Altered diversity and composition of gut microbiota in Wilson's disease

Wilson's disease (WD) is a hereditary disorder caused by mutations in ATP7B leading to impaired biliary copper excretion and toxic accumulation of copper in organs such as the liver and brain. Environmental and dietary factors can modulate gene expression relevant to WD, and penicillamine, a microbial-derived copper chelator, is a standard therapy. Given the GI tract is the primary site of copper entry and the gut microbiome influences host immunity, metabolism, and nervous system function, the study aimed to test whether WD is associated with altered gut microbiota diversity, composition, and predicted function, and to identify potential microbial biomarkers and pathways that might inform novel therapeutic strategies.

Prior work has linked gut microbiome dysbiosis to multiple metabolic and neurologic diseases (e.g., obesity, type 1 diabetes, Alzheimer's/Parkinson's disease) and shown that host genetics (e.g., HLA) shape microbiome composition. Antibiotics and microbial products can perturb gut communities and host metabolism. A previous study by Geng et al. characterized WD gut microbiota at the phylum level and reported shifts including increased Firmicutes; however, detailed multi-level taxonomic and functional differences remained unclear. The current study builds on these findings by profiling WD-associated changes at phylum, family, and genus levels and by predicting functional pathway alterations (KEGG/COG).

Study design: Case-control 16S rRNA gene sequencing of fecal microbiota. Participants: 14 newly diagnosed WD patients and 16 healthy controls. WD diagnosis confirmed by ATP7B genotyping and clinical criteria (family history, clinical/neurological evaluation, low serum ceruloplasmin, elevated 24 h urinary copper, liver function, liver ultrasound, brain MRI). Exclusions: respiratory/renal failure, congestive cardiac disease, severe liver dysfunction, or probiotic/antibiotic use within 1 month. Controls had no probiotic/antibiotic use within 1 month. Baseline characteristics (age, gender, weight) were comparable between groups (P>0.05). Sample handling: Fresh fecal samples collected into sterile tubes and stored at −80 °C until DNA extraction. DNA extraction and amplicon sequencing: Microbial DNA extracted using PowerSoil DNA Stool Mini Kit (MoBio). The V4 region of the 16S rRNA gene was amplified using primers 341F (CCTACGGGNGGCWGCAG) and 806R (GGACTACHVGGGTWTCTAAT). PCR conditions: 95 °C 2 min; 30 cycles of 95 °C 30 s, 52 °C 30 s, 72 °C 45 s; final extension 72 °C 5 min. Amplicons visualized by agarose gel electrophoresis, pooled equimolarly, and sequenced (paired-end) on Illumina HiSeq/MiniSeq. Bioinformatics: Raw FASTQ reads demultiplexed, quality-filtered, merged with FLASH; singletons and chimeras removed with UPARSE. OTUs clustered at 97% similarity. Taxonomic assignment via RDP classifier against Greengenes database. Diversity analyses: Alpha diversity (ACE, Observed species, Shannon, Chao1, Simpson, J) and rarefaction; beta diversity using Bray–Curtis, unweighted and weighted UniFrac distances; ordination by NMDS, PCA, PCoA; UPGMA clustering; group differences tested by ANOSIM. Differential taxa assessed by LEfSe. Functional prediction: PICRUSt used to infer KEGG pathways and COG categories; pathway/module abundances compared between groups. Statistics: Wilcoxon rank-sum test for diversity and abundance comparisons; Fisher’s exact and chi-square for categorical variables; significance at P<0.05. Analyses performed in R 3.0.3 and GraphPad Prism 7. Ethics: Approved by the Ethics Committee of the First Affiliated Hospital of Guangdong Pharmaceutical University; informed consent obtained.

