Systems Biology–Derived Genetic Signatures of Mastitis in Dairy Cattle: A New Avenue for Drug Repurposing

From Herd Health to Public Health: Digital Tools for Combating Antibiotic Resistance in Dairy Farms

The emergence of antimicrobial resistance (AMR) is a significant threat to global food security, human health, and the future of livestock production. Higher rates of antimicrobial use in dairy farming and the sheer lack of new antimicrobials available for use focused attention on the question of how the dairy production sector contributed to the development of AMR and paved the path toward taking action to curtail it on the targeted type of farms. This paper aims to provide an introduction to a phenomenon that has gained considerable attention in the recent past due to its ever-increasing impact, the use of antimicrobial drugs, the emergence of antimicrobial resistance (AMR) on dairy farms, and seeks to discuss the possibilities of approaches such as digital health monitoring and precision livestock farming. Using sensors, data, knowledge, automation, etc., digital health monitoring, as well as Precision Livestock Farming (PLF), is expected to enhance health control and minimize disease and antimicrobial usage. The work presents a literature review on the current status and trends of AMR in dairy farms, an understanding of the concept of digital health monitoring and PLF, and the presentation and usefulness of digital health monitoring and PLF in preventing AMR. The study also analyses the strengths and weaknesses of adopting and incorporating digital technologies and artificial intelligence for dairy farming and presents areas for further study and level of use.

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

Integrated co-expression analysis of regulatory elements (miRNA, lncRNA, and TFs) in bovine monocytes induced by Str. uberis

Non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), together with transcription factors, are critical pre-, co-, and post-transcriptional regulators. In addition to their criteria as ideal biomarkers, they have great potential in disease prognosis, diagnosis, and treatment of complex diseases. Investigation of regulatory mechanisms in the context of bovine mastitis, as most common and economic disease in the dairy industry, to identify elements influencing the expression of candidate genes as key regulators of the mammary immune response is not yet fully understood. Transcriptome profiles (50 RNA-Seq and 50 miRNA-Seq samples) of bovine monocytes induced by Str. uberis were used for co-expression module detection and preservation analysis using the weighted gene co-expression network analysis (WGCNA) approach. Assigned mi-, lnc-, and m-modules used to construct the integrated regulatory networks and miRNA-lncRNA-mRNA regulatory sub-networks. Remarkably, we have identified 18 miRNAs, five lncRNAs, and seven TFs as key regulators of str. uberis-induced mastitis. Most of the genes introduced here, mainly involved in immune response, inflammation, and apoptosis, were new to mastitis. These findings may help to further elucidate the underlying mechanisms of bovine mastitis, and the discovered genes may serve as signatures for early diagnosis and treatment of the disease.

Methylome and transcriptome data integration reveals potential roles of DNA methylation and candidate biomarkers of cow Streptococcus uberis subclinical mastitis

Background

Mastitis caused by different pathogens including Streptococcus uberis (S. uberis) is responsible for huge economic losses to the dairy industry. In order to investigate the potential genetic and epigenetic regulatory mechanisms of subclinical mastitis due to S. uberis, the DNA methylome (whole genome DNA methylation sequencing) and transcriptome (RNA sequencing) of milk somatic cells from cows with naturally occurring S. uberis subclinical mastitis and healthy control cows (n = 3/group) were studied.

Results

Globally, the DNA methylation levels of CpG sites were low in the promoters and first exons but high in inner exons and introns. The DNA methylation levels at the promoter, first exon and first intron regions were negatively correlated with the expression level of genes at a whole-genome-wide scale. In general, DNA methylation level was lower in S. uberis-positive group (SUG) than in the control group (CTG). A total of 174,342 differentially methylated cytosines (DMCs) (FDR < 0.05) were identified between SUG and CTG, including 132,237, 7412 and 34,693 DMCs in the context of CpG, CHG and CHH (H = A or T or C), respectively. Besides, 101,612 methylation haplotype blocks (MHBs) were identified, including 451 MHBs that were significantly different (dMHB) between the two groups. A total of 2130 differentially expressed (DE) genes (1378 with up-regulated and 752 with down-regulated expression) were found in SUG. Integration of methylome and transcriptome data with MethGET program revealed 1623 genes with significant changes in their methylation levels and/or gene expression changes (MetGDE genes, MethGET P-value < 0.001). Functional enrichment of genes harboring ≥ 15 DMCs, DE genes and MetGDE genes suggest significant involvement of DNA methylation changes in the regulation of the host immune response to S. uberis infection, especially cytokine activities. Furthermore, discriminant correlation analysis with DIABLO method identified 26 candidate biomarkers, including 6 DE genes, 15 CpG-DMCs and 5 dMHBs that discriminated between SUG and CTG.

Conclusion

The integration of methylome and transcriptome of milk somatic cells suggests the possible involvement of DNA methylation changes in the regulation of the host immune response to subclinical mastitis due to S. uberis. The presented genetic and epigenetic biomarkers could contribute to the design of management strategies of subclinical mastitis and breeding for mastitis resistance.

Genome-wide post-transcriptional regulation of bovine mammary gland response to Streptococcus uberis

MicroRNAs (miRNAs) as post-transcriptionally regulators of gene expression have been shown to be critical regulators to fine-tuning immune responses, besides their criteria for being an ideal biomarker. The regulatory role of miRNAs in responses to most mastitis-causing pathogens is not well understood. Gram-positive Streptococcus uberis (Str. uberis), the leading pathogen in dairy herds, cause both clinical and subclinical infections. In this study, a system biology approach was used to better understand the main post-transcriptional regulatory functions and elements of bovine mammary gland response to Str. uberis infection. Publicly available miRNA-Seq data containing 50 milk samples of the ten dairy cows (five controls and five infected) were retrieved for this current research. Functional enrichment analysis of predicted targets revealed that highly confident responsive miRNAs (4 up- and 19 downregulated) mainly regulate genes involved in the regulation of transcription, apoptotic process, regulation of cell adhesion, and pro-inflammatory signaling pathways. Time series analysis showed that six gene clusters significantly differed in comparisons between Str. uberis-induced samples with controls. Additionally, other bioinformatic analysis, including upstream network analysis, showed essential genes, including TP53 and TGFB1 and some small molecules, including glucose, curcumin, and LPS, commonly regulate most of the downregulated miRNAs. Upregulated miRNAs are commonly controlled by the most important genes, including IL1B, NEAT1, DICER1 enzyme and small molecules including estradiol, tamoxifen, estrogen, LPS, and epigallocatechin. Our study used results of next-generation sequencing to reveal key miRNAs as the main regulator of gene expression responses to a Gram-positive bacterial infection. Furthermore, by gene regulatory network (GRN) analysis, we can introduce the common upregulator transcription factor of these miRNAs. Such milk-based miRNA signature(s) would facilitate risk stratification for large-scale prevention programs and provide an opportunity for early diagnosis and therapeutic intervention.