cs.AIAug 29, 2026

Agentic AI uncovers conserved cross-tissue protein co-abundance programs inaccessible to single-dataset analysis

Authors: Runyu Guan, Dehao Wu, Qiqi Xie, Yang Li, Haohan Wang

Organizations: School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, 61820, IL, US · Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, 48109, MI, US

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Protein co-abundance clusters preserved across tissues can reveal shared disease mechanisms and candidate therapeutic targets, particularly when proteins implicated in organ-confined diseases converge in peripheral or accessible tissues. However, previous cross-tissue studies have focused on biologically pre-selected tissue pairs, leaving most possible combinations and non-obvious relationships unexplored. We present an LLM-agent framework for large-scale, evidence-grounded comparison of tissue-specific protein co-abundance networks. The framework constructs tissue networks, derives pairwise consensus clusters, and integrates evidence from expression atlases, protein interaction and complex databases, pathway annotations, disease catalogues, and literature. Applied to all 820 pairwise combinations of 41 human tissues and fluids, it identified 1,833 conserved co-abundance clusters across 406 tissue pairs. Colon, synovial fluid, blood, cerebrospinal fluid, and bone marrow were the most broadly connected tissues, while the most cluster-rich pairs were dominated by bone marrow. The analysis also highlighted non-obvious relationships: skin-bone marrow exceeded the anatomically adjacent bone-bone marrow pair, while colon-breast contained cancer-relevant clusters involving extracellular-matrix remodeling, lipid metabolism, and immune modulation. Cluster-level analyses generated further mechanistic hypotheses, including a brain-gut extracellular-vesicle/redox/serotonin-cofactor axis and a liver-bone marrow stress-response axis involving genes linked to white matter disease. These results provide a global, comparable landscape of conserved protein co-abundance and a hypothesis-generating resource for mechanistic and therapeutic exploration. Code and data are available at https://github.com/Gry1005/AgenticAI-conserved-cross-tissue-protein-co-abundance.

