Unraveling the molecular landscape of bovine subclinical mastitis in dairy cattle via RNA-sequencing and peptidomic profiling

Intramammary infection (IMI) and mastitis, especially in subclinical form, remain a major issue in the dairy sector, with strong implications for farm economy, animal welfare, and public health. Despite extensive research, the disease is complex and multifactorial, with many aspects still unclear. The advent of high-throughput technologies combined with advanced bioinformatics has opened new opportunities to deepen our knowledge of host–pathogen interactions, offering insights into the molecular mechanisms underlying IMI. Within this context, the aim of this PhD project was to investigate subclinical IMI at different biological and molecular levels, focusing on two etiological agents: the contagious bacterium Streptococcus agalactiae (Str. agalactiae) and the non-bacterial pathogen Prototheca spp. Three groups of Holstein cattle were considered: animals naturally infected with Str. agalactiae (Sa+), with Prototheca spp. (P+), and uninfected controls (Neg). In Chapter I, individual milk samples (n=12 Sa+, n=11 P+, n=15 Neg) were analyzed using untargeted and targeted mass spectrometry approach to explore peptidome changes driven by host and pathogen proteolytic activity. Sa+ and P+ samples showed higher peptide abundance than Neg, but no pathogen-specific effect was observed. Thirty-one peptides discriminated between infected and negative samples, mostly derived from caseins, including the antimicrobial casecidin 17. Here we aimed to analyze transcript expression, with focus on cases where multiple mRNA isoforms from alternative splicing were present. RNA-sequencing of milk somatic cells from n = 11 Sa+, n = 11 P+, and n = 9 Neg was performed, and data were analyzed to identify differentially expressed transcripts (DETs), distinguishing between previously annotated, annotated but with novel length, or novel from non-annotated genes. A functional analysis of DETs was then combined with the identification of functional variants (SNPs and INDELs) within splice sites. Both P+ vs Neg and Sa+ vs Neg comparisons revealed DETs, many encoded by immune-related genes. Pathway analysis showed distinct patterns, with immune response pathways enriched in P+, and metabolism/detoxification pathways in Sa+. Functional variants were detected in regions overlapping DETs potentially related to mastitis resistance/susceptibility traits. Finally, in Chapter III, we extended the work presented in Chapter II by expanding the analysis to all SNPs and INDELs detected in the coding regions, rather than focusing only on those within splice sites, all of which may be exploited in breeding program for the selection of mastitis resistant dairy cattle. Group-specific sequence variants were identified, then their functional effects were predicted by the Ensembl Variant Effect Predictor tool, followed by a QTL annotation and enrichment. A total of 246,777, 264,132, and 306,440 variants were uniquely identified in Sa+, P+, and Neg, respectively, with high-impact immune-related variants present in all groups and QTL enrichment showing milk trait associations, while clinical mastitis QTLs were specific to P+. The application of these different omics approaches provided the opportunity to uncover new features which may relevant for early diagnosis, therapeutic targeting, and resistance traits. Integrating these datasets could reveal more complete information about the molecular mechanisms involved, helping to identify true causal factors. These findings could improve the management of subclinical IMI, helping to develop screening and breeding strategies, improving animal welfare and dairy production.