Evidence map›Paper›PMID 41740447›Full record

ArticlePoultry science2026

Multi-tissue transcriptomic profiling reveals the internal physiological landscape of laying hens in cage and cage-free systems.

Nonoko N Shimura, Eiki Asagi, Tadahiro Matsubara, Itsufumi Sato, Yuki Higashiura, Saki Nakamura, Chihiro Kase, Atushi J Nagano, Shozo Tomonaga, Jun-Ichi Shiraishi and 6 more

Abstract read
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In one paragraph

Article in Poultry science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Nonoko N ShimuraDepartment of Biological Production, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan.
Eiki AsagiDepartment of Biological Production, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan.
Tadahiro MatsubaraDepartment of Biological Production, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan.
Itsufumi SatoDepartment of Biological Production, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan.
Yuki HigashiuraDepartment of Biological Production, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan.
Saki NakamuraDepartment of Biological Production, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan.
Chihiro KaseSchool of Veterinary Medicine, Azabu University, Sagamihara, Kanagawa 252-0206, Japan.
Atushi J NaganoBioscience and Biotechnology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan; Institute for Advanced Biosciences, Keio University, Yamagata 997-0017, Japan.
Shozo TomonagaDivision of Applied Biosciences, Graduate School of Agriculture, Kyoto University Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan.
Jun-Ichi ShiraishiFaculty of Applied Life Science, Nippon Veterinary and Life Science University, Musashino 180-8602, Japan.
Kaito KurogiLaboratory of Regulation in Metabolism and Behavior, Faculty of Agriculture, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
Ryohei MatsuoLaboratory of Regulation in Metabolism and Behavior, Faculty of Agriculture, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
Shinobu YasuoLaboratory of Regulation in Metabolism and Behavior, Faculty of Agriculture, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
Tatsuhiko GotoDepartment of Life and Food Sciences, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido 080-8555, Japan.
Kan SatoGraduate School of Agricultural Science, Tohoku University, Sendai, Miyagi 980-8572, Japan.
Tsuyoshi ShimmuraDepartment of Biological Production, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan; Research Area of Informatics for Human-Animal Interaction, Advanced Research Center for One Welfare, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan. Electronic address: shimmura@go.tuat.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Welfare-friendly housing systems for laying hens, such as cage-free, have become prevalent. However, the physiological effects of housing systems on the laying hens remain poorly understood. Here, we compared behavioral characteristics and transcriptomic profiles from 90 multi-tissue samples among three housing systems: battery cage (BC), barn (BR), and free-range (FR). BR and FR housing promoted behavioral diversity compared to cages. Transcriptome analysis of central tissues (diencephalon and cerebral hemisphere) and peripheral tissues involved in egg production (liver, ovary, oviductal segments of magnum, and uterine) revealed significant enrichment of insulin resistance-related pathways in both diencephalon and liver of BC hens, and enhanced norepinephrine signaling in the cerebrum of BR and FR hens. To validate these findings, we performed a glucose tolerance test to assess insulin sensitivity and quantified the cerebral norepinephrine concentrations by ECD-HPLC. The results showed that BR and FR hens tended to exhibit higher insulin sensitivity and enhanced norepinephrine signaling compared with BC hens. Taken together, our findings suggest that housing conditions markedly shape the internal physiological landscape of laying hens, and also that environment-enriched cage-free contributes to improving metabolic and neurophysiological signaling.

Indexed as

Animal HusbandryChickensHousing, AnimalTranscriptomeAnimalsAnimal WelfareFemaleGene Expression ProfilingAnimal welfareHousing systemLaying hensPhysiological landscapeTranscriptome analysis

Identifiers

PMID41740447
PMCPMC12974103

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.