Evidence map›Paper›PMID 41120833›Full record

ArticleBMC genomics2025

MicroRNA expression dynamics in mouse liver at different stages of Echinococcus multilocularis infection.

Ying Chen, Hai-Jun Gao, Chen Li, Xiao-Jin Mo, Gui-Rong Zheng, Jun Xie, Jie Gao, Bolor Bold, Zheng Feng, Ting Zhang and 1 more

Abstract read
In one paragraph

Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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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

2 citing papers in PubMed.

  1. Article
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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.

Ying Chen *The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010010, China.
Hai-Jun Gao *National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Center for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Chen Li *The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010010, China.
Xiao-Jin MoNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Center for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Gui-Rong ZhengThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010010, China.
Jun XieThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010010, China.
Jie GaoThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010010, China.
Bolor BoldNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Center for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Zheng FengNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Health Commission Key Laboratory of Parasite and Vector Biology; WHO Collaborating Center for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Ting ZhangThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010010, China. zhangting@nipd.chinacdc.cn.
Wei HuThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, 010010, China. huw@imu.edu.cn.

Funding

National Key Research and Development Program of China No. 2021YFC2300800, 2021YFC2300803NHC Key Laboratory of Echinococcosis Prevention and Control No. 2024WZK1002State Key Laboratory for Reproductive Regulation and Breeding of Grassland Livestock No. 2021KF0301
6 · The paper itself

Abstract

backgroundAlveolar echinococcosis (AE) is a globally widespread zoonotic disease caused by the larval stage of the tapeworm Echinococcus multilocularis, posing a high fatality rate and poor prognosis if not properly managed. Currently, effective vaccines or drugs for echinococcosis remain elusive. MicroRNAs (miRNAs) play crucial roles in various biological processes and are closely linked with parasite infection and pathogenicity. To date, there is limited knowledge about host miRNA profiles in E. multilocularis infection at different stages. Hence, exploring host miRNA expression patterns at different infection stages is vital for understanding miRNA transcriptional regulation mechanisms.

methodsThis study employs small RNA sequencing to depict the temporal dynamics of miRNAs in mice liver at 40, 80, and 120 dpi with E. multilocularis. Additionally, Short Time-series Expression Miner, Gene ontology, KEGG pathway, and miRNA-target gene-pathway network analysis were conducted to elucidate each miRNA's changing trends, focusing on the hub miRNAs during infection. Subsequently, miRNA altering patterns at 40, 80, and 120 dpi were confirmed via quantitative real-time PCR.

resultsThe findings reveal time-dependent miRNA expression profiles, categorized into three distinct patterns specific to early, middle, and late infection stages. Overall, 61 miRNAs were stage-differentially expressed in the livers of infected mice compared to uninfected mice, with 29 miRNAs up-regulated and 32 miRNAs down-regulated. Notably, in the early phase, 23 miRNAs showed differential expression, with 18 up-regulated and 5 down-regulated (|log₂FC| >1, P < 0.05). Moreover, genes regulated during this phase primarily involved in Th17 cell differentiation, AMPK signaling pathway, and Calcium signaling pathway. Subsequently, during the middle infection stage, a total of 16 miRNAs exhibited differential expression, with 8 up-regulated and 8 down-regulated (|log₂FC| >1, P < 0.05). These miRNAs are involved in prolactin signaling pathway, aldosterone synthesis secretion, and Cushing's syndrome. In the late infection stage, 22 differentially expressed miRNAs were identified, with 3 up-regulated and 19 down-regulated (|log₂FC| >1, P < 0.05). Furthermore, the identified target genes primarily participated in ECM-receptor interaction, TGF-β signaling pathway, and human papillomavirus infection pathway. Also, through network interaction analysis, it was speculated that Src, Jag1, and Mapk1 exhibited the most hub signaling genes during the early, middle, and late infection stages, respectively, while the Srf was dynamically expressed hub signaling gene throughout the whole infection stage. As the disease progresses, several hub networks have been identified around target genes, including the mmu-miR-1247-5p-Src-Rap1 signaling pathway, mmu-miR-149-5p-Jag1-Notch signaling pathway, mmu-miR-299a-5p-Mapk1-human papillomavirus infection pathway in the early, middle and late stages, respectively. Additionally, the mmu-miR-122-3p-Srf-MAPK signaling pathway was predicted to be the hub network in the whole infection stage.

conclusionsOverall, our investigation elucidates the temporal dynamic changes in host miRNAs and their potential target genes at the early, middle and late stages of E. multilocularis infection, which allows us to understand the roles of miRNAs in host-parasite interactions throughout infection and provides a reference for further studies of molecular pathogenesis and new drug or vaccine targets for control of AE.

Indexed as

EchinococcosisEchinococcus multilocularisLiverMicroRNAsAnimalsGene Expression ProfilingGene Regulatory NetworksMiceMicroRNAsEchinococcosisEchinococcus multilocularisMiRNARNA sequencing

Identifiers

PMID41120833
PMCPMC12538786

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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.