Skip to main content
NCBI home page
Arthritis Research & Therapy logoLink to Arthritis Research & Therapy
. 2026 Jul 6;28:176. doi: 10.1186/s13075-026-03860-4

Differential gene expression of retrotransposons (LTR and non-LTR) in peripheral blood leukocytes of people with gout

Ya-Sian Chang 1, Ming-Hon Hsu 2, Chieh-Min Chang 3, Ta-Chih Liu 4, Jan-Gowth Chang 1,2,5,6,7,✉
PMCID: PMC13617931  PMID: 42410622

Abstract

Background

To investigate the expression profiles of retrotransposons in people with gout and their clinical significance.

Methods

Peripheral blood leukocytes from 92 people with gout and 22 healthy controls were analyzed using Telescope and TEspeX to quantify human endogenous retroviruses (HERVs) and non-long terminal repeat (non-LTR). Gene Set Variation Analysis (GSVA) was used to assess the association of the alterations of HERV and viral related gene panels, and xCell to evaluate the changes of immune cell subpopulations. HEK293 and THP-1 cells were treated with uric acid (UA) and monosodium urate (MSU) to explore and confirm the mechanisms of retrotransposon changes.

Results

A total of 238 HERVs and 49 non-LTR were differentially expressed in people with gout as compared to healthy controls. Notably, HERVs exhibited lower expression levels overall, whereas most non‑LTR retrotransposons showed higher expression in people with gout, suggesting distinct immunological triggers. Among 397 genes located near differentially expressed HERVs, several displayed altered expressions, indicating potential locus-specific co-regulation. GSVA revealed that gene panels related to HERV regulation, including nearby genes, KRAB zinc-finger proteins, and stemness-associated genes, were less enriched in gout patients compared to controls. xCell analysis revealed significant changes in immune cell composition, including reduced proportions of neutrophils and NK cells, and increased CD4 + memory T-cells and regulatory T cells, aligning with gout-related immune migration and modulation. Consistent with the transcriptomic data, our cell-based assays demonstrated that UA and MSU differentially modulate LTR and non-LTR retrotransposon expression across distinct cell types.

Conclusions

This study reveals a distinct and dynamic retrotransposon signature in gout, with potential implications priming, immune cell behavior, and gene regulation.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s13075-026-03860-4.

Keywords: Gout, Retrotransposons, Total RNA sequencing, Telescope, TEspeX

Introduction

Gout is a crystal-associated joint disease caused by the precipitation of monosodium urate (MSU) resulting from hyperuricemia, which is defined as elevated levels of serum urate due to increased purine metabolism and/or decreased renal excretion of urate [1–3]. Owing to rapid economic development and associated changes in environment, lifestyle, and diet, the global burden of gout has been rising in recent decades [4]. However, the underlying drivers of this increase may vary across populations. For example, in East Asian and Polynesian populations, the rise in gout incidence appears to occur largely independently of changes in hyperuricemia prevalence. Gout is a complex disease resulting from interactions between genetic and environmental factors. Familial clustering of hyperuricemia and gout suggests a substantial heritable component. Previous genome-wide association study (GWAS) has estimated the heritability for serum urate concentration to be approximately 40–60%, and gout risk to be 30–50%, highlighting a major genetic contribution [5].

Our previous GWAS identified several genetic loci associated with gout susceptibility, highlighting the importance of inherited factors in disease development [6, 7]. To further elucidate the cellular mechanisms influenced by these genetic risk factors, we employed single-cell RNA sequencing to explore immune cell heterogeneity and inflammation-related transcriptional responses in acute gout [8]. However, despite these insights, the regulatory mechanisms linking genetic risk to immune dysregulation in gout remain incompletely understood—suggesting the involvement of additional regulatory layers beyond conventional coding genes.

Recent evidence indicates that transposable elements (TEs)—once considered “junk DNA”—play active roles in gene regulation and immune modulation. TEs are repetitive DNA sequences that can move or copy themselves to new genomic locations, and they account for nearly 50% of the human genome, vastly outnumbering the < 2% that encodes proteins [9]. Based on their mechanism of transposition, TEs are broadly classified into DNA transposons and retrotransposons. The latter move via an RNA intermediate in a “copy-and-paste” manner and remain transcriptionally active in select contexts [10, 11]. Under normal physiological conditions, TEs are epigenetically silenced to preserve genomic stability. However, in disease contexts such as inflammation, cancer, or autoimmunity, they may become reactivated. Reactivation refers to the re-expression of previously silenced TEs, which can contribute to pathology through various mechanisms, including immune system priming and modulation of gene expression.

Retrotransposons are subdivided into two main types: long terminal repeat (LTR) and non-LTR elements. LTR retrotransposons include human endogenous retroviruses (HERVs), remnants of ancestral viral infections that have become fixed in the germline [12]. Although most HERVs are no longer capable of retrotransposition, many retain promoter or enhancer sequences that can influence the expression of nearby genes. Aberrant HERV expression has been associated with autoimmune diseases, renal diseases, neurological disorders, and cancer [13–16].

Non-LTR retrotransposons, such as long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), are also transcriptionally active in some cells. LINE-1 (L1) elements retain retrotransposition activity, while SINEs like Alu rely on L1 machinery. These elements can produce RNA species that are sensed by pattern recognition receptors (PRRs), leading to the activation of innate immune responses [17]. Dysregulated expression of LINEs and SINEs has been observed in neurodegeneration, cancer, and chronic inflammatory conditions [18–20].

Despite growing interest in TEs and their roles in inflammation, their potential contribution to gout pathophysiology has not yet been examined. In this study, we analyze the retrotransposon expression landscape—including LTR and non-LTR elements—in peripheral blood from people with gout and healthy controls, aiming to identify disease-associated patterns. We also integrate transcriptomic data with immune cell profiling and examine nearby gene expression to explore potential regulatory relationships. Finally, using in vitro models, we evaluate how uric acid (UA) and MSU crystals influence retrotransposon expression, providing functional insights into TE behavior under gout-related stimuli.

