1. Medicine
Download icon

Reprogramming of bone marrow myeloid progenitor cells in patients with severe coronary artery disease

  1. Marlies P Noz
  2. Siroon Bekkering
  3. Laszlo Groh
  4. Tim MJ Nielen
  5. Evert JP Lamfers
  6. Andreas Schlitzer
  7. Saloua El Messaoudi
  8. Niels van Royen
  9. Erik HJPG Huys
  10. Frank WMB Preijers
  11. Esther MM Smeets
  12. Erik HJG Aarntzen
  13. Bowen Zhang
  14. Yang Li
  15. Manita EJ Bremmers
  16. Walter JFM van der Velden
  17. Harry Dolstra
  18. Leo AB Joosten
  19. Marc E Gomes
  20. Mihai G Netea
  21. Niels P Riksen  Is a corresponding author
  1. Department of Internal Medicine and Radboud Institute for Molecular Life Science (RIMLS), Radboud University Medical Center, Netherlands
  2. Department of Cardiology, Canisius Wilhelmina Hospital, Netherlands
  3. Quantitative Systems Biology, Life & Medical Sciences Institute, University of Bonn, Single Cell Genomics and Epigenomics Unit at the German Center for Neurodegenerative Diseases and the University of Bonn, Germany
  4. Department of Cardiology, Radboud University Medical Center, Netherlands
  5. Department of Laboratory Medicine – Laboratory for Haematology, Radboud University Medical Center, Netherlands
  6. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Netherlands
  7. Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Germany
  8. Department of Haematology, Radboud University Medical Center, Netherlands
  9. Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy, Romania
  10. Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, Germany
Research Article
Cite this article as: eLife 2020;9:e60939 doi: 10.7554/eLife.60939
8 figures, 4 tables and 2 additional files

Figures

Cytokine production capacity of circulating PBMCs.

(A) Cytokine production capacity of circulating PBMCs after LPS stimulation in control individuals (white bars, n = 13) and individuals with CAD (gray bars, n = 13). Geometric mean with 95% CI. (B) Table of cytokine/chemokine production (x-axis) after stimulation with LPS or P3C (y-axis) of PBMCs showing statistical differences between groups. The p-values are corrected for age and BMI with ANCOVA. Outliers were removed with an SD of >2.5 of Z-scores. * indicates p<0.05, **: p<0.01.

Figure 2 with 1 supplement
Progenitor cell populations in the bone marrow compartment (A–G) and in the circulation (H and I).

Control individuals (white bars, n = 13) and individuals with CAD (gray bars, n = 13). HSC and MPP cell populations were combined as the CD90 expression marker was not available for n = 6 in each study group. Geometric mean with 95% CI. The p-values are corrected for age and BMI with ANCOVA. * indicates p<0.05, **: p<0.01. In top-down order: HSC indicates hematopoietic stem cell, MPP: multipotent progenitor, CLP: common lymphoid progenitor, CMP: common myeloid progenitor, GMP: granulocyte-macrophage progenitor, MEP: megakaryocyte erythrocyte progenitor.

Figure 2—figure supplement 1
Gating strategy of hematopoietic stem and progenitor cells in the bone marrow.

HSPCs were defined as CD45+CD34+CD38dim cells, after exclusion of dead cells and doublets. Next, the lymphoid lineage was excluded in CD19-CD117+ cells. In CD45RAdimCD38+ cells, CMP, GMP, MEP, and R1-3 progenitor populations were identified using CD123 and CD45RA expression, see Table for details. CD90 expression in CD38-CD45RA- cells determined MPP and HSC populations.

Cytokine production capacity of bone marrow MNCs.

(A) Cytokine production capacity of BM-MNCs after LPS stimulation in control individuals (white bars, n = 13) and individuals with CAD (gray bars, n = 13). Geometric mean with 95% CI. (B) Table of cytokine/chemokine production (x-axis) after stimulation with LPS or P3C (y-axis) of BM-MNCs showing statistical differences between groups. The p-values are corrected for age and BMI with ANCOVA. Outliers were removed with an SD of >2.5 of Z-scores. * indicates p<0.05, **: p<0.01.

