Evidence map›Paper›PMID 41152791›Full record

ArticleBMC pulmonary medicine2025

Airway Candida diversity in COPD patients and its differential stimulatory effects on lung inflammation in mice.

Haiyue Liu, Feng Li, Jiaqin Zhang, Meizhen Li, Wenliang Zhu, Yihua Lin, Guolin Hong, Zuheng Liu

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2025. 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

8 authors.

Haiyue Liu *Department of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Feng Li *Department of Nutrition, School of Medicine, Cardiovascular Hospital of Xiamen University, Xiamen University, Xiamen, China.
Jiaqin ZhangDepartment of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Meizhen LiDepartment of Nutrition, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Wenliang ZhuDepartment of Pulmonary and Critical Care Medicine, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Yihua LinDepartment of Pulmonary and Critical Care Medicine, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China. lyh7@xmu.edu.cn.
Guolin HongDepartment of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. xmhgl9899@xmu.edu.cn.
Zuheng LiuDepartment of Cardiology, Xiamen Key Laboratory of Cardiac Electrophysiology, Xiamen Institute of Cardiovascular Diseases, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China. john_lau@126.com.

Funding

National Natural Science Foundation of China 82100385National Natural Science Foundation of China 82202629National Natural Science Foundation of China 82371200Xiamen Medical and Health Key Project 3502Z20234010
6 · The paper itself

Abstract

backgroundThe airway microbiome is altered in chronic obstructive pulmonary disease (COPD) individuals with high fungal diversity, which manifests mainly as an increase in the diversity of Candida genera. Acute exacerbation of COPD (AECOPD) is a crucial process in disease progression. The airway Candida species diversity in AECOPD patients and their differential stimulatory effects on airway inflammation in mice remain unclear. AIMS AND

methodsTo investigate the diversity of airway Candida species involved in AECOPD and assess their differential effects on airway inflammation in mice. We conducted airway bacterial and fungal microbiota surveys using 16S rRNA (V4 region) sequencing, ITS1 sequencing, and fungal cultures from sputum samples of 63 AECOPD patients. Subsequently, five Candida species isolated from airways were nasally colonized into mice. He staining, qPCR and 16S rRNA V4 region sequencing of mouse lung tissues were performed to explore the host inflammatory responses to different Candida species.

resultsCandida species were detected in the sputum of 50 (80.95%) of the 63 volunteers with AECOPD. Candida albicans, Candida glabrata, Candida parapsilosis, Candida orthopsilosis, and Candida dubliniensis were the five most prevalent species. Nasal Candida colonization in mice caused varying degrees of inflammation, with H&E staining scores of 2–7. Mouse body weight decreases significantly on days 11 and 13 after exposure to Candida albicans, Candida krusei, Candida parapsilosis, or Candida tropicalis. After a Candida albicans infection, significant increases in IL-8 and IL-1β in the mouse airways were observed. The mouse lung microbiota shifted after the addition of Candida species, with Prevotella, Streptococcus, Sutterella, Ralstonia, Sphingomonas, and Bradyrhizobiaceae being the genera identified among the six groups.

conclusionThe airways of AECOPD patients exhibit a high diversity of Candida species, each triggering distinct inflammatory patterns in mouse lungs. These findings warrant investigation into species-specific mechanisms in Candida-related COPD.

Indexed as

CandidaLungPneumoniaPulmonary Disease, Chronic ObstructiveAgedAnimalsDisease Models, AnimalDisease ProgressionFemaleHumansMaleMiceMice, Inbred C57BLMicrobiotaMiddle AgedRNA, Ribosomal, 16SRNA, Ribosomal, 16SAirway inflammationCandidaCOPD exacerbation

Identifiers

PMID41152791
PMCPMC12560385

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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.