Materials and methods

Blood samples and clinical data

Peripheral blood samples were collected from patients diagnosed according to the 2010 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) gouty Arthritis classification criteria. Both the Declaration of Helsinki and the Good Clinical Practice Guidelines were followed, and informed consent was obtained from all participants. The study was approved by the Research Ethics Committee of China Medical University Hospital, Taiwan (CMUH108-REC2-051).

The first cohort consisted of 92 individuals with gout and 22 healthy controls. Among the 92 gout patients, 42 were sampled during an acute gout flare, 32 had chronic tophaceous gout, and 18 were in the intercritical phase at the time of blood collection. The 92 gout patients included 87 males and 5 females, with a mean age of 50.90 ± 16.35 years. The mean BMI was 27.14 ± 5.01 kg/m², and mean serum urate concentration was 6.58 ± 1.97 mg/dL. Relevant clinical information including comorbidities (e.g., hypertension, diabetes mellitus, chronic kidney disease), use of urate-lowering therapy (e.g., allopurinol, febuxostat), and colchicine or nonsteroidal anti-inflammatory drugs (NSAIDs) use was recorded. A full summary of demographic and clinical features is provided in Supplementary Table S1. Peripheral blood leukocyte RNA from this group was used for differential expression analysis of retrotransposons and host genes using the Telescope and TEspeX pipelines.

The second, independent validation cohort included 7 individuals with gout who provided peripheral blood samples at both acute flare and intercritical (chronic) stages, along with 12 healthy controls. This cohort was used to evaluate the dynamic changes in retrotransposon expression during the course of gout. Detailed comparisons of acute versus chronic phase samples were performed to identify candidate elements with stage-specific expression patterns. In this study, the acute phase was defined as the symptomatic flare period characterized by abrupt joint inflammation and pain, typically lasting several days to a week, while the chronic phase encompassed the intercritical or tophaceous period occurring weeks to months after flare resolution, when patients were either asymptomatic or exhibited ongoing joint changes such as tophi.

RNA extraction and RNA sequencing (RNA-seq)

Peripheral white blood cells were isolated from whole blood using Ficoll-Paque density gradient centrifugation (GE Healthcare, Uppsala, Sweden) according to the manufacturer’s instructions. Total RNA was then extracted using the NucleoSpin® RNA Kit (Macherey-Nagel, Duren, Germany). The quality, quantity, and integrity of the total RNA were evaluated using the NanoDrop1000 spectrophotometer and Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA).

RNA-seq was performed as described in a previous study [21]. Briefly, samples with an RNA integrity number > 6.0 were used for RNA-seq. For each sample, 1 µg of total RNA was used as input for library preparation. Barcoded libraries were generated using the total RNA Library Preparation Kit (Illumina, San Diego, CA, USA). The libraries were sequenced on the Illumina NovaSeq 6000 instrument, using 2 × 151-bp paired-end sequencing flow cells, following the manufacturer’s instructions.

Retrotranscriptome and transcriptome quantification

After RNA-seq, raw data were processed using the Illumina DRAGAN RNA pipeline (v3.6 and v3.7), which includes read alignment using the DRAGAN aligner. This aligner retains multi-mapped reads, which is critical for accurate quantification of repetitive elements such as TEs. BAM files were used for retrotransposons and transcriptome analysis. For transcript quantification, featureCounts (v2.0.1) [22] was employed with annotations based on ENSEMBL hg38 release 99.

To quantify locus-specific expression of LTR elements (e.g., HERVs), we used Telescope (v1.0.3), which uses a Bayesian mixture model and expectation-maximization algorithm to probabilistically assign multi-mapped reads to their most likely source loci. This method improves quantification accuracy of individual HERVs loci. Telescope was applied using default parameters with the Telescope Meta-annotation resource (TE_annotation.v2.0.tsv) [23], which includes 14,968 annotated HERV loci in the hg38 genome. Definitions of nearby genes and enhancer overlap were also obtained from this meta-annotation. Differential expression analysis between gout and healthy control samples was performed independently within each RNA-seq experiment using DESeq2 (v1.36.0) [24], with p-values adjusted using the Benjamini–Hochberg procedure. Because control samples were distributed across two independently processed RNA‑seq experiments, we adopted a reproducibility‑based filtering strategy in lieu of a formal statistical batch correction. Differentially expressed HERVs were identified within each dataset using consistent thresholds (adjusted p-value < 0.05 and |log₂ fold change| > 3), and only those HERVs showing consistent differential expression in both experiments were retained for downstream analyses. This intersection‑based method yielded a conservative and robust set of reproducibly altered HERVs.

For non-LTR retrotransposons (e.g., LINEs, SINEs), expression was quantified using TEspeX (v2.2.0a) [25]. TEspeX aligns reads to a curated TE reference and implements a filtering strategy that excludes reads overlapping known exons of protein-coding or non-coding genes, in order to remove background signals from exonized TE fragments. Like Telescope, multi-mapped reads are retained during alignment, and filtering is performed at the quantification stage to improve specificity for autonomous TE expression. Differential expression was then analyzed using DESeq2, with significance thresholds set at adjusted p-value < 0.05 and |log₂ fold change| > 2.

For visualization only, gene expression levels were reported in transcripts per million (TPM) and TE expression in counts per million (CPM), as non-LTR elements lack gene length data for TPM conversion. TPM and CPM values were used in heatmaps and expression plots, but all statistical analyses were performed on raw counts using DESeq2. Visualization was performed using the Morpheus tool (http://software.broadinstitute.org/morpheus).

Analysis of the structures of gout-related HERVs and their genomic regions and nearby-related genes

We used the Teloscope tool “Telescope Meta-annotation” to annotate and analyze the nearby genes of HERVs including exonic, intronic, promoter, enhancer and other regulatory elements of coding and non-coding genes.

Gene Set Variation Analysis (GSVA) of HERV-related host gene panels

We selected gene panels from MSigDB [26] and our previous publication [27], which compiled genes from relevant recent literature. We collected 96 HERV restriction genes, 101 KRAB zinc-finger family protein (KZFP) genes, 263 immune-related genes, 70 nucleocytoplasmic transporting genes, 15 stimulated 3 prime antisense retroviral coding sequences (SPARCS) genes, 126 stemness-related genes, 45 human leukocyte antigen (HLA)-related genes, 54 inflammation and inflammatory genes, 22 immune checkpoint genes, and 51 metabolic genes.

