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Record W4415166402 · doi:10.1186/s12866-025-04315-7

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

2025· article· en· W4415166402 on OpenAlexaff
Yue Wang, Siyu Meng, Halihaxi Bahetijiang, Haoxia Li, Tian Wang, Talgat Assabayev, Herman W. Barkema, John P. Kastelic, Bo Han, Zhaoju Deng

Bibliographic record

VenueBMC Microbiology · 2025
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsUniversity of Calgary
FundersHigh-end Foreign Experts Recruitment Plan of ChinaNational Natural Science Foundation of China
KeywordsStreptococcus agalactiaeMultilocus sequence typingMastitisVirulenceSerotypeAntibiotic resistanceGenotypePathogen

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.241
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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