Evidence map›Paper›PMID 42410501›Full record

ArticleBMC genomics2026

Integration of bulk RNA-seq and scRNA-seq reveals cell subsets and gene signatures associated with Glaesserella parasuis infection.

Ke Xu, Zixin Wang, Ying Zhu, Zhengfang Liu, Min Xiang, Huanhuan Zhou, Ao Zhou, Qing Liu, Liangyu Shi, Xiangwei Deng and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 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

13 authors.

Ke Xu *Laboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Zixin Wang *Laboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Ying ZhuLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Zhengfang LiuLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Min XiangInstitute of Animal Science and Veterinary Medicine, Wuhan Academy of Agricultural Sciences, Wuhan, 430208, China.
Huanhuan ZhouLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Ao ZhouLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Qing LiuLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Liangyu ShiLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Xiangwei DengLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Xinqi ZengLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China.
Lei ChengInstitute of Animal Science and Veterinary Medicine, Wuhan Academy of Agricultural Sciences, Wuhan, 430208, China. chenglei@wuhanagri.com.
Hongbo ChenLaboratory of Genetic Breeding, Reproduction and Precision Livestock Farming & Hubei Provincial Center of Technology Innovation for Domestic Animal Breeding, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, 430023, China. chenhongbo@whpu.edu.cn.ORCID https://orcid.org/0000-0002-6051-3321

Funding

National Natural Science Foundation of China No. 32172712the Science and Technology Program of Education Department of Hubei Province No. B2023048
6 · The paper itself

Abstract

backgroundGlaesserella parasuis (GPS) is a major bacterial pathogen threatening the pig industry worldwide. It is widely prevalent in pig farms across China; however, to date, no effective strategies for its prevention and control are available. It causes divergent outcomes ranging from asymptomatic carriage to lethal disease; however, the underlying mechanisms remain unclear. In this study, we integrated bulk- and single-cell RNA-seq of over 98,000 porcine alveolar macrophages from a well-defined piglet infection model (including control, mild, and severe groups).

resultsThis is the first multi-omics dissection of macrophage heterogeneity in GPS infection at single-cell resolution. We identified four transcriptionally distinct macrophage subpopulations, with two inflammation-associated subsets (Im-Mac and Inf- Mac) that expanded dramatically with increasing disease severity. Pseudotime analysis revealed that severe infection skews macrophage differentiation almost exclusively toward pro-inflammatory fates. Integrated analysis revealed two opposing cellular programs dictating clinical outcomes: a protective program (active in mild infections, marked by CXCL10, CD69, SERPING1, and KMO) balancing immune recruitment with regulation, and a pathogenic program (dominating severe infections, characterized by a 16-gene signature including AMCF-II, CCL2, SERPINB2, CD14, ECE1, and IL7R). Notably, 9 of these 16 genes overlapped with a core set of 58 genes that were positively correlated with disease progression.

conclusionsWe propose a balanced model in which the equilibrium between protective and pathogenic macrophage programs dictates the outcome of GPS infection. These findings provide novel cell-state-specific biomarkers and a molecular framework for understanding the divergent clinical outcomes of GPS infection.

Indexed as

Haemophilus InfectionsHaemophilus parasuisRNA-SeqSwine DiseasesTranscriptomeAnimalsGene Expression ProfilingMacrophages, AlveolarSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisSwineDisease severityGlaesserella parasuisPorcine alveolar macrophagesSingle-cell RNA sequencing

Identifiers

PMID42410501
PMCPMC13621471

What Socratic holds

Textmetadata
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Registered trials

None linked

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 Socratic graph.