The rationale for selecting these gene panels was to investigate whether transcriptional changes in HERVs might be functionally linked to known pathways involved in immune regulation, inflammation, host restriction of retroelements, and cellular differentiation. For example, KZFPs and HERV restriction factors represent key epigenetic regulators that suppress TE activity and are implicated in antiviral immunity. Similarly, immune- and inflammation-related genes reflect the central role of immune dysregulation in gout. Stemness- and metabolism-related genes were included based on prior findings linking TE expression to pluripotency and metabolic stress, respectively [27].

To explore whether the altered retrotransposon landscape in gout was associated with broader transcriptional changes in these biological programs, we conducted GSVA, a non-parametric method for estimating pathway activity variation across a sample population [28]. Heatmaps of GSVA scores were generated using the Morpheus tool (http//software.broadinstitute.org/morpheus) to visualize sample-wise expression and to perform hierarchical clustering of HERV-related host gene panels.

Immune cell analysis

For cell enrichment analysis of various immune cells in gouty patients and healthy controls, xCell was used. xCell generates enrichment scores from the expression matrix, as described previously [21], and infers the relative abundance of 64 immune and stromal cell types based on gene signatures. It incorporates a spillover compensation technique to improve cell-type specificity. TPM-normalized gene expression data were used as input, and the analysis was conducted using the default reference signatures provided by xCell [29]. Enrichment scores were then compared between groups to identify differences in immune cell infiltration.

Statistical analyses

Differential expression analyses for both transcriptome and retrotransposons were conducted using DESeq2 (v1.36.0). For HERVs (LTR elements), significance was defined as adjusted p-value (FDR) < 0.05 and |log₂ fold change| > 3. For non-LTR retrotransposons (LINEs, SINEs), the cutoff was adjusted p-value < 0.05 and |log₂ fold change| > 2. All p-values were adjusted for multiple testing using the Benjamini–Hochberg procedure.

GSVA was used to calculate enrichment scores for selected gene panels across samples, using the GSVA R package. Differential GSVA scores between gout patients and healthy controls were then compared using linear modeling via the limma package, and p-values were adjusted for multiple testing using the Benjamini–Hochberg method.

The Mann–Whitney U test, a non-parametric method appropriate for non-normally distributed data and small sample sizes, was used to compare both TPM gene expression values and xCell immune cell enrichment scores between groups (e.g., Acute vs. Chronic or Gout vs. Control). Statistical analyses were calculated using Python statistics. A p-value of less than 0.05 was considered statistically significant.

Cell culture, treatment, and RNA expression analysis

RNA-seq data from UA- and MSU-treated HEK293 and THP-1 cells were obtained from a previously published study [30]. While that study focused on RNA modifications and splicing, the current study presents new analyses of retrotransposon expression (including both LTR and non-LTR elements) using those same datasets. Briefly, THP-1 cells were grown in lipopolysaccharide-free complete RPMI medium containing 10% fetal bovine serum. HEK293 cells were grown in Dulbecco’s modified Eagle’s medium containing 10% fetal bovine serum. These cells were grown at 37 °C in an incubator with 5% CO2. For in vitro stimulation, UA and MSU were used at a final concentration of 10.5 mg/dL. Soluble UA was prepared by dissolving UA (Sigma-Aldrich, St Louis, MO, USA) in 1 N NaOH at 60 °C and adjusting the pH to 7.4 with HCl. The UA-containing medium was freshly prepared and visually inspected under light microscopy to ensure the absence of crystal formation prior to cell treatment. MSU crystals were prepared following a previously published method [31, 32]. Briefly, UA was dissolved in boiling 0.01 N NaOH, cooled to room temperature, and adjusted to pH 7.2. The solution was incubated at 4 °C for 24 h to allow crystal formation. Crystals were washed with 100% ethanol and acetone, dried, and sterilized by baking at 180 °C for 2 h. The resulting MSU crystals were suspended in sterile PBS and added to culture media at the desired concentration. Cells were seeded on a culture dish and treated with UA- or MSU-containing media for 48 h. Expression analyses in cell lines were performed using poly(A)-selected RNA-seq, in contrast to total RNA-seq used for peripheral blood samples. Despite this difference in library preparation, downstream computational analyses—including alignment, quantification, and differential expression—followed the same pipeline as used for human blood specimens.

To determine whether urate-related stimuli recapitulate in vivo retrotransposon expression patterns, we performed comparative analyses between differentially expressed retrotransposons identified in peripheral blood of people with gout and those observed in RNA-seq datasets from HEK293 and THP-1 cells treated with MSU or UA. Analyses were conducted separately for HERVs and non-LTR elements to evaluate overlap and concordance in expression changes across in vivo and in vitro conditions.

Results

Expression levels of HERVs in people with gout and healthy control subjects

A total of 238 out of 14,968 HERVs loci were found to be differentially expressed between gouty and healthy control samples (Fig. 1), while the remaining 14,730 loci were not differentially expressed. The 238 differentially expressed HERVs included 138 ERVLs, six ERVKs, and 94 ERV1s. We further analyzed these HERVs and found that 236 HERVs were more lowly expressed, and only two were more highly expressed in people with gout compared to healthy controls (Fig. 2 and Supplementary Table S2). The two HERVs showing higher expression in people with gout were ERV3-16A3_I-int_1946 and HERV9-int_0171.

Fig. 1.

Fig. 1

Open in a new tab

Overview of the reproducibility‑based filtering strategy used to derive a robust set of differentially expressed HERVs. Peripheral blood leukocytes from people with gout and healthy controls were analyzed in two independent RNA‑seq experiments. Differential expression of HERV loci was assessed separately in each dataset using identical thresholds (adjusted p-value < 0.05 and |log₂ fold change| > 3). HERVs with consistent differential expression across both experiments were retained, resulting in a conservative set of 238 reproducibly altered HERVs used for downstream analyses

Fig. 2.

