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Povzetek: Machine learning models provide a scalable approach for predicting the diversity of eukaryotic microbial plankton from environmental predictors. However, the extent to which these models generalize to data outside the training set remains poorly quantified. In this study, XGBoost was used to predict the 18S rRNA gene Shannon Diversity Index (SDI) from seven environmental predictors derived from satellite and model data. Surface samples were collected between 2001 and 2025 at two fixed stations in the northwestern Mediterranean (BBMO and SOLA), one fixed station in the northern Adriatic Sea (VIDA), and during the HOTMIX expedition, which sampled an east-west open-sea transect across the Mediterranean Sea. Model performance was assessed using standard repeated K-fold cross-validation (CV), Leave-One-Dataset-Out CV (LODO-CV), and a blocked spatiotemporal CV that combined LODO with temporal forward chaining. Under standard K-fold CV, the model showed moderate performance (R² = 0.44, RMSE = 0.59). In contrast, performance declined substantially under LODO-CV (R² = 0.09, RMSE = 0.73), with uniformly low per-dataset generalization, a pattern also observed with blocked spatiotemporal CV. VIDA and HOTMIX sampled environmental regimes distinct from those at BBMO and SOLA, which may partly explain their poor transferability. Additionally, BBMO and SOLA, despite similar environmental conditions, exhibited poor transferability, indicating that technical differences among independently collected 18S rRNA datasets likely constrain transferability, although their effects cannot be disentangled from environmental variation. Overall, these results highlight the limitations of imbalanced training data and underscore the importance of spatially explicit evaluation, protocol standardization, and environmentally representative coverage.
Ključne besede: cross-validation, data leakage, eukaryotic plankton, machine learning, marine biodiversity, Mediterranean Sea, omics, shannon diversity index
Objavljeno v DiRROS: 29.07.2026; Ogledov: 156; Prenosov: 97
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Povzetek: Background/Objectives: In recent years, interest in studying changes in cancer metabolites has resulted in significant advances in the metabolomics field. Glioblastoma remains the most aggressive and lethal brain malignancy, which presents with notable metabolic reprogramming. Methods: We performed literature research from the PubMed database and considered research articles focused on the key metabolic pathways altered in glioblastoma (e.g., glycolysis, lipid metabolism, TCA cycle), the role of oncometabolites and metabolic plasticity, and the differential expression of metabolites in glioblastoma. Currently used metabolomics approaches can be either targeted, focusing on specific metabolites and pathways, or untargeted, which involves data-driven exploration of the metabolome and also results in the identification of new metabolites. Data processing and analysis is of great importance and can be improved with the integration of machine learning approaches for metabolite identification. Results: Changes in α/β-glucose, lactate, choline, and 2-hydroxyglutarate were detected in glioblastoma compared with non-tumor tissues. Different metabolites such as fumarate, tyrosine, and leucine, as well as citric acid, isocitric acid, shikimate, and GABA were detected in blood and CSF, respectively. Conclusions: Although promising new technological and bioinformatic approaches help us understand glioblastoma better, challenges associated with biomarker availability, tumor heterogeneity, interpatient variability, standardization, and reproducibility still remain. Metabolomics research, either alone or combined with genomics or proteomics (i.e., multiomics) in glioblastoma, can lead to biomarker identification, tracking of metabolic therapy response, discovery of novel metabolites and pathways, and identification of potential therapeutic targets.
Ključne besede: Omics, metabolome, multiomic integration, tumor metabolic reprogramming, biomarker, diagnosis, therapy, precision oncology
Objavljeno v DiRROS: 22.04.2026; Ogledov: 268; Prenosov: 240
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Povzetek: Fuchs’ endothelial corneal dystrophy (FECD) is a progressive eye disease characterized by accelerated loss of endothelial cells and the development of focal excrescence (guttae) on Descemet’s membrane, resulting in cornea opacity and vision deterioration. The development of FECD is assumed to be due to the interplay between genetic and environmental factor risks, causing abnormal extracellular-matrix organization, increased oxidative stress, apoptosis and unfolded protein response. However, the molecular knowledge of FECD is limited. The development of genome-wide platforms and bioinformatics approaches has enabled us to identify numerous genetic loci that are associated with FECD. In this review, we gathered genome-wide studies (n = 31) and sorted them according to genomics (n = 9), epigenomics (n = 3), transcriptomics (n = 15), proteomics (n = 3) and metabolomics (n = 1) levels to characterize progress in understanding FECD. We also extracted validated differentially expressed/spliced genes and proteins identified through comparisons of FECD case and control groups. In addition, highlighted loci from each omics layer were combined according to a comparison with similar study groups from original studies for downstream gene-set enrichment analysis, which provided the most significant biological pathways related to extracellular-matrix organization. In the future, multiomics study approaches are needed to increase the sample size and statistical power to identify strong candidate genes for functional studies on animal models and cell lines for better understanding FECD.
