Microbial genes and pathways in inflammatory bowel disease.
Melanie SchirmerAshley GarnerHera VlamakisRamnik J XavierPublished in: Nature reviews. Microbiology (2020)
Perturbations in the intestinal microbiome are implicated in inflammatory bowel disease (IBD). Studies of treatment-naive patients have identified microbial taxa associated with disease course and treatment efficacy. To gain a mechanistic understanding of how the microbiome affects gastrointestinal health, we need to move from census to function. Bacteria, including those that adhere to epithelial cells as well as several Clostridium species, can alter differentiation of T helper 17 cells and regulatory T cells. Similarly, microbial products such as short-chain fatty acids and sphingolipids also influence immune responses. Metagenomics and culturomics have identified strains of Ruminococcus gnavus and adherent invasive Escherichia coli that are linked to IBD and gut inflammation. Integrated analysis of multiomics data, including metagenomics, metatranscriptomics and metabolomics, with measurements of host response and culturomics, have great potential in understanding the role of the microbiome in IBD. In this Review, we highlight current knowledge of gut microbial factors linked to IBD pathogenesis and discuss how multiomics data from large-scale population studies in health and disease have been used to identify specific microbial strains, transcriptional changes and metabolic alterations associated with IBD.
Keyphrases
- regulatory t cells
- microbial community
- escherichia coli
- healthcare
- immune response
- dendritic cells
- public health
- end stage renal disease
- ulcerative colitis
- newly diagnosed
- electronic health record
- fatty acid
- gene expression
- ejection fraction
- chronic kidney disease
- induced apoptosis
- transcription factor
- genome wide
- risk assessment
- peritoneal dialysis
- combination therapy
- big data
- human health
- case control
- machine learning
- staphylococcus aureus
- multidrug resistant
- cell cycle arrest
- heat shock
- deep learning
- heat stress
- heat shock protein