Fig. 2

Open in a new tab

Heatmap showing the differential expression of 238 HERVs found across all samples. Columns are sample id and annotated sample type with gout (green) and healthy (orange), gender status with male (blue) and female (pink). Rows are HERVs from Telescope. Values in heatmap plot are CPM of each category and samples, and the color map is from blue (min) to red (max)

We collected blood samples from seven patients at two stages (acute and chronic) to investigate the dynamic changes in HERVs during the clinical course, along with samples from 12 healthy non-gout controls to validate our findings. The results revealed that seven HERVs were reactivated, which may also serve as candidate markers to differentiate gouty patients (Fig. 3 and Supplementary Table S3).

Fig. 3.

Fig. 3

Open in a new tab

Another cohort of samples from people with gout and healthy individuals was used to validate the previous findings. Blood samples from this group of people with gout were collected during both the acute and chronic phases. Seven HERVs were identified that not only distinguish people with gout from healthy individuals but also differentiate between the acute phase and the chronic phase (lasting weeks to months) of gout

Analysis of nearby genes of the 238 differentially expressed HERVs by telescope

Of the 238 differentially expressed HERVs, 48, 166, and 24 were in intronic, intergenic and exonic regions, respectively; 20 of these 238 HERVs contained protein coding transcripts. One HERV in exonic region, seven in intergenic regions, and two in intronic regions, were enhancers (Supplementary Table S4).

We identified 397 nearby genes of the 238 differentially expressed HERVs. The expression levels of 135 nearby genes were higher in gouty patients than healthy non-gout controls, including 10 genes with almost no expression in healthy non-gout controls. Forty-nine nearby genes showed higher expression in healthy non-gout controls, including seven with almost no expression in gouty patients (LINC00355, AMY1A, SYPL2, PRSS3, ZNF812P, LINC02527, and EQTN) (Supplementary Table S5). In this study, “almost no expression” was defined as a gene having a median TPM (Q50) of 0 across all individuals within a given group, indicating that the gene was largely unexpressed in that group.

Among the 236 HERVs that were more lowly expressed in people with gout, 49 nearby genes also showed lower expression compared to healthy controls (concordant), whereas 135 exhibited higher expression (discordant); the remaining 205 genes did not show differences. These findings indicate that nearby gene expression does not follow a uniform trend relative to HERV expression. The predominance of discordant cases suggests complex, locus-specific regulatory relationships rather than a global co-regulatory pattern.

Analysis of 10 gene panels and nearby genes of the gouty patients

GSVA revealed that several HERV-related host gene panels, including nearby genes, KZFPs, and stemness-related genes, were more highly expressed in healthy non-gout controls compared with people with gout (Fig. 4 and Supplementary Table S6).

Fig. 4.

Fig. 4

Open in a new tab

GSVA analysis of the 11 HERV-related host gene panels. Heatmap showing all pathway heatmap quantified by GSVA. Red represents high enrichment, and blue indicates the opposite

Expression levels of non-long terminal repeat (non-LTR) retrotransposons in people with gout and healthy control subjects

Ultimately, 49 of 125 non-LTR retrotransposons were differentially expressed between 92 individuals with gout and 22 healthy controls. Of these, 30 non-LTR elements were more highly expressed and 19 were more lowly expressed in people with gout (Fig. 5 and Supplementary Table S7). The more highly expressed non-LTR retrotransposons included 24 LINE-1 (L1) elements and six Alu elements, while the more lowly expressed group comprised 13 Alu, three L1, and three SINE-VNTR-Alus (SVA) elements.

Fig. 5.

Fig. 5

Open in a new tab

Heatmap showing the differential expression 49 non-LTR found across all samples. Columns are sample id and annotated sample type with gout (green) and healthy (orange). Rows are non-LTR from TEspeX. Values in heatmap plot are CPM of each category and samples, and the color map is from blue (min) to red (max)

Differences of immune cell populations in people with gout and healthy control subjects

Compared to healthy control subjects, gouty patients showed increased levels of adaptive immune cells, including CD4 + memory T-cells, CD8+ naïve T-cells, and regulatory T cells (Tregs). In contrast, several innate immune cells, such as eosinophils, neutrophils, nature killer (NK) cells, dendritic cells (DC), and mast cells, were more abundant in healthy individuals (Fig. 6 and Supplementary Table S8).

Fig. 6.

Fig. 6

Open in a new tab

Eight types of immune cells exhibited differential enrichment between people with gout and healthy individuals: (A) immune cells that were elevated in people with gout compared to healthy individuals, and (B) immune cells that were reduced in people with gout compared to healthy individuals

Given the central role of the NLRP3 inflammasome in gout pathogenesis, we examined the expression of NLRP3 in peripheral blood transcriptomes. NLRP3 mRNA levels did not differ between people with gout and healthy controls (Supplementary Figure S1), indicating that inflammasome activation in gout is not associated with altered transcriptional expression of NLRP3.

Analysis of the effects of uric acid (UA) and monosodium urate (MSU) on the expression of HERVs

In HEK293 cells, MSU treatment resulted in lower expression of 194 HERVs and higher expression of 41 HERVs. Similarly, UA treatment led to lower expression of 190 HERVs and higher expression of 47. Among the HERVs showing higher expression, 13 were unique to MSU, 19 were unique to UA, and 28 were shared by both treatments. Regarding HERVs with lower expression, 17 were specific to MSU, 13 to UA, and 177 were shared (Fig. 7A and Supplementary Table S9).

Fig. 7.

Fig. 7

Open in a new tab

Venn diagrams illustrating HERVs with higher or lower expression levels following treatment with uric acid or monosodium urate in (A) HEK293 cells and (B) THP-1 cells. Shared and treatment-specific HERVs are shown to highlight common and distinct expression patterns between the two conditions

In THP-1 cells, MSU treatment resulted in lower expression of 166 HERVs and higher expression of 69 HERVs, while UA treatment led to lower expression of 108 HERVs and higher expression of 129 HERVs. Among the HERVs showing higher expression, 19 were unique to MSU, 79 were unique to UA, and 50 were shared between the two treatments. In contrast, 77 HERVs were uniquely reduced by MSU, 19 by UA, and 89 were shared between both conditions (Fig. 7B and Supplementary Table S9).

