To identify the genetic determinants of domestication and productivity of Asian water buffaloes (Bubalus bubalis), 470 genomes of domesticated river and swamp buffaloes along with their putative ancestors, the wild water buffaloes (Bubalus arnee) are sequenced and integrated. The swamp buffaloes inherit the morphology of the wild buffaloes. In contrast, most river buffaloes are unique in their morphology, but their genomes cluster with the wild buffaloes. The levels of genomic diversity in Italian river and Indonesian swamp buffaloes decrease at opposite extremes of their distribution range. Purifying selection prevented the accumulation of harmful loss‐of‐function variants in the Indonesian buffaloes. Genes that evolved rapidly (e.g., GKAP1) following differential selections in the river and swamp buffaloes are involved in their reproduction. Genes related to milk production (e.g., CSN2) and coat color (e.g., MC1R) underwent strong selections in the dairy river buffaloes via soft and hard selective sweeps, respectively. The selective sweeps and single‐cell RNA‐seq data revealed the luminal cells as the key cell type in response to artificial selection for milk production of the dairy buffaloes. These findings show how artificial selection has been driving the evolutionary divergence and genetic differentiation in morphology and productivity of Asian water buffaloes.

Household buffalo dairy farming is gaining popularity nowadays in Bangladesh because of the outstanding food value of buffalo milk as well as the lower production cost of buffalo compared to cattle. An initiative has recently been taken for the genetic improvement of indigenous dairy buffaloes. The present study was carried out to determine the influence of some environmental factors like age, parity, season of calving, calving interval, dry period on the lactation yield, and lactation curve of indigenous dairy buffaloes of Bangladesh. A total of 384 indigenous dairy buffaloes from the 3rd and 4th parity of seven herds under two different agroecological zones covering four seasons were selected and ear tagged for individual buffalo milk recording. A milk yield of 300 days (MY300d) was calculated following the International Committee for Animal Recording (ICAR) and the data were evaluated using the generalized linear model (GLM). In production traits, the mean of calculated lactation period (CLP), calculated lactation yield (CLY), and milk yield of 300 days (MY300d) of the overall population were 267.28 days, 749.36 kg, and 766.92 kg, respectively, whereas calving interval (CI) and dry period (DP) as reproductive traits were 453.06 days and 185.78 days, respectively. The season of calving, age of buffalo cows, population or herd, agroecological zone, calving interval, and dry period had significant effects on production traits (p < 0.05 to p < 0.001). The season of calving, level of milk production of 300 days, population, and agroecological zone significantly affected the reproduction traits (p < 0.01 to p < 0.001). Parity was found to be non-significant for both types of traits. The average peak yield of test day (TD) milk production was highest at TD4 (4.47 kg, 98th day of lactation). The average MY300d of milk production was the highest in the Lalpur buffalo population (1076.13 kg) and the lowest in the buffalo population of Bhola (592.44 kg). The correlations between milk production traits (CLP, CLY, and MY-300d) and reproduction traits (CI and DP) were highly significant (p < 0.01 to p < 0.001). Positive and high correlation was found within milk traits and reproduction traits, but correlation was negative between milk traits and reproduction traits. Therefore, these non-genetic factors should be considered in the future for any genetic improvement program for indigenous dairy buffaloes in Bangladesh.

We generated 73 transcriptomic data of water buffalo, which were integrated with publicly available data in this species, yielding a large dataset of 355 samples representing 20 major tissue categories. We established a multi-tissue gene expression atlas of water buffalo. Furthermore, by comparing them with 4866 cattle transcriptomic data from the cattle genotype–tissue expression atlas (CattleGTEx), we found that the transcriptomes of the two species exhibited conservation in their overall gene expression patterns, tissue-specific gene expression and house-keeping gene expression. We further identified conserved and divergent expression genes between the two species, with the largest number of differentially expressed genes found in the skin, which may be related to structural and functional differences in the skin of the two species. This work provides a source of functional annotation of the buffalo genome and lays the foundations for future genetic and evolutionary studies in water buffalo.

Mastitis caused by Staphylococcus aureus (S. aureus) infection is one of the most difficult diseases to treat in dairy cattle. Exploring the biological progression of S. aureus mastitis via the interaction between host, pathogen, and environment is the key to an effective and sustainable improvement of animal health. Here, two strains of S. aureus and a strain of MRSA (Methicillin-resistant Staphylococcus aureus) isolated from cows with different inflammation phenotypes were used to challenge Mac-T cells and to investigate their effects on the global transcriptome of the cells, then to explore the potential regulatory mechanisms of folic acid on S. aureus mastitis prevention. Differential gene expression or splicing analysis showed that different strains of S. aureus led to distinct transcriptional responses from the host immune system. Folic acid could protect host defense against the challenge of S. aureus and MRSA partially through activating cytoplasmic DNA sensing and tight junction pathway. ZBP1 at the upstream of cytoplasmic DNA sensing pathway was verified and related to anti-pathogen through RNA interference. Further enrichment analysis using these transcriptome data with cattle large-scale genome-wide association study (GWAS) data confirmed that ZBP1 gene is highly associated with bovine somatic cell score (SCS) trait. Our data shed light on the potential effect of FA through regulating key gene and then protect host cells’ defense against S. aureus and MRSA.

Background: Gene expression profiles hold potentially valuable information for the prediction of breeding values and phenotypes. However, in practical breeding programs, most reference population individuals typically have only genomic data, lacking transcriptomic data. Predicting gene expression based on genetic markers and integrating the genetically predicted gene expression data into genomic prediction may offer a potential solution. Results: This study extends kernel ridge regression (KRR) to weighted multiple kernel ridge regression (WMKRR), which integrates genomic data and transcriptomic data predicted from genetic markers through a multiple kernel learning (MKL) approach. We evaluated the predictive ability of WMKRR compared to traditional genomic best linear unbiased prediction (GBLUP) and a combined genomic and transcriptomic best linear unbiased prediction (GTBLUP) in both genotype feature selection and non-feature selection scenarios in two datasets: (i) 3305 simulated data based on the Cattle Genotype-Tissue Expression (CattleGTEx) dataset, (ii) 5515 real dairy cattle data. Our results show that WMKRR yielded higher predictive abilities than GBLUP And GTBLUP in both simulated And real dairy cattle data. For the simulated data based on CattleGTEx, WMKRR achieved an average improvement in predictive ability of 1.12% And 1.13% over GBLUP And GTBLUP, respectively, under the non-feature selection scenario, And 3.17% And 3.23%, respectively, under the feature selection scenario. For the real dairy cattle data, in cross-validation, WMKRR improved over GBLUP And GTBLUP by An average of 5.56% And 7.23%, respectively, without feature selection, And by 5.66% And 6.40%, respectively, with feature selection. In forward validation, WMKRR improved over GBLUP And GTBLUP by An average of 5.68% And 8.41%, respectively, without feature selection, And by 4.66% And 7.06%, respectively, with feature selection. Conclusions: Our result demonstrates that the WMKRR model, which integrates genomic and genetically predicted transcriptomic data, achieves better prediction performance compared to traditional genomic prediction models. This study showed the potential of enhanced genomic breeding application using omics data with no further omics sequencing cost.