Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels

Subtype-specific gout susceptibility loci and enrichment of selection pressure on ABCG2 and ALDH2 identified by subtype genome-wide meta-analyses of clinically defined gout patients

OBJECTIVES

Genome-wide meta-analyses of clinically defined gout were performed to identify subtype-specific susceptibility loci. Evaluation using selection pressure analysis with these loci was also conducted to investigate genetic risks characteristic of the Japanese population over the last 2000-3000 years.

METHODS

Two genome-wide association studies (GWASs) of 3053 clinically defined gout cases and 4554 controls from Japanese males were performed using the Japonica Array and Illumina Array platforms. About 7.2 million single-nucleotide polymorphisms were meta-analysed after imputation. Patients were then divided into four clinical subtypes (the renal underexcretion type, renal overload type, combined type and normal type), and meta-analyses were conducted in the same manner. Selection pressure analyses using singleton density score were also performed on each subtype.

RESULTS

In addition to the eight loci we reported previously, two novel loci, PIBF1 and ACSM2B, were identified at a genome-wide significance level (p<5.0×10-8) from a GWAS meta-analysis of all gout patients, and other two novel intergenic loci, CD2-PTGFRN and SLC28A3-NTRK2, from normal type gout patients. Subtype-dependent patterns of Manhattan plots were observed with subtype GWASs of gout patients, indicating that these subtype-specific loci suggest differences in pathophysiology along patients' gout subtypes. Selection pressure analysis revealed significant enrichment of selection pressure on ABCG2 in addition to ALDH2 loci for all subtypes except for normal type gout.

CONCLUSIONS

Our findings on subtype GWAS meta-analyses and selection pressure analysis of gout will assist elucidation of the subtype-dependent molecular targets and evolutionary involvement among genotype, phenotype and subtype-specific tailor-made medicine/prevention of gout and hyperuricaemia.

High-Protein Diet Induces Hyperuricemia in a New Animal Model for Studying Human Gout

Hyperuricemia is a central risk factor for gout and increases the risk for other chronic diseases, including cardiometabolic disease, kidney disease, and hypertension. Overproduction of urate is one of the main reasons for hyperuricemia, and dietary factors including seafoods, meats, and drinking are contributed to the development of it. However, the lack of a suitable animal model for urate metabolism is one of the main reasons for the delay and limitations of hyperuricemia research. Combining evolutionary biological studies and clinical studies, we conclude that chicken is a preferred animal model for hyperuricemia. Thus, we provided chickens a high-protein diet (HPD) to evaluate the changes in the serum urate levels in chickens. In our study, the HPD increased the serum urate level and maintained it at a long-term high level in chickens. Long-term high serum urate levels induced an abnormal chicken claw morphology and the precipitation of monosodium urate (MSU) in joint synovial fluid. In addition, a long-term HPD also decreased the glomerular filtration rate and induced mild renal injury. Most importantly, allopurinol and probenecid displayed the positive effects in decreasing serum urate and then attenuated hyperuricemia in chicken model. These findings provide a novel model for hyperuricemia and a new opportunity to further investigate the effects of long-term hyperuricemia on other metabolic diseases.

Pleiotropic effect of the ABCG2 gene in gout: involvement in serum urate levels and progression from hyperuricemia to gout

BACKGROUND

The ABCG2 Q141K (rs2231142) and rs10011796 variants associate with hyperuricaemia (HU). The effect size of ABCG2 rs2231142 on urate is ~ 60% that of SLC2A9, yet the effect size on gout is greater. We tested the hypothesis that ABCG2 plays a role in the progression from HU to gout by testing for association of ABCG2 rs2231142 and rs10011796 with gout using HU controls.

METHODS

We analysed 1699 European gout cases and 14,350 normouricemic (NU) and HU controls, and 912 New Zealand (NZ) Polynesian (divided into Eastern and Western Polynesian) gout cases and 696 controls. Association testing was performed using logistic and linear regression with multivariate adjusting for confounding variables.

