Background: Gout is a prevalent inflammatory arthropathy driven by monosodium urate crystal deposition, yet the causal relationships between circulating biomarkers and disease susceptibility remain incompletely characterized. Establishing robust causal associations and mapping them to specific effector genes and tissues is essential for identifying mechanistically informed therapeutic targets. Methods: We conducted a comprehensive multi-omics Mendelian randomization study integrating a meta-analysis of three large-scale gout genome-wide association studies (N = 1,538,494) with genome-wide data for 233 metabolites, 179 lipid species, and 926 plasma proteins. Findings were replicated in an independent cohort (N = 327,457). Summary-data-based Mendelian randomization and Bayesian colocalization (HyPrColoc) were applied to map causal biomarkers to tissue-specific effector genes using expression quantitative trait loci data from kidney, liver, and whole blood. Candidate genes were experimentally validated in monosodium urate-stimulated THP-1 macrophages. Results: We identified 32 metabolites, one lipid species (TAG 54:3), and two protective plasma proteins (ISLR2, ITIH3) with replicated causal associations with gout. Triglyceride-rich very-low-density lipoprotein particles and circulating isoleucine emerged as prominent risk factors. Multi-tissue transcriptomic mapping prioritized PRELID1 (kidney), NIPAL1 (liver), LMAN2 (whole blood), and CAD as high-confidence effector genes with strong colocalization evidence (posterior probability >0.70). Functional validation confirmed concordant transcriptional and translational dysregulation of these genes following inflammatory stimulation. Conclusion: This integrative analysis establishes a causal framework linking specific lipoprotein subfractions, amino acid metabolism, and novel effector genes to gout pathogenesis, elucidating the systemic metabolic architecture of the disease and identifying potential therapeutic candidates warranting further preclinical investigation before clinical translation.

Osteoarthritis (OA) is a prevalent, disabling joint disease with no approved disease modifying treatments. We present a knowledge-graph based approach to discover candidate treatments for OA by integrating large-scale biomedical data. We introduce the Osteoarthritis Knowledge-graph (OKG), a comprehensive network derived from the Drug Repurposing Knowledge-graph (DRKG) and enriched with causal genetic associations from OA genome-wide association study (GWAS) involving nearly 2 million individuals. We propose CausalPathKG, a knowledge-graph embedding model built upon RotatE that integrates domain specific innovations: (i) weighted gene OA edges reflecting GWAS significance, (ii) a path based regularization term to encourage drug gene OA causal connectivity, (iii) multi hop graph attention to prioritize informative paths, and (iv) self adversarial negative sampling with type consistent corruptions for robust training. CausalPathKG was trained to predict missing links, while withholding known OA-related edges for testing. In experiments, CausalPathKG outperformed TransE and RotatE baselines in predicting held out OA treatments, achieving higher link prediction accuracy and classification performance. Case studies highlight that top ranked repurposed drugs engage key OA-associated genes and pathways identified in human genetics. These results demonstrate that incorporating genetic evidence into knowledge-graph models can improve the discovery of therapeutics, offering a computational strategy to bridge human genomic data with drug repurposing.

Selective autophagy is a critical cellular process in eukaryotic cells, characterized by the targeted degradation of specific organelles and proteins. This

Tumor-associated macrophages (TAMs) adapt to hypoxia, nutrient competition, lipid accumulation, and anticancer treatment. Mitochondria contribute to this adaptation through energy metabolism, redox signaling, mitochondrial DNA (mtDNA) sensing, and metabolite-dependent regulation of gene expression. This Review examines mitochondrial dynamics, mitophagy, proteostasis, respiratory remodeling, and lipid-droplet interactions, distinguishing direct TAM evidence from findings in other experimental systems. Tumor studies link these processes to IL-12 and IL-10 production, interferon responses, antigen presentation, and lymphocyte recruitment, with effects that vary by pathway and macrophage state. These findings do not establish a single optimal level of mitochondrial quality control. We discuss how macrophage lineage, spatial niche, and treatment exposure may influence the response to mitochondrial interventions. Therapeutic development will require causal validation in defined TAM populations, selective delivery, and measurements of immune function alongside mitochondrial changes. Evidence in human TAMs and direct studies of organelle contacts remain limited. Somatic mtDNA variation is considered separately from altered gene expression and DNA release, with lineage-tracing evidence distinguished from functional causation.