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Debeljak, P.

Publications and source records attributed to Debeljak, P..

5 recordsLinked to original sources

FlavoTyper: a genome-based in-silico serotyping tool for the fish pathogen Flavobacterium psychrophilum

Flavobacterium psychrophilum is a devastating pathogen of fish reared in freshwater worldwide. Serotyping is a relevant method for epidemiological surveillance and outbreak detection, as well as for a better understanding of host-pathogen interactions. Serological diversity may also have important consequences for the selection of appropriate strains for vaccine development and for selective breeding for increased disease resistance. F. psychrophilum serotyping relies on structural variations in the O-polysaccharide (O-PS) moiety of the cell surface lipopolysaccharide (LPS). However, conventional serotyping is costly, labor-intensive and requires significant technical expertise. Moreover, divergent scheme proposals highlighted the absence of harmonization among laboratories. In this context, the development of an mPCR-based serotyping scheme targeting wzy genes greatly improved the reliability and standardization of serotyping. Nevertheless, the proposed mPCR scheme did not capture the entire diversity of genomic variability. The aim of this study was to establish a robust and publicly available tool for F. psychrophilum genome-based serotyping. Extensive genome analysis of the O-antigen biosynthesis locus allowed the identification of biomarkers enabling the development of FlavoTyper, an in-silico-based serotyping tool. The FlavoTyper tool was evaluated on all F. psychrophilum genome assemblies publicly available, providing sound and sensitive predictions and easily interpretable results. When applied to a curated collection of publicly available genomes, the in-silico O-types assigned by the tool were statistically significantly associated with host fish species, confirming previous studies (coho salmon with O:0, rainbow trout with O:1 and O:2, and ayu with O:3) and their distribution across MLST clonal complexes revealed that the O-antigen locus is frequently rearranged independently of the core-genome lineage, consistent with the extensive recombination that shapes the evolution and genomic diversity of this species.

microbiology↗

Interpretable Machine Learning on Soybean Multi Omics Data Reveals Drought-Driven Shifts of Plant-Microbe Interactions

BackgroundPlant-microbe interactions in the rhizosphere are central to plant growth, nutrient acquisition, and stress resilience. Although multi-omics approaches enable comprehensive profiling of different biological layers, integrating these data to understand the mechanisms underlying plant-microbe symbiosis, particularly under drought stress, remains a challenge. ResultsGenomic, metabolomic, and microbiome data from 198 soybean accessions grown under both control and drought conditions were integrated to identify environment-specific predictive features of the plant phenotypes. We compared best linear unbiased prediction (BLUP), genome-wide association study (GWAS), and a nonlinear machine learning model to evaluate their ability to detect informative features. The machine learning models provided flexible variable selection and outperformed linear models in capturing nonlinear dependencies. Model interpretation using SHapley Additive exPlanations (SHAP) indicated that the isoflavone derivative, daidzin, and the drought-tolerant Candidatus Nitrosocosmicus, were major contributors to phenotypic variation, specifically under drought stress. SHAP-based interaction networks indicated cross-omics links, including connections between daidzin, gamma-aminobutyric acid (GABA), and Paenibacillus. ConclusionThe proposed interpretable machine learning approach for plant phenotype prediction identified multi-omics biomarkers and interactions, providing insights into plant adaptation to drought stress through environment-dependent rhizosphere networks and symbiotic associations.

bioinformatics↗

Tagging of water masses with covariance of trace metals and prokaryotic taxa in the Southern Ocean

Marine microbes are strongly interrelated to trace metals in the ocean. How the availability of trace metals selects for prokaryotic taxa and the potential feedbacks of microbial processes on the trace metal distribution in the ocean remains poorly understood. We investigate here the potential reciprocal links between diverse prokaryotic taxa and iron (Fe), manganese (Mn), copper (Cu), Nickel (Ni) as well as apparent oxygen utilization (AOU) across 12 well-defined water masses in the Southern Indian Ocean (SWINGS-South West Indian Ocean GEOTRACES GS02 Section cruise). Applying Partial Least Square Regression (PLSR) analysis we show that the water masses are associated with particular latent vectors that are a combination of the spatial distribution of prokaryotic taxa, trace elements and AOU. This approach provides novel insights on the potential interactions between prokaryotic taxa and trace metals in relation to organic matter remineralization in distinct water masses of the ocean.

microbiology↗

Picoeukaryotic photosynthetic potential is functionally redundant but taxonomically structured at global scale

Primary production, performed by RUBISCO, and often associated with carbon concentration mechanisms, is of major importance in the oceans. Thanks to growing metagenomic resources (e.g., eukaryotic Metagenome-Assembled-Genomes; MAGs), we provide the first reproducible machine-learning-based framework to derive the potential biogeography of a given function, through the multi-output regression of the standardized number of reads of the associated genes on environmental climatologies. We use it to study the genomic potential of C4-photosynthesis of picoeukaryotes, a diverse and abundant group of marine unicellular photosynthetic organisms. We show that the genomic potential supporting C4-enzymes and RUBISCO exhibit strong functional redundancy and an important affinity towards tropical oligotrophic waters. This redundancy is then structured taxonomically by the dominance of Mamiellophyceae and Prymnesiophyceae in mid and high latitudes. Finally, unlike the genomic potential related to most C4-enzymes, the one of RUBISCO showed a clear pattern affinity for temperate waters.

ecology↗

Beyond the limits of the unassigned protist microbiome: inferring large-scale spatio-temporal patterns of marine parasites

Marine protists are major components of the oceanic microbiome that remain largely unrepresented in culture collections and genomic reference databases. The exploration of this uncharted protist diversity in oceanic communities relies essentially on studying genetic markers from the environment as taxonomic barcodes. Here we report that across 6 large scale spatio-temporal planktonic surveys, half of the genetic barcodes remain taxonomically unassigned at the genus level, preventing a fine ecological understanding for numerous protist lineages. Among them, parasitic Syndiniales (Dinoflagellata) appear as the least described protist group. We have developed a computational workflow, integrating diverse 18S rDNA gene metabarcoding datasets, in order to infer large-scale ecological patterns at 100% similarity of the genetic marker, overcoming the limitation of taxonomic assignment. From a spatial perspective, we identified 2 171 unassigned clusters exclusively shared between the Tropical/Subtropical Ocean and the Mediterranean Sea among all Syndiniales orders and 25 ubiquitous clusters shared within all the studied marine regions. From a temporal perspective, over 3 time-series, we highlighted 38 unassigned clusters that follow rhythmic patterns of recurrence and are the best indicators of parasite communitys variation. These clusters withhold potential as ecosystem change indicators, mirroring their associated host community responses. Our results underline the importance of Syndiniales in structuring planktonic communities through space and time, raising questions regarding host-parasite association specificity and the trophic mode of persistent Syndiniales, while providing an innovative framework for prioritizing unassigned protist taxa for further description.

genomics↗