Healthy Cotwins Share Gut Microbiome Signatures With Their Inflammatory Bowel Disease Twins and Unrelated Patients

Abstract

BACKGROUND & AIMS: It is currently unclear whether reported changes in the gut microbiome are cause or consequence of inflammatory bowel disease (IBD). Therefore, we studied the gut microbiome of IBD-discordant and -concordant twin pairs, which offers the unique opportunity to assess individuals at increased risk of developing IBD, namely healthy cotwins from IBDdiscordant twin pairs. METHODS: Fecal samples were obtained from 99 twins (belonging to 51 twin pairs), 495 healthy age-, sex-, and body mass index-matched controls, and 99 unrelated patients with IBD. Whole-genome metagenomic shotgun sequencing was performed. Taxonomic and functional (pathways) composition was compared among healthy cotwins, IBD-twins, unrelated patients with IBD, and healthy controls with multivariable (ie, adjusted for potential confounding) generalized linear models.RESULTS: No significant differences were observed in the relative abundance of species and pathways between healthy cotwins and their IBD-twins (false discovery rate <0.10). Compared with healthy controls, 13, 19, and 18 species, and 78, 105, and 153 pathways were found to be differentially abundant in healthy cotwins, IBD-twins, and unrelated patients with IBD, respectively (false discovery rate <0.10). Of these, 8 (42.1%) of 19 and 1 (5.6%) of 18 species, and 37 (35.2%) of 105 and 30 (19.6%) of 153 pathways overlapped between healthy cotwins and IBDtwins, and healthy cotwins and unrelated patients with IBD, respectively. Many of the shared species and pathways have previously been associated with IBD. The shared pathways include potentially inflammation-related pathways, for example, an increase in propionate degradation and L-arginine degradation

Compartment-driven imprinting of intestinal CD4 (regulatory) T cells in inflammatory bowel disease and homeostasis

“…Future research should include newly diagnosed patients or prediagnostic samples (with the latter being hard to collect) to assess how CD4 T cells at baseline or before diagnosis are altered. This could be pre-onset changes as shown by presence of an IBD-like microbiome in healthy co-twins at risk of developing IBD, 51 the influence of (previous) administered medication as shown in the blood of IBD patients, 52 or the effect of previous flares. The fact that CD patients with an ileocecal resection often experience relapses at the anastomosis 53 suggests that the gut mucosa of CD patients exhibits (pre-existent) changes, although the occurrence of relapses could also be (partly) caused by non-immune cell related factors.…”

Inflammatory potential of gut microbiota from monozygotic twin pair discordant for ulcerative colitis

“…In co-twins discordant for IBD, the healthy twin has an increased risk of developing IBD 28,29 . Recently, it has been reported that the healthy co-twin has a gut microbiota more similar to that of their diseased twin or IBD patients in general compared to healthy individuals 29,30 .…”

“…In co-twins discordant for IBD, the healthy twin has an increased risk of developing IBD 28,29 . Recently, it has been reported that the healthy co-twin has a gut microbiota more similar to that of their diseased twin or IBD patients in general compared to healthy individuals 29,30 . However, the diseased twin living with an IBD diagnosis may have evolved a less in ammatory gut microbiota due to treatment and lifestyle changes such as diet.…”

multiMiAT: An optimal microbiome-based association test for multicategory phenotypes

“…Microbial studies usually perform global hypothesis tests to explore the association between the microbiome and host phenotype, where the type of phenotype is determined by the distribution it follows, for example, Gaussian (BMI), Bernoulli (health/disease) and multinomial distribution (health/moderate disease/severe disease). Adonis [23] and ANOSIM [24,25] are classic distance-based statistical methods widely used to detect the association between the microbiome and host phenotypes in most microbial studies [26][27][28][29]. However, the disadvantage of these methods is that they do not adequately consider the effects of confounding factors, resulting in power loss.…”