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

In silico analysis of virulence factors of Streptococcus uberis for a chimeric vaccine design

Streptococcus uberis is one of the causative agents of bovine mastitis, which has detrimental effects on animal health and the dairy industry. Despite decades of research, the requirement for effective vaccines against the disease remains unmet. The goal of this study was to create a multi-epitope vaccine using five virulence factors of S. uberis through the reverse vaccinology approach, which has been employed due to its high efficiency and applicability. Plasminogen activator A (PauA), glyceraldehyde-3-phosphate dehydrogenase C (GapC), C5a peptidase, S. uberis adhesion molecule (SUAM), and sortase A (SrtA) were selected for the T cytotoxic (CTL) and B cell epitope analyses as they were extensively studied in S. uberis or other pathogens. Eighteen CTL and ten B cell epitopes that were antigenic, non-toxic, and non-allergenic were selected in order to design a chimeric vaccine candidate that in silico analysis revealed to be potentially immunogenic, non-allergenic, and stable. Molecular docking analysis of the vaccine candidate with Toll-like receptor (TLR) 2 and TLR 4 revealed stable interactions between the candidate and the immune receptors. Meanwhile, the stability of the docked complexes was confirmed using normal mode analysis. Additionally, in silico immune simulation of the vaccine candidate demonstrated the stimulation of primary immune responses, indicating that the chimeric protein can hold promise as a viable vaccine candidate for preventing S. uberis mastitis. Moreover, the current study can provide a background for designing epitope-based vaccines based on the explored epitopes.

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.

Integrated analysis of inflammatory mRNAs, miRNAs, and lncRNAs elucidates the molecular interactome behind bovine mastitis

Mastitis is known as intramammary inflammation, which has a multifactorial complex phenotype. However, the underlying molecular pathogenesis of mastitis remains poorly understood. In this study, we utilized a combination of RNA-seq and miRNA-seq techniques, along with computational systems biology approaches, to gain a deeper understanding of the molecular interactome involved in mastitis. We retrieved and processed one hundred transcriptomic libraries, consisting of 50 RNA-seq and 50 matched miRNA-seq data, obtained from milk-isolated monocytes of Holstein-Friesian cows, both infected with Streptococcus uberis and non-infected controls. Using the weighted gene co-expression network analysis (WGCNA) approach, we constructed co-expressed RNA-seq-based and miRNA-seq-based modules separately. Module-trait relationship analysis was then performed on the RNA-seq-based modules to identify highly-correlated modules associated with clinical traits of mastitis. Functional enrichment analysis was conducted to understand the functional behavior of these modules. Additionally, we assigned the RNA-seq-based modules to the miRNA-seq-based modules and constructed an integrated regulatory network based on the modules of interest. To enhance the reliability of our findings, we conducted further analyses, including hub RNA detection, protein-protein interaction (PPI) network construction, screening of hub-hub RNAs, and target prediction analysis on the detected modules. We identified a total of 17 RNA-seq-based modules and 3 miRNA-seq-based modules. Among the significant highly-correlated RNA-seq-based modules, six modules showed strong associations with clinical characteristics of mastitis. Functional enrichment analysis revealed that the turquoise module was directly related to inflammation persistence and mastitis development. Furthermore, module assignment analysis demonstrated that the blue miRNA-seq-based module post-transcriptionally regulates the turquoise RNA-seq-based module. We also identified a set of different RNAs, including hub-hub genes, hub-hub TFs (transcription factors), hub-hub lncRNAs (long non-coding RNAs), and hub miRNAs within the modules of interest, indicating their central role in the molecular interactome underlying the pathogenic mechanisms of S. uberis infection. This study provides a comprehensive insight into the molecular crosstalk between immunoregulatory mRNAs, miRNAs, and lncRNAs during S. uberis infection. These findings offer valuable directions for the development of molecular diagnosis and biological therapies for mastitis.

Prevalence and antimicrobial resistance profile of bacterial pathogens isolated from poultry in Jiangxi Province, China from 2020 to 2022

Poultry is one of the most commonly farmed species and the most widespread meat industries. However, numerous poultry flocks have been long threatened by pathogenic bacterial infections, especially antimicrobial resistant pathogens. Here the prevalence and the antimicrobial resistance (AMR) profiles of bacterial pathogens isolated from poultry in Jiangxi Province, China were investigated. From 2020 to 2022, 283 tissue and liquid samples were collected from clinically diseased poultry, including duck, chicken, and goose, with an overall positive isolation rate of 62.90%. Among all the 219 bacterial isolates, 29 strains were gram-positive and 190 strains were gram-negative. Major bacteria species involved were avian pathogenic Escherichia coli (APEC; 57.53%; 126/219), followed by Salmonella spp. (11.87%, 26/219), Pasteurella multocida (6.39%, 14/219), and Staphylococcus spp. (1.22%, 11/219). Antimicrobial susceptibility testing showed the APEC isolates displayed considerably higher levels of AMR than the Salmonella and P. multocida isolates. The APEC isolates showed high resistance rate to amoxicillin (89.68%), ampicillin (89.68%), and florfenicol (83.33%), followed by streptomycin (75.40%), cefradine (65.87%), and enrofloxacin (64.29%). Multidrug-resistant isolates were observed in APEC (99.21%), Salmonella spp. (96.16%), and P. multocida (85.71%), and nearly 3 quarters of the APEC strains were resistant to 7 or more categories of antimicrobial drugs. Moreover, blaNDM genes associated with carbapenemase resistance and mcr-1 associated with colisitin resistance were detected in the APEC isolates. Our findings could provide evidence-based guidance for veterinarians to prevent and control bacterial diseases, and be helpful for monitoring the emerging and development of AMR in poultry bacterial pathogens.

Genomic and Phenotypic Udder Evaluation for Dairy Cattle Selection: A Review

The traditional point of view regarding dairy cattle selection has been challenged by recent genomic studies indicating that livestock productivity prediction can be redefined based on the evaluation of genomic and phenotypic data. Several studies that included different genomic-derived traits only indicated that interactions among them or even with conventional phenotypic evaluation criteria require further elucidation. Unfortunately, certain genomic and phenotypic-derived traits have been shown to be secondary factors influencing dairy production. Thus, these factors, as well as evaluation criteria, need to be defined. Owing to the variety of genomic and phenotypic udder-derived traits which may affect the modern dairy cow functionality and conformation, a definition of currently important traits in the broad sense is indicated. This is essential for cattle productivity and dairy sustainability. The main objective of the present review is to elucidate the possible relationships among genomic and phenotypic udder evaluation characteristics to define the most relevant traits related to selection for function and conformation in dairy cattle. This review aims to examine the potential impact of various udder-related evaluation criteria on dairy cattle productivity and explore how to mitigate the adverse effects of compromised udder conformation and functionality. Specifically, we will consider the implications for udder health, welfare, longevity, and production-derived traits. Subsequently, we will address several concerns covering the application of genomic and phenotypic evaluation criteria with emphasis on udder-related traits in dairy cattle selection as well as its evolution from origins to the present and future prospects.