Associations between interleukin and interleukin receptor gene polymorphisms and risk of gout

The causal association between immune cells and gout: A bidirectional two-sample Mendelian randomization study

Gout, a metabolic disorder, is increasingly being linked to immune cells. However, the causal relationships between these factors remain unclear. Our study aimed to elucidate the causal relationship between immune cells and gout. Our study used 2-sample Mendelian randomization (MR) to explore the causal relationship between immune cells and gout. It is noteworthy that we utilized 5 methods MR-Egger, weighted median, inverse variance weighted, weighted mode, and simple mode to ensure the reliability of the results. Comprehensive sensitivity analyses were performed to verify the robustness, heterogeneity, and horizontal pleiotropy of the results. After false discovery rate correction (PFDR <0.20), 3 immunophenotypes were identified: one in the B cell panel, one in the regulatory T cells panel, and another in the T lymphocytes, B lymphocytes, Natural Killer cells panel. Among them, 2 immunophenotypes (CD4-CD8-T cell absolute count and CD25 on IgD + CD24 + B cell) increased the risk of developing gout, whereas the other one immunophenotype (CD45RA + CD28- CD8 + T cell %T cell) decreased the risk of gout. Subsequently, we did not observe heterogeneity and horizontal pleiotropy stable in these data through comprehensive sensitivity analyses. Furthermore, we identified some positive results in reverse MR analysis, but after false discovery rate correction (PFDR <0.20), no significant results were detected. Our study revealed causal relationships between immune cells and gout, providing novel insights into the prevention and treatment of gout.

