The Association between Gut Microbiome Diversity and Composition and Heat Tolerance in Cattle

Abstract

Cattle are raised around the world and are frequently exposed to heat stress, whether in tropical countries or in regions with temperate climates. It is universally acknowledged that compared to those in temperate areas, the cattle breeds developed in tropical and subtropical areas have better heat tolerance. However, the underlying mechanism of heat tolerance has not been fully studied, especially from the perspective of intestinal microbiomics. The present study collected fecal samples of cattle from four representative climatic regions of China, namely, the mesotemperate (HLJ), warm temperate (SD), subtropical (HK), and tropical (SS) regions. Then, the feces were analyzed using high-throughput 16S rRNA sequencing. The results showed that with increasing climatic temperature from HLJ to SS, the abundance of Firmicutes increased, accompanied by an increasing Firmicutes to Bacteroidota ratio. Proteobacteria showed a trend of reduction from HLJ to SS. Patescibacteria, Chloroflexi, and Actinobacteriota were particularly highest in SS for adapting to the tropical environment. The microbial phenotype in the tropics was characterized by an increase in Gram-positive bacteria and a decrease in Gram-negative bacteria, aerobic bacteria, and the forming of_biofilms. Consistently, the functional abundances of organismal systems and metabolism were decreased to reduce the material and energy demands in a hot environment. Genetic information processing and information storage and processing may be how gut flora deals with hot conditions. The present study revealed the differences in the structure and function of gut microbes of cattle from mesotemperate to tropical climates and provided an important reference for future research on the mechanism of heat tolerance regulated by the gut microbiota and a potential microbiota-based target to alleviate heat stress.

Keywords: cattle; fecal microbiome; heat tolerance; regional climates.

Figure 1

Histogram of the top 10 microbiota in each sampling location at the levels of phylum (A) and genus (B).

Figure 2

The microbiota diversity of cattle display an evident association with geographical position. (A) Beta diversity analysis shows the sequential association of microbiota diversity among the sampled locations in the dimension of PC1. (B,C) the distant separations among the four locations are confirmed using the methods of PERMANOVA and ANOSIM, respectively. (D,E) Kruskal–Wallis rank sum test further analyzes the statistical significance of microbiome partitions above at the phylum and genus levels. Data are expressed as mean ± standard deviation. * p < 0.05, ** p < 0.01.

Figure 3

LEfSe analysis shows 94 biomarkers of fecal microbiota from four sampled locations. Histogram of LDA scores represent the differences in the proportions of fecal microbiota. Taxa meeting an LDA significant threshold of >4 are shown.

Figure 4

Fecal microbiome functions between comparisons through the enriched KEGG metabolic pathways.

Figure 5

Fecal microbiome functions between comparisons with the COG functions analysis.

Figure 6

Differences in microbiota phenotype among different sampled locations.