Pan-tissue transcriptomic profiling of dairy cattle

Abstract Background Milk production in dairy cattle is a paradigmatic complex trait emerging from coordinated regulatory programs across multiple tissues and molecular layers, while previous transcriptomic studies have largely focused on a limited number of key tissues, most notably the mammary gland and liver. Consequently, how transcriptional regulatory mechanisms underlying milk traits are coordinated across the whole organism remains poorly understood. In particular, the contributions of non-coding RNA, alternative splicing and alternative polyadenylation vary across tissues and contribute to tissue-specific regulatory landscapes has not been comprehensively profiled at a pan-tissue scale. This study aims to systematically characterize tissue-specific transcriptomic and post-transcriptional profiles across multiple tissues in dairy cattle. Results We generated RNA-seq data for 99 tissues from two adult Holstein cows and integrated these with 400 publicly available RNA-seq samples from 182 adult Holstein cows covering 127 tissues, yielding a profiling that spans 166 tissues. Using a one-versus-all framework, we identified the tissue-specific genes thereby revealing distinct tissue metabolic demands and physiological specialization, with highly active tissues (brain, mammary gland and reproductive organs) harboring larger numbers of tissue specific genes. Further, we predicted potential RNA–RNA interactions and found that tissue-specific genes may be associated with coordinated non-coding RNA interaction networks. At the post-transcriptional level, rMATS profiling revealed skipped exons and mutually exclusive exons as the predominant alternative splicing classes across tissues, whereas proximal polyadenylation site usage was widespread, although brain-expressed genes more frequently used distal sites. Cross-species analysis based on 11,547 one-to-one orthologues shared among cattle, humans, and pigs showed that tissues from the same organ or system of cattle, humans and pigs generally clustered together, whereas the oviduct displayed expression patterns more similar to tissues within the hypothalamic-pituitary-gonadal axis. Conclusions This study establishes an integrative multi-tissue transcriptomic and post-transcriptional regulatory profile for dairy cattle across a broad range of organs, providing a valuable resource for investigating tissue-specific variation in transcriptional and post-transcriptional regulation.

Authors

Publication Details

Journal
Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology
Published
2026-06-13
DOI
https://doi.org/10.1186/s40104-026-01440-9
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pan-tissue transcriptomic profiling of dairy cattle

Weijie Zheng, Ruike Jia, Liu Y, Bo Han et al.
Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology
RNA Research and Splicing
article

Pan-tissue transcriptomic profiling of dairy cattle

Weijie Zheng, Ruike Jia, Liu Y, Bo Han, Dongxiao Sun, Aixia Du, Yali Hou, Qi Zhang, Zijiao Guo, Jinfeng He
article en

Abstract

Abstract Background Milk production in dairy cattle is a paradigmatic complex trait emerging from coordinated regulatory programs across multiple tissues and molecular layers, while previous transcriptomic studies have largely focused on a limited number of key tissues, most notably the mammary gland and liver. Consequently, how transcriptional regulatory mechanisms underlying milk traits are coordinated across the whole organism remains poorly understood. In particular, the contributions of non-coding RNA, alternative splicing and alternative polyadenylation vary across tissues and contribute to tissue-specific regulatory landscapes has not been comprehensively profiled at a pan-tissue scale. This study aims to systematically characterize tissue-specific transcriptomic and post-transcriptional profiles across multiple tissues in dairy cattle. Results We generated RNA-seq data for 99 tissues from two adult Holstein cows and integrated these with 400 publicly available RNA-seq samples from 182 adult Holstein cows covering 127 tissues, yielding a profiling that spans 166 tissues. Using a one-versus-all framework, we identified the tissue-specific genes thereby revealing distinct tissue metabolic demands and physiological specialization, with highly active tissues (brain, mammary gland and reproductive organs) harboring larger numbers of tissue specific genes. Further, we predicted potential RNA–RNA interactions and found that tissue-specific genes may be associated with coordinated non-coding RNA interaction networks. At the post-transcriptional level, rMATS profiling revealed skipped exons and mutually exclusive exons as the predominant alternative splicing classes across tissues, whereas proximal polyadenylation site usage was widespread, although brain-expressed genes more frequently used distal sites. Cross-species analysis based on 11,547 one-to-one orthologues shared among cattle, humans, and pigs showed that tissues from the same organ or system of cattle, humans and pigs generally clustered together, whereas the oviduct displayed expression patterns more similar to tissues within the hypothalamic-pituitary-gonadal axis. Conclusions This study establishes an integrative multi-tissue transcriptomic and post-transcriptional regulatory profile for dairy cattle across a broad range of organs, providing a valuable resource for investigating tissue-specific variation in transcriptional and post-transcriptional regulation.

Journal of Animal Science and Biotechnology/Journal of animal science and biotechnologyVol. 17(1)
Openalex Percentile: Top 9%
RNA Research and Splicing
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.