Genetic and Epigenetic Regulation of the Innate Immune Response to Gout

Gout is a disease caused by uric acid (UA) accumulation in the joints, causing inflammation. Two UA forms - monosodium urate (MSU) and soluble uric acid (sUA) have been shown to interact physically with inflammasomes, especially with the nod-like receptor (NLR) family pyrin domain containing 3 (NLRP3), albeit the role of the immune response to UA is poorly understood, given that asymptomatic hyperuricemia does also exist. Macrophage phagocytosis of UA activate NLRP3, lead to cytokines release, and ultimately, lead to chemoattract neutrophils and lymphocytes to the gout flare joint spot. Genetic variants of inflammasome genes and of genes encoding their molecular partners may influence hyperuricemia and gout susceptibility, while also influencing other comorbidities such as metabolic syndrome and cardiovascular diseases. In this review, we summarize the inflammatory responses in acute and chronic gout, specifically focusing on innate immune cell mechanisms and genetic and epigenetic characteristics of participating molecules. Unprecedently, a novel UA binding protein - the neuronal apoptosis inhibitor protein (NAIP) - is suggested as responsible for the asymptomatic hyperuricemia paradox.Abbreviation: β2-integrins: leukocyte-specific adhesion molecules; ABCG2: ATP-binding cassete family/breast cancer-resistant protein; ACR: American college of rheumatology; AIM2: absent in melanoma 2, type of pattern recognition receptor; ALPK1: alpha-protein kinase 1; ANGPTL2: angiopoietin-like protein 2; ASC: apoptosis-associated speck-like protein; BIR: baculovirus inhibitor of apoptosis protein repeat; BIRC1: baculovirus IAP repeat-containing protein 1; BIRC2: baculoviral IAP repeat-containing protein 2; C5a: complement anaphylatoxin; cAMP: cyclic adenosine monophosphate; CARD: caspase activation and recruitment domains; CARD8: caspase recruitment domain-containing protein 8; CASP1: caspase 1; CCL3: chemokine (C-C motif) ligand 3; CD14: cluster of differentiation 14; CD44: cluster of differentiation 44; Cg05102552: DNA-methylation site, usually cytosine followed by guanine nucleotides; contains arbitrary identification code; CIDEC: cell death-inducing DNA fragmentation factor-like effector family; CKD: chronic kidney disease; CNV: copy number variation; CPT1A: carnitine palmitoyl transferase - type 1a; CXCL1: chemokine (CXC motif) ligand 1; DAMPs: damage associated molecular patterns; DC: dendritic cells; DNMT(1): maintenance DNA methyltransferase; eQTL: expression quantitative trait loci; ERK1: extracellular signal-regulated kinase 1; ERK2: extracellular signal-regulated kinase 2; EULAR: European league against rheumatism; GMCSF: granulocyte-macrophage colony-stimulating factor; GWAS: global wide association studies; H3K27me3: tri-methylation at the 27th lysine residue of the histone h3 protein; H3K4me1: mono-methylation at the 4th lysine residue of the histone h3 protein; H3K4me3: tri-methylation at the 4th lysine residue of the histone h3 protein; HOTAIR: human gene located between hoxc11 and hoxc12 on chromosome 12; IκBα: cytoplasmatic protein/Nf-κb transcription inhibitor; IAP: inhibitory apoptosis protein; IFNγ: interferon gamma; IL-1β: interleukin 1 beta; IL-12: interleukin 12; IL-17: interleukin 17; IL18: interleukin 18; IL1R1: interleukin-1 receptor; IL-1Ra: interleukin-1 receptor antagonist; IL-22: interleukin 22; IL-23: interleukin 23; IL23R: interleukin 23 receptor; IL-33: interleukin 33; IL-6: interleukin 6; IMP: inosine monophosphate; INSIG1: insulin-induced gene 1; JNK1: c-jun n-terminal kinase 1; lncRNA: long non-coding ribonucleic acid; LRR: leucine-rich repeats; miR: mature non-coding microRNAs measuring from 20 to 24 nucleotides, animal origin; miR-1: miR followed by arbitrary identification code; miR-145: miR followed by arbitrary identification code; miR-146a: miR followed by arbitrary identification code, "a" stands for mir family; "a" family presents similar mir sequence to "b" family, but different precursors; miR-20b: miR followed by arbitrary identification code; "b" stands for mir family; "b" family presents similar mir sequence to "a" family, but different precursors; miR-221: miR - followed by arbitrary identification code; miR-221-5p: miR followed by arbitrary identification code; "5p" indicates different mature miRNAs generated from the 5' arm of the pre-miRNA hairpin; miR-223: miR followed by arbitrary identification code; miR-223-3p: mir followed by arbitrary identification code; "3p" indicates different mature miRNAs generated from the 3' arm of the pre-miRNA hairpin; miR-22-3p: miR followed by arbitrary identification code, "3p" indicates different mature miRNAs generated from the 3' arm of the pre-miRNA hairpin; MLKL: mixed lineage kinase domain-like pseudo kinase; MM2P: inductor of m2-macrophage polarization; MSU: monosodium urate; mTOR: mammalian target of rapamycin; MyD88: myeloid differentiation primary response 88; n-3-PUFAs: n-3-polyunsaturated fatty-acids; NACHT: acronym for NAIP (neuronal apoptosis inhibitor protein), C2TA (MHC class 2 transcription activator), HET-E (incompatibility locus protein from podospora anserina) and TP1 (telomerase-associated protein); NAIP: neuronal apoptosis inhibitory protein (human); Naip1: neuronal apoptosis inhibitory protein type 1 (murine); Naip5: neuronal apoptosis inhibitory protein type 5 (murine); Naip6: neuronal apoptosis inhibitory protein type 6 (murine); NBD: nucleotide-binding domain; Nek7: smallest NIMA-related kinase; NET: neutrophil extracellular traps; Nf-κB: nuclear factor kappa-light-chain-enhancer of activated b cells; NFIL3: nuclear-factor, interleukin 3 regulated protein; NIIMA: network of immunity in infection, malignancy, and autoimmunity; NLR: nod-like receptor; NLRA: nod-like receptor NLRA containing acidic domain; NLRB: nod-like receptor NLRA containing BIR domain; NLRC: nod-like receptor NLRA containing CARD domain; NLRC4: nod-like receptor family CARD domain containing 4; NLRP: nod-like receptor NLRA containing PYD domain; NLRP1: nucleotide-binding oligomerization domain, leucine-rich repeat, and pyrin domain containing 1; NLRP12: nucleotide-binding oligomerization domain, leucine-rich repeat, and pyrin domain containing 12; NLRP3: nod-like receptor family pyrin domain containing 3; NOD2: nucleotide-binding oligomerization domain; NRBP1: nuclear receptor-binding protein; Nrf2: nuclear factor erythroid 2-related factor 2; OR: odds ratio; P2X: group of membrane ion channels activated by the binding of extracellular; P2X7: p2x purinoceptor 7 gene; p38: member of the mitogen-activated protein kinase family; PAMPs: pathogen associated molecular patters; PBMC: peripheral blood mononuclear cells; PGGT1B: geranylgeranyl transferase type-1 subunit beta; PHGDH: phosphoglycerate dehydrogenase; PI3-K: phospho-inositol; PPARγ: peroxisome proliferator-activated receptor gamma; PPARGC1B: peroxisome proliferative activated receptor, gamma, coactivator 1 beta; PR3: proteinase 3 antigen; Pro-CASP1: inactive precursor of caspase 1; Pro-IL1β: inactive precursor of interleukin 1 beta; PRR: pattern recognition receptors; PYD: pyrin domain; RAPTOR: regulatory associated protein of mTOR complex 1; RAS: renin-angiotensin system; REDD1: regulated in DNA damage and development 1; ROS: reactive oxygen species; rs000*G: single nuclear polymorphism, "*G" is related to snp where replaced nucleotide is guanine, usually preceded by an id number; SLC2A9: solute carrier family 2, member 9; SLC7A11: solute carrier family 7, member 11; SMA: smooth muscular atrophy; Smac: second mitochondrial-derived activator of caspases; SNP: single nuclear polymorphism; Sp3: specificity protein 3; ST2: serum stimulation-2; STK11: serine/threonine kinase 11; sUA: soluble uric acid; Syk: spleen tyrosine kinase; TAK1: transforming growth factor beta activated kinase; Th1: type 1 helper T cells; Th17: type 17 helper T cells; Th2: type 2 helper T cells; Th22: type 22 helper T cells; TLR: tool-like receptor; TLR2: toll-like receptor 2; TLR4: toll-like receptor 4; TNFα: tumor necrosis factor alpha; TNFR1: tumor necrosis factor receptor 1; TNFR2: tumor necrosis factor receptor 2; UA: uric acid; UBAP1: ubiquitin associated protein; ULT: urate-lowering therapy; URAT1: urate transporter 1; VDAC1: voltage-dependent anion-selective channel 1.