Metabolism of BM-MNCs assessed with Seahorse respirometry in unstimulated condition and 2 hours after IFN-γ+LPS stimulation.

(A, B) Oxygen consumption and extracellular acidification rates over time using treatment with Oligomycin, FCCP, and Rotenone/Antimycin A. (C, D) Bar graphs of control individuals (white bars, n = 13) and individuals with CAD (gray bars, n = 13). Geometric mean with 95% CI. The p-values are corrected for age and BMI with ANCOVA. * indicates p<0.05, **: p<0.01. IFN-γ+LPS: 2 hr IFN-γ and LPS stimulation.

Proliferation capacity of bone marrow MNCs.

Counted colonies per 103 cultured BM-MNCs of control individuals (white bars, n = 13) and individuals with CAD (gray bars, n = 13). Geometric mean with 95% CI. The p-values are corrected for age and BMI with ANCOVA. BFU-E indicates erythroid progenitor population, CFU-GEMM: myeloid progenitor population, CFU-GM: granulocyte-macrophage progenitor population.

Figure 6 with 3 supplements
Transcriptome analyses of HSC, MPP, and GMP populations.

Control individuals (n = 10) versus individuals with CAD (n = 10) for each cell population. (A) Principle component analysis (PCA) based on differentially expressed (DE) genes of the HSC population; (B) Volcano plot showing differential expressed genes between patients with CAD and individuals without atherosclerosis, controlled for age, in a combined analysis of HSC, MPP, and GMP population. Genes with an FDR <0.1 are named; (C) Gene ontology enrichment analysis of DE genes from HSCs, MPPs, and GMPs, depicting the FDR and enrichment ratio.

Figure 6—source data 1

Contains source data for Figure 6B, and Figure 6—figure supplements 1B, 2, 3.

Table of differential expressed genes between patients with CAD and individualswithout atherosclerosis, controlled for age, in a combined/separated analysis of HSCs, MPPs, and GMPs population.

https://cdn.elifesciences.org/articles/60939/elife-60939-fig6-data1-v2.xlsx
Figure 6—source data 2

Contains source data for Figure 6C.

Table of enrichment analysis of gene ontology terms and involved DE genes of HSCs, MPPs, and GMPs population.

https://cdn.elifesciences.org/articles/60939/elife-60939-fig6-data2-v2.xlsx
Figure 6—figure supplement 1
Transcriptome of HSC, MPP, and GMP populations.

(A) PCA analysis based on DE genes of the GMP and MPP cell population; (B) Volcano plot showing differential expressed genes between CAD patients and controls, controlled for age, in the HSC, MPP, and GMP cell populations. Genes with an FDR <0.1 are called out.

Figure 6—figure supplement 2
Combined heatmap showing the top 50 DE genes for the HSC, MPP, and GMP cell populations in the patients and the control subjects.

Genes with an FDR <0.1 are colored in red and blue for up- and down-regulated, respectively.

Figure 6—figure supplement 3
Separated heatmap showing the top 50 DE genes for each of the HSC, MPP, and GMP cell populations in the patients and the control subjects.

Genes with an FDR <0.1 are colored in red and blue for up- and down-regulated, respectively.

Figure 7 with 1 supplement
Vascular wall inflammation and hematopoietic tissue activation on [18F]FDG PET/CT scan.

Standard uptake value of each region in control individuals (white bars, n = 13) and individuals with CAD (gray bars, n = 13). Geometric mean with 95% CI. The p-values are corrected for age and BMI with ANCOVA.* indicates p<0.05, **: p<0.01. TBR: target SUV/mean blood pool SUV or mean liver SUV as background.

Figure 7—figure supplement 1
Splenic activity correlates with progenitor cells and circulating immune cells.

Linear regression with 95% CI (n = 26). Spearman correlation coefficient (rs). * indicates p<0.05, **: p<0.01. HSPCs: hematopoietic stem and progenitor cells, GMP: granulocyte macrophage progenitor cells, WBC: white blood cells.

