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Research Article
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Profiles of histidine-rich glycoprotein associate with age and risk of all-cause mortality

View ORCID ProfileMun-Gwan Hong, View ORCID ProfileTea Dodig-Crnković, Xu Chen, Kimi Drobin, Woojoo Lee, Yunzhang Wang, Fredrik Edfors, View ORCID ProfileDavid Kotol, View ORCID ProfileCecilia Engel Thomas, Ronald Sjöberg, View ORCID ProfileJacob Odeberg, Anders Hamsten, View ORCID ProfileAngela Silveira, View ORCID ProfilePer Hall, View ORCID ProfilePeter Nilsson, Yudi Pawitan, View ORCID ProfileMathias Uhlén, Nancy L Pedersen, Sara Hägg, Patrik KE Magnusson, View ORCID ProfileJochen M Schwenk  Correspondence email
Mun-Gwan Hong
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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Tea Dodig-Crnković
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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  • ORCID record for Tea Dodig-Crnković
Xu Chen
2Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Kimi Drobin
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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Woojoo Lee
2Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
3Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Korea
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Yunzhang Wang
2Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Fredrik Edfors
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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David Kotol
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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  • ORCID record for David Kotol
Cecilia Engel Thomas
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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  • ORCID record for Cecilia Engel Thomas
Ronald Sjöberg
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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Jacob Odeberg
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
4Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Solna, Sweden
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Anders Hamsten
5Department of Medicine Solna, Cardiovascular Medicine Unit, Karolinska Institutet, Solna, Sweden
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Angela Silveira
5Department of Medicine Solna, Cardiovascular Medicine Unit, Karolinska Institutet, Solna, Sweden
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Per Hall
2Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
6Department of Oncology, Södersjukhuset, Stockholm, Sweden
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Peter Nilsson
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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Yudi Pawitan
2Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Mathias Uhlén
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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  • ORCID record for Mathias Uhlén
Nancy L Pedersen
2Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Sara Hägg
2Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Patrik KE Magnusson
2Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Jochen M Schwenk
1Department of Protein Science, Science for Life Laboratory, KTH–Royal Institute of Technology, Solna, Sweden
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  • ORCID record for Jochen M Schwenk
  • For correspondence: jochen.schwenk@scilifelab.se
Published 31 July 2020. DOI: 10.26508/lsa.202000817
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Figures

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  • Figure S1.
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    Figure S1. Study design.

    This illustration describes the steps of the present investigation.

  • Figure S2.
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    Figure S2. Protein profiles of HPA045005 in every sample set.

    The solid lines illustrate the trends estimated by linear regression, and the shades around them show 95% confidence intervals of fitted values. At the bottom right corner, all trend lines were overlapped to present the increasing trends in all nine sample sets.

  • Figure 1.
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    Figure 1. Meta-analysis from nine different sample sets.

    In the forest plot, the numbers in parenthesis indicate the age range of the included subjects. For each sample set, the estimated effect of age on HPA045005-derived profiles from the linear regression model, 95% confidence interval of it, and study weight in the meta-analysis are shown in the middle as a tick, a line, and a gray box, respectively. The numeric value of the effect is clarified at the right side.

  • Figure 2.
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    Figure 2. Genome-wide association study results and histidine-rich glycoprotein (HRG) domains.

    (A) Manhattan plot. The significance of association between genotypes and HPA045005 profiles is presented vertically. The dashed guide line marks the stringent threshold of P-value for genome-wide association study, which is P = 0.01 after Bonferroni correction. One peak in chromosome 3 indicates strong association of a locus with the molecular phenotype. (B) LocusZoom (Pruim et al, 2010) on associated locus. The illustration shows the elements of chromosome 3 associated with HPA045005 profiles. (A) Zooming in on the peak of the Manhattan plot in (A), the genes around the locus are presented together with the associated single-nucleotide polymorphisms. (C) Box plots to show the association between genotypes of rs9898 and two antibody profiles, HPA045005 and BSI0137. The trends were opposite. (D) Representation of HRG to illustrate how two separate domains of the HRG protein affected the profiles of the antibodies. Protein domains, glycosylation sites and disulfide bonds of HRG were schematically illustrated using black round, purple dots and yellow lines, respectively. The figure was prepared based on the schematic representation of Poon et al (2011).

  • Figure S3.
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    Figure S3. Additional validation of molecular target.