Ključne besede: Fuchs’ endothelial corneal dystrophy, FECD, omics study, genomics
Objavljeno v DiRROS: 26.02.2026; Ogledov: 399; Prenosov: 330
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Povzetek: Components of the renin-angiotensin system (RAS) are expressed in both female and male reproductive tracts, with angiotensin I converting enzyme (ACE) being an important component for male reproductive function, as shown in animal models. The most studied ACE polymorphism is the Alu insertion-deletion (I/D), which has been proposed to have a negative effect on male fertility. Given the conflicting evidence in the literature, we conducted a multicentric case-control study to investigate the association between the ACE Alu I/D polymorphism and impaired spermatogenesis. Using PCR amplification and agarose electrophoresis, we genotyped the ACE gene Alu I/D polymorphism in 745 South Slavic men. The study group consisted of 457 patients with impaired spermatogenesis, 239 with non-obstructive azoospermia (NOA) and 218 with oligoasthenoteratozoospermia (OAT) and a control group of 288 fertile men. No association was found between the Alu I/D polymorphism and these semen phenotypes, suggesting that it is not associated with NOA or severe OAT in this cohort. To provide a broader regulatory context, we also developed an integrative atlas of ACE regulatory elements by in silico multi-omics analysis using genomics databases and bioinformatics tools. Data integration revealed various regulatory mechanisms at multiple omics levels, including genomics, epigenomics, miRNAomics, transcriptomics, proteomics and epiproteomics. These include genomic variants with predicted deleterious effects, a CpG island, microRNAs (miRNAs) and post-translational modifications (PTMs). In addition, protein interaction analysis revealed that ACE is indirectly linked to several proteins previously associated with male infertility and is also targeted by miRNA previously associated with oligozoospermia. This comprehensive, multi-faceted approach, combining genetic association analysis with bioinformatics, provides insights into ACE regulation in its broader molecular context. These results emphasize the importance of further integrative multi-omics and systems biology research to better understand the role of ACE in male reproductive function.
Ključne besede: angiotensin I converting enzyme (ACE), male infertility, azoospermia, oligozoospermia, multi-omics
Objavljeno v DiRROS: 09.12.2025; Ogledov: 466; Prenosov: 343
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Povzetek: Potato (Solanum tuberosum) is highly water and space efficient but susceptible to abiotic stresses such as heat, drought, and flooding, which are severely exacerbated by climate change. Our understanding of crop acclimation to abiotic stress, however, remains limited. Here, we present a comprehensive molecular and physiological high-throughput profiling of potato (Solanum tuberosum, cv. Désirée) under heat, drought, and waterlogging applied as single stresses or in combinations designed to mimic realistic future scenarios. Stress responses were monitored via daily phenotyping and multi-omics analyses of leaf samples comprising proteomics, targeted transcriptomics, metabolomics, and hormonomics at several timepoints during and after stress treatments. Additionally, critical metabolites of tuber samples were analyzed at the end of the stress period. We performed integrative multi-omics data analysis using a bioinformatic pipeline that we established based on machine learning and knowledge networks. Waterlogging produced the most immediate and dramatic effects on potato plants, interestingly activating ABA responses similar to drought stress. In addition, we observed distinct stress signatures at multiple molecular levels in response to heat or drought and to a combination of both. In response to all treatments, we found a downregulation of photosynthesis at different molecular levels, an accumulation of minor amino acids, and diverse stress-induced hormones. Our integrative multi-omics analysis provides global insights into plant stress responses, facilitating improved breeding strategies toward climate-adapted potato varieties.
Ključne besede: potato, Solanum tuberosum, abiotic stress responses, heat, drought, waterlogging, multi-omics, integrative omics, adaptomics, panomics
Objavljeno v DiRROS: 14.04.2025; Ogledov: 1446; Prenosov: 1171
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