Analysis of the effects of UA and MSU on the expression of non-LTR retrotransposons

In HEK293 cells, MSU treatment resulted in lower expression of 25 non-LTRs and higher expression of 14; whereas UA treatment led to lower expression of 20 non-LTRs and higher expression of 19. Among the non-LTRs showing higher expression, six were specific to MSU, 11 to UA, and eight were shared between treatments. Among those with lower expression, nine were unique to MSU, four to UA, and 16 were shared (Fig. 8A and Supplementary Table S10).

Fig. 8.

Fig. 8

Open in a new tab

Venn diagrams illustrating non LTR retrotransposons with higher or lower expression levels following treatment with uric acid or monosodium urate in (A) HEK293 cells and (B) THP 1 cells. Overlapping and unique elements indicate common and condition specific expression responses

In THP-1 cells, MSU treatment resulted in lower expression of 30 non-LTRs and higher expression of nine, while UA treatment led to lower expression of 29 non-LTRs and higher expression of 11. Among the non-LTRs elements showing higher expression, two were specific to MSU, four to UA, and seven were shared between treatments. Among those with lower expression, three were unique to MSU, two to UA, and 27 were shared (Fig. 8B and Supplementary Table S10).

Comparison of retrotransposon expression between patient samples and cell line models

For HERVs, MSU-treated HEK293 cells showed a high degree of concordance with peripheral blood samples from people with gout. Specifically, 193 of the 236 HERVs showing lower expression in patient blood (81.4%) also exhibited lower expression in MSU-treated HEK293 cells, and one of the two HERVs showing higher expression in patient blood also showed higher expression in HEK293 cells. THP-1 cells demonstrated a moderate level of concordance, with 165 of 236 HERVs (69.6%) showing lower expression overlapping with those observed in patient blood. In contrast, UA treatment induced fewer overlapping changes in both cell types. Together, these results indicate that MSU treatment, particularly in epithelial cells, more closely recapitulates the HERV expression patterns observed in people with gout (Supplementary Figures S2–S3).

For non-LTR retrotransposons, overall concordance between patient samples and cell line models was lower. In MSU-treated HEK293 cells, 25.7% of non-LTR elements showing higher expression and 46.7% of those showing lower expression overlapped with the corresponding changes observed in patient blood, with similar overlap proportions observed following UA treatment. In THP-1 cells, overlap rates ranged from 14.7 to 20.6% for elements showing higher expression and 44.1–45.5% for those showing lower expression across UA and MSU conditions (Supplementary Figures S4–S5). These findings suggest that regulation of non-LTR retrotransposons may be more cell type– or stimulus–specific, exhibiting lower overall concordance with peripheral blood profiles compared to HERVs.

Discussion

Although retrotransposons are widely recognized for their pivotal role in regulating gene expression, particularly in complex diseases such as cancer [14, 17, 33], their role in gout remains unexplored. In this study, we investigated the expression of retrotransposons, including LTRs (HERVs) and non-LTRs (LINEs, SINEs, and retroposons), using the Telescope and TEspeX tools, respectively. We provided evidence that gout impacts HERVs in addition to genes. Notably, our data revealed differences in retrotransposon expression between samples collected during acute and chronic phases of gout. However, due to the limited size of the dynamic cohort (n = 7 paired samples) and the potential influence of treatment during acute flares, analyses assessing concordant versus discordant TE–gene expression patterns across disease stages are underpowered and should be interpreted as exploratory. Accordingly, these findings are best viewed as identifying candidate loci that require validation in larger, well-controlled longitudinal cohorts.

All seven patients in the dynamic cohort received anti-inflammatory treatment during the acute flare phase. Specifically, all were treated with colchicine and NSAIDs; five of seven received corticosteroids; and two were on febuxostat. No patients were taking allopurinol at the time of sampling. These medications, particularly corticosteroids and urate-lowering agents, have the potential to influence gene and TE expression profiles. As such, medication use represents a potential confounding factor that may contribute to the observed transcriptional changes between acute and chronic phases. Accordingly, our findings should be interpreted with caution, and future studies involving medication-naïve patients or standardized treatment regimens are warranted to better disentangle disease-stage effects from pharmacological influences.

Retrotransposon-derived RNAs or DNAs can be detected by pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs) TLR7 and TLR8 for single-stranded RNA, TLR3 for double-stranded RNA, and cytosolic sensors like RIG-I, MDA5, and cGAS–STING [34, 35]. These pathways link TE activity to immune and inflammatory responses. In gout, MSU crystals provide the “signal 2” that activates the NLRP3 inflammasome, while PRR signaling provides the “signal 1” for priming [31, 36]. Our results showing broad HERV repression but increased expression of non-LTR elements suggest that different TE types may activate distinct PRR pathways, potentially influencing IL-1β–mediated inflammation. Although we did not observe differential expression of NLRP3 mRNA between people with gout and healthy controls (as shown in the Results), this suggests that inflammasome activation in gout may be regulated not at the transcriptional level, but rather via upstream priming signals and post-transcriptional mechanisms. A prior study observed elevated NLRP3 mRNA levels in individuals with hyperuricemia [37], however, elevated transcript levels alone appear insufficient to trigger full inflammasome activation or gout flares. Given that the NLRP3 inflammasome is broadly expressed across immune cell types [38], its activation likely depends more on priming and post-transcriptional regulation than on mRNA abundance alone.

Growing evidence continues to understand the impact of retrotransposons on gene expression [39, 40]. In this analysis, we identified 397 genes with HERVs located either immediately upstream/downstream or within their gene regions. The GSVA results demonstrated that pathways related to 397 nearby genes, 101 KZFPs, and 126 stemness-related genes were enriched in healthy non-gout controls. We initially hypothesized that these HERVs are not transcribed in people with gout either due to, or as a result of, the lower expression of nearby genes, suggesting a possible co-regulatory relationship.