Author response image 1

Tables

Table 1
Group characteristics.
CharacteristicsIndividuals with CAD (n = 13)Individuals without atherosclerosis (n = 13)
Age (years)59.8 ± 9.752.2 ± 10.4
Sex (% men, n)100 (13)100 (13)
BMI (kg/m2)27.8 ± 2.825.8 ± 2.5
SBP (mm Hg)133 ± 15126 ± 10
DBP (mm Hg)90 ± 8*84 ± 6
Hypertension (%, n)93 (12)**31 (4)
Current smoking (%, n)23 (3)31 (4)
Calcium score (HU)445 [213-781]**0 [0]
Total Plaque score (0–16)‡14 [9-15]***0 [0]
Lipid-lowering therapy (%, n)77 (10)***8 (1)
Acetylsalicylic acid use (%, n)69 (9)***0 (0)
ACE-inhibitor use (%, n)23 (3)8 (1)
β-blocker use (%, n)23 (3)8 (1)
Glucose (mmol/L)5.9 ± 0.85.7 ± 0.7
Creatinine (µmol/L)89 ± 1491 ± 16
Tchol (mmol/L)4.51 ± 0.86**5.61 ± 0.61
LDLc (mmol/L)2.52 ± 0.97**3.56 ± 0.64
HDLc (mmol/L)1.25 ± 0.311.51 ± 0.34
TG (mmol/L)1.98 ± 1.971.20 ± 0.38
nHDLc (mmol/L)3.26 ± 1.06*4.11 ± 0.75
  1. Data are reported as mean ± SD, as mean (number of participants), or as median [interquartile range] and compared with the appropriate statistical tests. ‡ TPS was calculated for participants with a calcium score of <400 HU (n = 6). † Data is missing for one participant. * indicates p<0.05, **: p<0.01, ***: p<0.001.

Table 2
Circulating immune cells and inflammatory markers in patients and controls.
Cell typesIndividuals with CADIndividuals without atherosclerosis
WBC (106/mL)5.5 [4.9–6.0]5.4 [4.8–6.7]
Neutrophils (106/mL)3.2 [2.5–3.6]3.0 [2.6–4.0]
Lymphocytes (106/mL)1.7 [1.3–1.9]1.8 [1.5–2.5]
Monocytes (106/mL)0.53 [0.42–0.67]0.55 [0.45–0.66]
Monocytes (%)9.8 [8.0–11.5]9.3 [7.7–11.1]
Classical monocytes (%gated)78.1 [72.8–80.3]72.8 [70.1–85.5]
Intermediate monocytes (%gated)9.8 [8.2–14.2]10.1 [7.6–13.7]
Nonclassical monocytes (%gated)12.2 [9.3–14.3]13.1 [6.3–18.2]
CCR2+ monocytes (%gated)80.5 [73.0–82.3]77.6 [71.4–86.3]
CD11b expression monocytes (MFI)10490 [7814–12025]^††6978 [6512–10041]
CD41+ monocytes (%gated)7.8 [6.5–9.7]8.7 [7.6–8.9]
Inflammatory markers
IL-1β (pg/mL)0.12 [0.09–0.17]0.12 [0.06–0.15]
IL-1Ra (pg/mL)271 [197-338]212 [165-253]
IL-6 (pg/mL)2.31 [1.37–2.86]1.61 [1.23–2.19]
IL-18 (pg/mL)162 [127-227]195 [144-236]
hsCRP (pg/mL)1.66 [0.83–4.87]1.37 [0.53–3.84]
E-selectin (ng/mL)11.74 [7.65–15.10]*8.45 [4.52–14.06]
VCAM-1 (ng/mL)773 [711-859]^769 [643-844]
MMP2 (ng/mL)354 [264-434]341 [250-427]
  1. Circulating concentrations of cells and inflammatory markers in individuals with CAD (n = 13) compared to individuals without atherosclerosis (n = 13). Median with [IQR]. P-values are corrected for age and BMI with ANCOVA. Outliers were removed with an SD of >2.5 of Z-scores. † Data is missing for one participant. *p<0.05, **p<0.01. HSPCs: hematopoietic stem and progenitor cells.

