Systemic Investigation of Promoter-wide Methylome and Genome Variations in Gout

Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1

Background

Psoriasis represents a persistent, immune-driven inflammatory condition affecting the skin, characterized by a lack of well-established biologic treatments without adverse events. Consequently, the identification of novel targets and therapeutic agents remains a pressing priority in the field of psoriasis research.

Methods

We collected single-cell RNA sequencing (scRNA-seq) datasets and inferred T cell differentiation trajectories through pseudotime analysis. Bulk transcriptome and scRNA-seq data were integrated to identify differentially expressed genes (DEGs). Machine learning was employed to screen candidate genes. Correlation analysis was used to predict the interactions between cells expressing insulin-induced gene 1 (INSIG1) and other immune cells. Finally, drug docking was performed on INSIG1, and the expression levels of INSIG1 in psoriasis were verified through clinical and in vivo experiments, and further in vivo experiments established the efficacy of tetrandrine in the treatment of psoriasis.

Results

T cells were initially categorized into seven states, with differentially expressed genes in T cells (TDEGs) identified and their functions and signaling pathways. INSIG1 emerged as a characteristic gene for psoriasis and was found to be downregulated in psoriasis and potentially negatively associated with T cells, influencing psoriasis fatty acid metabolism, as inferred from enrichment and immunoinfiltration analyses. In the cellular communication network, cells expressing INSIG1 exhibited close interactions with other immune cells through multiple signaling channels. Furthermore, drug sensitivity showed that tetrandrine stably binds to INSIG1, could be a potential therapeutic agent for psoriasis.

Conclusion

INSIG1 emerges as a specific candidate gene potentially regulating the fatty acid metabolism of patients with psoriasis. In addition, tetrandrine shows promise as a potential treatment for the condition.

The causal association between thyroid disease and gout: A Mendelian randomization study

Observational studies have reported some associations between thyroid disease and gout, but the causal relationship between the 2 is not clear. We used Mendelian randomization (MR) Analysis to investigate the causal association between some thyroid diseases (autoimmune hypothyroidism, autoimmune hyperthyroidism, thyroid nodules, and thyroid cancer) and gout. GWAS data were used for analysis. The exposure factors were autoimmune hypothyroidism, autoimmune hyperthyroidism, thyroid nodules and thyroid cancer, and the outcome variables were gout. IVW, MR-Egger, Weighted median and Weighted mode were used for MR analysis. Cochran Q test MR-PRESSO and MR-Egger intercept analysis were used to detect heterogeneity and multi directivity. Autoimmune hypothyroidism has a causal effect on gout, IVW results show (OR = 1.13, 95% CI = 1.03-1.21, PFDR = 0.0336); Autoimmune hyperthyroidism has a causal effect on gout, IVW results show (OR = 1.07, 95% CI = 1.01-1.12, PFDR = 0.0314); Thyroid cancer has no causal effect on gout, IVW results show (OR = 1.03, 95% CI = 0.98-1.09, PFDR = 0.297); Thyroid nodules has no causal effect on gout, IVW results show (OR = 1.03, 95% CI = 0.98-1.08, PFDR = 0.225); Reverse MR Studies show that gout have no causal effect on the above thyroid diseases. Autoimmune hypothyroidism and autoimmune hyperthyroidism increase the risk of gout.

Regulation of protein prenylation

Prenyltransferases (PTases) are known to play a role in embryonic development, normal tissue homeostasis and cancer by posttranslationally modifying proteins involved in these processes. They are being discussed as potential drug targets in an increasing number of diseases, ranging from Alzheimer's disease to malaria. Protein prenylation and the development of specific PTase inhibitors (PTIs) have been subject to intense research in recent decades. Recently, the FDA approved lonafarnib, a specific farnesyltransferase inhibitor that acts directly on protein prenylation; and bempedoic acid, an ATP citrate lyase inhibitor that might alter intracellular isoprenoid composition, the relative concentrations of which can exert a decisive influence on protein prenylation. Both drugs represent the first approved agent in their respective substance class. Furthermore, an overwhelming number of processes and proteins that regulate protein prenylation have been identified over the years, many of which have been proposed as molecular targets for pharmacotherapy in their own right. However, certain aspects of protein prenylation, such as the regulation of PTase gene expression or the modulation of PTase activity by phosphorylation, have attracted less attention, despite their reported influence on tumor cell proliferation. Here, we want to summarize the advances regarding our understanding of the regulation of protein prenylation and the potential implications for drug development. Additionally, we want to suggest new lines of investigation that encompass the search for regulatory elements for PTases, especially at the genetic and epigenetic levels.