- Cohort: 14 WD and 16 controls with similar age, sex, and weight; urinary copper markedly higher in WD (726.44±383.62 mg/L) vs controls (58.65±23.23 mg/L), P<0.001. - Sequencing/OTUs: 1604 OTUs identified across 15 phyla, 85 families, and 153 genera; unique OTUs: 674 (WD) vs 843 (controls). - Alpha diversity: WD showed significantly reduced diversity and evenness (Shannon P=0.0369; Simpson P=0.0386). Observed species and ACE also differed significantly (P=0.0493 and P=0.0357, respectively). No significant differences for J and Chao1 (P=0.061; P=0.0784). - Beta diversity: Community structure differed significantly between groups (NMDS separation; ANOSIM R=0.407, P=0.001). PCA and PCoA (unweighted/weighted UniFrac) showed distinct clustering of WD vs controls. - Phylum-level shifts: WD vs controls—Actinobacteria 4.67% vs 7.94% (P<0.05, lower in WD); Firmicutes 53.39% vs 61.77% (P<0.05, lower); Verrucomicrobia 0.24% vs 0.44% (P<0.05, lower); Bacteroidetes 28.13% vs 21.59% (P<0.05, higher); Proteobacteria 13.01% vs 8.00% (P<0.05, higher); Fusobacteria 0.29% vs 0.15% (P<0.05, higher); Cyanobacteria 0.12% vs 0 (P<0.05, higher). Firmicutes/Bacteroidetes ratio reduced in WD: 1.90 vs 2.86 (P<0.05). - Family-level: WD exhibited unique presence/enrichment of Gemellaceae, Pseudomonadaceae, and Spirochaetaceae (rare/undetected in controls). Dominant families in WD included Lachnospiraceae, Ruminococcaceae, Bacteroidaceae, Veillonellaceae, Prevotellaceae, Enterobacteriaceae, Bifidobacteriaceae, and Coriobacteriaceae. - Genus-level: Enriched in WD—Bacteroides, Enterobacteriaceae (unclassified at genus level), Megamonas, Megasphaera (P<0.05). Depleted in WD—Lachnospiraceae and Ruminococcaceae lineages (unclassified genera), Blautia, Ruminococcus, Coprococcus, Clostridium, Lachnospira (P<0.05). Prevotella showed comparable abundance between groups. - Functional prediction (PICRUSt): WD microbiomes showed reduced abundance of pathways related to transcription factors and ABC-type transporters (F=31.745, P<0.001). Decreases also observed in fructose and mannose metabolism (F=31.2291), butanoate metabolism (F=31.385), glyoxylate and dicarboxylate metabolism (F=30.7384), pentose and glucuronate interconversions (F=32.6781), other ion-coupled transporters (F=31.4571), and carbon fixation in photosynthetic organisms (F=30.6242) (all P<0.001). Specific COG functions reduced included TRAP-type C4-dicarboxylate transport system components (periplasmic, small permease, large permease; F≈36–38), dihydroxyacetone kinase (F=37.2417), demethylmenaquinone methyltransferase (F=41.6979), and several uncharacterized bacterial proteins (all P<0.001).

The study demonstrates that WD is associated with significant gut microbiome dysbiosis characterized by reduced alpha diversity, distinct beta-diversity separation, depletion of beneficial Firmicutes (including SCFA producers) and Actinobacteria/Verrucomicrobia, and enrichment of Bacteroidetes, Proteobacteria, Fusobacteria, and Cyanobacteria. The lower Firmicutes/Bacteroidetes ratio and reduced predicted butanoate metabolism suggest diminished SCFA production, potentially impairing gut barrier integrity and immune regulation. Enrichment of opportunistic/pathobiont taxa (e.g., Enterobacteriaceae) and depletion of commensals (e.g., Blautia, Ruminococcus, Coprococcus, Lachnospira) indicate a shift toward a proinflammatory, metabolically altered ecosystem in WD. Predicted functional decreases in transport systems (ABC transporters, TRAP-type C4-dicarboxylate) and carbohydrate metabolism pathways align with altered microbial energy handling and could intersect with copper metabolism and host metabolic disturbances in WD. Compared with earlier WD microbiome work limited to phylum-level summaries, this study provides finer taxonomic resolution and functional inferences, strengthening the link between WD pathophysiology and gut microbial alterations. These findings support exploring microbiome-targeted interventions (e.g., SCFA supplementation, fecal microbiota transplantation, or modulation of specific taxa) as adjunctive strategies for WD management.

WD patients exhibit a distinct gut microbiome characterized by reduced diversity, a lower Firmicutes/Bacteroidetes ratio, depletion of key commensals, enrichment of potential pathobionts, and reduced predicted functions in transport and carbohydrate/SCFA metabolism. The results provide new insight into WD pathogenesis and identify potential microbial biomarkers and pathways (e.g., ABC and TRAP transport systems) that could be targeted therapeutically. Future work should validate these findings using shotgun metagenomics and metabolomics in larger, multicenter cohorts and assess the efficacy of microbiome-based interventions (e.g., FMT, SCFA/butyrate supplementation) in WD.

Small sample size due to disease rarity limits statistical power and generalizability. Functional insights are based on 16S rRNA gene-based predictions (PICRUSt) rather than direct metagenomic/metabolomic measurements. Single-center design; potential confounders (diet, geography) not deeply profiled.