RESULTS

In Europeans and Polynesians, the ABCG2 141K (T) allele was associated with gout using HU controls (OR = 1.85, P = 3.8E- 21 and ORmeta = 1.85, P = 1.3E- 03, respectively). There was evidence for an effect of 141K in determining HU in European (OR = 1.56, P = 1.7E- 18) but not in Polynesian (ORmeta = 1.49, P = 0.057). For SLC2A9 rs11942223, the T allele associated with gout in the presence of HU in European (OR = 1.37, P = 4.7E- 06), however significantly weaker than ABCG2 rs2231142 141K (PHet = 0.0023). In Western Polynesian and European, there was epistatic interaction between ABCG2 rs2231142 and rs10011796. Combining the presence of the 141K allele with the rs10011796 CC-genotype increased gout risk, in the presence of HU, 21.5-fold in Western Polynesian (P = 0.009) and 2.6-fold in European (P = 9.9E- 06). The 141K allele of ABCG2 associated with increased gout flare frequency in Polynesian (Pmeta = 2.5E- 03).

CONCLUSION

These data are consistent with a role for ABCG2 141K in gout in the presence of established HU.

A genome-wide multiphenotypic association analysis identified candidate genes and gene ontology shared by four common risky behaviors

BACKGROUND

Risky behaviors can lead to huge economic and health losses. However, limited efforts are paid to explore the genetic mechanisms of risky behaviors.

RESULT

MASH analysis identified a group of target genes for risky behaviors, such as APBB2, MAPT and DCC. For GO enrichment analysis, FUMA detected multiple risky behaviors related GO terms and brain related diseases, such as regulation of neuron differentiation (adjusted P value = 2.84×10^-5), autism spectrum disorder (adjusted P value =1.81×10^-27) and intelligence (adjusted P value =5.89×10^-15).

CONCLUSION

We reported multiple candidate genes and GO terms shared by the four risky behaviors, providing novel clues for understanding the genetic mechanism of risky behaviors.

METHODS

Multivariate Adaptive Shrinkage (MASH) analysis was first applied to the GWAS data of four specific risky behaviors (automobile speeding, drinks per week, ever-smoker, number of sexual partners) to detect the common genetic variants shared by the four risky behaviors. Utilizing genomic functional annotation data of SNPs, the SNPs detected by MASH were then mapped to target genes. Finally, gene set enrichment analysis of the identified candidate genes were conducted by the FUMA platform to obtain risky behaviors related gene ontology (GO) terms as well as diseases and traits, respectively.

Whole-Exome Sequencing Reveals a Rare Missense Variant in SLC16A9 in a Pedigree with Early-Onset Gout

Gout is a common inflammatory arthritis triggered by monosodium urate deposition after longstanding hyperuricemia. In the general community, the disease is largely polygenic in genetic architecture, with many polymorphisms having been identified in gout or urate-associated traits. In a small proportion of cases, rare high penetrant mutations associated with monogenic segregation of the disease in families have been demonstrated to be disease causative. In this study, we recruited a two-generation pedigree with early-onset gout. To elucidate the genetic predisposition, whole-exome sequencing (WES) was performed. After comprehensive variant analyses and cosegregation testing, we identified a missense variant (c.277C>A, p.L93M) in SLC16A9, an extremely rare variant in genetic databases. Moreover, in silico assessments showed strong pathogenicity. This variant cosegregated with the disease phenotype perfectly in the family and is located in a highly conserved functional domain. A few studies supported our results of the association between SLC16A9 and gout and serum urate levels. In conclusion, we provide the first evidence for the association of rare missense in SLC16A9 with early-onset gout. These findings not only expand our current understanding of gout but also may have further implications for the treatment and prevention of gout.

Association of serum uric acid with visceral, subcutaneous and hepatic fat quantified by magnetic resonance imaging

Elevated serum uric acid (SUA) is associated with a variety of medical conditions, such as hypertension, diabetes and obesity. Analyses investigating uric acid and obesity were primarily conducted using anthropometric measures like BMI and waist circumference. However, different adipose tissue depots might be differentially affected in uric acid metabolism. We analyzed the relation of SUA with visceral, subcutaneous and hepatic fat as quantified by Magnetic Resonance Imaging in N = 371 individuals from a cross-sectional sample of a population-based cohort. Associations of SUA and fat depots were calculated by regressions adjusted for potential confounders. We found that SUA was correlated with all fat measures (e.g. Pearson’s r between SUA and hepatic fat: 0.50, 95%-CI: 0.42, 0.57). Associations with visceral and hepatic fat, but not with subcutaneous fat, remained evident after adjustment for anthropometric measures (e.g. visceral fat: β = 0.51 l, 95%-CI: 0.30 l, 0.72 l). In conclusion, these results show how different adipose tissue compartments are affected by SUA to varying degrees, thus emphasizing the different physiological roles of these adipose tissues in uric acid metabolism.