Evidence map›Paper›PMID 41087872›Full record

ArticleBMC microbiology2025

Phenotypic and genotypic characterization of ST103 serotype Ia Streptococcus agalactiae isolated from bovine mastitis in China.

Yue Wang, Siyu Meng, Halihaxi Bahetijiang, Haoxia Li, Tian Wang, Talgat Assabayev, Herman W Barkema, John P Kastelic, Bo Han, Zhaoju Deng

Abstract read
In one paragraph

Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Veterinary macrolides againstFrontiers in veterinary science · 2026
    Pooled it
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.

Yue WangCollege of Veterinary Medicine, China Agricultural University, Beijing, 100193, People's Republic of China.
Siyu MengCollege of Veterinary Medicine, China Agricultural University, Beijing, 100193, People's Republic of China.
Halihaxi BahetijiangCollege of Veterinary Medicine, China Agricultural University, Beijing, 100193, People's Republic of China.
Haoxia LiCollege of Veterinary Medicine, China Agricultural University, Beijing, 100193, People's Republic of China.
Tian WangCollege of Veterinary Medicine, China Agricultural University, Beijing, 100193, People's Republic of China.
Talgat AssabayevCollege of Veterinary Medicine, China Agricultural University, Beijing, 100193, People's Republic of China.
Herman W BarkemaFaculty of Veterinary Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada.
John P KastelicFaculty of Veterinary Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada.
Bo HanCollege of Veterinary Medicine, China Agricultural University, Beijing, 100193, People's Republic of China. hanbo@cau.edu.cn.
Zhaoju DengCollege of Veterinary Medicine, China Agricultural University, Beijing, 100193, People's Republic of China. zhaoju_deng@cau.edu.cn.

Funding

High-end Foreign Experts Recruitment Plan of China G2022108009LNational Natural Science Foundation of China 31772813Natural Science Foundation of Beijing, China 6244052
6 · The paper itself

Abstract

Streptococcus agalactiae is a contagious pathogen responsible for bovine mastitis, leading to significant economic losses in the global dairy industry. Our objectives were to determine the population structure, to profile the antimicrobial resistance and to investigate the pathogenicity and genes associated with pathogenicity in S. agalactiae isolated from Chinese dairy herds. A total of 266 milk samples were collected on three dairy farms in Ningxia (herd A) and Hebei provinces (herds B & C) with outbreaks of bovine mastitis from September 2020 to April 2021. There were 116 isolates identified as Streptococcus agalactiae by 16S rRNA sequencing. Twenty-seven S. agalactiae isolates were randomly selected using a stratified approach from the three farms for whole genome sequencing analysis and phenotypic analyses, including antimicrobial resistance profiling, identification of adhesion, invasion and virulence genes using in vitro bovine mammary epithelial cell models and in vivo Galleria mellonella models. Multilocus sequence typing and serotyping showed that all isolates belonged to sequence type ST103 and serotype Ia. In total, 34 genes were identified as virulence genes in Streptococcus species. Isolates from herd C had significantly higher virulence than those from herd B. Genome-wide association analysis revealed 166 virulence-related genes, 221 adhesion-related genes and 218 invasion-related genes. Furthermore, 47 genes were associated with pathogenicity in infecting G. mellonella. Resistance to tetracycline and macrolides was related to the presence of antimicrobial resistance genes tetO, tetM, and ermB. Pan-genome analyses revealed that 1,421 S. agalactiae isolates (27 from our study and 1,394 from the NCBI genome database) had 20,955 genes, including 666 and 20,289 genes in the core and accessory genomes, respectively. This study characterized the phenotypic and genotypic profiles for S. agalactiae, and identified associations between phenotypic traits and genetic determinants of virulence and antimicrobial resistance, providing new insights into controlling S. agalactiae mastitis in Chinese dairy herds.

Indexed as

Mastitis, BovineStreptococcal InfectionsStreptococcus agalactiaeAnimalsAnti-Bacterial AgentsCattleChinaDrug Resistance, BacterialFemaleGenome, BacterialGenotypeMicrobial Sensitivity TestsMilkMultilocus Sequence TypingPhenotypeRNA, Ribosomal, 16SAnti-Bacterial AgentsRNA, Ribosomal, 16SVirulence FactorsBovine mastitisPhenotypesStreptococcus agalactiaeVirulence genesWhole-genome sequencing

Identifiers

PMID41087872
PMCPMC12522963

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.