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Biological sample (Homo sapiens)Peripheral bloodThrough venous punctureFreshly isolated from Homo sapiens, men, 18–75 years
Biological sample (Homo sapiens)Bone Marrow aspirateFrom the posterior iliac
crest according to standard practice
Freshly isolated from Homo sapiens
AntibodyMouse monoclonal CD45 KOBeckman CoulterClone J33 Cat# B36294, RRID:AB_2833027(1:25)
AntibodyMouse monoclonal HLA-DR PEBeckman CoulterClone immu-357 Cat# IM1639U RRID:AB_2876782(1:10)
AntibodyMouse monoclonal CD14 PECy7eBioscienceClone 61D3
Cat# 25-0149-42 RRID:AB_1582276
(1:25)
AntibodyMouse monoclonal CD16 FITCeBioscienceClone CB16 Cat# 11-0168-42
RRID:AB_10805747
(1:25)
AntibodyMouse monoclonal CD3 APC-Alexa750Beckman CoulterClone UCTH1 Cat# A66329
RRID:AB_2876783
(1:25)
AntibodyMouse monoclonal CD56 APCBeckman CoulterClone N901 Cat# IM2474U
RRID:AB_2876784
(1:25)
AntibodyMouse monoclonal CD192 BV421BD BiosciencesClone 48607 Cat# 564067, RRID:AB_2738573(1:50)
AntibodyMouse monoclonal CD11b BV785BiolegendClone ICRF44
Cat# 301346, RRID:AB_2563794
(1:50)
AntibodyMouse monoclonal CD41 PerCP-Cy5.5BiolegendClone Hip8
Cat# 303719, RRID:AB_2561731
(1:50)
AntibodyMouse monoclonal CD90 FITCBiolegendClone 5E10 Cat# 328107, RRID:AB_893438(1:50)
AntibodyMouse monoclonal CD123 PEBD BiosciencesClone 9F5 Cat# 555644, RRID:AB_396001(1:40)
AntibodyMouse monoclonal CD19 ECDBeckman CoulterClone J4.119 Cat# IM2708U, RRID:AB_130854(1:20)
AntibodyMouse monoclonal CD38 PC5.5Beckman CoulterClone LS198-4-3 Cat# IM2651U, RRID:AB_131166(1:20)
AntibodyMouse monoclonal CD117 PEC7Beckman CoulterClone 104D2D1 Cat# IM3698, RRID:AB_131184(1:20)
AntibodyMouse monoclonal CD45RA APCBeckman CoulterClone 2H4LDH11LD89 (2H4) Cat# B14807
RRID:AB_2876787
(1:20)
AntibodyMouse monoclonal CD34-APC A750Beckman CoulterClone 581 Cat# A89309
RRID:AB_2876786
(1:20)
Commercial assay or kitDRAQ7BiostatusLive/Dead stain(1:500)
Commercial assay or kitHuman Cytokine Magnetic Magpix 25-plex panelInvitrogenMAGPIX platform
Commercial assay or kitSimplePlex cartridgeProteinSimpleElla platform
Commercial assay or kitTruseq small RNA primersIllumina
Commercial assay or kithsCRP ELISAR&DDY1707
Commercial assay or kitVCAM-1 ELISAR&DDY809
Commercial assay or kitMMP2 ELISAR&DDY902
Commercial assay or kitE-selectin ELISAR&DDY724
Chemical compound, drugPharm Lyse lysing bufferBD Biosciences
Chemical compound, drugGlutamineInvitrogen2 mmol/L in RPMI
Chemical compound, drugGentamycinCentrafarm10 mg/mL in RPMI
Chemical compound, drugPyruvateInvitrogen1 mmol/L in RPMI
Chemical compound, drugMethocult GFStemcell TechnologiesH84435
Sequence-based reagentHg19 human Refseq transcriptomeLi and Durbin, 2010To align RNAseq
Peptide, recombinant proteinLipopolysaccharide from Escherichia coliSigma-AldrichSerotype 055:B5, L288010 ng/mL
Peptide, recombinant proteinPam3CysK4EMC MicrocollectionsL200010 ug/mL
