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Genetic variation of Golgi membrane protein 1 is associated with COVID-19 disease

  • Jiantao Fu
    Affiliations
    Department of Translational Medicine Center, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, Zhejiang, PR China

    Institute of Hepatology and Metabolic Diseases, Hangzhou Normal University, Hangzhou 310015, Zhejiang, PR China
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  • Yuxiao Luo
    Affiliations
    Department of Translational Medicine Center, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, Zhejiang, PR China

    Department of orthopedics, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, Zhejiang, PR China
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  • Xin Fang
    Affiliations
    Department of orthopedics, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, Zhejiang, PR China
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  • Jianmin Lu
    Affiliations
    Department of orthopedics, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, Zhejiang, PR China
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  • Jin Yang
    Correspondence
    Corresponding author.
    Affiliations
    Department of Translational Medicine Center, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, Zhejiang, PR China

    Institute of Hepatology and Metabolic Diseases, Hangzhou Normal University, Hangzhou 310015, Zhejiang, PR China
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      Highlights

      • There is an causality between GOLM1 and COVID-19.
      • GOLM1 levels may be linked to high glucose levels during COVID-19 infection.
      • GOLM1 might be a novel biomarker for COVID-19.
      Dear Editor
      We read with interest a recent work by Zoha Kamali and colleagues, who found IL-13 as a risk factor for severe COVID-19.
      • Kamali Z
      • Vonk JM
      • Thio CHL
      • Vaez A
      • Snieder H.
      A Mendelian randomization cytokine screen reveals IL-13 as causal factor in risk of severe COVID-19.
      Similarly, other investigators discovered that interleukin (IL) pathways, including IL-1, IL-1R1, and IL-6, etc, were associated with the severity of COVID-19 disease.
      • Wang R.
      Genetic variation of interleukin-1 receptor type 1 is associated with severity of COVID-19 disease.
      ,
      • Giannitrapani L
      • Augello G
      • Mirarchi L
      • Amodeo S
      • Veronese N
      • Sasso BL
      • et al.
      Outcome predictors in SARS-CoV-2 disease (COVID-19): The prominent role of IL-6 levels and an IL-6 gene polymorphism in a western Sicilian population.
      In the present work, we used the previously identified association of single-nucleotide polymorphisms (SNPs) for circulating Golgi membrane protein 1 (GOLM1) levels to evaluate its causal role in COVID-19.
      • Sun BB
      • Maranville JC
      • Peters JE
      • Stacey D
      • Staley JR
      • Blackshaw J
      • et al.
      Genomic atlas of the human plasma proteome.
      GOLM1, also known as GOLPH2 and GP73, is a type II transmembrane protein that cycles across membrane compartments. Once considered a valuable serum marker for hepatocellular carcinoma,
      • Mao Y
      • Yang H
      • Xu H
      • Lu X
      • Sang X
      • Du S
      • et al.
      Golgi protein 73 (GOLPH2) is a valuable serum marker for hepatocellular carcinoma.
      GOLM1 was shown to exacerbate CD8+ T cell suppression in liver cancer by facilitating exosomal PD-L1 trafficking into tumor-associated macrophages.
      • Chen J
      • Lin Z
      • Liu L
      • Zhang R
      • Geng Y
      • Fan M
      • et al.
      GOLM1 exacerbates CD8(+) T cell suppression in hepatocellular carcinoma by promoting exosomal PD-L1 transport into tumor-associated macrophages.
      More recently, it was discovered that GOLM1 connects SARS-CoV-2 infection with dysglycemia.
      • Coate KC.
      GP73 links SARS-CoV-2 infection with dysglycaemia.
      In order to infer potential causality of risk factor-disease associations, we used the promising approach of Mendelian randomization (MR).
      • Hemani G
      • Zheng J
      • Elsworth B
      • Wade KH
      • Haberland V
      • Baird D
      • et al.
      The MR-Base platform supports systematic causal inference across the human phenome.
      This strategy is based on the premise that genetic variations are distributed randomly during meiosis, hence minimizing confounding bias. The design for this MR study is shown in Suppl. Fig. 1.
      Fig 1:
      Fig. 1Causal relationships of GOLM1 genetic liability on COVID-19. (A) Individual estimates about the causal effect of GOLM1 on COVID-19 GWAS dataset ebi-a-GCST90000256. The x-axis and y-axis show the SNP effect and SE (standard error) on GOLM1 and COVID-19, respectively. The regression line for MR Egger, weighted median, IVW, simple mode, and weighted mode is shown. (B) Forest plot of GOLM1 associated SNPs with risk of COVID-19. The x-axis shows MR effect size for GOLM1 on COVID-19. The y-axis shows the analysis for each of SNPs. (C) MR leave-one-out sensitivity analysis for the effect of GOLM1 on COVID-19. (D) The association between genetically increased GOLM1 and Odds of multiple COVID-19 GWASs.
      The GOLM1 genetic instrumental variables (IVs) were selected on the basis of cis-protein quantitative trait loci (cis-pQTLs) identified in recent proteomics genome-wide association study (GWAS) including 3,301 European individuals.
      • Sun BB
      • Maranville JC
      • Peters JE
      • Stacey D
      • Staley JR
      • Blackshaw J
      • et al.
      Genomic atlas of the human plasma proteome.
      pQTLs strongly associated with GOLM1 at a threshold of p < 5e-6 were chosen. Linkage disequilibrium (LD) analysis by the LDlinkR package was used to eliminate cis-pQTLs (r2 > 0.1) based on the 1000-genome European reference panel. F statistics were assessed to determine the instrument strength, and F ≥ 10 indicates strong instruments. Finally, the candidate GOLM1 genetic IVs were listed in Suppl. Table 1.
      Table 1Corona Virus Disease 2019 (COVID-19) GWAS datasets.
      GWAS IDtraitncasencontrolnsnppopulation
      ebi-a-GCST011074COVID-19 (RELEASE 5)3249413162078666451European
      ebi-a-GCST010776COVID-19 (RELEASE 4)14134128487611435708European
      ebi-a-GCST010780COVID-19 (RELEASE 4)14134128487612508741European