A review on gout: Looking back and looking ahead

Gout is a metabolic disease caused by the deposition of monosodium urate (MSU) crystals inside joints, which leads to inflammation and tissue damage. Increased concentration of serum urate is an essential step in the development of gout. Serum urate is regulated by urate transporters in the kidney and intestine, especially GLUT9 (SLC2A9), URAT1 (SLC22A12) and ABCG. Activation of NLRP3 inflammasome bodies and subsequent release of IL-1β by monosodium urate crystals induce the crescendo of acute gouty arthritis, while neutrophil extracellular traps (NETs) are considered to drive the self-resolving of gout within a few days. If untreated, acute gout may eventually develop into chronic tophaceous gout characterized by tophi, chronic gouty synovitis, and structural joint damage, leading the crushing burden of treatment. Although the research on the pathological mechanism of gout has been gradually deepened in recent years, many clinical manifestations of gout are still unable to be fully elucidated. Here, we reviewed the molecular pathological mechanism behind various clinical manifestations of gout, with a view to making contributions to further understanding and treatment.

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.

Epigenetic regulation in metabolic diseases: mechanisms and advances in clinical study

Epigenetics regulates gene expression and has been confirmed to play a critical role in a variety of metabolic diseases, such as diabetes, obesity, non-alcoholic fatty liver disease (NAFLD), osteoporosis, gout, hyperthyroidism, hypothyroidism and others. The term 'epigenetics' was firstly proposed in 1942 and with the development of technologies, the exploration of epigenetics has made great progresses. There are four main epigenetic mechanisms, including DNA methylation, histone modification, chromatin remodelling, and noncoding RNA (ncRNA), which exert different effects on metabolic diseases. Genetic and non-genetic factors, including ageing, diet, and exercise, interact with epigenetics and jointly affect the formation of a phenotype. Understanding epigenetics could be applied to diagnosing and treating metabolic diseases in the clinic, including epigenetic biomarkers, epigenetic drugs, and epigenetic editing. In this review, we introduce the brief history of epigenetics as well as the milestone events since the proposal of the term 'epigenetics'. Moreover, we summarise the research methods of epigenetics and introduce four main general mechanisms of epigenetic modulation. Furthermore, we summarise epigenetic mechanisms in metabolic diseases and introduce the interaction between epigenetics and genetic or non-genetic factors. Finally, we introduce the clinical trials and applications of epigenetics in metabolic diseases.

Metagenomic Analysis of Gut Microbiome in Gout Patients with Different Chinese Traditional Medicine Treatments

Introduction

Changes in eating habits have made gout a metabolic disease of increasing concern. Previous studies have indicated that there are significant differences in species composition and abundance of gut microbiome in gout patients compared with average. Considering that traditional Chinese medicine has a momentous effect in treating gout, the research study aimed to explore the differences of genomic and metabolomics of gut microbiome before and after traditional Chinese medicine treatment in patients with gout.

Method

30 patients with gout and 29 matched controls were recruited of which 16 patients took H treatment and 14 patients took T treatment. Stools were collected twice for patients before and after treatment and only once for controls. A total of 89 samples were annotated with metagenomic species and functions, and the enrichment analysis of differential genes and KO pathway was carried out.

Result

The results showed a decrease in the diversity of gut microbiome in gout patients and the gene abundance and metabolomics had great differences among study groups. The number of bacterial genera also had significant differences among treatment groups. Moreover, among different groups, the regulation of different species was variously correlated. The correlation between species and clinical laboratory indicators in the rising group was stronger than that in the decreasing group and the upregulation of some strain was related to the content of urea nitrogen.

Conclusion

After the traditional Chinese medicine treatment, the glutathione pathway was significantly enriched and some pathogenic bacteria were significantly inhibited. The study suggests that traditional Chinese medicine treatment may exert its therapeutic effect by inhibiting relevant pathways.

A novel mutation in the UBAP1 gene causing hereditary spastic paraplegia: A case report and overview of the genotype-phenotype correlation

