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Chronic stress primes innate immune responses in mice and humans

Barrett, Tessa J., Corr, Emma M., van Solingen, Coen, Schlamp, Florencia, Brown, Emily J., Koelwyn, Graeme J., Lee, Angela H., Shanley, Lianne C., Spruill, Tanya M., Bozal, Fazli, de Jong, Annika, Newman, Alexandra A.C., Drenkova, Kamelia, Silvestro, Michele, Ramkhelawon, Bhama, Reynolds, Harmony R., Hochman, Judith S., Nahrendorf, Matthias, Swirski, Filip K., Fisher, Edward A., Berger, Jeffrey S., Moore, Kathryn J.·2021·Cell Reports
DOI 10.1016/j.celrep.2021.109595PMC PMC8493594
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Figure 1. Chronic variable stress activates hematopoiesis in mice (A) Experimental outline of 3-week chronic variable stress protocol. (B) Circulating corticosterone levels in control (white circles) and stressed (red circles) mice at the end of the 3-week chronic variable stress protocol. (C and D) Total bone marrow cell count (C) and characterization of hematopoietic and stem cell populations by flow cytometry (D) in the bone marrow of stressed and control mice. (E) Levels of hematopoietic progenitor cells (HPCs), Lin−Sca1+cKit+ (LSK), common myeloid progenitors (CMPs), and granulocyte macrophage progenitors (GMPs) in the bone marrow of stressed and control mice. (F and G) Relative mRNA levels of (F) the cytokine macrophage colony-stimulating factor (Mcsf) and stem cell factor (Scf), and (G) the retention factor Cxcl12 in the bone marrow of stressed and control mice. (H) Circulating white blood cell (WBC), monocyte, and neutrophil counts in stressed and control mice. Data are the means ± SEMs. p values were calculated using a Mann-Whitney t test; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001. n = 3–15 mice/group.
Figure 1. Chronic variable stress activates hematopoiesis in mice (A) Experimental outline of 3-week chronic variable stress protocol. (B) Circulating corticosterone levels in control (white circles) and stressed (red circles) mice at the end of the 3-week chronic variable stress protocol. (C and D) Total bone marrow cell count (C) and characterization of hematopoietic and stem cell populations by flow cytometry (D) in the bone marrow of stressed and control mice. (E) Levels of hematopoietic progenitor cells (HPCs), Lin−Sca1+cKit+ (LSK), common myeloid progenitors (CMPs), and granulocyte macrophage progenitors (GMPs) in the bone marrow of stressed and control mice. (F and G) Relative mRNA levels of (F) the cytokine macrophage colony-stimulating factor (Mcsf) and stem cell factor (Scf), and (G) the retention factor Cxcl12 in the bone marrow of stressed and control mice. (H) Circulating white blood cell (WBC), monocyte, and neutrophil counts in stressed and control mice. Data are the means ± SEMs. p values were calculated using a Mann-Whitney t test; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001. n = 3–15 mice/group.CC BY-NC-ND · PubMed Central
Figure 2. Chronic variable stress leads to transcriptome reprogramming in mouse monocytes (A) RNA-seq heatmap displaying differentially expressed transcripts (adjusted p < 0.05 and |log2FC| > 1.5) between monocytes isolated from mice exposed to chronic variable stress and nonstressed control mice (n = 3 mice/group). (B) Top canonical pathways of genes upregulated in monocytes from stressed versus control mice (p < 0.01). (C) Normalized expression counts of genes in the NF-κB, IL-6, and inflammasome signaling pathways in monocytes from stressed and control mice. (D and E) Ingenuity Pathway Analysis of predicted upstream (D) transcriptional and (E) cytokine regulators of genes differentially expressed in monocytes from stressed versus control mice. The number next to each bar represents genes in our dataset that have a measurement direction consistent with the activation of that transcriptional regulator. (F) Normalized expression counts of interferon receptor-related genes in monocytes from stressed and control mice. (G) Heatmap showing RNA-seq normalized gene expression values of interferon-stimulated genes (row Z score) in monocytes isolated from the bone marrow of stressed or control mice (n = 3). Data in (C) and (F) are means ± SEMs. p values were calculated using a Mann-Whitney test; *p ≤ 0.05, ***p ≤ 0.001.
