A meta-analysis resolves the huntingtin interactome into coactivator losses and a robust proteostatic and synaptic gain network

This record contains the derived data and analysis code accompanying the manuscript: Seefelder, M. A meta-analysis resolves the huntingtin interactome into coactivator losses and a robust proteostatic and synaptic gain network (2026).The study integrates four previously published huntingtin (HTT) affinity-proteomics datasets and contrasts wild-type and polyglutamine-expanded HTT within a single Bayesian differential-interactomics model (BayesInteractomics), assigning every protein a calibrated, condition-dependent interaction call. Of 4,338 proteins evaluated, 275 are condition-dependent, describing a bidirectional remodelling of the HTT interactome — a loss of transcription-activation coactivators (Mediator, the ASCOM H3K4-methyltransferase, CREBBP, CDK9) and a gain of proteostatic and synaptic contacts (the 26S proteasome, HSP70 chaperones, synaptic and actin-cytoskeletal networks), around an intact chaperonin–HAP40 core. Contents Source differential-interactome table (differential_results.xlsx, all.csv, HTT_unchanged.csv) — per-protein posterior interaction probabilities, differential calls and false-discovery rates for wild-type and mutant HTT. Call-specific protein lists (gene-symbol lists for each differential class). Derived over-representation / enrichment results. Per-figure source data for all main and supplementary figures. A snapshot of the analysis and figure-generation code used to produce all results and figures. A snapshot of the BayesInteractomics.jl framework version used for interaction scoring. Raw data provenance This is a re-analysis of published datasets. The primary (raw) affinity-proteomics data are not re-hosted here and remain available from the original publications and their associated repositories: Greco et al. (2022), Justice et al. (2025), Sap et al. (2021) and Gutiérrez-García et al. (2023). Reproducibility Running the deposited code against the deposited derived data regenerates the figures and tables of the manuscript. The BayesInteractomics method is developed openly at https://github.com/ma-seefelder/BayesInteractomics.jl and described in full in a companion methods paper (Seefelder, submitted).