      ebi-a-GCST011072COVID-19 (RELEASE 5)3156210268487750967European
      ebi-a-GCST011073COVID-19 (RELEASE 5)3898416447848660177European
      ebi-a-GCST011071COVID-19 (RELEASE 5)2907115597128103014European
      ebi-a-GCST010781COVID-19 (predicted covid from self-reported symptoms vs predicted or self-reported non-covid) RELEASE 432043572811379674European
      ebi-a-GCST010778COVID-19 (covid vs lab/self reported negative) RELEASE 4881810180612832272European
      ebi-a-GCST011075COVID-19 (very severe respiratory confirmed vs population) RELEASE 5510113832419739225European
      ebi-a-GCST011076COVID-19 (very severe respiratory confirmed vs population) RELEASE 546067028017475770European
      ebi-a-GCST011078COVID-19 (very severe respiratory confirmed vs population) RELEASE 5479210546649817241European
      ebi-a-GCST010783COVID-19 (very severe respiratory confirmed vs population) RELEASE 4388662226511678750European
      ebi-a-GCST010777COVID-19 (hospitalized vs population) RELEASE 4640690208812832272European
      ebi-a-GCST010779COVID-19 (hospitalized vs population) RELEASE 4640690208811272365European
      ebi-a-GCST011077COVID-19 (very severe respiratory confirmed vs population) RELEASE 5479210546647496658European
      ebi-a-GCST011084COVID-19 (hospitalized vs population) RELEASE 5937311972567534178European
      ebi-a-GCST90000256Severe COVID-19 infection with respiratory failure (analysis II)161021808095992European
      ebi-a-GCST90000255Severe COVID-19 infection with respiratory failure (analysis I)161022058095360European
      ebi-a-GCST011082COVID-19 (hospitalized vs population) RELEASE 5831615490956814406European
      GWAS ID: Genome wide association study identity; ncase: the number of COVID-19 case; ncontrol: the number of the control; nsnp: the number of single-nucleotide polymorphism.
      The instrumental variables for COVID-19 were retrieved at the genome-wide significance (p < 5e-8) from the largest GWAS meta-analysis of COVID-19 to date, by the COVID-19 Host Genetics Initiative.
      COVID-19 Host Genetics Initiative
      The COVID-19 Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic.
      In total, we used twenty COVID-19 GWASs for COVID-19 severity (e.g. “Severe COVID-19 infection with respiratory failure, id:ebi-a-GCST90000256”, etc) or susceptibility (e.g. “COVID-19 RELEASE 5, id:ebi-a-GCST011072”, etc) respectively. Summary statistics about twenty COVID-19 GWASs of persons with European ancestry are shown in Table 1, and GWAS summary datasets are available in https://gwas.mrcieu.ac.uk/datasets/.
      The independent GOLM1 genetic IVs from twenty COVID-2019 GWAS datasets were then standardized. Potential proxy SNPs were identified by the LD proxy tool (r2 > 0.80) when these IVs could not be found. The association of these IVs with the twenty COVID-19 GWAS datasets is shown in Suppl. Table 2.
      The MR-PRESSO, MR-Egger_intercept, MR-Egger, and Inverse variance weighted (IVW) methods in Cochran's Q statistic were used to examine the pleiotropy or heterogeneity of the independent GOLM1 genetic IVs in the COVID-19 GWASs. No evident pleiotropy or heterogeneity of these IVs was seen in the COVID-19 GWAS datasets (Suppl. Table 3). Consequently, all identified GOLM1 genetic variations may be regarded as effective IVs in our MR investigation.
      Further, we used MR to analyze the effect of the GOLM1 genetic IVs on the risk of contracting COVID-19. Interestingly, we found that as GOLM1 genetically increased, the risk of severe respiratory COVID-19 (ebi-a-GCST90000256) had an increased trend using MR Egger (Beta = 0.823, p = 6.96E-03; OR = 2.277), weighted mode (Beta = 0.587, p = 2.07E-03; OR = 1.799), weighted median (Beta = 0.573, p = 9.81E-06; OR = 1.773), and IVW (Beta = 0.410, p = 1.81E-04; OR = 1.507) (Fig. 1; Suppl. Table 4). In addition, the impact of a single SNP on COVID-19 risk rose as the effect of a single SNP on GOLM1 increased, as measured by IVW, weighted median, simple mode, and weighted mode (Fig. 1A). Critically, each effect size (Fig. 1B) and leave-one-out sensitivity (Fig. 1C) suggested that each effect of GOLM1-associated SNPs on COVID-19 risk were robust. Our MR results were replicated in other 19 COVID-19 GWASs to ensure robustness and reduce false positives (Suppl. Table 4; Suppl. Fig. 2-4).
      In summary, we found an OR of about 1.20 for COVID-19 per 1 SD increase in GOLM1 levels and replicated in multiple independent datasets (Fig. 1D). Warranting further investigations, severe cases of SARS-CoV-2 infection are related with high blood glucose levels and metabolic complications. Recent research suggested that GOLM1 is a glucogenic hormone that contributes to the SARS-CoV-2-induced change in systemic glucose metabolism and increased hepatic gluconeogenesis.
      • Wan L
      • Gao Q
      • Deng Y
      • Ke Y
      • Ma E
      • Yang H
      • et al.
      GP73 is a glucogenic hormone contributing to SARS-CoV-2-induced hyperglycemia.
      We then conducted a MR study to investigate the associations of genetically predicted GOLM1 with glucose (Suppl. Table 5). Our MR result of a favorable impact of GOLM1 on glucose levels is consistent with a prior finding that plasma GOLM1 levels are increased in COVID-19 patients and positively correlate with blood glucose levels.
      • Wan L
      • Gao Q
      • Deng Y
      • Ke Y
      • Ma E
      • Yang H
      • et al.
      GP73 is a glucogenic hormone contributing to SARS-CoV-2-induced hyperglycemia.
      This study has several limitations. First, GOLM1 genetic IVs and COVID-19 GWAS are from European ancestry. Our conclusion need be proven in other ancestries. Second, GOLM1 blockade with an antibody inhibits excessive glucogenesis stimulated by SARS-CoV-2 in vitro and lowers elevated fasting blood glucose levels in infected mice.
      • Wan L
      • Gao Q
      • Deng Y
      • Ke Y
      • Ma E
      • Yang H
      • et al.
      GP73 is a glucogenic hormone contributing to SARS-CoV-2-induced hyperglycemia.
      It is necessary to clarify whether inhibiting GOLM1 could reduce the risk of severe respiratory COVID-19 in the future research.
      To conclude, our study provides evidence for a causal effect of GOLM1 on COVID-19. As such, further investigation is warranted exploring GOLM1 as a potential novel biomarker and therapeutic target for COVID-19 patients or those at risk of acquiring severe symptoms.