However, further analysis showed that this relationship may not be uniform across all loci. While many HERVs were consistently expressed at lower levels in gout samples, the expression of their nearby genes varied—some showed higher expression, while others showed lower expression. This suggests that the reduction in HERV expression could influence enhancer activity in a locus-specific manner, particularly in regions associated with immune-related genes. The expression pattern we observed align with known antiviral and epigenetic silencing mechanisms, particularly the KZFP–TRIM28–SETDB1–H3K9me3 pathway. Silencing of HERVs through this pathway may lead to alterations in chromatin accessibility or enhancer function, thereby affecting the expression of adjacent genes in diverse ways. In other words, the activity of TEs and the expression of nearby genes may not always follow the same trend, with outcomes that depend on the specific genomic context. These findings are correlative, and additional targeted experiments will be required to determine causality at specific loci.

LINE-1, Alu, and SVA are recognized as active retrotransposons in the human genome, with LINE-1 and Alu being more abundant than SVA [17, 20]. Using the TEspeX tool, we analyzed non-LTR retrotransposons and identified differential expression of LINE-1, Alu, and SVA between people with gout and healthy controls. Compared with healthy individuals, most differentially expressed non-LTR elements in people with gout were expressed at higher levels (30 elements) than at lower levels (19 elements), in contrast to the pattern observed for LTR elements, which were predominantly expressed at lower levels in gout samples (236 elements) with only two showing higher expression.

Gout flares are characterized by strong recruitment of neutrophils and the formation of neutrophil extracellular traps (NETs) within the joint [41]. Although our xCell analysis showed a decrease in circulating neutrophils, this likely reflects their migration to and retention at inflamed sites. TE-derived nucleic acids can influence neutrophil priming through TLR signaling and type I interferon pathways, making neutrophils more prone to NET formation [34, 42]. Interestingly, we observed opposite expression patterns between LTR and non-LTR elements, suggesting that different TE types might affect neutrophil activation in distinct ways. This hypothesis should be tested in primary neutrophils exposed to UA or MSU crystals.

xCell analysis showed higher proportions of CD4 + memory T cells, CD8+ naïve T cells, and Tregs, together with lower levels of eosinophils, neutrophils, NK cells, DCs, and mast cells in the peripheral blood of people with gout compared with healthy controls. While these findings may suggest altered immune cell dynamics in gout, they should be interpreted with caution. The cohort included individuals sampled across different disease stages, including acute flare, chronic tophaceous gout, and intercritical gout, and peripheral cell depletion may not uniformly reflect tissue recruitment. For instance, the observed reduction in neutrophils might be due to factors beyond joint infiltration, such as chronic immune modulation, medication effects, or other systemic changes. Discrepancies between our findings and prior reports also highlight the need for further validation using flow cytometry or single-cell methods in well-defined clinical phases of gout. Recent studies have shown that LINE-1 elements can influence T-cell translational fitness and overall function [43]. In line with this, our observation of increased non-LTR element expression may impact T-cell activity in gout. While xCell analysis provided estimates of T-cell subset proportions, bulk RNA data alone cannot determine which subsets are functionally affected or responsible for the observed TE changes. Nevertheless, these findings support a model in which non-LTR activity modulates the Th17/Treg balance, potentially via PRR-mediated cytokine pathways such as IL-1, IL-6, and IL-23. Further validation using single-cell or subset-specific approaches will be needed to test this hypothesis.

Gout is a metabolic disorder characterized by the accumulation of UA or urate in the blood and tissues. When tissue urate concentrations become sufficiently high, urate salts crystallize, leading to the formation of MSU crystals [1–3]. In two cell lines treated with UA or MSU, most HERVs were expressed at lower levels compared with untreated cells, consistent with the pattern observed in people with gout. Across both cell lines, several HERVs exhibited concordant changes in expression, showing either higher or lower expression relative to controls. With respect to non-LTR retrotransposons, five elements showed consistently lower expression under both UA and MSU treatments, mirroring their reduced expression in people with gout (AluSx_short_#SINE/Alu, AluSx#SINE/Alu, AluYk2#SINE/Alu, SVA_C#Retroposon/SVA, and SVA_D#Retroposon/SVA). The identification of these specific retrotransposons highlights them as candidate loci that may be associated with gout-related inflammatory states. However, given the limited sample size of the dynamic cohort and the absence of prospective validation, their potential clinical relevance will require confirmation in larger, longitudinal studies.

Among nearby genes, we observed apparent expression differences in CCR2 and PLA2G4A compared to controls. Although these changes did not reach statistical significance, their potential relevance cannot be excluded and warrants further investigation. These genes are key regulators of monocyte recruitment and eicosanoid biosynthesis, respectively. Specifically, HERVH-int_0243, which is located near CCR2, showed markedly reduced expression in people with gout (20.65 vs. 1.30), while CCR2 itself was modestly more highly expressed (34.98 vs. 36.17), raising the possibility of a context-dependent regulatory association. In contrast, both PABL_A-int_0009 and its nearby gene PLA2G4A exhibited lower expression in people with gout (50.42 vs. 2.22 for the TE; 2.17 vs. 2.14 for the gene), indicating a possible positive association. In the context of gout, CCR2–CCL2 signaling is known to promotes monocyte and macrophage infiltration [44], while PLA2–COX/LOX pathways contribute to the production of PGE₂ and LTB₄, shaping the inflammatory milieu together with IL-1β [45]. Because some HERV regions have been reported to act as gene enhancers, and we observed broad suppression of TE activity, it is possible that this reduced activity might affect enhancer function at specific genome loci. Although our findings are based on correlative data, they point to several candidate TE–gene pairs—such as HERVH-int_0243–CCR2, PABL_A-int_0009–PLA2G4A, and ERV3-16A3_I-int_2359–PLA2G4D/F—that could be functionally linked. These observations are hypothesis-generating and should be validated using larger cohorts and functional perturbation experiments before any clinical implications are considered. Specifically, locus-specific perturbation studies using CRISPR interference (CRISPRi) to silence, or CRISPR activation (CRISPRa) to induce, expression of the implicated TE loci could be employed to directly test their regulatory impact on adjacent genes such as CCR2 and PLA2G4A. Coupled with downstream assays measuring IL-1β secretion and inflammatory pathway activation, these experiments would provide mechanistic insight into how TEs modulate immune gene networks in gout. In addition to transcriptomic changes, integration with GWAS of serum urate and gout (e.g., SLC2A9, ABCG2, and other loci) may help to interpret whether differentially expressed retrotransposon loci colocalize with known genetic risk signals. This was beyond the scope of the current analysis but represents an important future direction for elucidating how TE regulation may intersect with inherited susceptibility to gout.