Figure 2. Chronic variable stress leads to transcriptome reprogramming in mouse monocytes (A) RNA-seq heatmap displaying differentially expressed transcripts (adjusted p < 0.05 and |log2FC| > 1.5) between monocytes isolated from mice exposed to chronic variable stress and nonstressed control mice (n = 3 mice/group). (B) Top canonical pathways of genes upregulated in monocytes from stressed versus control mice (p < 0.01). (C) Normalized expression counts of genes in the NF-κB, IL-6, and inflammasome signaling pathways in monocytes from stressed and control mice. (D and E) Ingenuity Pathway Analysis of predicted upstream (D) transcriptional and (E) cytokine regulators of genes differentially expressed in monocytes from stressed versus control mice. The number next to each bar represents genes in our dataset that have a measurement direction consistent with the activation of that transcriptional regulator. (F) Normalized expression counts of interferon receptor-related genes in monocytes from stressed and control mice. (G) Heatmap showing RNA-seq normalized gene expression values of interferon-stimulated genes (row Z score) in monocytes isolated from the bone marrow of stressed or control mice (n = 3). Data in (C) and (F) are means ± SEMs. p values were calculated using a Mann-Whitney test; *p ≤ 0.05, ***p ≤ 0.001.CC BY-NC-ND · PubMed Central
Figure 3. Epigenetic reprogramming of inflammatory gene loci by stress (A) Experimental design of assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) of bone marrow monocytes from control and stressed mice and integration with RNA-seq. (B) Volcano plot showing chromatin loci identified by ATAC-seq to be more (red) or less (blue) accessible in bone marrow monocytes from mice exposed to chronic variable stress versus control (n = 6, 3 mice/phenotype). (C) Biological pathways were identified using the genomic regions enrichment of annotations tool (GREAT) analysis of loci with increased and decreased accessibility following chronic stress. (D) Oxygen consumption rate (OCR) of monocytes isolated from the bone marrow of stressed or control mice (n = 4) and sequentially treated with oligomycin, carbonyl cyanide p-trifluoro-methoxyphenylhydrazone (FCCP), and rotenone plus antimycin (Rtn/AA). Quantification of spare and maximal respiration. (E) Pie chart showing distribution of open chromatin loci in genes overlapping in RNA-seq and ATAC-seq datasets. (F) Integration scatterplot showing overlap between chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) in monocytes from stressed versus nonstressed mice. Differentially expressed (DE) genes adjusted p value < 0.05, FC > 1.5. (G) Reactome pathways of overlapping ATAC-seq peaks and RNA-seq reads that were activated or inhibited in stressed mice relative to control mice. (H) RNA-seq (top) and ATAC-seq (bottom) reads in bone marrow monocytes at the Nlrp3, Casp1, and Il6 loci. Enhancer-like sequences (ELSs) are indicated by boxed regions at bottom. Data in (D) are means ± SEMs. p values were calculated using a Mann-Whitney t test; **p ≤ 0.01 n = 4 mice/group.
Figure 3. Epigenetic reprogramming of inflammatory gene loci by stress (A) Experimental design of assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) of bone marrow monocytes from control and stressed mice and integration with RNA-seq. (B) Volcano plot showing chromatin loci identified by ATAC-seq to be more (red) or less (blue) accessible in bone marrow monocytes from mice exposed to chronic variable stress versus control (n = 6, 3 mice/phenotype). (C) Biological pathways were identified using the genomic regions enrichment of annotations tool (GREAT) analysis of loci with increased and decreased accessibility following chronic stress. (D) Oxygen consumption rate (OCR) of monocytes isolated from the bone marrow of stressed or control mice (n = 4) and sequentially treated with oligomycin, carbonyl cyanide p-trifluoro-methoxyphenylhydrazone (FCCP), and rotenone plus antimycin (Rtn/AA). Quantification of spare and maximal respiration. (E) Pie chart showing distribution of open chromatin loci in genes overlapping in RNA-seq and ATAC-seq datasets. (F) Integration scatterplot showing overlap between chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) in monocytes from stressed versus nonstressed mice. Differentially expressed (DE) genes adjusted p value < 0.05, FC > 1.5. (G) Reactome pathways of overlapping ATAC-seq peaks and RNA-seq reads that were activated or inhibited in stressed mice relative to control mice. (H) RNA-seq (top) and ATAC-seq (bottom) reads in bone marrow monocytes at the Nlrp3, Casp1, and Il6 loci. Enhancer-like sequences (ELSs) are indicated by boxed regions at bottom. Data in (D) are means ± SEMs. p values were calculated using a Mann-Whitney t test; **p ≤ 0.01 n = 4 mice/group.CC BY-NC-ND · PubMed Central
Figure 4. Stress primes monocytes toward a hyperinflammatory phenotype characterized by alterations to metabolic processes, cytokine production, and efferocytotic capacity Heatmap showing the RNA-seq gene expression profile of the mammalian target of rapamycin (mTOR) signaling pathway in monocytes from mice exposed to chronic variable stress or nonstressed mice (n = 3/phenotype). (B) Lipopolysaccharide (LPS) stimulated (10 ng/mL; 6 h) cytokine response of bone marrow monocytes isolated from mice exposed to chronic variable stress or nonstressed mice (n = 4–5 mice/phenotype). (C) Phagocytosis of E. coli bioparticles by bone marrow monocytes isolated from stressed and nonstressed mice (n = 6 per phenotype). (D) Gene expression heatmap of monocytes isolated from stressed and nonstressed mice, and subsequently stimulated with LPS (n = 4/group); DE genes defined from effect of stress treatment factor alone (297 genes, p < 0.01) segregate into 3 clusters using hierarchical clustering (cluster 1 = 80 genes, cluster 2 = 92 genes, cluster 3 = 125 genes). (E) Mean expression of genes in each cluster for stress and nonstressed (control) mice in both basal and LPS stimulation. (F) GO enrichment analysis was performed separately for each cluster using the ClusterProfiler package from BioConductor, using an adjusted p value cutoff of 0.05. Data in (B) and (C) are means ± SEMs. p values were calculated using a Mann-Whitney test; *p ≤ 0.05.
