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Objective The gut microbial composition has been linked to metabolic and autoimmune diseases, including arthritis. However, there is a dearth of knowledge on the gut bacteriome, mycobiome, and virome in patients with gouty arthritis (GA). Methods We conducted a comprehensive analysis of the multi-kingdom gut microbiome of 26 GA patients and 28 healthy controls, using whole-metagenome shotgun sequencing of their stool samples. Results Profound alterations were observed in the gut bacteriome, mycobiome, and virome of GA patients. We identified 1,117 differentially abundant bacterial species, 23 fungal species, and 4,115 viral operational taxonomic units (vOTUs). GA-enriched bacteria included Escherichia coli_D GENOME144544, Bifidobacterium infantis GENOME095938, Blautia_A wexlerae GENOME096067, and Klebsiella pneumoniae GENOME147598, while control-enriched bacteria comprised Faecalibacterium prausnitzii_G GENOME147678, Agathobacter rectalis GENOME143712, and Bacteroides_A plebeius_A GENOME239725. GA-enriched fungi included opportunistic pathogens like Cryptococcus neoformans GCA_011057565, Candida parapsilosis GCA_000182765, and Malassezia spp., while control-enriched fungi featured several Hortaea werneckii subclades and Aspergillus fumigatus GCA_000002655. GA-enriched vOTUs mainly attributed to Siphoviridae , Myoviridae , Podoviridae , and Microviridae , whereas control-enriched vOTUs spanned 13 families, including Siphoviridae , Myoviridae , Podoviridae , Quimbyviridae , Phycodnaviridae , and crAss-like . A co-abundance network revealed intricate interactions among these multi-kingdom signatures, signifying their collective influence on the disease. Furthermore, these microbial signatures demonstrated the potential to effectively discriminate between patients and controls, highlighting their diagnostic utility. Conclusions This study yields crucial insights into the characteristics of the GA microbiota that may inform future mechanistic and therapeutic investigations. Key messages (1) Gut bacteriome, mycobiome, and virome of GA patients were substantially altered compared with controls. (2) GA-enriched bacteria and fungi include potential pathogens. (3) Multi-kingdom microbial signatures may be used to predict and discriminate GA patients and controls.

Idiopathic inflammatory myopathies (IIMs) are systemic autoimmune disorders with unknown etiology. Despite the established link between gut microbes and immunity, the roles of gut bacteriome, mycobiome, and virome in IIM are unexplored. We performed shotgun metagenomic sequencing on fecal samples from 34 IIM patients and 37 healthy controls to profile gut microbiota. Taxonomic, functional, network, and machine-learning analyses revealed microbial dysbiosis and its potential for discriminating IIM. All three microbial kingdoms were significantly altered in IIM. Several inflammation-associated bacterial taxa (e.g., Rothia mucilaginosa , Streptococcus parasanguinis , Trueperella pyogenes ) and opportunistic fungi (e.g., Aspergillus spp.) were enriched in IIM, while SCFA-producing bacteria and fungi were depleted. Virome analysis revealed substantial shifts, with higher abundance of Siphoviridae in IIM. Altered viral functional gene profiles suggesting enhanced phage-mediated genome integration, recombination, and bacterial stress adaptation. Multi-kingdom network analysis showed extensive rewiring in IIM, characterized by increased network connectivity and a shift toward fungi-centered ecological hubs, contrasting with bacteria/virus-dominated networks in controls. In machine-learning models, the virome demonstrated the strongest discriminatory power, and viral signatures dominated the combined multi-kingdom classifier (AUC = 0.997). This first comprehensive multi-kingdom gut microbiota analysis in IIM provides a foundation for developing diagnostic and therapeutic strategies.