Aberrant messenger RNA expression in peripheral blood mononuclear cells is associated with gouty arthritis

AIM

Gouty arthritis (GA) is a type of self-limiting inflammatory arthritis caused by deposition of monosodium urate (MSU). This study aimed to analyze the expression variation of messenger RNAs (mRNAs) in GA patients and investigated the role of mRNAs in GA pathogenesis.

METHODS

Five patients with acute GA (AGA), 5 with non-acute GA (NAGA), and 5 healthy controls (HC) were recruited to examine differential mRNA expression profiles in peripheral blood mononuclear cells (PBMCs) and explore whether mRNA is involved in the pathogenesis of AGA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to study the biological functions of differentially expressed mRNA and the relationship between genes and signal pathways.

RESULTS

Compared with HC, the AGA group had 1456 differentially expressed mRNAs, while the NAGA group had 437 differentially expressed mRNAs and compared with the NAGA group, 115 differentially expressed mRNAs were found in the AGA group. GO analysis showed that the differentially expressed mRNA in the AGA group was mainly enriched in processes related to leukocyte activation and immune response, while KEGG analysis showed that "Staphylococcus aureus infection" and "Cytokine-cytokine receptor interaction" are enriched in the up-regulated mRNAs in the AGA group.

CONCLUSION

This study identified genes and pathways that are differentially expressed during the onset of AGA, which might reveal part of the pathogenesis of the disease and provide clues to explaining the severe pain associated with disease onset and the rapid development of inflammatory response that subsides by itself.

Multiple Membrane Transporters and Some Immune Regulatory Genes are Major Genetic Factors to Gout

Gout is a common form of inflammatory arthritis caused by hyperuricemia and the deposition of Monosodium Urate (MSU) crystals. It is also considered as a complex disorder in which multiple genetic factors have been identified in association with its susceptibility and/or clinical outcomes. Major genes that were associated with gout include URAT1, GLUT9, OAT4, NPT1 (SLC17A1), NPT4 (SLC17A3), NPT5 (SLC17A4), MCT9, ABCG2, ABCC4, KCNQ1, PDZK1, NIPAL1, IL1β, IL-8, IL-12B, IL-23R, TNFA, MCP-1/CCL2, NLRP3, PPARGC1B, TLR4, CD14, CARD8, P2X7R, EGF, A1CF, HNF4G and TRIM46, LRP2, GKRP, ADRB3, ADH1B, ALDH2, COMT, MAOA, PRKG2, WDR1, ALPK1, CARMIL (LRRC16A), RFX3, BCAS3, CNIH-2, FAM35A and MYL2-CUX2. The proteins encoded by these genes mainly function in urate transport, inflammation, innate immunity and metabolism. Understanding the functions of gout-associated genes will provide important insights into future studies to explore the pathogenesis of gout, as well as to develop targeted therapies for gout.

Interleukin-23 receptor polymorphism (rs10889677 A/C) in ankylosing spondylitis: Meta-analysis in Caucasian and Asian populations

BACKGROUND

The association between interleukin-23 receptor (IL23R) gene rs10889677 polymorphism and ankylosing spondylitis (AS) susceptibility was inconsistent in the recent literatures. A systematic review and meta-analysis was therefore performed.

