{
  "abstract": "Repeated pregnancy loss (RPL) is a multifactorial condition in which the underlying immunological mechanisms, particularly the disruption of maternal-fetal tolerance, remain incompletely understood. Although immune tolerance is critical for pregnancy success, the specific immune dysregulations contributing to RPL, particularly in euploid pregnancies, have been difficult to characterize. To address this, we performed single-cell RNA sequencing of decidual tissues from RPL patients and first-trimester controls. Our analysis initially revealed elevated expression of a transcriptional module of immune activation genes in RPL decidual tissues. To dissect the cellular drivers of this complex landscape, we employed genotype-based origin analysis coupled with a supervised machine learning model and a transformer-based foundation model (scGPT). This hierarchical approach prioritized maternal T cells over other immune subsets as the population carrying the most distinct and generalizable RPL-associated signatures. Through the convergence of computational drug repurposing, network centrality analysis, and a rigorous origin-controlled expression filtering strategy, we identified CXCR4 and JUN as druggable molecular candidates strongly associated with this T-cell dysregulation. Collectively, our machine learning–driven approach characterizes the maternal immune landscape of euploid RPL in the context of immune tolerance breakdown, and nominates candidate targets for future functional investigation. Author summary: Repeated Pregnancy Loss (RPL) is a devastating condition affecting many couples. It is particularly perplexing when it occurs even though the fetus is genetically normal, leaving doctors and patients without clear answers. Successful pregnancy requires the mother’s immune system to tolerate the fetus. However, the exact reasons why this tolerance fails in RPL have remained a mystery. In this study, we first found that in these unexplained miscarriages, maternal immune cells in the decidual tissue show increased activity of genes involved in immune activation. To characterize this immune activation in more detail, we applied advanced artificial intelligence to analyze the gene activity of individual cells at the interface between the mother and the fetus. This analysis identified maternal T cells as the immune cells most strongly and consistently linked to RPL. Furthermore, by mapping the complex molecular networks governing these cells, we identified specific molecules (CXCR4 and JUN) strongly associated with this immune dysregulation. This study provides a new way of understanding RPL in relation to immune tolerance failure and identifies these specific molecules as candidate targets for developing future treatments.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Biomedical Computing, Korea Institute of Science and Technology Information (KISTI), andDepartment of Data and HPC Science, Korea National University of Science and Technology (UST), Daejeon, Republic of Korea",
    "Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea, and Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom",
    "Department of Obstetrics and Gynecology, College of Medicine, Myunggok Medical Research Institute, Konyang University, Daejeon, Republic of Korea",
    "Department of Biological Sciences, Sungkyunkwan University, Suwon, Republic of Korea"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tae Lyun Ko"
    },
    {
      "affs": [
        1
      ],
      "name": "Jaesub Park"
    },
    {
      "affs": [
        2
      ],
      "name": "Dongju Leem"
    },
    {
      "affs": [
        3
      ],
      "name": "Junho Kim"
    },
    {
      "name": "Jae won Han"
    },
    {
      "name": "Jin Sol Park"
    },
    {
      "name": "Sung Ki Lee"
    },
    {
      "name": "Hyojung Paik"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6tym2qngtjz71.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014806",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014806",
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "2403|Reproductive System and Pregnancy"
    ],
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      "/Biology and life sciences/Biochemistry/Proteomics/Protein interaction networks"
    ],
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      "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Blood cells/White blood cells/T cells"
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      "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Immune cells"
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    ],
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      "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis"
    ],
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      "/Biology and life sciences/Immunology/Immune cells"
    ],
    [
      "plos",
      "/Biology and life sciences/Immunology/Immune cells/White blood cells/T cells"
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      "/Computer and information sciences/Artificial intelligence/Machine learning"
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      "/Computer and information sciences/Network analysis/Centrality"
    ],
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      "plos",
      "/Computer and information sciences/Network analysis/Protein interaction networks"
    ],
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      "plos",
      "/Medicine and health sciences/Immunology/Immune cells"
    ],
    [
      "plos",
      "/Medicine and health sciences/Immunology/Immune cells/White blood cells/T cells"
    ],
    [
      "plos",
      "/Medicine and health sciences/Women's health/Maternal health/Pregnancy"
    ],
    [
      "plos",
      "/Medicine and health sciences/Women's health/Obstetrics and gynecology/Pregnancy"
    ]
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  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014806&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/6tym2qngtjz71.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pcbi.1014806",
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      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014806"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:cell biology",
      "subfield": "cell-biology"
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    {
      "field": "biology",
      "origin": "plos:immunology",
      "subfield": "immunology"
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    {
      "field": "medicine",
      "origin": "plos:women's health",
      "subfield": "reproductive-child"
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  ],
  "title": "Machine learning–driven decoding of maternal immune signatures in repeated pregnancy loss",
  "title_text": "Machine learning–driven decoding of maternal immune signatures in repeated pregnancy loss",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/6tym2qngtjz71",
  "venue": "PLOS Computational Biology",
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}