Conclusions

This study reveals a distinct expression pattern of retrotransposons in gout, highlighting the downregulation of HERVs and upregulation of specific non-LTR elements. These changes are associated with altered expression of nearby genes and shifts in immune cell composition. Cell-based assays further support the regulatory impact of UA and MSU on retrotransposon expression. Our findings suggest that retrotransposon dysregulation may contribute to gout pathogenesis and immune modulation, offering novel insights into disease mechanisms and potential diagnostic or therapeutic targets.

Supplementary Information

Supplementary Material 1. (11.7MB, xlsx)
Supplementary Material 2. (1.5MB, docx)

Acknowledgements

Not applicable.

Authors’ contributions

Y.S.C. and J.G.C participated in the concept and design the experiments. M.H.H. analyzed the data. C.M.C. collected samples and performed the experiments. T.C.L. revised the manuscript. Y.S.C. wrote the first draft of the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by the Ministry of Science and Technology of Taiwan (grants no. 114-2320-B-442-002-) and Hsinchu Science Park Bureau, National Science and Technology Council (grants no. B11402).

Data availability

The sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive under BioProject accession number PRJNA1321092.

Declarations

Ethics approval and consent to participate

The study was approved by the Research Ethics Committee of China Medical University Hospital, Taiwan (CMUH108-REC2-051) and was conducted in accordance with the Declaration of Helsinki.