Figure 4. Stress primes monocytes toward a hyperinflammatory phenotype characterized by alterations to metabolic processes, cytokine production, and efferocytotic capacity Heatmap showing the RNA-seq gene expression profile of the mammalian target of rapamycin (mTOR) signaling pathway in monocytes from mice exposed to chronic variable stress or nonstressed mice (n = 3/phenotype). (B) Lipopolysaccharide (LPS) stimulated (10 ng/mL; 6 h) cytokine response of bone marrow monocytes isolated from mice exposed to chronic variable stress or nonstressed mice (n = 4–5 mice/phenotype). (C) Phagocytosis of E. coli bioparticles by bone marrow monocytes isolated from stressed and nonstressed mice (n = 6 per phenotype). (D) Gene expression heatmap of monocytes isolated from stressed and nonstressed mice, and subsequently stimulated with LPS (n = 4/group); DE genes defined from effect of stress treatment factor alone (297 genes, p < 0.01) segregate into 3 clusters using hierarchical clustering (cluster 1 = 80 genes, cluster 2 = 92 genes, cluster 3 = 125 genes). (E) Mean expression of genes in each cluster for stress and nonstressed (control) mice in both basal and LPS stimulation. (F) GO enrichment analysis was performed separately for each cluster using the ClusterProfiler package from BioConductor, using an adjusted p value cutoff of 0.05. Data in (B) and (C) are means ± SEMs. p values were calculated using a Mann-Whitney test; *p ≤ 0.05.CC BY-NC-ND · PubMed Central
Figure 5. Stress alters the human blood transcriptional profile (A) Study design: whole blood was collected from women in PAXgene Blood RNA tubes, and RNA isolated and analyzed by RNA-seq. At the time of enrollment, the stress of the participants was determined by the validated Perceived Stress Scale 4 (PSS-4) questionnaire. Subjects were stratified into those with low stress (PSS-4 < 5, n = 15) and those with high stress (PSS-4 > 5, n = 12). See demographics in Table S6. (B) Heatmap of the top 100 DE transcripts between low- and high-stress subjects, ranked by p value. Subjects and genes were hierarchically clustered with each row representing a single gene and each column representing an individual subject. (C) Canonical pathways upregulated and downregulated as determined by Ingenuity Pathway Analysis (IPA) of significantly DE transcripts in low-stress versus high-stress subjects (p < 0.05). (D and E) Expression of (D) interferon-related and (E) NF-κB-related transcripts DE in subjects with low and high stress. (F and G) IPA upstream analysis of predicted (F) cytokines and (G) transcriptional regulators of genes differentially expressed between subjects with high and low stress. The number next to each bar represents genes in our dataset that have a measurement direction consistent with the activation of that cytokine or transcriptional regulator. Data in (D) and (E) are means ± SEMs. p values were calculated using a Mann-Whitney t test; *p ≤ 0.05, **p ≤ 0.01.
