Inflammatory bowel disease
Inflammatory bowel disease (IBD) encompasses multiple chronic inflammatory conditions that affect the gastrointestinal tract. Although the prevalence of this disease is increasing worldwide, it occurs most frequently in North America and Europe, with approximately 0.3% of the European population being affected (Burisch et al., 2013). Over the past decade, the gut microbiome has been found to be implicated in disease etiology (Huttenhower et al., 2014). Yet, the numerous factors that appear to play a role in IBD make this disease challenging to study.
In their study, Franzosa et al. (2019) used metabolomics and metagenomics approaches to profile a cohort of people with IBD. They included people affected by either Crohn’s disease or ulcerative colitis, two of the most common manifestations of IBD. The aim of their study was to identify microbes and metabolites that were different in patients with IBD. Specifically, they found 122 associations between species and metabolites that were differentially abundant across patients, suggesting potential mechanisms of disease.
In this case study, we are going to use data generated by Franzosa et al. (2019), construct microbe-function associations and upload these to a Neo4j database. For convenience, we have processed these files in advance so they are ready to use in R, but they are all derived from supplementary files provided by Franzosa et al. (2019). Later on, we will use Cypher to find taxa with the largest number of metabolite associations.
The data have been pre-processed already; relative abundances were estimated with MetaPhlan2, function abundances with HUMAnN2, and species below 0.1% abundance in at least five samples were excluded.
Download pre-processed files here:
ibd_lineages.csv
ibd_metabolite_abundances.csv
ibd_metabolite_features.csv
ibd_metadata.csv
ibd_taxa.csv