Combining mucosal microbiome and host multi-omics data shows prognostic potential in paediatric ulcerative colitis
Maria Kulecka, Jill O’Sullivan, Rachel S. Fitzgerald, Ana Velikonja, Chloe E. Huseyin, Emilio J. Laserna‐Mendieta, Patricia Ruiz‐Limón, Julia Eckenberger, Miriam Vidal-Marín, Bastian-Alexander Truppel, Raminder Singh, Sandhia Naik, Nicholas M. Croft, Andriy Temko, Aldert Zomer, John MacSharry, Silvia Melgar, Protima Deb, Ian R. Sanderson, Marcus J. Claesson
Abstract
Current first-line treatments of paediatric ulcerative colitis (UC) maintain a 6-month remission in only half of the patients. Relapse prediction at diagnosis could enable earlier introduction of immunosuppressants. We collected intestinal biopsies from 56 treatment-naïve children, combining mucosal quantitative microbial profiling with host epigenomics, transcriptomics, genotyping, and in vitro and in vivo experiments on selected bacteria. Baseline bacterial diversity is lower in relapsing children, who have fewer butyrate producers but more oral-associated bacteria, whereof Veillonella parvula induces inflammation in epithelial cell lines and IL10−/− mice. Microbiota has the strongest association with future relapse, followed by host epigenome and transcriptome. Interferon gamma signalling is also linked to relapse-associated bacteria. Relapse-prediction using separate omics data is outperformed by a robust machine learning approach combining microbiomes and epigenomes. In summary, host-microbe data have prognostic potential in paediatric UC. Our translational findings also suggest that pro-inflammatory oral-associated colonizers can exploit the reduced colonic bacterial diversity of relapsing children. Current first-line treatments of pediatric UC maintain a 6-month remission in only half of the patients. Here, applying multi-omics on intestinal biopsies from treatment-naïve children, the authors show that relapse-prediction using separate omics data is outperformed by a robust machine learning approach combining microbiomes and epigenomes.