Figure 5. Stress alters the human blood transcriptional profile (A) Study design: whole blood was collected from women in PAXgene Blood RNA tubes, and RNA isolated and analyzed by RNA-seq. At the time of enrollment, the stress of the participants was determined by the validated Perceived Stress Scale 4 (PSS-4) questionnaire. Subjects were stratified into those with low stress (PSS-4 < 5, n = 15) and those with high stress (PSS-4 > 5, n = 12). See demographics in Table S6. (B) Heatmap of the top 100 DE transcripts between low- and high-stress subjects, ranked by p value. Subjects and genes were hierarchically clustered with each row representing a single gene and each column representing an individual subject. (C) Canonical pathways upregulated and downregulated as determined by Ingenuity Pathway Analysis (IPA) of significantly DE transcripts in low-stress versus high-stress subjects (p < 0.05). (D and E) Expression of (D) interferon-related and (E) NF-κB-related transcripts DE in subjects with low and high stress. (F and G) IPA upstream analysis of predicted (F) cytokines and (G) transcriptional regulators of genes differentially expressed between subjects with high and low stress. The number next to each bar represents genes in our dataset that have a measurement direction consistent with the activation of that cytokine or transcriptional regulator. Data in (D) and (E) are means ± SEMs. p values were calculated using a Mann-Whitney t test; *p ≤ 0.05, **p ≤ 0.01.CC BY-NC-ND · PubMed Central
Figure 6. Stress priming induces hyperinflammatory immune phenotype characterized by increased cytokine production (A) Ratios of neutrophils to lymphocytes and monocytes to lymphocytes in peripheral blood of women enrolled in the HARP study (demographics, Table 1) stratified by low stress (PSS-4 < 5, n = 11) and high stress (PSS-4 > 5, n = 7). (B and C) Cytokine production in monocyte purified PBMCs from low stress and high stress subjects following stimulation with (B) Pam3Cys (10 μg/mL) or (C) LPS (10 ng/mL). Data are means ± SEMs. p values were calculated using a Mann-Whitney test; *p ≤ 0.05 relative to the low-stress group. (D) Comparison of human RNA-seq to mouse RNA-seq between high and low stress. Venn diagram depicting the 421-gene overlap in stress-induced differential gene expression in humans and mice. DE genes are defined by p < 0.05 in the human RNA-seq and adjusted p < 0.05 in the mouse RNA-seq. (E) Two-way volcano plot depicting overlap between human DE genes (y axis) and mouse DE genes (x axis); red dots indicate genes upregulated (n = 98) and blue dots indicate genes downregulated (n = 152) in both datasets. (F) Top common pathways of genes with concordant expression directionality in myeloid cells from chronically stressed humans and mice as determined by IPA (n = 250, 98 genes upregulated, 152 genes downregulated). (G) Heatmap of human and mouse mRNA expression values for genes found in the top enriched pathways common to high-stress mice and humans. Fold change of mRNA expression is scaled to represent relative maximum (1) and minimum (−1) expression of genes within each organism. Pathway assignment of genes is indicated by dots below the heatmap.
Figure 6. Stress priming induces hyperinflammatory immune phenotype characterized by increased cytokine production (A) Ratios of neutrophils to lymphocytes and monocytes to lymphocytes in peripheral blood of women enrolled in the HARP study (demographics, Table 1) stratified by low stress (PSS-4 < 5, n = 11) and high stress (PSS-4 > 5, n = 7). (B and C) Cytokine production in monocyte purified PBMCs from low stress and high stress subjects following stimulation with (B) Pam3Cys (10 μg/mL) or (C) LPS (10 ng/mL). Data are means ± SEMs. p values were calculated using a Mann-Whitney test; *p ≤ 0.05 relative to the low-stress group. (D) Comparison of human RNA-seq to mouse RNA-seq between high and low stress. Venn diagram depicting the 421-gene overlap in stress-induced differential gene expression in humans and mice. DE genes are defined by p < 0.05 in the human RNA-seq and adjusted p < 0.05 in the mouse RNA-seq. (E) Two-way volcano plot depicting overlap between human DE genes (y axis) and mouse DE genes (x axis); red dots indicate genes upregulated (n = 98) and blue dots indicate genes downregulated (n = 152) in both datasets. (F) Top common pathways of genes with concordant expression directionality in myeloid cells from chronically stressed humans and mice as determined by IPA (n = 250, 98 genes upregulated, 152 genes downregulated). (G) Heatmap of human and mouse mRNA expression values for genes found in the top enriched pathways common to high-stress mice and humans. Fold change of mRNA expression is scaled to represent relative maximum (1) and minimum (−1) expression of genes within each organism. Pathway assignment of genes is indicated by dots below the heatmap.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Barrett, T. J., Corr, E. M., van Solingen, C., Schlamp, F., Brown, E. J., Koelwyn, G. J., Lee, A. H., Shanley, L. C., Spruill, T. M., Bozal, F., de Jong, A., Newman, A. A. C., Drenkova, K., Silvestro, M., Ramkhelawon, B., Reynolds, H. R., Hochman, J. S., Nahrendorf, M., Swirski, F. K., Fisher, E. A., Berger, J. S., & Moore, K. J.
Venue
Cell Reports
Publisher
Elsevier BV
Published
2021-09
Volume
36
Issue
10
Pages
109595
ISSN
2211-1247
DOI
10.1016/j.celrep.2021.109595
PMC
PMC8493594
Cited by
169
References
13
Language
en
Licence
Funders
  • Medtronic
  • American Heart Association (16SFRN28730002, 19CDA34630066, 118CDA34110203)
  • National Institutes of Health (R01HL084312)
  • Pfizer
  • Eli Lilly and Company
  • Biotronik
  • Novartis
  • CSL Behring
  • Biogen
  • American Society of Hematology (18-A0-00-1001884)
  • GlaxoSmithKline
  • NHLBI (R35HL135799, P01HL131478, R35HL144993, T32HL098129)

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