The gut microbiome of Baka forager-horticulturalists from Cameroon is optimized for wild plant foods

Abstract

The human gut microbiome is losing biodiversity, due to the "microbiome modernization process" that occurs with urbanization. To keep track of it, here we applied shotgun metagenomics to the gut microbiome of the Baka, a group of forager-horticulturalists from Cameroon, who combine hunting and gathering with growing a few crops and working for neighboring Bantu-speaking farmers. We analyzed the gut microbiome of individuals with different access to and use of wild plant and processed foods, to explore the variation of their gut microbiome along the cline from hunter-gatherer to agricultural subsistence patterns. We found that 26 species-level genome bins from our cohort were pivotal for the degradation of the wild plant food substrates. These microbes include Old Friend species and are encoded for genes that are no longer present in industrialized gut microbiome. Our results highlight the potential relevance of these genes to human biology and health, in relation to lifestyle.

Keywords: Diet; Microbiome.

Graphical abstract

Figure 1

Geographic map of sample collection locations Baka and Nzime live in South-East part of Cameroon, as indicated in the box on the top-right of the figure. Fecal samples from Baka adults were collected in the Kungu forest camp (Baka forest) and in the Le Bosquet village (Baka settlement). Samples from Nzime individuals were collected in the Nkeadinako village. Such locations are indicated by red dot on the map.

Figure 2

Differences in GM compositions among individuals of the Baka forest (green), Baka settled (light green), and Nzime village (yellow-green) groups (A) PCoA plots based on unweighted and weighted UniFrac distances and (B) boxplots for SGBs abundances, in terms of genome copies per million of sequenced reads. p values are obtained using Kruskal-Wallis test. See also Figure S2 for the distribution of all the SGBs across the entire cohort and Figure S3 for the same analysis combining Baka forest and Baka settled in a unique group.

Figure 3

Comparison of cumulative abundances of wpSGBs Highlighting comparison between fecal samples from individuals of the Baka forest (green), Baka settled (light green), and Nzime village (yellow-green) groups, represented by boxplots. Values in genome copies per million reads. ∗p = 0.02, Kruskal-Wallis rank-sum test.

Figure 4

Co-abundance analysis highlights distinct bacterial networks characterizing the three groups (A) A network heatmap based on Kendall’s correlation coefficient and GM data was generated using the most abundant SGBs across all samples (see complete list of taxa and their abundance in Table S4). The most dominant clusters identified are highlighted by different colored boxes and were confirmed by permutation tests with pseudo-F ratios (p < 0.05, adonis of the R package vegan). One setting was used for cluster analysis (gray dashed lines), which identified seven clusters. The Cryptobacteroides cluster is highlighted in brown, the Treponema cluster in green, the Succinivibrio cluster in cyan, Phascolarctobacterium in red, Prevotella (cluster 1) in pink, Bacteroides fragilis in gray, and Prevotella (cluster 2) in blue. (B) Network scheme illustrating the relationships between bacterial clusters. The leading taxa in each network are highlighted. A positive correlation is shown with a gray line and a negative correlation with a red line. Clusters are colored as in (A). (C) Cumulative relative abundance of the different clusters of taxa among the three groups (∗p < 0.01, ∗∗p < 0.001; Wilcoxon test). See also Figure S4.

Figure 5

Prevalence of wpSGB genes and BGCs across human populations Heatmaps showing the prevalence of the 29 wpSGB genes showing a propensity for the degradation of polysaccharides and phytochemicals (A) and of 34 BGCs for secondary metabolites (B), which were not present in the other SGBs. Datasets comprised individuals relying on both rural and industrialized lifestyle from different geographical origin (see also Table S5 for further details). SWE, Sweden; ITA, Italy (industrial); DEU, Germany (industrial); USA, USA (industrial); IND, India (industrial); CHN, China (industrial); BRA, Brasil (rural); PER, Peru (rural); TZA, Tanzania (rural).