samples were clustered using average linkage and Spearman correlation distance.

  • FIGURE 2
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    FIGURE 2

    Genes differentially expressed between the smoke-exposed mice in each model and the control group. Increasing fold changes are highlighted with the increasing level of shading. In each category the genes are sorted by the fold-change values obtained from GSE18344. The colour coding is different for the all fold changes and fold change ≥2.

  • FIGURE 3
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    FIGURE 3

    Functional interaction network based on the 234 differentially expressed genes. a) Functional network with annotated gene clusters. Each cluster is contained within a separate NTA: grey oval, with the ID superimposed on it. b) Expression of the gene clusters in each of the mouse models. Clusters containing at least one upregulated gene are marked in pink; clusters containing at least one downregulated gene are marked in green; clusters containing both up- and downregulated genes are marked in grey. Clusters not containing any differentially expressed genes are marked by black contour. Models are sorted based on the total particulate matter used in the model: from lowest (lower left corner) to highest (upper right corner).

  • FIGURE 4
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    FIGURE 4

    Clusters from the functional network. a) Models clustered by the numbers of differentially expressed genes in each of the clusters. Numbers of the differentially expressed genes are indicated on the heatmap. Branches of the vertical dendrogram are named using the cluster IDs (0–16). Each cluster is functionally annotated based on the Pathway Enrichment and Gene Ontology analyses (see table 3). Cluster 9 is marked by “–” since it did not show any significant representation of any of the known processes. #: cluster contained both up- and downregulated genes. b) Correlation between total particulate matter (TPM) and number of differentially regulated clusters across mouse models. *: indicates mouse models. TLR: Toll-like receptor; MHC: major histocompatibility complex; NOD: nucleotide-binding oligomerisation domain. r2 =0.9348, p-value = 0.0072.

  • FIGURE 5
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    FIGURE 5

    Regulation of clusters in human datasets. a) Expression of the gene clusters in each human cohort. Clusters containing at least one upregulated gene are marked in pink; clusters containing at least one downregulated gene are marked in green; clusters containing both up- and downregulated genes are marked in grey. Clusters not containing any differentially expressed genes are marked by black contour. b) Models clustered by the numbers of differentially expressed genes in each of the clusters across human data and mouse models. Numbers of the differentially expressed genes are indicated on the heatmap. Branches of the vertical dendrogram are named using the cluster IDs (0–16). Each cluster is functionally annotated based on the Pathway Enrichment and Gene Ontology analyses (see table 3). Cluster 9 is marked by “–” since it did not show any significant representation of any of the known processes. #: cluster contained both up- and downregulated genes. c) Correlation between total particulate matter (TPM) and number of differentially regulated clusters across mouse models and human datasets. Human datasets are marked by horizontal lines indicating the number of regulated clusters in each dataset. *: indicates mouse models. Laval: Laval University; GRNG: Groningen; UBC: University of British Columbia; TLR: Toll-like receptor; MHC: major histocompatibility complex; NOD: nucleotide-binding oligomerisation domain.

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  • TABLE 1

    Mouse datasets downloaded from Gene Expression Omnibus (GEO)

    GEO accession [ref.]Exposure duration weeksStrainSexAge weeksSamplesTPM µg·L−1Cigarette
    GSE8790 [9]6A/JMale83 RA/3 CS902R4F
    GSE33561 [11]6–7AKR/JMale6–83 RA/3 CS902R4F
    GSE33512 [7]16C57BL/6Male124 RA/4 CS100–1201R3F
    GSE52509 [10]16C57BL/6Female8–103 RA/3 CS5003R4F
    GSE1773712C57BL/6FemaleNA5 FA/5 CSNANA
    GSE55127 [6]8BALB/CFemale6–85 RA/5 CS>6003R4F#
    GSE18344 [8]8CD-1Female134 RA/4 CS7502R4F
    • TPM: total particulate matter; RA: room air; FA: forced air; CS: cigarette smoke; NA: not applicable. #: filters removed.

  • TABLE 2

    Functional annotation of the gene clusters

    ClusterGenes in clusterFunction derived from Pathway Enrichment
    and Gene Ontology analyses
    Gene list
    06TLR signalling: unfolded protein responseDNAJB1, DNAJC5B, HSPA1A, HSPB1, SAA1, TLR2
    102TLR signallingCD86, CXCL9
    16Chemokines: signallingCCL17, CCL22, CCL4, CCR1, CXCL13, CXCL5
    73Chemokines: activityCCL2, CCL3, CCL7
    112Chemokines: NOD-like receptor signallingCXCL1, CXCL2
    25Muscle contractionACTN2, MYH6, MYL1, TCAP, TNNI3
    35IntegrinsCD14, ITGAM, ITGAX, ITGB2, SPON2
    44Circadian clockARNTL, DBP, NR1D1, PER1
    53Osteoclast differentiationCLEC5A, TREM2, TYROBP
    63Interleukins: FC-epsilon receptor I signallingFCER1G, FCGR2B, IL12B
    142InterleukinsIL1R2, IL1RN
    83p53 pathwayIGF1, IGFBP3, IGFBP6
    93–AHRR, ARNT2, MAFB
    122Class I MHC mediated antigen processing and presentationCYBA, NCF4
    132Immune response (complement pathway)C1QA, C1QB
    152Glutathione metabolismGCLC, GCLM
    162Heterotrimeric G-protein signalling pathway-Gq alpha and Go alpha mediated pathwayRGS1, RGS16
    • There were no pathways or processes significantly represented by cluster 9. TLR: Toll-like receptor; NOD: nucleotide-binding oligomerisation domain; MHC: major histocompatibility complex.

  • TABLE 3

    Clinical characteristics of subjects

    VariablesGroningenLAVALUBC
    Never-smokersCurrent smokersNever-smokersCurrent smokersNever-smokersCurrent smokers
    Subjects124527821788
    Male/female5 (42%)/723 (51%)/226 (22%)/2137 (45%)/458 (47%)/954 (61%)/34
    Age years48.3±15.457.5±8.755.8±11.663±9.259.6±14.662±9.4
    Smoking pack-years037.9±17.4053±21.8053.7±27.1
    FEV1 % pred98.6±9.38 (7)73.3±19.8 (17)94.1±13.2 (4)75.1±14.4 (1)105.4±33.1 (7)76.4±18.2 (7)
    FVC % pred98.8±7.3 (7)87.1±16.3 (18)96.5±12.5 (6)87. 1±13.5 (5)99.1±31.2 (6)86.7±15.7 (7)
    • Data are presented as mean±sd, unless otherwise stated. The number of missing values is shown in parentheses. LAVAL: Laval University; UBC: University of British Columbia; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity.

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Total particulate matter concentration skews cigarette smoke's gene expression profile
Anna Dvorkin-Gheva, Gilles Vanderstocken, Ali Önder Yildirim, Corry-Anke Brandsma, Ma'en Obeidat, Yohan Bossé, John A. Hassell, Martin R. Stampfli
ERJ Open Research Oct 2016, 2 (4) 00029-2016; DOI: 10.1183/23120541.00029-2016

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Total particulate matter concentration skews cigarette smoke's gene expression profile
Anna Dvorkin-Gheva, Gilles Vanderstocken, Ali Önder Yildirim, Corry-Anke Brandsma, Ma'en Obeidat, Yohan Bossé, John A. Hassell, Martin R. Stampfli
ERJ Open Research Oct 2016, 2 (4) 00029-2016; DOI: 10.1183/23120541.00029-2016
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