{
  "abstract": "Dysregulated signaling drives Metabolic Dysfunction-Associated Steatohepatitis (MASH) through a complex crosstalk between pro- and anti-fibrotic cues. We previously observed potential competition between IFN-mediated JAK-STAT signaling and TGF{beta} in modulating MASH fibrosis, but the precise mechanism of that competition, and whether it can act directly on hepatocytes, is yet unknown. We used DT-LEMBAS, an interactome-based deep learning framework, training an ensemble of 50 models on drug-induced transcriptomics from primary human hepatocytes. The ensemble captured transcriptional changes induced by simulated receptor knockouts and aligned predicted profiles with known TGF{beta}- and IFN-induced phenotypes. Systematic simulations demonstrate their competitive modulation of MASH phenotype scores benchmarked against human cohorts. To extract mechanistic insights, we developed a gradient-based approach, where we computed the Jacobian of the used recurrent signaling network and applied chain rule decomposition to identify a four-node module of SMAD3, TP63, STAT2, and STAT3. Mechanistic hypotheses involving these canonical and non-canonical TF mediators were experimentally validated in primary hepatocyte spheroids. This work establishes a generalizable and scalable approach to decode large signaling neural networks into interpretable biological subnetworks.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biological Engineering, Massachusetts Institute of Technology"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "N. Meimetis"
    },
    {
      "name": "T. Matsuzaki"
    },
    {
      "name": "X. Mou"
    },
    {
      "name": "L. Foster"
    },
    {
      "name": "L. G. Griffith"
    },
    {
      "affs": [
        0
      ],
      "name": "D. A. Lauffenburger"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7a2d007v8zmyn.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.24.761530",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.24.761530v1.full",
  "id": "7a2d007v8zmyn",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.24.761530v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "systems biology"
    ],
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7223829779",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.24.761530v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7a2d007v8zmyn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:48Z",
      "native_id": "10.64898/2026.10.24.761530",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.24.761530v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Interactome-based deep learning and path-level decomposition reveal TGFβ-IFNα competition in MASH",
  "title_text": "Interactome-based deep learning and path-level decomposition reveal TGFβ-IFNα competition in MASH",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:48Z",
  "url": "https://research.draconitic.com/paper/7a2d007v8zmyn",
  "venue": "bioRxiv",
  "versions": []
}