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Climate changes affects animal physiology. In particular, rising ambient temperatures reduce animal vitality due to heat stress and this can be observed at various levels which included genome, transcriptome, and microbiome. In a previous study, microbiota highly associated with changes in cattle physiology were identified under heat stress conditions which included rectal temperature, drooling score, and respiratory score. In the present study, genes differentially expressed between individuals were selected representing different additive genetic effects towards the heat stress response in cattle in their production condition. Moreover, a correlation network analysis was performed to identify interactions between the transcriptome and microbiome for 71 Chinese Holstein cows sequenced for mRNA from blood samples and for 16S rRNA genes from fecal samples. Bioinformatics analysis was performed comprising: i) clustering and classification of 16S rRNA sequence reads, ii) mapping cows’ transcripts to the reference genome and their expression quantification, and iii) statistical analysis of both data types – including differential gene expression analysis and gene set enrichment analysis. A weighted co-expression network analysis was carried out to assess changes in the association between gene expression and microbiota abundance as well as to find hub genes/microbiota responsible for the regulation of gene expression under heat stress. Results showed 1,851 differentially expressed genes were found that were shared by three heat stress phenotypes. These genes were predominantly associated with the cytokine-cytokine receptor interaction pathway. The interaction analysis revealed three modules of genes and microbiota associated with rectal temperature with which two hubs of those modules were bacterial species, demonstrating the importance of the microbiome in the regulation of gene expression during heat stress. Genes and microbiota from the significant modules can be used as biomarkers of heat stress in cattle.

Humans have been influencing climate changes by burning fossil fuels, farming livestock, and cutting down rainforests, which has led to global temperature rise. This problem of global warming affects animals by causing heat stress, which negatively affects their health, biological functions, and reproduction. On the molecular level, it has been proved that heat stress changes the expression level of genes and therefore causes changes in proteome and metabolome. The importance of a microbiome in many studies showed that it is considered as individuals’ “second genome”. Physiological changes caused by heat stress may impact the microbiome composition. In this study, we identified fecal microbiota associated with heat stress that was quantified by three metrics – rectal temperature, drooling, and respiratory scores and represented by their Estimated Breeding Values. For this purpose, the 16S rRNA sequencing technique was used. We analyzed the microbiota from 136 fecal samples of Chinese Holstein cows through a 16S rRNA gene sequencing approach. Sequence data were processed using a pipeline involving QIIME2 software together with SILVA database. Statistical modeling was performed using a negative binomial regression. The analysis revealed the total number of 24 genera and 12 phyla associated with heat stress metrics. Rhizobium and Pseudobutyrivibrio turned out to be the most significant genera, while Acidobacteria and Gemmatimonadetes were the most significant phyla. Phylogenetic analysis revealed that that three heat stress indicators quantify different metabolic ways of animals’ reaction to heat stress. Other studies already identified that those genera had significantly increased abundance in mice exposed to stressor-induced changes. Moreover, identified microbiota significantly associated with heat stress measures were mostly mesophilic, so their association seems to be due to heat stress-induced secondary, metabolic changes, and not directly by temperature. Moreover, high fold changes of many genera suggest that they may be used as biomarkers for monitoring the level of heat stress in cattle.