Genetics Of Osteopontin in Patients With Chronic Kidney Disease: The German Chronic Kidney Disease Study Ⅳ

Plasma proteome.

In addition, we used GWAS summary statistics of plasma proteins (pGWAS) by Sun et al. [22] to run colocalization analysis to identify consistent association signals between OPN and proteins with effects in cis as well as in trans. In contrast to data from GTEx and NephQTL, the genome-wide available pGWAS summary does allow the assessment of both effects. To detect colocalization with cis-pQTLs, pGWAS summary statistics of any pro-tein-gene region (gene region ±500kb, cis region) were extracted. Per the OPN locus and a 100kb region around it, we checked if any of the cis pGWAS extracts overlapped and had a pGWAS association p-value of <0.05/2 (Bonferroni correction for two OPN loci). For all hereby selected proteins, we then extracted the protein-gene-region from the OPN GWAS summary statistics and ran colocalization within the protein-gene-region. For potential colocalization with trans-pQTLs, we selected all proteins with pGWAS association p-values <0.05/2/3,000 (Bonferroni correction for the two OPN loci and number of proteins evaluated in pGWAS) within a 100kb region around an OPN-associated index SNP. For all hereby selected proteins, colocalization analyses were conducted within the ±500kb region of the OPN-associated index SNP.

Organic Health Food For Kidney Health

For colocalizing proteins, a Gene ontology (GO) enrichment analysis (http://geneontology. org/, [101,102]) in the form of a PANTHER [103] overrepresentation test with the two annotation data sets of GO cellular component and GO molecular function (homo sapiens) as references were conducted to assess enriched categories to which identified proteins were assigned to. Overall, 20,595 human genes are mapped to various terms related to cellular components and molecular function. A category is considered enriched if both, the Bonferroni-corrected p-value of the Fisher's exact test and the false discovery rate based on the Benjamini-Hochberg procedure, are <0.05

UKB diseases. Finally, we used the GWAS from the GeneAtlas database (http://geneatlas. Roslin.ed.ac.uk/) to perform colocalization analysis for the two replicated OPN loci (±500kb) and all UKB binary disease traits that showed genome-wide significant associations (p-value <5.0E-08) in at least one of the two replicated OPN loci. Overall, GeneAtlas comprises GWAS results of 660 binary disease traits of ~450,000 UKB participants [23]. In addition, we adopted the conditional colocalization analysis approach which was first applied in a GWAS of plasma proteome [104].

 Performing colocalization on conditionally independent association statistics could reveal true colocalization signals that were missing when using marginal association statistics in the presence of multiple independent association signals. We applied the GCTA COJO Slct algorithm to identify independent association signals in the OPN region for the seven traits [105], which showed a trait association signal different from the OPN signal (H3: p1.2>0.8). The cleaned and imputed GCKD genotype dataset mentioned before was used as an LD reference by GCTA. We set the collinearity cutoff at 0.1 to be conservative. For loci with more than 1 independent signal, an approximate conditional analysis was conducted by the GCTA COJO-Cond algorithm to generate conditional association statistics conditioned on the other independent SNPs in the region [105]. Finally, the colocalization analyses were performed as before for each of the independent SNPs using the conditional association statistics as input.

Aggregated rare variant testing

Overall, 4,879 GCKD participants with complete data on genotyping (Exome chip), eGFR, UACR, and log2(OPN) measurements were included in the analysis of aggregated rare variant testing (S1 Fig). As previously described [106], two types of rare variant aggregation tests (burden test, sequence kernel association test [SKAT]) implemented in the R package seqMeta (v1.6.7, [107]) were conducted using exome chip data and log2(OPN) measurements (outcome). Per gene, variants with MAF <1% and having a major effect on the gene product (nonsynonymous, stop gain/loss, splicing; "qualifying variants") as annotated by dbNSFP v.2.0 were aggregated [24,25]. Results were filtered to retain genes with cumulative minor allele count (MAC) �10 and with �2 contributing variants per gene. Analyses were adjusted for age, sex, log(eGFR), and log(UACR). To adjust for multiple testing, the statistical significance level was corrected for the number of assessed genes (N = 17,575) and the two conducted tests: 0.05/(2×17,575) = 1.4E-06. Moreover, analyses were repeated for significantly associated genes additionally adjusted for the two replicated OPN loci.

