Fig 1.
Analytical workflow for this study.
Blood and fecal samples from animals were used for metabolite analysis, RNA-seq and 16S rRNA-seq, respectively, and the generated data were analyzed and integrated with their correlation.
Fig 2.
(A) PCA plot showing the distance between two groups. (B) Score plot derived from OPLS-DA model of blood samples, tend to be divided between two groups but not significant.
Fig 3.
The relative amounts of metabolites.
Box plot showing significant metabolites with VIP ≥1 and p <0.05, these metabolites reduced in Severe as follows: Ornithine, Tryptophan, and Tyrosine from amino acids, Hexadecanoic acid and Linoleic acid from fatty acids, Fructose and Myo-Inositol from sugar derivatives, and Organic acids from organic acids Lactic acid and uric acid.
Fig 4.
The identification of gene expression patterns and DEGs.
(A) and (B) PCA plot showing count data before the batch effect correction and after the removed unwanted factors. (C) Volcano plot showing the up- and down-regulated DEGs removed unwanted factors with FDR < 0.05 and log2FC ≥ 1.0 in Severe. (D) Heatmap clustering showing the degrees of DEGs and the clustered samples in two groups, colors indicated pink as up- and blue as down-regulated.
Table 1.
The top 10 DEGs, gene expression level and statistical significance in the heat stress experimental group.
Fig 5.
The enriched terms of upregulated DEGs with EASE < 0.1 and p < 0.05 in the heat stress experimental group.
Colors indicated each category, green (Biological Process), blue (Cellular Component), periwinkle (Molecular Function), and pink (KEGG pathway) in upregulated DEGs. The upregulated DEGs in the heat stress experimental group are considered downregulated DEGs in control group.
Fig 6.
The enriched of upregulated gene network with p < 0.05 in the heat stress experimental group.
Shapes indicated octagon as GO terms and hexagon as KEGG pathways in upregulated DEGs, and circle as DEGs. The upregulated DEGs in the heat stress experimental group are considered downregulated DEGs in control group.
Table 2.
The enriched of upregulated gene network and the associated with up regulated DEGs in the heat stress experimental group.
Fig 7.
Alpha, beta diversity metrics and alpha rarefaction of microbial communities.
(A) Alpha diversity showing Observed species, Shannon, Simpson, and Chao1 index. (B) Beta diversity showing distance of samples through unweighted and weighted UniFrac. (C) The generated alpha rarefaction curves showing sampling depth.
Fig 8.
(A) phylum level. (B) genus level. Each color indicated taxa, respectively, taxon sequences were assigned to OTUs in each sample, and abundant taxa more than 1% indicated their name whereas <1% was included in the other on the plots.
Fig 9.
Microbial abundance showing significant difference at genus level.
The characterized genus were compared significantly with at linear discriminant analysis (LDA) ≥ 2.
Table 3.
Top 10 genus of microbial differential abundance.
Fig 10.
The predicted functions of microbial genome.
The functional pathways of microbial communities were compared significantly with LDA ≥ 2.
Table 4.
Microbial genome predictions and statistical analysis.
Fig 11.
Significant correlation network between metabolome, transcriptome, and microbiome.
The correlation network is considered p <0.05 and r2 ± ≥ 0.95 with Spearman’s method. (A) control group. (B) heat stress experimental group.