    (A) Comparative sandwich immunoassays for histidine-rich glycoprotein (HRG). To confirm the molecular binding of HPA045005 to HRG, dilution of spiked-in HRG protein levels were studied. Different colors represent additional capture antibodies, such as the anti-HRG binder HPA054598. All were used for a multiplexed assessment of HRG binding. For the detection, a labeled version of HPA054598 was used. (B) High-density protein array analysis of HPA045005. The signal intensities from the interaction of the antibody with any of the 16,728 immobilized unique protein fragments on the glass slide are illustrated. For the antibody HPA045005, one peak was exclusively observed for the protein fragment that had been used for the production of the antibody (red bar), as well as for the positive controls detecting the presence of the rabbit IgG antibody (orange and green bars).

  • Figure S4.
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    Figure S4. Manhattan plot and LocusZoom plot for BSI0137 antibody profile.

    Because the P-values of the top single-nucleotide polymorphisms in the genome-wide association study for BSI0137 was too low, the computation tool for this analysis, PLINK, gave us 0 s. (A, B) The values were manually set to the lowest values the visualization software allowed, 1 × 10−320 in (A) or 1 × 10−350 in (B).

  • Figure S5.
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    Figure S5. Probabilistic identification of causal SNPs (PICS) analysis for the associated single-nucleotide polymorphisms with HPA045005 and BSI0137.

    The top two figures display the distribution of -log of P-values obtained from the linear regression model for the effects of the genotype of rs1042464 in 100,000 permutations, assuming rs9898 was causal for individual antibody profiles. The bottom figures show the results of rs9898 assuming rs1042464 was causal genetic variant. The green vertical lines indicate the observed significances.

  • Figure S6.
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    Figure S6. Detection of histidine-rich glycoprotein peptides in human serum by MS/MS analysis.

    Peptides from histidine-rich glycoprotein detected in serum samples using a bottom-up proteomic experimental workflow and liquid chromatography tandem mass spectrometry for read-out. Top panel: five different enzymes (y-axis; GluC, LysN, LysC, chymotrypsin, and trypsin) and the peptide amino acid sequence coverage (x-axis) of their experimental peptides. Four single-nucleotide polymorphism variants (rs10770, rs9898, rs2228243, and rs1042464) have been indicated by vertical black lines. Bottom panel: experimental peptides reported by the PeptideAtlas (accessed 16 May, 2017). The peptide amino acid sequence coverage (x-axis) is visualized in relation to experimental peptides for each experiment (y-axis). Highlighted in this example, rs9898 has only a few theoretical peptide sequences that can be observed with the bottom up method (because of sequence length) using this set of enzymes. Either one tryptic peptide with one missed cleavage NCPRHHFPR (identified once highlighted in the figure) or the chymotryptic peptide SCRNCPRH (based on low enzymatic specificity). For both panels, blue indicates the most abundant protein form and red is for any variation from the form.

  • Figure 3.
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    Figure 3. Survival analysis comparing upper and lower quarters of histidine-rich glycoprotein levels.

    The individuals of sample set 3 were divided into four subsets by the quartiles of histidine-rich glycoprotein levels. Differential mortality across follow-up time is illustrated by the survival curves, where age was used as the time scale (Thiébaut & Bénichou, 2004). The number at risk at 10 yr intervals were displayed below the survival curve. Some detailed statistics related to this survival analysis are presented in Table S4.

  • Figure S7.
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    Figure S7. Survival curves for women and men, comparing two extreme quartiles of set 3 by histidine-rich glycoprotein profiles.

    Detailed statistics are provided in Table S5.

  • Figure S8.
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    Figure S8. Location of the 204th residue of histidine-rich glycoprotein.

    The amino acid variant that affected HPA045005 profiles is highlighted in a 3D model predicted by SWISS-MODEL for the peptide of 19–250th amino acids of the protein using the homology with the template 6ht9.1B “Fetuin-B” (Waterhouse et al, 2018).

  • Figure S9.
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    Figure S9. Difference between serum and plasma in sample sets 1 and 2.

    Each plot displays the first two principal components of the MFIs of one assay, where the profiles of a set of 384 antibodies were obtained. The points of the scatter plot were colored by sample preparation types, such as plasma and serum.

Tables

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    Table 1.

    Description of sample sets.