METHODS

Online electronic databases were searched for relevant studies published up to November 2017. Meta-analyses were performed for the comparisons of allele (A versus C) and multiple genetic models, including dominant, recessive, heterozygous, and homozygous models using fixed or random effects models. Odds ratios (OR) with 95% confidence intervals (95%CI) were utilized to assess the potential relationship.

RESULTS

Sixteen studies containing 19 separate comparisons, totaling 6450 cases and 8009 controls were included. A significant association between rs10889677 A allele and AS susceptibility was detected (OR=1.136, 95%CI=1.043-1.236, P=0.003). Stratified analysis by ethnicity indicated that rs10889677 A allele was significantly associated with AS in Europeans (OR=1.192, 95%CI=1.080-1.315, P<0.001), but not Asians (OR=1.045, 95%CI=0.913-1.197, P=0.523). In addition, there were no significant associations between rs10889677 polymorphism and AS susceptibility in any of dominant, recessive, homozygous and heterozygous models.

CONCLUSION

This meta-analysis demonstrates that IL23R gene rs10889677 A allele confers increased risk of AS in Europeans, but its role in Asian populations needs further exploration.

Regulatory T cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to intravenous immunoglobulin therapy

Background

Intravenous immunoglobulin (IVIG) is a polyspecific pooled immunoglobulin G preparation and one of the commonly used therapeutics for autoimmune diseases including those of neurological origin. A recent report in murine model proposed that IVIG expands regulatory T (T_reg) cells via induction of interleukin 33 (IL-33). However, translational insight on these observations is lacking.

Methods

Ten newly diagnosed Guillain-Barré syndrome (GBS) patients were treated with IVIG at the rate of 0.4 g/kg for three to five consecutive days. Clinical evaluation for muscular weakness was performed by Medical Research Council (MRC) and modified Rankin scoring (MRS) system. Heparinized blood samples were collected before and 1, 2, and 4–5 weeks post-IVIG therapy. Peripheral blood mononuclear cells were stained for surface CD4 and intracellular Foxp3, IFN-γ, and tumor necrosis factor alpha (TNF-α) and were analyzed by flow cytometry. IL-33 and prostaglandin E2 in the plasma were measured by ELISA.

Results

The fold changes in plasma IL-33 at week 1 showed no correlation with the MRC and MRS scores at weeks 1, 2, and ≥4 post-IVIG therapy. Clinical recovery following IVIG therapy appears to be associated with T_reg cell response. Contrary to murine study, there was no association between the fold changes in IL-33 at week 1 and T_reg cell frequency at weeks 1, 2, and ≥4 post-IVIG therapy. T_reg cell-mediated clinical response to IVIG therapy in GBS patients was associated with reciprocal regulation of effector T cells-expressing TNF-α.

Conclusion

T_reg cell expansion by IVIG in patients with autoimmune diseases lack correlation with IL-33. T_reg cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to IVIG therapy.

Polymorphisms in key bone modulator cytokines genes influence bisphosphonates therapy in postmenopausal women

Osteoporosis is a multifactorial and debilitating disease resulting from decreased bone mineral density (BMD) and loss of tissue microarchitecture. Ineffective therapies may lead to bone fractures and subsequent death. Single nucleotide polymorphisms (SNPs) in key immune regulator genes have been associated with therapeutic response to bisphosphonates, which are the first therapeutic line of choice for osteoporosis. However, cytokine pathways and their relation with therapeutic adhesion remain to be fully elucidated. Aimed at better understanding these processes, we investigated the response to bisphosphonate therapy in postmenopausal women and four SNPs in key proinflammatory cytokines genes: IL23R +2284 (C>A) (rs10889677), IL17A +672 (G>A) (rs7747909), IL12B +1188 (T>G) (rs3212227) and INF-γ -1616 (G>A) (rs2069705). A total of 69 patients treated with bisphosphonate were followed for a period of 1 up to 4 years, genotyped and compared according to their changes in bone mineral density (BMD) and level of biochemical markers during their treatment. The INF-γ -1616 G/G associated with increased BMD values in femoral neck (GG/AA, p = 0.016) and decreased BMD values in total hip (GG/GA, p = 0.019; GG/AA, p = 0.011). In relation to biochemical markers, INF-γ -1616 SNP associated with increased alkaline phosphatase (GG/AA; p < 0.0001) and parathyroid hormone levels (AA/GA; p = 0.017). Vitamin D values changes were related to IL17A +672 (GG/GA, p = 0.034) and to IL12B +1188 (TT/TG, p = 0.046) SNPs. Besides, significant differences in changes of calcium levels correlated with IL23R +2284 (CC/CA, p = 0.016) genotypes. Altogether, we suggest that these polymorphisms may play an important role for therapeutic decisions in osteoporosis treatment.