Replication of identified loci in Young Finns Study 

The three OPN loci identified in the GWAS of GCKD participants were tested for replication in the Cardiovascular Risk in Young Finns Study (YFS) cohort. Here, plasma OPN was measured by enzyme-linked immunosorbent assay (Human Osteopontin Quantikine kit, R&D Systems, USA) from samples thawed for the first time for the assay in 2007. Samples of 2,442 participants and 546,677 genotyped SNPs were available for further analysis after QC and imputation. Further details can be found in S1 Methods. Per the selected locus, association analysis of log2(OPN) on SNP dosage (additive) was formed by fitting linear regression models adjusted for age, sex, and eGFR by using SNPTEST v2.5.4 [89]. GFR was estimated with the MDRD study equation and log2-transformed before analysis [108]. Replication was defined by a one-sided association p-value <0.05/3 (Bonferroni correction for three OPN loci).

Acknowledgments We are grateful for the willingness of the CKD patients to participate in the GCKD study. The enormous effort of the study personnel of the various regional centers is highly appreciated. We thank the large number of nephrologists who provide routine care for the patients and collaborate with the GCKD study. The GCKD Investigators are listed in the S1 Information. A complete list of nephrologists currently collaborating with the GCKD study is available at (http://gckd.org). 

Author Contributions Conceptualization: Peggy Sekula, Ulla T. Schultheiss. 

Data curation: Yurong Cheng, Yong Li, Nora Scherer, Franziska Grundner-Culemann, Terho Lehtima¨ki, Binisha H. Mishra, Olli T. Raitakari, Matthias Nauck, Kai-Uwe Eckardt, Peggy Sekula, Ulla T. Schultheiss. 

Formal analysis: Yurong Cheng, Yong Li, Nora Scherer, Franziska Grundner-Culemann, Binisha H. Mishra, Peggy Sekula, Ulla T. Schultheiss.

Funding acquisition: Terho Lehtima¨ki, Olli T. Raitakari, Kai-Uwe Eckardt, Peggy Sekula, Ulla T. Schultheiss. 

Investigation: Binisha H. Mishra, Olli T. Raitakari, Peggy Sekula, Ulla T. Schultheiss. 

Methodology: Yurong Cheng, Yong Li, Nora Scherer, Franziska Grundner-Culemann, Binisha H. Mishra, Matthias Nauck, Peggy Sekula, Ulla T. Schultheiss. 

Project administration: Peggy Sekula, Ulla T. Schultheiss.

Resources: Peggy Sekula, Ulla T. Schultheiss. 

Software: Yurong Cheng, Yong Li, Binisha H. Mishra, Peggy Sekula, Ulla T. Schultheiss.

Supervision: Nora Scherer, Franziska Grundner-Culemann, Peggy Sekula, Ulla T. Schultheiss.

Validation: Yurong Cheng, Yong Li, Nora Scherer, Franziska Grundner-Culemann, Terho Lehtima¨ki, Binisha H. Mishra, Olli T. Raitakari, Matthias Nauck, Kai-Uwe Eckardt, Peggy Sekula, Ulla T. Schultheiss. 

References

1. Kaleta B. The role of osteopontin in kidney diseases. Inflamm Res. 2019; 68(2):93–102. https://doi. org/10.1007/s00011-018-1200-5 PMID: 30456594. 

2. Denhardt DT, Guo X. Osteopontin: a protein with diverse functions. Faseb j. 1993; 7(15):1475–82. Epub 1993/12/01. PMID: 8262332. 

3. Ross FP, Chappel J, Alvarez JI, Sander D, Butler WT, Farach-Carson MC, et al. Interactions between the bone matrix proteins osteopontin and bone sialoprotein and the osteoclast integrin alpha v beta 3potentiate bone resorption. The Journal of Biological Chemistry. 1993; 268(13):9901–7. Epub 1993/05/ 05. PMID: 8486670. 