Consent for publication

All participants signed informed consent.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Dalbeth N, Gosling AL, Gaffo A, Abhishek A. Gout. Lancet. 2021;397(10287):1843–55. [DOI] [PubMed] [Google Scholar]
  • 2.Keenan RT. The biology of urate. Semin Arthritis Rheum. 2020;50(3S):S2–10. [DOI] [PubMed] [Google Scholar]
  • 3.Dalbeth N, Phipps-Green A, Frampton C, Neogi T, Taylor WJ, Merriman TR. Relationship between serum urate concentration and clinically evident incident gout: an individual participant data analysis. Ann Rheum Dis. 2018;77(7):1048–52. [DOI] [PubMed] [Google Scholar]
  • 4.Chen-Xu M, Yokose C, Rai SK, Pillinger MH, Choi HK. Contemporary Prevalence of Gout and Hyperuricemia in the United States and Decadal Trends: The National Health and Nutrition Examination Survey, 2007–2016. Arthritis Rheumatol. 2019;71(6):991–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Tin A, Marten J, Halperin Kuhns VL, Li Y, Wuttke M, Kirsten H, Sieber KB, Qiu C, Gorski M, Yu Z, et al. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels. Nat Genet. 2019;51(10):1459–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Chang YS, Lin CY, Liu TY, Huang CM, Chung CC, Chen YC, Tsai FJ, Chang JG, Chang SJ. Polygenic risk score trend and new variants on chromosome 1 are associated with male gout in genome-wide association study. Arthritis Res Ther. 2022;24(1):229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Lin CY, Chang YS, Liu TY, Huang CM, Chung CC, Chen YC, Tsai FJ, Chang JG, Chang SJ. Genetic contributions to female gout and hyperuricaemia using genome-wide association study and polygenic risk score analyses. Rheumatology (Oxford). 2023;62(2):638–46. [DOI] [PubMed] [Google Scholar]
  • 8.Chang JG, Tu SJ, Huang CM, Chen YC, Chiang HS, Lee YT, Yen JC, Lin CL, Chung CC, Liu TC, et al. Single-cell RNA sequencing of immune cells in patients with acute gout. Sci Rep. 2022;12(1):22130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Nurk S, Koren S, Rhie A, Rautiainen M, Bzikadze AV, Mikheenko A, Vollger MR, Altemose N, Uralsky L, Gershman A, et al. The complete sequence of a human genome. Science. 2022;376(6588):44–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Hayward A, Gilbert C. Transposable elements. Curr Biol. 2022;32(17):R904–9. [DOI] [PubMed] [Google Scholar]
  • 11.Wells JN, Feschotte C. A Field Guide to Eukaryotic Transposable Elements. Annu Rev Genet. 2020;54:539–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Stein RA, DePaola RV. Human endogenous retroviruses: our genomic fossils and companions. Physiol Genomics. 2023;55(6):249–58. [DOI] [PubMed] [Google Scholar]
  • 13.Iwata S, Tanaka Y. Association of Viral Infection With the Development and Pathogenesis of Systemic Lupus Erythematosus. Front Med (Lausanne). 2022;9:849120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Kitsou K, Lagiou P, Magiorkinis G. Human endogenous retroviruses in cancer: Oncogenesis mechanisms and clinical implications. J Med Virol. 2023;95(1):e28350. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Dhillon P, Mulholland KA, Hu H, Park J, Sheng X, Abedini A, Liu H, Vassalotti A, Wu J, Susztak K. Increased levels of endogenous retroviruses trigger fibroinflammation and play a role in kidney disease development. Nat Commun. 2023;14(1):559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Gruchot J, Herrero F, Weber-Stadlbauer U, Meyer U, Kury P. Interplay between activation of endogenous retroviruses and inflammation as common pathogenic mechanism in neurological and psychiatric disorders. Brain Behav Immun. 2023;107:242–52. [DOI] [PubMed] [Google Scholar]
  • 17.Jiang Y, Zong W, Ju S, Jing R, Cui M. Promising member of the short interspersed nuclear elements (Alu elements): mechanisms and clinical applications in human cancers. J Med Genet. 2019;56(10):639–45. [DOI] [PubMed] [Google Scholar]
  • 18.Cavalier AN, Smith ME, McWilliams GT, McEntee CM, Bettcher BM, Coughlan C, LaRocca TJ. Epigenetic dysregulation of transposable elements in cognitive impairment and Alzheimer’s disease. Geroscience. 2025;47(6):6731–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Saleh A, Macia A, Muotri AR. Transposable Elements, Inflammation, and Neurological Disease. Front Neurol. 2019;10:894. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Lavasanifar A, Sharp CN, Korte EA, Yin T, Hosseinnejad K, Jortani SA. Long interspersed nuclear element-1 mobilization as a target in cancer diagnostics, prognostics and therapeutics. Clin Chim Acta. 2019;493:52–62. [DOI] [PubMed] [Google Scholar]
  • 21.Chang YS, Hsu MH, Tu SJ, Yen JC, Lee YT, Fang HY, Chang JG. Metatranscriptomic Analysis of Human Lung Metagenomes from Patients with Lung Cancer. Genes (Basel). 2021;12(9):1458. [DOI] [PMC free article] [PubMed]
  • 22.Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. [DOI] [PubMed] [Google Scholar]
  • 23.Bendall ML, de Mulder M, Iniguez LP, Lecanda-Sanchez A, Perez-Losada M, Ostrowski MA, Jones RB, Mulder LCF, Reyes-Teran G, Crandall KA, et al. Telescope: Characterization of the retrotranscriptome by accurate estimation of transposable element expression. PLoS Comput Biol. 2019;15(9):e1006453. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Ansaloni F, Gualandi N, Esposito M, Gustincich S, Sanges R. TEspeX: consensus-specific quantification of transposable element expression preventing biases from exonized fragments. Bioinformatics. 2022;38(18):4430–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1(6):417–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Chang YS, Hsu MH, Chung CC, Chen HD, Tu SJ, Lee YT, Yen JC, Liu TC, Chang JG. Comprehensive Analysis and Drug Modulation of Human Endogenous Retrovirus in Hepatocellular Carcinomas. Cancers (Basel). 2023;15(14):3664. [DOI] [PMC free article] [PubMed]
  • 28.Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18(1):220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Huang CM, Chen YC, Lai IL, Chen HD, Huang PH, Tu SJ, Lee YT, Yen JC, Lin CL, Liu TY, et al. Exploring RNA modifications, editing, and splicing changes in hyperuricemia and gout. Front Med (Lausanne). 2022;9:889464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Martinon F, Petrilli V, Mayor A, Tardivel A, Tschopp J. Gout-associated uric acid crystals activate the NALP3 inflammasome. Nature. 2006;440(7081):237–41. [DOI] [PubMed] [Google Scholar]
  • 32.Lee CP, Chiang SL, Ko AM, Liu YF, Ma C, Lu CY, Huang CM, Chang JG, Kuo TM, Chen CL, et al. ALPK1 phosphorylates myosin IIA modulating TNF-alpha trafficking in gout flares. Sci Rep. 2016;6:25740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Rodriguez-Martin B, Alvarez EG, Baez-Ortega A, Zamora J, Supek F, Demeulemeester J, Santamarina M, Ju YS, Temes J, Garcia-Souto D, et al. Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition. Nat Genet. 2020;52(3):306–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Chiappinelli KB, Strissel PL, Desrichard A, Li H, Henke C, Akman B, Hein A, Rote NS, Cope LM, Snyder A, et al. Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses. Cell. 2015;162(5):974–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Russ E, Iordanskiy S. Endogenous Retroviruses as Modulators of Innate Immunity. Pathogens. 2023;12(2):162. [DOI] [PMC free article] [PubMed]
  • 36.Bauernfeind FG, Horvath G, Stutz A, Alnemri ES, MacDonald K, Speert D, Fernandes-Alnemri T, Wu J, Monks BG, Fitzgerald KA, et al. Cutting edge: NF-kappaB activating pattern recognition and cytokine receptors license NLRP3 inflammasome activation by regulating NLRP3 expression. J Immunol. 2009;183(2):787–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Wu M, Ma Y, Chen X, Liang N, Qu S, Chen H. Hyperuricemia causes kidney damage by promoting autophagy and NLRP3-mediated inflammation in rats with urate oxidase deficiency. Dis Model Mech. 2021;14(3):dmm048041. [DOI] [PMC free article] [PubMed]
  • 38.Honda TSB, Ku J, Anders HJ. Cell type-specific roles of NLRP3, inflammasome-dependent and -independent, in host defense, sterile necroinflammation, tissue repair, and fibrosis. Front Immunol. 2023;14:1214289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Dopkins N, Nixon DF. Activation of human endogenous retroviruses and its physiological consequences. Nat Rev Mol Cell Biol. 2023;25(3):212–22. [DOI] [PubMed]
  • 40.Dopkins N, O’Mara MM, Lawrence E, Fei T, Sandoval-Motta S, Nixon DF, Bendall ML. A field guide to endogenous retrovirus regulatory networks. Mol Cell. 2022;82(20):3763–8. [DOI] [PubMed] [Google Scholar]
  • 41.Chen T, Zhou J, Dang W. Mechanism of neutrophil extracellular traps in the pathogenesis of gout. Clin Exp Rheumatol. 2024;42(11):2272–9. [DOI] [PubMed] [Google Scholar]
  • 42.Jorch SK, Kubes P. An emerging role for neutrophil extracellular traps in noninfectious disease. Nat Med. 2017;23(3):279–87. [DOI] [PubMed] [Google Scholar]
  • 43.Burattin FV, Vadala R, Panepuccia M, Ranzani V, Crosti M, Colombo FA, Ruberti C, Erba E, Prati D, Nittoli T, et al. LINE1 modulate human T cell function by regulating protein synthesis during the life span. Sci Adv. 2024;10(41):eado2134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Grainger R, McLaughlin RJ, Harrison AA, Harper JL. Hyperuricaemia elevates circulating CCL2 levels and primes monocyte trafficking in subjects with inter-critical gout. Rheumatology (Oxford). 2013;52(6):1018–21. [DOI] [PubMed] [Google Scholar]
  • 45.Wang B, Wu L, Chen J, Dong L, Chen C, Wen Z, Hu J, Fleming I, Wang DW. Metabolism pathways of arachidonic acids: mechanisms and potential therapeutic targets. Signal Transduct Target Therapy. 2021;6(1):94. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (11.7MB, xlsx)
Supplementary Material 2. (1.5MB, docx)

Data Availability Statement

The sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive under BioProject accession number PRJNA1321092.

Articles from Arthritis Research & Therapy are provided here courtesy of BMC

RESOURCES