bioRxiv Science⌕ Search

bioRxiv · 10.1101/301424

Microevolution of aquatic Streptococcus agalactiae ST-261 from Australia indicates dissemination via imported tilapia and ongoing adaptation to marine hosts or environment

Abstract

Streptococcus agalactiae (GBS) causes disease in a wide range of animals. The serotype 1b lineage is highly adapted to aquatic hosts, exhibiting substantial genome reduction compared with terrestrial con-specifics. Here we sequence genomes from 40 GBS isolates including 25 from wild fish and captive stingrays in Australia, six local veterinary or human clinical isolates, and nine isolates from farmed tilapia in Honduras and compare with 42 genomes from public databases. Phylogenetic analysis based on non-recombinant core genome SNPs indicated that aquatic serotype Ib isolates from Queensland were distantly related to local veterinary and human clinical isolates. In contrast, Australian aquatic isolates are most closely related to a tilapia isolate from Israel, differing by only 63 core-genome SNPs. A consensus minimum spanning tree based on core genome SNPs indicates dissemination of ST-261 from an ancestral tilapia strain, which is congruent with several introductions of tilapia into Australia from Israel during the 1970s and 1980s. Pan-genome analysis identified 1,440 genes as core with the majority being dispensable or strain-specific with non-protein-coding intergenic regions (IGRs) divided amongst core and strain-specific genes. Aquatic serotype Ib strains have lost many virulence factors during adaptation, but six adhesins were well conserved across the aquatic isolates and might be critical for virulence in fish and targets for vaccine development. The close relationship amongst recent ST-261 isolates from Ghana, USA and China with the Israeli tilapia isolate from 1988 implicates the global trade in tilapia seed for aquaculture in the widespread dissemination of serotype Ib fish-adapted GBS.\n\nImportanceStreptococcus agalactiae (GBS) is a significant pathogen of humans and animals. Some lineages have become adapted to particular hosts and serotype Ib is highly specialized to fish. Here we show that this lineage is likely to have been distributed widely by the global trade in tilapia for aquaculture, with probable introduction into Australia in the 1970s and subsequent dissemination in wild fish populations. We report variability in the polysaccharide capsule amongst this lineage, but identify a cohort common surface proteins that may be a focus of future vaccine development to reduce the biosecurity risk in international fish trade.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kawasaki, M., Delamare-Deboutteville, J., Bowater, R., Walker, M. J., Beatson, S. A., Ben Zakour, N., Barnes, A. C.. 2018-04-14. Microevolution of aquatic Streptococcus agalactiae ST-261 from Australia indicates dissemination via imported tilapia and ongoing adaptation to marine hosts or environment. https://doi.org/10.1101/301424

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Almond Expression Atlas: A comprehensive multi-tissue transcriptomic resource for almond gene expression

Background: Improving crop adaptation and enhancing agronomic and quality traits requires a deeper understanding of gene expression regulation, for which expression atlases are valuable tools. Almond (Prunus dulcis (Mill.) D.A. Webb) is one of the most economically important tree nut crops worldwide. However, an integrated transcriptomic resource for this species is still lacking. Available data remain fragmented across independent studies, limiting their comparability and reuse. Results: Here, we present the first almond gene expression atlas: a comprehensive multi-tissue transcriptomic resource integrating publicly available RNA sequencing data. A total of 142 high-quality untreated and control samples from 17 independent projects were curated through a metadata standardization process and analyzed using a unified bioinformatics pipeline. Standardized expression profiles were generated for 25,225 annotated genes in the Prunus dulcis Texas v2.0 reference genome. The atlas integrates two complementary expression approaches: a median TPM approach capturing quantitative expression levels and a rank-based strategy implemented using the Bgee expression calling method, designed to improve comparability across heterogeneous datasets. This design enables the identification of tissue-specific candidate genes and the exploration of expression patterns across tissues and developmental stages. Its applicability is demonstrated through two use cases, highlighting its ability to identify biologically meaningful gene sets associated with bud dormancy transitions and to prioritize candidate reference genes for RT-qPCR normalization in almond. Conclusions: The Almond Expression Atlas v1.0 is publicly available through FruitDBase (fruitdbase.csic.es), providing an interactive platform for gene expression exploration and visualization. This resource supports functional genomics studies and facilitates candidate gene discovery in almond, while ensuring data traceability and promoting data reusability.

bioinformatics↗

Leakage-Controlled Benchmarking of Variant Effect Predictors and Development of Gradient-Boosting Consensus Model

Single amino acid mutations are among the most common forms of genetic variation, with effects ranging from negligible to severe disruption of protein function. Accurate quantitative evaluation of protein variants therefore represents a key challenge in medicine and protein engineering. Computational variant effect predictors (VEPs) are widely used to prioritize missense variants, yet their fair comparison is hindered by data circularity, benchmarks restricted to human clinical annotations, and limited availability of code and training data, all of which can inflate apparent performance. Here, we reviewed 18 recent and 3 older, widely adopted variant effect predictors for reproducible benchmarking and assembled a rigorously curated, leakage-controlled Benchmark dataset by integrating clinically annotated human variants from ClinVar and Humsavar as well as cross-species variants with functional annotations. The evaluation on this dataset revealed a narrow overall performance range for the benchmarked tools (AUC 0.631-0.758) and a consistent drop from clinical to functional data. Surprisingly, several tools published two decades ago demonstrated comparable performance to mid-ranked modern predictors. Building on these results, we developed a gradient-boosting consensus model, PredictSNPprot, which achieved an AUC of 0.82 on a held-out test set, outperforming the original PredictSNP meta-predictor and the best individual state-of-the-art tool by 13.6 and 5.7 percentage points, respectively. The Benchmark dataset, including the predictions from the evaluated tools, is available at https://doi.org/10.5281/zenodo.23597130 and PredictSNPprot consensus predictor is available at https://github.com/loschmidt/PredictSNPprot.

bioinformatics↗

MaCADEMIA: a software toolbox for single-molecule fluorescence image quantification

Single-molecule localisation microscopy (SMLM) is a powerful tool in molecular and cellular biology and part of the broad umbrella of super-resolution microscopy. A distinct feature of SMLM, is its capacity for deep quantification, beyond just spatial information, to copy number, stoichiometry, molecular interaction and dynamics in living cells. The hardware required is increasingly straightforward to implement. However, complexity remains in the image analysis where much remains fragmented across specialised software tools. Here, we present MaCADEMIA (Matlab Computational Analysis of Diffusion, Emitter Molecular Intensity and Abundance), a simple yet powerful toolbox of MATLAB functions and associated documentation to enable straightforward extraction of quantitative single-molecule information. MaCADEMIA performs ensemble particle detection achieving localisation accuracies down to 0.5 pixels Root mean squared error (RMSE) across wide signal-to-noise (SNR) ranges on standardised particle tracking benchmarks, consistently matching or exceeding the performance of 14 leading methods. Our integrated workflow enables molecular counting and stoichiometry determination through stepwise photobleaching analysis, mobility measurement via mean squared displacement, colocalisaton analysis and absolute protein copy number quantification using three-dimensional cellular models.

bioinformatics↗