Microbiome insights into pediatric familial adenomatous polyposis

BACKGROUND

Individuals with familial adenomatous polyposis (FAP) harbor numerous polyps with inevitable early progression to colon cancer. Complex microbiotic-tumor microenvironment perturbations suggest a dysbiotic relationship between polyp and microbiome. In this study, we performed comprehensive analyses of stool and tissue microbiome of pediatric FAP subjects and compared with unaffected cohabiting relatives through 16S V4 region amplicon sequencing and machine learning platforms.

RESULTS

Within our FAP and control patient population, Firmicutes and Bacteroidetes were the predominant phyla in the tissue and stool samples, while Proteobacteria dominated the polyp/non-polyp mucosa. A decline in Faecalibacterium in polyps contrasted with a decline in Bacteroides in the FAP stool. The alpha- and beta-diversity indices differed significantly within the polyp/non-polyp groups, with a concurrent shift towards lower diversity in polyps. In a limited 3-year longitudinal study, the relative abundance of Proteobacteria and Fusobacteria was higher in polyps compared to non-polyp and stool specimens over time. Through machine learning, we discovered that Archaeon_enrichment_culture_clone_A13, Micrococcus_luteus, and Eubacterium_hallii in stool and PL-11B10, S1-80, and Blastocatellaceae in tissues were significantly different between patients with and without polyps.

CONCLUSIONS

Detection of certain bacterial concentrations within stool or biopsied polyps could serve as adjuncts to current screening modalities to help identify higher-risk patients.

Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging

Aims

We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress.

Methods and results

In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function—including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = −0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28–0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = −0.40) of sulfated androgen.

Conclusion

Using computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.

Three-dimensional cardiovascular imaging-genetics: a mass univariate framework

Motivation

Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D).

Results

High-resolution cardiac magnetic resonance images were automatically segmented in 1124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts.

Availability and implementation

The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work.

Contact

declan.oregan@imperial.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.