Publications

Publications 2019 – Partner 4

  • NOD2 Expression in Intestinal Epithelial Cells Protects Toward the Development of Inflammation and Associated Carcinogenesis. https://www.ncbi.nlm.nih.gov/pubmed/30704984?dopt=Abstract Icon for Elsevier Science Icon for PubMed Central Related Articles

    NOD2 Expression in Intestinal Epithelial Cells Protects Toward the Development of Inflammation and Associated Carcinogenesis.

    Cell Mol Gastroenterol Hepatol. 2019;7(2):357-369

    Authors: Ferrand A, Al Nabhani Z, Tapias NS, Mas E, Hugot JP, Barreau F

    Abstract
    Nucleotide-binding oligomerization domain 2 (NOD2) is an intracellular pattern recognition receptor that senses bacterial peptidoglycan-conserved motifs in cytosol and stimulates host immune response including epithelial and immune cells. The association of NOD2 mutations with a number of inflammatory pathologies including Crohn's disease (CD), graft-versus-host diseases, or Blau syndrome, highlights its pivotal role in inflammatory response and the associated-carcinogenesis development. Since its identification in 2001 and its association with CD, the role of NOD2 in epithelial cells and immune cells has been investigated extensively but the precise mechanism by which NOD2 mutations lead to CD and the associated carcinogenesis development is largely unknown. In this review, we present and discuss recent developments about the role of NOD2 inside epithelial cells on the control of the inflammatory process and its linked carcinogenesis development.

    PMID: 30704984 [PubMed - indexed for MEDLINE]

  • Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis. https://www.ncbi.nlm.nih.gov/pubmed/30223112?dopt=Abstract Icon for Elsevier Science Related Articles

    Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis.

    Clin Gastroenterol Hepatol. 2019 Apr;17(5):905-913

    Authors: Morilla I, Uzzan M, Laharie D, Cazals-Hatem D, Denost Q, Daniel F, Belleannee G, Bouhnik Y, Wainrib G, Panis Y, Ogier-Denis E, Treton X

    Abstract
    BACKGROUND & AIMS: Acute severe ulcerative colitis (ASUC) is a life-threatening condition managed with intravenous steroids followed by infliximab, cyclosporine, or colectomy (for patients with steroid resistance). There are no biomarkers to identify patients most likely to respond to therapy; ineffective medical treatment can delay colectomy and increase morbidity and mortality. We aimed to identify biomarkers of response to medical therapy for patients with ASUC.
    METHODS: We performed a retrospective analysis of 47 patients with ASUC, well characterized for their responses to steroids, cyclosporine, or infliximab, therapy at 2 centers in France. Fixed colonic biopsies, collected before or within the first 3 days of treatment, were used for microarray analysis of microRNA expression profiles. Deep neural network-based classifiers were used to derive candidate biomarkers for discriminating responders from non-responders to each treatment and to predict which patients would require colectomy. Levels of identified microRNAs were then measured by quantitative PCR analysis in a validation cohort of 29 independent patients-the effectiveness of the classification algorithm was tested on this cohort.
    RESULTS: A deep neural network-based classifier identified 9 microRNAs plus 5 clinical factors, routinely recorded at time of hospital admission, that associated with responses of patients to treatment. This panel discriminated responders to steroids from non-responders with 93% accuracy (area under the curve, 0.91). We identified 3 algorithms, based on microRNA levels, that identified responders to infliximab vs non-responders (84% accuracy, AUC = 0.82) and responders to cyclosporine vs non-responders (80% accuracy, AUC = 0.79).
    CONCLUSION: We developed an algorithm that identifies patients with ASUC who respond vs do not respond to first- and second-line treatments, based on microRNA expression profiles in colon tissues.

    PMID: 30223112 [PubMed - in process]

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