Peptide, recombinant proteinInterferon gammaImmukine, Boehringer Ingelheim BV50 ng/mL for Seahorse
Peptide, recombinant proteinOligomycinSigma-Aldrich753511 mM for Seahorse
Peptide, recombinant proteinFCCPSigma-AldrichC29201 mM for Seahorse
Peptide, recombinant proteinRotenoneSigma-AldrichR88751.25 mM
Peptide, recombinant proteinAntimycin ASigma-AldrichA86742.5 mM
Software,
algorithm
KaluzaBeckman CoulterVersion 2.1 RRID:SCR_016182Flow Cytometry
analysis
Software, algorithmMultiQCEwels et al., 2016RRID:SCR_014982Quality check RNAseq
Software, algorithmDEseq2 v1.22.0Love et al., 2014
BioConductor
RRID:SCR_015687Differential gene expression RNAseq
Software, algorithmclusterProfiler v3.10.1Yu et al., 2012
BioConductor
RRID:SCR_016884RNAseq
Software, algorithmR 3.6.1https://www.r-project.org/RRID:SCR_001905
Software, algorithmTrueX algorithmEARL protocols
Software, algorithmInveon Research Workspace 4.2Preclinical Solutions, Siemens Medical Solutions3D Gaussian filter kernel, 3.0 mmPostprocessing of FDG PET CT scanning
Software, algorithmPyRadiomics toolboxvan Griethuysen et al., 2017Analysis FDG PET CT
Software, algorithmSPSS V25.0SPSSRRID:SCR_002865Data analysis
Software, algorithmPrism v6.0GraphPad softwareRRID:SCR_002798Figures
OtherSysmex-XN 450 hematology analyzerSysmexFor total blood counts
OtherCytoFLEX flow cytometerBeckman Coulter13 color on CytExpert RRID:SCR_017217Flow Cytometry Peripheral blood
OtherNavios flow cytometerBeckman CoulterRRID:SCR_014421Flow Cytometry Bone marrow Progenitors
OtherXFp AnalyzerSeahorse Bioscience
OtherBD FACSAria II SORPBecton DickinsonRRID:SCR_018091Flow cytometry sorting
OtherIllumina Nextseq500 platformIlluminaRRID:SCR_014983RNAseq
OtherBiograph 40 mCT scannerSiemens Healthineers~2.1 MGq/kg FDG i.v.FDG PET CT
Table 3
Cell composition of PBMC and BM-MNC fraction.
Cell types in PBMC fractionControlsPatients
Lymphocytes (%)73 [68-79]65 [62-75]*
Monocytes (%)25 [19-31]32 [23-35]
Neutrophils (%)0.7 [0.6–1.1]1.2 [0.6–1.7]
Cell types in BM-MNC fraction
HSPCs (%)1.6 [1.2–2.0]1.4 [1.2–4.7]
  1. Cellular composition after mononuclear cell enrichment of peripheral blood and bone marrow. Median with [IQR]. Mann-Whitney U test. *: p<0.05, **: p<0.01. HSPCs: hematopoietic stem and progenitor cells.

Additional files

Supplementary file 1

Gating strategy of circulating immune cells (related to Table 2).

Monocytes were selected based on CD45+ HLA-DR+ and monocyte scatter properties, after exclusion of dead cells and doublets. Then CD3+ lymphocytes and CD56+ NK-cells were excluded, and monocyte subsets were identified in the CD14/CD16 plot as the percentage of gated. CD11b and CCR2 expression was determined on the monocyte population.

https://cdn.elifesciences.org/articles/60939/elife-60939-supp1-v2.pdf
Transparent reporting form
https://cdn.elifesciences.org/articles/60939/elife-60939-transrepform-v2.docx

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)