    Study setAge (yr)Gender (F:M)IndicationaCohort nameSample TypeReferences
    Set 150–9278:78PopulationTwinGeneSerumLichtenstein et al (2002) and Magnusson et al (2013)
    Set 29–63102:102PopulationLifeGenePlasmaAlmqvist et al (2011)
    Set 348–931,613:1,386PopulationTwinGeneSerumLichtenstein et al (2002) and Magnusson et al (2013)
    Set 451–8650:0Breast cancer
    Set 556–750:50Prostate cancer
    Set 655–7816:27Cardiovascular diseaseIMPROVEPlasmaBaldassarre et al (2010)
    Set 741–6012:31Myocardial infarctionSCARFPlasmaSamnegård et al (2005)
    Set 848–7320:23Acute coronary heart syndromeCHAPSPlasmaOdeberg et al (2014)
    Set 940–73600:0MammographyKARMAPlasmaGabrielson et al (2017)
    • ↵a Subjects included in the presented study did not include individuals diagnosed with the disease of the indication area, but the subjects assigned as controls for the different disease cohorts.

    • View popup
    Table 2.

    Associations of histidine-rich glycoprotein profiles to various traits in set 3.

    TraitHPA045005Bsi0137
    EstimateaP-valueEstimateaP-value
    Aging
     Age0.0101.53 × 10−60.00170.44
     Mortality risk1.256.45 × 10−51.030.59
    Genetic/protein variants
     rs9898 (Pro204Ser)0.152.35 × 10−97−0.231.85 × 10−177
     rs1042464 (Asn493Ile)0.101.9 × 10−44−0.33<1 × 10−300
    Clinical trait
     APOA1−0.269.47 × 10−6−0.010.76
     APOB−0.238.46 × 10−40.306.81 × 10−10
     C-reactive protein0.0127.33 × 10−50.00030.88
     Glucose−0.0280.0610.0130.21
     Hb−0.0086.45 × 10−90.00040.68
     HbA1C−0.0470.0700.0300.092
     High density lipoprotein−0.0350.41−0.1205.65 × 10−5
     Low density lipoprotein−0.0441.53 × 10−20.0711.90 × 10−8
     TC−0.0601.37 × 10−40.0604.92 × 10−8
     TG−0.0862.73 × 10−50.0742.36 × 10−7
    • ↵a The estimates from selected models for individual associations. For clinical traits, the values are the estimated slope from linear regression models with adjustment for age and the top single-nucleotide polymorphism (rs9898 for HPA045005 and rs1042464 for BSI0137). Linear models were also used for age and genetic/protein variants, whereas Cox models for mortality risk (more details in the Materials and Methods section). For the trait, hazard ratios are presented in the column.

    • The two distinct histidin-rich glycoprotein profiles were compared with respect to the association with various traits in set 3, which are 2,999 samples from the TwinGene cohort (Lichtenstein et al, 2002; Magnusson et al, 2013).

Supplementary Materials

  • Figures
  • Tables
  • Table S1 The association between age and individual protein profiles.

  • Supplemental Data 1.

    Materials and methods.[LSA-2020-00817_Supplemental_Data_1.docx]

  • Table S2 The histidine-rich glycoprotein associated single-nucleotide polymorphisms that are non-synonymous or located near to transcription start site.

  • Table S3 Enzymes used for digestion of human serum for MS/MS analysis.

  • Table S4 The results of stratified analysis by genotype of rs9898.

  • Table S5 Statistics for survival analyses from Figs 3 and S7.

  • Table S6 Summary of survival analyses.

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Profiles of HRG associate with aging
Mun-Gwan Hong, Tea Dodig-Crnković, Xu Chen, Kimi Drobin, Woojoo Lee, Yunzhang Wang, Fredrik Edfors, David Kotol, Cecilia Engel Thomas, Ronald Sjöberg, Jacob Odeberg, Anders Hamsten, Angela Silveira, Per Hall, Peter Nilsson, Yudi Pawitan, Mathias Uhlén, Nancy L Pedersen, Sara Hägg, Patrik KE Magnusson, Jochen M Schwenk
Life Science Alliance Jul 2020, 3 (10) e202000817; DOI: 10.26508/lsa.202000817

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Profiles of HRG associate with aging
Mun-Gwan Hong, Tea Dodig-Crnković, Xu Chen, Kimi Drobin, Woojoo Lee, Yunzhang Wang, Fredrik Edfors, David Kotol, Cecilia Engel Thomas, Ronald Sjöberg, Jacob Odeberg, Anders Hamsten, Angela Silveira, Per Hall, Peter Nilsson, Yudi Pawitan, Mathias Uhlén, Nancy L Pedersen, Sara Hägg, Patrik KE Magnusson, Jochen M Schwenk
Life Science Alliance Jul 2020, 3 (10) e202000817; DOI: 10.26508/lsa.202000817
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