Decoding the lncRNA landscape in bovine mastitis: insights into immune regulation and gene networks underlying host response

Abstract Background Long non-coding RNAs (lncRNAs) are critical regulatory elements of gene expression and play a crucial role in immune regulation of disease, such as bovine mastitis. With this study we evaluated expression profile of lncRNAs in subclinical intramammary infection (sIMI) from two pathogens: Streptococcus agalactiae ( S. agalactiae ; Sa+) and Prototheca spp. (P+) and predicted their interaction with protein coding genes in 31 Holstein cows. Quantitative trait loci (QTL) annotation and enrichment analysis was carried out on the lncRNAs partner genes for a comprehensive overview of their potential association with phenotypic traits. Finally, Partial Correlation and Information Theory (PCIT) and Regulatory Impact Factor (RIF) algorithms were adopted to assess co-expression network of differentially expressed lncRNAs (DELs) and genes (DEGs). Results A total of 211, 146, 353 and 184 DELs were identified when comparing Sa+ vs. Neg, P+ vs. Neg, Sa+ vs. P+, and uninfected vs. infected animals (Mast vs. Neg), respectively. Most of these DELs were mainly located within genic regions, and 78% had not been previously annotated in the bovine reference genome. Overall S. agalactiae infection modulated both immune and metabolic pathways (lipid metabolism, extracellular matrix synthesis), whereas Prototheca spp. infection activated immune signaling networks (B-cell and innate immunity). Unique lncRNAs induced by Sa+ were predicted to target genes controlling fatty acid synthesis and milk fat QTLs, while P+-induced lncRNAs were found associated mostly with immune genes and somatic cell count QTLs. The co-expression network also revealed shared lncRNAs between the two types of infection suggesting a core response: both infections converge on common hub genes (e.g., EBF1 , BTLA ), indicating potential conserved host-defense mechanisms. Conclusions These results suggest that, despite distinct lncRNA expression patterns, the downstream regulatory pathways—particularly those linked to immune function—may converge toward a shared host response during subclinical intramammary infections. Together, these findings underscore the complexity of lncRNA-mediated regulation in the bovine mammary gland and highlight the need for functional studies to clarify the biological roles of these novel transcripts.

Authors

Publication Details

Journal
Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology
Published
2026-09-30
DOI
https://doi.org/10.1186/s40104-026-01500-0
Primary Topic
Milk Quality and Mastitis in Dairy Cows
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Decoding the lncRNA landscape in bovine mastitis: insights into immune regulation and gene networks underlying host response

Diana Giannuzzi, G. Giannotta, A. Vanzin, S. Pegolo et al.
Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology
Milk Quality and Mastitis in Dairy Cows
article

Decoding the lncRNA landscape in bovine mastitis: insights into immune regulation and gene networks underlying host response

Diana Giannuzzi, G. Giannotta, A. Vanzin, S. Pegolo, A. Cecchinato, Á. Canovas, V. Asselstine, V. Bisutti, G. Polizel
article en

Abstract

Abstract Background Long non-coding RNAs (lncRNAs) are critical regulatory elements of gene expression and play a crucial role in immune regulation of disease, such as bovine mastitis. With this study we evaluated expression profile of lncRNAs in subclinical intramammary infection (sIMI) from two pathogens: Streptococcus agalactiae ( S. agalactiae ; Sa+) and Prototheca spp. (P+) and predicted their interaction with protein coding genes in 31 Holstein cows. Quantitative trait loci (QTL) annotation and enrichment analysis was carried out on the lncRNAs partner genes for a comprehensive overview of their potential association with phenotypic traits. Finally, Partial Correlation and Information Theory (PCIT) and Regulatory Impact Factor (RIF) algorithms were adopted to assess co-expression network of differentially expressed lncRNAs (DELs) and genes (DEGs). Results A total of 211, 146, 353 and 184 DELs were identified when comparing Sa+ vs. Neg, P+ vs. Neg, Sa+ vs. P+, and uninfected vs. infected animals (Mast vs. Neg), respectively. Most of these DELs were mainly located within genic regions, and 78% had not been previously annotated in the bovine reference genome. Overall S. agalactiae infection modulated both immune and metabolic pathways (lipid metabolism, extracellular matrix synthesis), whereas Prototheca spp. infection activated immune signaling networks (B-cell and innate immunity). Unique lncRNAs induced by Sa+ were predicted to target genes controlling fatty acid synthesis and milk fat QTLs, while P+-induced lncRNAs were found associated mostly with immune genes and somatic cell count QTLs. The co-expression network also revealed shared lncRNAs between the two types of infection suggesting a core response: both infections converge on common hub genes (e.g., EBF1 , BTLA ), indicating potential conserved host-defense mechanisms. Conclusions These results suggest that, despite distinct lncRNA expression patterns, the downstream regulatory pathways—particularly those linked to immune function—may converge toward a shared host response during subclinical intramammary infections. Together, these findings underscore the complexity of lncRNA-mediated regulation in the bovine mammary gland and highlight the need for functional studies to clarify the biological roles of these novel transcripts.

Journal of Animal Science and Biotechnology/Journal of animal science and biotechnologyVol. 17(1)
Openalex Percentile: Top 10%
Milk Quality and Mastitis in Dairy Cows
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.