      Funding

      This study was supported by grants from National Natural Science Foundation of China (81772520). The funder had no role in the study design, collection, analysis and interpretation of data, in the writing of the manuscript or in the decision to submit the manuscript for publication.

      Authors’ contributions

      JY conceived and initiated the project, and were responsible for the design of the study. F-JT, L-YX, FX and L-JM access all the data in the study and took responsibility for the accuracy of the data analysis. JY and F-JT performed the statistical analysis. All authors were involved in the writing and revision of the article, and all authors approved the submitted version to be published.

      Declaration of competing interest

      The authors have no potential conflicts of interest to disclose.

      Acknowledgement

      We thank the COVID-19 Host Genetics Initiative for making GWAS summary statistics publicly available. We also thank ieu open gwas project (https://gwas.mrcieu.ac.uk/datasets/) for providing summary data for these analyses.

      Appendix. Supplementary materials

      • Supplementary materials

        Supplementary Figure 1. The design flow chart for the Mendelian randomization (MR) study. MR assumptions: hypothesis 1, 2 and 3. Solid line represents direct causal effects that GOLM1 genetic instrumental variants are reliably associated with GOLM1 levels, and influence the risk of Corona Virus Disease 2019 (COVID-19) through the GOLM1, respectively, in H1. Dotted line represents that GOLM1 genetic instrumental variants are not associated with any measured and unmeasured confounders and do not influence the risk of COVID-19 through other pathways in H2 and H3, respectively.

        Supplementary Figure 2. Individual estimates about the causal effect of GOLM1 on COVID-19.

        Supplementary Figure 3. Forest plot of GOLM1 associated SNPs with risk of COVID-19.

        Supplementary Figure 4. MR leave-one-out sensitivity analysis for the effect of GOLM1 SNPs on COVID-19.

        Supplementary Table 1. GOLM1 genetic instrumental variants (IVs).

        Supplementary Table 2. Association of GOLM1 genetic IVs in COVID-19 GWAS datasets.

        Supplementary Table 3. Pleiotropy and heterogeneity test of GOLM1 genetic IVs in COVID-19 GWAS datasets.

        Supplementary Table 4. The causal association of GOLM1 with COVID-19.

        Supplementary Table 5. The causal association of GOLM1 with glucose.

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