Figures & tables

Figure 1: Pairwise tissue comparison across 41 human tissues and biofluids. Lower triangle: number of shared protein co-abundance clusters between each tissue pair. Upper triangle: expression similarity (Pearson rr of log-transformed mean iBAQ values). Tissues are ordered by hierarchical clustering of the shared cluster count matrix.
Tissue or fluidCountTissue or fluidCount
Colon40Ileum14
Synovial fluid39Adrenal13
Blood39Heart13
Cerebrospinal fluid38Adipose13
Bone marrow38Cell line13
Liver37Stem cells13
Table 1: Number of tissues or fluids sharing at least one conserved protein co-abundance cluster with the reference tissue
SourceCategoryTerm#Genespp /FDR
GOCCMitochondrion23.39×10−43.39\times 10^{-4}
GOBPMitochondrial calcium ion homeostasis—2.61×10−22.61\times 10^{-2}
KEGGPathwayPeroxisome—4.55×10−24.55\times 10^{-2}
GOMFL-xylulose reductase (NADPH) activity15.00×10−25.00\times 10^{-2}
GOMFPeptide serotonyltransferase activity15.00×10−25.00\times 10^{-2}
GOMFPeptide dopaminyltransferase activity15.00×10−25.00\times 10^{-2}
Table 2: Liver–Adipose co-abundance Cluster 1 (7 proteins: ANXA6, DCXR, IDH2, LACTB, PECR, PPM1A, TGM2) — Integrated Enrichment Profile. HPA: 7/7. hu.MAP 3.0: none. STRING PPI: 0/21 pairs (0%).
Figure 2: Cross-tissue co-abundance cluster sharing connectivity of 41 human tissues and biofluids. Each bar represents the number of other tissues sharing at least one conserved protein co-abundance cluster with the reference tissue. Tissues are colored by significance (binomial test, BH-FDR corrected): hub tissues (blue, FDR << 0.05) show significantly elevated connectivity, isolated tissues (orange, FDR << 0.05) show significantly reduced connectivity, and neutral tissues (light blue) show connectivity consistent with random expectation (dashed line, expected =19.8=19.8 ).
Figure 3: Pairwise cross-tissue conservation of protein co-abundance clusters. The top 10 (left) and bottom 10 (right) tissue pairs ranked by shared co-abundance cluster count are shown. Significance was assessed using a degree-preserving network rewiring permutation test ( n=1,000n=1{,}000 permutations) with Benjamini-Hochberg FDR correction. Colored bars indicate significant enrichment (blue, FDR << 0.05) or depletion (dark orange, FDR << 0.05) of shared co-abundance clusters relative to degree-matched random networks.
Figure 4: Protein co-abundance network of Cluster 0 from the brain–gut consensus analysis. Nodes are colored by functional group. Gold rings denote the EV/cargo-sorting and complement-associated ensemble (CD9, VPS4B, ATP6V1A, TRIM25, UBE2N, C3), which shares only 2 of 190 possible pairwise STRING interactions and has no corresponding complex in hu.MAP 3.0. Nodes with a blue outer ring indicate proteins with STRING-documented interactions that fall below the co-abundance threshold and are therefore absent from the consensus network (VPS4B, PDCD6, TRIM25, UBE2N); nodes carrying both rings belong to both categories. The amber dashed edge between SPR and TXNRD1 represents a novel co-abundance association (weight =0.926=0.926 ) with no prior STRING or hu.MAP 3.0 evidence. Edge opacity is proportional to co-abundance weight.
Figure 5: Protein co-abundance network of Cluster 0 from the liver–bone marrow consensus analysis. Nodes are colored by functional group. Gold rings denote proteins independently implicated in heritable white matter diseases (AIMP1: Pelizaeus-Merzbacher-like disease; CAPN1: hereditary spastic paraplegia type 76; EIF2B5: vanishing white matter disease) whose co-occurrence in a single co-abundance cluster was identified without prior disease-focused selection. Blue edges indicate STRING high-confidence interactions (3 of 253 possible pairs, FDR<0.05\text{FDR}<0.05 ); no complexes containing any co-abundance cluster members are recorded in hu.MAP 3.0. Edge opacity is proportional to co-abundance weight.
Figure 41: Overview of the LLM-agent pipeline. Stage 1: tissue-specific co-abundance network construction with agent-driven threshold refinement. Stage 2: pairwise consensus network, Leiden community detection, and module filtering. Stage 3: RAG-based module annotation against HPA, STRING, hu.MAP 3.0, GO, and KEGG. Stage 4: agent-driven module-function inference, gene-level profiling, and novelty assessment via Open Targets and Europe PMC.
SourceCategoryTerm#Genespp /FDR
GOCCExtracellular exosome141.85×10−81.85\times 10^{-8}
GOCCExtracellular vesicle—2.24×10−82.24\times 10^{-8}
GOCCExtracellular organelle—2.25×10−82.25\times 10^{-8}
GOCCExtracellular membrane-bounded organelle—2.25×10−82.25\times 10^{-8}
GOCCExtracellular space155.11×10−75.11\times 10^{-7}
GOCCExtracellular region—1.74×10−51.74\times 10^{-5}
Table 51: Brain–Gut Module 0 (20 proteins) — Integrated Enrichment Profile. HPA: 20/20. hu.MAP 3.0: none. STRING PPI: 2/190 pairs (1.05%).
SourceCategoryTerm#Genespp -value
GOCCCytosol132.13×10−42.13\times 10^{-4}
GOCCExtracellular exosome114.88×10−44.88\times 10^{-4}
GOMFProtein-disulfide reductase activity32.55×10−32.55\times 10^{-3}
GOMFDisulfide oxidoreductase activity34.19×10−34.19\times 10^{-3}
GOBPNucleobase-containing small molecule metabolic process68.17×10−38.17\times 10^{-3}
GOMFOxidoreductase activity, sulfur group donors31.10×10−21.10\times 10^{-2}
Table 52: Liver–Bone Marrow Module 0 (23 proteins) — GO/KEGG Functional Enrichment. HPA: 23/23. hu.MAP 3.0: none.
CategoryTerm#GenesFDR
KeywordAcetylation162.58×10−52.58\times 10^{-5}
TISSUESDigestive gland141.10×10−31.10\times 10^{-3}
WikiPathwaysMethionine de novo and salvage pathway32.20×10−32.20\times 10^{-3}
TISSUESViscus175.40×10−35.40\times 10^{-3}
KeywordRedox-active center31.10×10−21.10\times 10^{-2}
ComponentExtracellular exosome111.60×10−21.60\times 10^{-2}
Table 53: Liver–Bone Marrow Module 0 (23 proteins) — STRING Network Enrichment & Protein–Protein Interactions. STRING PPI: 3/253 pairs (1.19%).

Explore similar work

CardsList

  1. spora: A Unified Multimodal Dataset and Platform for Spatial Proteomics

    Oct 15, 2026Benedikt von Querfurth, Johann Wenckstern, Eeshaan Jain +9Multimodal DatasetMedical Image Benchmarks

    EPFL · University Hospital Zurich · University of Zurich +2

  2. Beyond Expression Similarity: Contrastive Learning Recovers Functional Gene Associations from Protein Interaction Structure

    Mar 21, 2026Jason DuryContrastive LearningBioinformatics

    Eridos AI

  3. SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis

    Jun 23, 2026Yucheng Yuan, Yuanfeng Ji, Zhongxiao Li +1AI Agent BenchmarksTool-Using Agents

    Department of Computer Science, Stanford University, Stanford, USA · Department of Radiation Oncology, Stanford University, Stanford, USA