4. Green PM, Ludbrook SB, Miller DD, Horgan CM, Barry ST. Structural elements of the osteopontin SVVYGLR motif are important for the interaction with alpha(4) integrins. FEBS Lett. 2001; 503(1):75–9. https://doi.org/10.1016/s0014-5793(01)02690-4 PMID: 11513858. 

5. Hu DD, Lin EC, Kovach NL, Hoyer JR, Smith JW. Biochemical characterization of the binding of osteopontin to integrins alpha v beta 1 and alpha v beta 5. J Biol Chem. 1995; 270(44):26232–8. https://doi.org/10.1074/jbc.270.44.26232 PMID: 7592829. 

6. Xie Y, Sakatsume M, Nishi S, Narita I, Arakawa M, Gejyo F. Expression, roles, receptors, and regulation of osteopontin in the kidney. Kidney Int. 2001; 60(5):1645–57. https://doi.org/10.1046/j.1523- 1755.2001.00032.x PMID: 11703581. 

7. Rabb H, Barroso-Vicens E, Adams R, Pow-Sang J, Ramirez G. Alpha-V/beta-3 and alpha-V/beta-5 integrin distribution in neoplastic kidney. Am J Nephrol. 1996; 16(5):402–8. https://doi.org/10.1159/ 000169032 PMID: 8886177. 

8. Lopez CA, Hoyer JR, Wilson PD, Waterhouse P, Denhardt DT. Heterogeneity of osteopontin expression among nephrons in mouse kidneys and enhanced expression in sclerotic glomeruli. Laboratory investigation; a journal of technical methods and pathology. 1993; 69(3):355–63. Epub 1993/09/01. PMID: 8377476. 

9. Giacopelli F, Marciano R, Pistorio A, Catarsi P, Canini S, Karsenty G, et al. Polymorphisms in the osteopontin promoter affect its transcriptional activity. Physiological genomics. 2004; 20(1):87–96. Epub 2004/10/14. https://doi.org/10.1152/physiolgenomics.00138.2004 PMID: 15479859. 

10. Salimi S, Noora M, Nabizadeh S, Rezaei M, Shahraki H, Milad MK, et al. Association of the osteopontintin rs1126616 polymorphism and a higher serum osteopontin level with lupus nephritis. Biomed Rep. 2016; 4(3):355–60. Epub 2016/03/22. https://doi.org/10.3892/br.2016.589 PMID: 26998275; PubMed Central PMCID: PMC4774351.

11. Forton AC, Petri MA, Goldman D, Sullivan KE. An osteopontin (SPP1) polymorphism is associated with systemic lupus erythematosus. Hum Mutat. 2002; 19(4):459. Epub 2002/04/05. https://doi.org/10. 1002/humu.9025 PMID: 11933203. 

12. Xu AP, Bai J, Lu¨ J, Liang YY, Li JG, Lai DY, et al. Osteopontin gene polymorphism in association with systemic lupus erythematosus in Chinese patients. Chin Med J (Engl). 2007; 120(23):2124–8. Epub 2008/01/03. PMID: 18167187. 

13. Fathy R, Elsayed GR, Hasan AS. Osteopontin 9250 C/T Gene Polymorphism in Egyptian Lupus Nephritis Patients. SL Clinical Medicine: Research. 2021; 4(1):120. 

14. Kaleta B, Krata N, Zagozdzon R, Mucha K. Osteopontin Gene Polymorphism and Urinary OPN Excretion in Patients with Immunoglobulin A Nephropathy. Cells. 2019; 8(6). https://doi.org/10.3390/ cells8060524 PMID: 31159229; PubMed Central PMCID: PMC6628186.

15. Cheema BS, Iyengar S, Ahluwalia TS, Kohli HS, Sharma R, Shah VN, et al. Association of an Osteopontin gene promoter polymorphism with susceptibility to diabetic nephropathy in Asian Indians. Clinica chimica Acta; International Journal of Clinical Chemistry. 2012; 413(19–20):1600–4. Epub 2012/05/ 16. https://doi.org/10.1016/j.cca.2012.04.028 PMID: 22584029.