Hereditary Spastic Paraplegia (HSP) is considered to be one of the common neurodegenerative diseases with marked genetic heterogeneity. Recently, the mutations in ubiquitin-associated protein 1 (UBAP1) have been described in patients with HSP, known as spastic paraplegias 80 (SPG80). Here, we reported a Chinese HSP family presenting a frameshift mutation in the UBAP1 gene leading to complex HSP. Their clinical features encompassed spastic paraparetic gait, exaggerated patellar tendon reflexes, bilateral Babinski signs, and hyperactive Achilles tendon reflex. The proband also had severe urinary incontinence and a dermoid cyst at the lumbar 4–5 spinal cord, which rarely occurs in HSP patients. Following whole-exome sequencing, a novel heterozygous mutation (c.437dupG, NM_016,525) was identified in the UBAP1 that segregated with the family’s phenotype and resulted in truncating UBAP1 protein (p.Ser146ArgfsTer13). Moreover, we reviewed the genotypes of UBAP1 and the phenotypic variability in 90 HSP patients reported in the literature. We found that the age of onset in UBAP1-related patients was juvenile, and there were population differences in the age of onset. The main complications were lower extremity spasticity, hyperreflexia, and the Babinski sign. Exon 4 of UBAP1 was identified as a mutation hotspot region. Our study expands the knowledge of UBAP1 mutations, which will aid in HSP patient counseling. Further molecular biological research is needed to explore the genotype-phenotype correlations of UBAP1-related HSP.

Angptl2 is a Marker of Cellular Senescence: The Physiological and Pathophysiological Impact of Angptl2-Related Senescence

Cellular senescence is a cell fate primarily induced by DNA damage, characterized by irreversible growth arrest in an attempt to stop the damage. Senescence is a cellular response to a stressor and is observed with aging, but also during wound healing and in embryogenic developmental processes. Senescent cells are metabolically active and secrete a multitude of molecules gathered in the senescence-associated secretory phenotype (SASP). The SASP includes inflammatory cytokines, chemokines, growth factors and metalloproteinases, with autocrine and paracrine activities. Among hundreds of molecules, angiopoietin-like 2 (angptl2) is an interesting, although understudied, SASP member identified in various types of senescent cells. Angptl2 is a circulatory protein, and plasma angptl2 levels increase with age and with various chronic inflammatory diseases such as cancer, atherosclerosis, diabetes, heart failure and a multitude of age-related diseases. In this review, we will examine in which context angptl2 was identified as a SASP factor, describe the experimental evidence showing that angptl2 is a marker of senescence in vitro and in vivo, and discuss the impact of angptl2-related senescence in both physiological and pathological conditions. Future work is needed to demonstrate whether the senescence marker angptl2 is a potential clinical biomarker of age-related diseases.

The epigenome: key to understanding and predicting gout flares

Gout is a form of arthritis, resulting from an inflammatory reaction to the deposition of monosodium urate (MSU) crystals in the synovial fluid of the joint space. It is characterised by periods of acute inflammation in the affected joint, or joints (known as gout flares), separated by asymptomatic periods. There seems to be substantial overlap between environmental triggers of gout flares and common environmental modifiers (diet, pharmaceuticals, and stress) of epigenetic markers (DNA methylation, histone modifications, and ncRNA). Very few studies have looked at whether environment is influencing gout through epigenetic mechanisms. The pathogenesis of gouty inflammation is well understood but understanding the variation of response to hyperuricaemia in terms of gout flare initiation is less well known. In this review, we will examine the potential of epigenomics in understanding how gout flares may occur, both in terms of development of hyperuricaemia and the inflammatory response. Looking at the epigenome and its intersection with lifestyle could help identify new targets and strategies for effective management of gout flares.

Autoinflammatory Features in Gouty Arthritis

In the panorama of inflammatory arthritis, gout is the most common and studied disease. It is known that hyperuricemia and monosodium urate (MSU) crystal-induced inflammation provoke crystal deposits in joints. However, since hyperuricemia alone is not sufficient to develop gout, molecular-genetic contributions are necessary to better clinically frame the disease. Herein, we review the autoinflammatory features of gout, from clinical challenges and differential diagnosis, to the autoinflammatory mechanisms, providing also emerging therapeutic options available for targeting the main inflammatory pathways involved in gout pathogenesis. This has important implication as treating the autoinflammatory aspects and not only the dysmetabolic side of gout may provide an effective and safer alternative for patients even in the prevention of possible gouty attacks.

Genetics of hyperuricemia and gout: Insights from recent genome-wide association studies and Mendelian randomization studies

Gout is the most common form of inflammatory arthritis in adults. Elevation serum uric acid (SUA) concentration is known to be the key to gout pathogenesis. Since the first genome-wide association study (GWAS) for SUA was performed in 2007, the number of gene loci known to be associated with hyperuricemia and gout has grown rapidly. GWASs and Mendelian randomization studies have also reported numerous novel results regarding the genetics of hyperuricemia and gout since 2018. We concisely review recent advances in scholarship on the effects of genetics on hyperuricemia and gout risk. We also review data from genetic association studies in Taiwan and perform GWASs of SUA levels among Taiwan Biobank participants.