ArticleScience signaling2020
An interspecies translation model implicates integrin signaling in infliximab-resistant inflammatory bowel disease.
Article in Science signaling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 46 citations in OpenAlex.
- Systems biology framework for the rational design of operational conditions for in vitro/in vivo translation of tissue models.Science advances · 2026Article
- Proteomic analysis reveals shared biological pathways linking acrolein to biomolecular changes in the acute phase of rat spinal cord injury.bioRxiv : the preprint server for biology · 2026Article
- Computational translation of mouse models of osteoarthritis predicts human disease.Osteoarthritis and cartilage · 2026Article
- Integrated cross-species translation and biophysical multi-scale modeling links molecular signatures and locomotory phenotypes in spaceflight-induced sarcopenia.NPJ microgravity · 2026Article
- Multi-omics analysis identifies fibroblast IGFBP5 as a key target of anti-TNFα therapy in ulcerative colitis.Frontiers in immunology · 2026Article
- Cross-species blood transcriptional correlates of BCG-mediated protection against tuberculosis include innate and adaptive immune processes.JCI insight · 2025Article
- Translatability of Animal Models for Alzheimer's Disease Using a Machine Learning Based Workflow.Clinical and translational science · 2025Article
- Integrated Cross-Species Translation and Biophysical Multi-Scale Modeling Links Molecular Signatures and Locomotory Phenotypes in Spaceflight-Induced Sarcopenia.bioRxiv : the preprint server for biology · 2025Article
- Noncanonical T cell responses are associated with protection from tuberculosis in mice and humans.The Journal of experimental medicine · 2025Article
- Translational disease modeling of peripheral blood identifies type 2 diabetes biomarkers predictive of Alzheimer's disease.NPJ systems biology and applications · 2025Article
- Analysis of systemic effects of dioxin on human health through template-and-anchor modeling.PLoS computational biology · 2025Article
- Computational Translation of Mouse Models of Osteoarthritis Predicts Human Disease.bioRxiv : the preprint server for biology · 2025Article
- Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection.NPJ systems biology and applications · 2025Article
- Cross-Species Modeling Identifies Gene Signatures in Type 2 Diabetes Mouse Models Predictive of Inflammatory and Estrogen Signaling Pathways Associated with Alzheimer's Disease Outcomes in Humans.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Decoding the mosaic of inflammatory bowel disease: Illuminating insights with single-cell RNA technology.Computational and structural biotechnology journal · 2024Review
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- Systematic Analysis of Human Colorectal Cancer scRNA-seq Revealed Limited Pro-tumoral IL-17 Production Potential in Gamma Delta T Cells.bioRxiv : the preprint server for biology · 2024Article
- AutoTransOP: translating omics signatures without orthologue requirements using deep learning.NPJ systems biology and applications · 2024Article
- Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.Briefings in bioinformatics · 2024Article
- Key advances in vaccine development for tuberculosis-success and challenges.NPJ vaccines · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 1 country.
Funding
Abstract
Anti-tumor necrosis factor (anti-TNF) therapy resistance is a major clinical challenge in inflammatory bowel disease (IBD), due, in part, to insufficient understanding of disease-site, protein-level mechanisms. Although proteomics data from IBD mouse models exist, data and phenotype discrepancies contribute to confounding translation from preclinical animal models of disease to clinical cohorts. We developed an approach called translatable components regression (TransComp-R) to overcome interspecies and trans-omic discrepancies between mouse models and human subjects. TransComp-R combines mouse proteomic data with patient pretreatment transcriptomic data to identify molecular features discernable in the mouse data that are predictive of patient response to therapy. Interrogating the TransComp-R models revealed activated integrin pathway signaling in patients with anti-TNF-resistant colonic Crohn's disease (cCD) and ulcerative colitis (UC). As a step toward validation, we performed single-cell RNA sequencing (scRNA-seq) on biopsies from a patient with cCD and analyzed publicly available immune cell proteomics data to characterize the immune and intestinal cell types contributing to anti-TNF resistance. We found that
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.