Microbial gut evaluation in an angolan paediatric population with sickle cell disease

Abstract

Sickle cell disease (SCD) is one of the most common genetic conditions worldwide. It can contribute up to 90% of under-5 mortality in sub-Saharan Africa. Clinical manifestations are very heterogeneous, and the intestinal microbiome appears to be crucial in the modulation of inflammation, cell adhesion and induction of aged neutrophils, the main interveners of recurrent vaso-occlusive crisis. Enterocyte injury, increased permeability, altered microbial composition and bacterial overgrowth have all been documented as microbial and pathophysiologic changes in the gut microbiome of SCD patients in recent studies. Our aim was to sequence the bacterial 16S rRNA gene in order to characterize the gut microbiome of Angolan children with SCA and healthy siblings as a control. A total of 72 stool samples were obtained from children between 3 and 14 years old. Our data showed that the two groups exhibit some notable differences in microbiota relative abundance at different classification levels. Children with SCA have a higher number of the phylum Actinobacteria. As for the genus level, Clostridium cluster XI bacteria was more prevalent in the SCA children, whereas the siblings had a higher abundance of Blautia, Aestuariispira, Campylobacter, Helicobacter, Polaribacter and Anaerorhabdus. In this study, we have presented the first microbiota analysis in an Angolan paediatric population with SCD and provided a detailed view of the microbial differences between patients and healthy controls. There is still much to learn before fully relying on the therapeutic approaches for gut modulation, which is why more research in this field is crucial to making this a reality.

Keywords: 16S rRNA; foetal haemoglobin; microbiome; sickle cell disease.

FIGURE 1

Prevalence of each BMI group classification in the studied population (n = 72)

FIGURE 2

Heatmap of the 10 most prevalent bacterial phylum in the population. The colour intensity in each panel shows the relative abundance in percentage of each phylotype. Metagenomic samples were grouped by similarity and dendrograms were constructed by the average neighbour algorithm and unweighted pair group method with arithmetic mean (UPGMA)

FIGURE 3

Boxplots of alpha diversity calculated by Shannon index, relatively to the: (A) Patient genotype group, siblings (AA+AS) or SCA children; (B) Foetal haemoglobin level and (C) BMI group. p‐values were calculated with Mann–Whitney U‐test

FIGURE 4

Distribution of metagenomic data in SCA children and healthy siblings for the levels of: (A) phylum (B) class (C) order (D) family (E) genus (F) species. The results were filtered using a p‐value of 0.05 with a Welch's t‐test, effect size of 0.01 threshold and removing unclassified reads in STAMP software. The difference in mean proportions indicates the mean proportion of certain bacteria in SCA children minus the mean proportion in siblings

FIGURE 5

Relative abundance of the ten most prevalent bacterial genera between SCA children and corresponding healthy siblings

FIGURE 6

(A) Extended error bar plot and (B) Bar plot with the proportion of sequences of the genus Blautia in each sample. The results were filtered using a p‐value of 0.05 with a Welch's t‐test, effect size of 0.50 threshold and retaining unclassified reads in STAMP software

FIGURE 7

Microbiota analysis plots of the 36 SCA children grouped in relation to their HbF group: (A) Principal component analysis (PCA) plots, where the percentage of variation explained by the principal coordinates is indicated on each axis. Grey circles represent the SCA children with <5% of HbF (n = 27) and yellow squares with above 5% (n = 9); (B) Bar plots representing the significant genus: Ruminococcus and Kandleria. The star symbol indicates the mean and the plus symbol the outliers. This analysis was performed with Welch's t‐test and an effect size filter of 0.5