Evidence map›Paper›PMID 42679725›Full record

ArticlePoultry science2026

MicroRNA expression profiling in fowl adenovirus serotype 4-infected Leghorn male hepatoma cells using small RNA deep sequencing.

Fengjiao Wu, Xi Chen, Yunyun Zhang, Shirong Chen, Yumeng Liang, Jiao Xu, Fengping He, Lei Li, Huixin Liu, Liangyu Yang and 1 more

Abstract read
PubMed Full text at PMC Publisher
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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Fengjiao WuCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Xi ChenCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Yunyun ZhangCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Shirong ChenCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Yumeng LiangCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Jiao XuCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Fengping HeCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Lei LiCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Huixin LiuCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Liangyu YangCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China.
Bin XiangCollege of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China; Center for Poultry Disease Control and Prevention, Yunnan Agricultural University, Kunming 650201, China. Electronic address: xiangbin2018@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fowl adenovirus serotype 4 (FAdV-4) is the causative agent of hydropericardium-hepatitis syndrome (HHS), a disease that causes severe economic losses to the global poultry industry. MicroRNAs (miRNAs) are key regulators of host-pathogen interactions; yet, their temporal expression dynamics and functional roles during FAdV-4 replication in chicken-derived cells remain poorly defined. This study characterized the temporal expression profile of host miRNAs during FAdV-4 infection in Leghorn male hepatoma (LMH) cells to elucidate their regulatory roles in viral replication through small RNA deep sequencing and functional validation. We identified 63 and 126 differentially expressed (DE) miRNAs at 12 hpi and 24 hpi, respectively. These DE miRNAs exhibited time-dependent regulatory functions throughout FAdV-4 infection, and KEGG enrichment confirmed persistent enrichment of their potential target genes within the MAPK signaling and metabolic pathways at both 12 hpi and 24 hpi. Moreover, we also revealed that gga-miR-184-5p could inhibit FAdV-4 replication, while gga-miR-7447-3p could promote FAdV-4 replication. Dual-luciferase reporter assays demonstrated that Family with Sequence Similarity 135 Member B (FAM135B) is directly targeted by gga-miR-184-5p. Overexpression of FAM135B suppressed FAdV-4 replication, an effect that was partially reversed by the introduction of gga-miR-184-5p. These findings deepen our understanding of host-FAdV-4 interaction mechanisms and provide valuable candidate targets for future development of antiviral strategies against FAdV-4 infection in poultry.

Indexed as

FAdV-4FAM135BGga-miR-184-5pLMH cellmiRNA expression profile

Identifiers

PMID42679725
PMCPMC13570354

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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.