Evidence map›Paper›PMID 40439157›Full record

ArticleGenomics, proteomics & bioinformatics2025

Unveiling Neonatal Pneumonia Microbiome by High-throughput Sequencing and Droplet Culturomics.

Zerui Wang 王则锐, Xin Cheng 程欣, Yibin Xu 徐义斌, Zhiyi Wang 汪之一, Liyan Ma 马立艳, Caiming Li 李埰明, Shize Jiang 姜世泽, Yuchen Li 黎雨尘, Shuilong Guo 郭水龙, Wenbin Du 杜文斌

Abstract read
In one paragraph

Article in Genomics, proteomics & bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

10 authors.

Zerui Wang 王则锐Biomedical Sciences College & Shandong Medical Biotechnology Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, China.ORCID 0000-0002-1535-8772
Xin Cheng 程欣Clinical Laboratory Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.ORCID 0000-0002-9325-3351
Yibin Xu 徐义斌State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0002-7591-2421
Zhiyi Wang 汪之一State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0009-0006-1302-8215
Liyan Ma 马立艳Clinical Laboratory Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.ORCID 0009-0006-9816-0134
Caiming Li 李埰明State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0002-8355-0109
Shize Jiang 姜世泽State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0009-0007-7288-9549
Yuchen Li 黎雨尘Biomedical Sciences College & Shandong Medical Biotechnology Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, China.ORCID 0009-0003-1049-9861
Shuilong Guo 郭水龙Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.ORCID 0000-0001-6705-9339
Wenbin Du 杜文斌Biomedical Sciences College & Shandong Medical Biotechnology Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, China.ORCID 0000-0002-7401-1410

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neonatal pneumonia is a leading cause of infant mortality worldwide; however, a lack of microbial profiling, especially of low-abundance species, makes accurate diagnosis challenging. Traditional methods can fail to capture the complexity of the neonatal respiratory microbiota, thereby obscuring its role in disease progression. Here, we describe a novel approach that combines high-throughput sequencing with droplet-based microfluidic cultivation to investigate microbiome shifts in neonates with pneumonia. Using 16S ribosomal RNA (rRNA) gene sequencing of 71 pneumonia cases and 49 controls, we identified 1009 genera, including 930 low-abundance taxa, which showed significant compositional differences between groups. Linear discriminant analysis effect size identified key pneumonia-associated genera, such as Streptococcus, Rothia, and Corynebacterium. Droplet-based cultivation recovered 299 strains from 94 taxa, including rare species and ESKAPE pathogens, thereby supporting targeted antimicrobial management. Host-pathogen interaction assays showed that Rothia and Corynebacterium induced inflammation in lung epithelial cells, likely via dysregulation of the PI3K-Akt pathway. Integrating these marker taxa with clinical factors, such as gestational age and delivery type, offers the potential for precise diagnosis and treatment. The recovery of diverse species can support the construction of a biobank of neonatal respiratory microbiota to advance mechanistic studies and therapeutic strategies.

Indexed as

High-Throughput Nucleotide SequencingMicrobiotaPneumoniaFemaleHumansInfant, NewbornMaleRNA, Ribosomal, 16SRNA, Ribosomal, 16SDroplet microfluidicsHost–pathogen interactionNeonatal pneumoniaRespiratory microbiotaSingle-cell cultivation

Identifiers

PMID40439157
PMCPMC12721867

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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.