Metagenomic analysis reveals the community composition of the microbiome in different segments of the digestive tract in donkeys and cows: implications for microbiome research

Abstract

Introduction: The intestinal microbiota plays a crucial role in health and disease. This study aimed to assess the composition and functional diversity of the intestinal microbiota in donkeys and cows by examining samples collected from different segments of the digestive tract using two distinct techniques: direct swab sampling and faecal sampling.

Results: In this study, we investigated and compared the effects of multiple factors on the composition and function of the intestinal microbial community. Approximately 300 GB of metagenomic sequencing data from 91 samples obtained from various segments of the digestive tract were used, including swabs and faecal samples from monogastric animals (donkeys) and polygastric animals (cows). We assembled 4,004,115 contigs for cows and 2,938,653 contigs for donkeys, with a total of 9,060,744 genes. Our analysis revealed that, compared with faecal samples, swab samples presented a greater abundance of Bacteroidetes, whereas faecal samples presented a greater abundance of Firmicutes. Additionally, we observed significant variations in microbial composition among different digestive tract segments in both animals. Our study identified key bacterial species and pathways via different methods and provided evidence that multiple factors can influence the microbial composition. These findings provide new insights for the accurate characterization of the composition and function of the gut microbiota in microbiome research.

Conclusions: The results obtained by both sampling methods in the present study revealed that the composition and function of the intestinal microbiota in donkeys and cows exhibit species-specific and region-specific differences. These findings highlight the importance of using standardized sampling protocols to ensure accurate and consistent characterization of the intestinal microbiota in various animal species. The implications and underlying mechanisms of these associations provide multiple perspectives for future microbiome research.

Keywords: Digestive tract segments; Intestinal microbiota; Metagenomic analysis; Microbiota composition.

Fig. 1

Graphical representation of samples collected from distinct sites along the digestive tract of cows and donkeys

Fig. 2

Overview of commonalities and differences among all samples. (a) Pangene rarefaction curve. The curve nearly plateaued when sufficient sequence data were inputted. (b) Heatmap showing the correlation between the samples and the gene table. We divided the heatmap into four parts, A, B, C, and D, whose primary variables were digestive tract segments, animals, sampling methods and animal and sampling methods, respectively. (c) Microbial composition of all samples at the kingdom level and the top 5 dominant phyla of bacteria. (d) The composition of microbial species depending on the animal (donkey, cow), sampling method (S: swab, F:faecal) and digestive tract segment (A-E: stomach, small intestine, caecum, colon, rectum). CowF denotes the cow faecal samples. (e) The enriched KEGG pathways

Fig. 3

Compared with the two donkey sampling methods. (a) Partial least squares discriminant analysis (PLS-DA) at the phylum level. (b) Analysis of similarities (ANOSIM) at the phylum level. (c) Top 30 phyla. (d) The species compositions of the samples collected using the two sampling methods. (e) The distribution of the unique species. (f) Species variable importance dot plot. The horizontal axis is the measure of importance, and the vertical axis is the phylum name in order of importance (the same applies below). (g) Linear discriminant analysis (LDA) effect size (LEfSe) analysis

Fig. 4

The two sampling methods were compared. (a) PLS-DA results of bacteria at the phylum level. (b) ANOSIM results of bacteria at the phylum level. (b) Top 30 differentially abundant bacteria at the phylum level. (d) The microbial composition of the samples collected via the two methods. (e) The distribution of unique microbial species. (f) Species variable importance dot plot

Fig. 5

Comparison of donkey digestive tract segments. (a-b) Bacterial composition of donkey digestive tract segments at the genus and species levels. (c) Venn diagram of bacteria in different donkey digestive tract segments at the species level. (d) Flower plot of bacteria in different donkey digestive tract segments at the phylum level. (e) UpSet plot of bacteria in different donkey digestive tract segments at the phylum level. The horizontal axis is the combination of different digestive tract segments, and the vertical axis is the intersection size of the segment combination (the same below). (f) Variable importance dot plot of bacteria at the phylum level

Fig. 6

Comparison of digestive tract segments in cows. (a-b) Bacterial composition of cow digestive tract segments at the genus and species levels. (c) Venn diagram of bacteria in different cow digestive tract segments at the species level. (d) UpSet plot of bacteria in the different cow digestive tract segments at the phylum level. (e) Variable importance dot plot of bacteria at the phylum level. (f) The species composition of the cow rumen, reticulum, omasum, abomasum and donkey stomach

Fig. 7

Comparison of monogastric and polygastric animals. (a-b) Bacterial composition of donkeys and cows at the genus and species levels. (c) PLS-DA results showing the differences in the bacterial species compositions of the animals. (d) Venn diagram of the number of species shared by different animals. (e) The number of unique species between the animals and their distributions. (f) Species variable importance dot plot

Fig. 8

The results of microbiome functional comparisons and codifference networks. Comparison of the main KEGG functions of cows (a, c) and donkeys (b, d) grouped by sampling method and digestive tract segment. Comparison of the levels of GH, GT, CBM, CE, PL, and AA among different digestive tract segments of cows (e) and donkeys (f). (g) The distributions of function nodes (N_f), microbe nodes (N_m) and graph complexity (C) of the codifference network. Here, we used the formula (C = E-N_f-N_m + 1) to calculate the graph complexity (E represents the number of edges in the network). (h) The database sources of differential function nodes. (i) The major bacteria in different groups at the species level