The role of gut microbiota in a generalist, golden snub-nosed monkey, adaptation to geographical diet change

Abstract

Changes in diet causing ecological stress pose a significant challenge to animal survival. In response, the gut microbiota, a crucial part of the host's digestive system, exhibits patterns of change reflective of alterations in the host's food component. The impact of temporal dietary shifts on gut microbiota has been elucidated through multidimensional modeling of both food component and macronutrient intake. However, the broad distribution of wild generalist and the intricate complexity of their food component hinder our capacity to ascertain the degree to which their gut microbiota assist in adapting to spatial dietary variations. We examined variation in patterns of the gut microbial community according to changes in diet and in a colobine monkey with a regional variable diet, the golden snub-nosed monkey (Rhinopithecus roxellana). Specifically, we analyse the interactions between variation in food component, macronutrient intake and the gut microbial community. We compared monkeys from four populations by quantifying food component and macronutrient intake, and by sequencing 16S rRNA and the microbial macro-genomes from the faecal samples of 44 individuals. We found significant differences in the diets and gut microbial compositions, in nutrient space and macronutrient intake among some populations. Variations in gut microbiota composition across distinct populations mirror the disparities in macronutrient intake, with a notable emphasis on carbohydrate. Geographical differences in the diet among of golden snub-nosed monkey populations will result in macronutrient intake variation, with corresponding differences in macronutrient intake driving regional differences in the compositions and abundances of gut microbiota. Importantly, the gut microbiota associated with core digestive functions does not vary, with the non-core gut microbiota fluctuating in response to variation in macronutrient intake. This characteristic may enable species heavily reliant on gut microbiota for digestion to adapt to diet changes. Our results further the understanding of the roles gut microbiota play in the formation of host dietary niches.

Keywords: Dietary niches; Gut microbiota; Macronutrient intake; Primate; Weighted gene co-expression network.

Fig. 1

Food component, nutrient space and macronutrient intake of golden snub-nosed monkey from different regions. (a) Heat map of food intake quality of golden snub-nosed monkeys in different regions. The darker colour indicates a higher proportion of such food intake in this area compared to other areas. (b) Nutrient space of golden snub-nosed monkey populations in different regions. The figure’s points show the percentage of macronutrient energy from each food item relative to total nutrient supply. The x-axis measures carbohydrate energy’s share of total macronutrients, and the y-axis does the same for available protein. Closed shapes indicate the nutrient space for different monkey populations: blue dots for HY, yellow squares for HBY, red triangles for FP, and green pentagrams for LGT. (c) The comparison of available protein intake. (d) The comparison of carbohydrate intake. (e) The comparison of lipid intake. The asterisk (*) indicates statistical significance at the P < 0.05 (Kruskal-Wallis test)

Fig. 2

Differences in the gut microbiota of golden snub-nosed monkeys from different regions. (a) PCoA based on OTU level gut microbiota data of golden snub-nosed monkey from different regions. Analysing regional differences in dominant bacterial populations. Relative abundance of dominant phylum (b) and order (c) in four regions based on 16S rRNA gene pools

Fig. 3

Differential gut microbiota, environmental influences and functional differences of golden snub-nosed monkey from different regions. (a) Differential gut microbiotas between FP and LGT populations of golden snub-nosed monkey. The groups of gut microbiotas shown are those that differ in different groupings, and the length of the bar graph indicates the magnitude of the differences. (b) Nutritional effects on microbial communities in FP and LGT. Each sample is represented by a point, while arrows emanating from the origin represent different environmental factors. The length of the arrows indicates the strength of the influence of each environmental factor on community variation; longer arrows signify greater impact. The angle between the arrows reflects their correlation: acute angles indicate positive correlation between two environmental factors, while obtuse angles indicate negative correlation. (c) PCA based on gut microbiota CAZy-family data of golden snub-nosed monkey from different regions

Fig. 4

Identification of key module and hub OTUs based on WGCNA. (a) Correlation between module eigenvalues and traits of golden snub-nosed monkey. Depth of colour corresponds to depth of correlation and P value of each module. (b) Network graph of the hub OTUs. Each node represented the OTUs who’s between centrality value was in the top 50%, and its colour represented the corresponding module, the size of each node represented the betweenness centrality value, the size of each line thickness represented the weight value between nodes (OTUs). Visualization of the complete weighted networks comprising 58 candidate hub OTUs associated with SC.TNC. Among them, 36 red nodes represent unique bacterial species in the LGT population, while 22 blue nodes denote species shared between the LGT and FP populations