Actinobacteria belonging to the genus Rubrobacter are known for their multi-extremophilic growth conditions—they are highly radiation-resistant, halotolerant, thermotolerant or even thermophilic. This work demonstrates that the members of the genus are capable of accumulating polyhydroxyalkanoates (PHA) since PHA-related genes are widely distributed among Rubrobacter spp. whose complete genome sequences are available in public databases. Interestingly, all Rubrobacter strains possess both class I and class III synthases (PhaC). We have experimentally investigated the PHA accumulation in two thermophilic species, R. xylanophilus and R. spartanus. The PHA content in both strains reached up to 50% of the cell dry mass, both bacteria were able to accumulate PHA consisting of 3-hydroxybutyrate and 3-hydroxyvalerate monomeric units, none other monomers were incorporated into the polymer chain. The capability of PHA accumulation likely contributes to the multi-extremophilic characteristics since it is known that PHA substantially enhances the stress robustness of bacteria. Hence, PHA can be considered as extremolytes enabling adaptation to extreme conditions. Furthermore, due to the high PHA content in biomass, a wide range of utilizable substrates, Gram-stain positivity, and thermophilic features, the Rubrobacter species, in particular Rubrobacter xylanophilus, could be also interesting candidates for industrial production of PHA within the concept of Next-Generation Industrial Biotechnology.

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

Acclimation and adaptation of metabolism to a changing environment are key processes for plant survival and reproductive success. In the present study, 241 natural accessions of Arabidopsis (Arabidopsis thaliana) were grown under two different temperature regimes, 16 °C and 6 °C, and growth parameters were recorded, together with metabolite profiles, to investigate the natural genome × environment effects on metabolome variation. The plasticity of metabolism, which was captured by metabolic distance measures, varied considerably between accessions. Both relative growth rates and metabolic distances were predictable by the underlying natural genetic variation of accessions. Applying machine learning methods, climatic variables of the original growth habitats were tested for their predictive power of natural metabolic variation among accessions. We found specifically habitat temperature during the first quarter of the year to be the best predictor of the plasticity of primary metabolism, indicating habitat temperature as the causal driver of evolutionary cold adaptation processes. Analyses of epigenome- and genome-wide associations revealed accession-specific differential DNA-methylation levels as potentially linked to the metabolome and identified FUMARASE2 as strongly associated with cold adaptation in Arabidopsis accessions. These findings were supported by calculations of the biochemical Jacobian matrix based on variance and covariance of metabolomics data, which revealed that growth under low temperatures most substantially affects the accession-specific plasticity of fumarate and sugar metabolism. Our findings indicate that the plasticity of metabolic regulation is predictable from the genome and epigenome and driven evolutionarily by Arabidopsis growth habitats.

The salmonid pathogen Flavobacterium psychrophilum poses a significant economic threat to global aquaculture, yet our understanding of its genetic and phenotypic diversity remains incomplete across much of its geographic range. In this study, we characterise the genetic and phenotypic diversity of 70 isolates collected from rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo trutta m. fario) from fish farms in the Czech Republic between 2012 and 2019 to compare their genomic content with all draft or complete genomes present in the NCBI database (n = 187). The Czech isolates underwent comprehensive evaluation, including multiplex PCR-based serotyping, genetic analysis, antimicrobial resistance testing, and assessment of selected virulence factors. Multiplex PCR serotyping revealed 43 isolates as Type 1, 23 as Type 2, with sporadic cases of Types 3 and 4. Multi-locus sequence typing unveiled 12 sequence types (ST), including seven newly described ones. Notably, 24 isolates were identified as ST329, a novel sequence type, while 22 were classified as the globally-distributed ST2. Phylogenetic analysis demonstrated clonal distribution of ST329 in the Czech Republic, with these isolates lacking a phage sequence in their genomes. Antimicrobial susceptibility testing revealed a high proportion of isolates classified as non-wild type with reduced susceptibility to oxolinic acid, oxytetracycline, flumequine, and enrofloxacin, while most isolates were classified as wild type for florfenicol, sulfamethoxazole-trimethoprim, and erythromycin. However, 31 isolates classified as wild type for florfenicol exhibited minimum inhibitory concentrations at the susceptibility breakpoint. The prevalence of the Czech F. psychrophilum serotypes has evolved over time, likely influenced by the introduction of new isolates through international trade. Thus, it is crucial to monitor F. psychrophilum clones within and across countries using advanced methods such as MLST, serotyping, and genome sequencing. Given the open nature of the pan-genome, further sequencing of strains promises exciting discoveries in F. psychrophilum genomics.

Advanced sequencing techniques have propelled molecular biology research into what is often termed the post-genomics era. Consequently, there is a heightened focus on unraveling the functional connections between individual genes and their ultimate phenotype, an area referred to as predictive genomics. To support this quest, we developed an intuitive platform, PANOMICS, that we believe substantially facilitates the statistical model building to pathway inference, with applications ranging from agriculture to the biomedical field. PANOMICS, designed using Matlab2023b App Designer, allows for the uploading of genomics data, such as Single Nucleotide Polymorphisms (SNPs) information, metabolomics, other omics data, and phenotype information. The integration of different omics layers for the prediction of phenotypic outcomes promises to improve the accuracy and shifts the original genomic prediction, based solely on genomic inforamtion, to panomic prediction, that integrates multiple layers of molecular organization. PANOMICS facilitates data visualization and predictive analysis using various approaches and methods, such as linear, non-linear, and deep learning approaches. These analyses include statistical prediction models calculated based on Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RR), Elastic Net Regression (ENR), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Partial Least Squares (PLS) regression, and Random Forest (RF), as well as sophisticated Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models. Genome-Wide Association Studies (GWAS) with visualization based on an implementation of mixed linear models such as Ridge Regression with Best Linear Unbiased Prediction (rrBLUP) and Polygenic Risk Scores (PRS) are all put into practice within the app. The integration of a full range of advanced techniques within the application enhances its utility for researchers aiming to identify and interpret complex genetic interactions. This can lead to a more efficient use of Machine Learning (ML) algorithms in fields ranging from agriculture to medicine.

Motivation: The increasing use of big data and optimized prediction methods in metabolomics requires techniques aligned with biological assumptions to improve early symptom diagnosis. One major challenge in predictive data analysis is handling confounding factors—variables influencing predictions but not directly included in the analysis. Results: Detecting and correcting confounding factors enhances prediction accuracy, reducing false negatives that contribute to diagnostic errors. This study reviews concept drift detection methods in metabolomic predictions and selects the most appropriate ones. We introduce a new implementation of concept drift analysis in predictive classifiers using metabolomics data. Known confounding factors were confirmed, validating our approach and aligning it with conventional methods. Additionally, we identified potential confounding factors that may influence biomarker analysis, which could introduce bias and impact model performance. Availability and implementation: Based on biological assumptions supported by detected concept drift, these confounding factors were incorporated into correction of prediction algorithms to enhance their accuracy. The proposed methodology has been implemented in Semi-Automated Pipeline using Concept Drift Analysis for improving Metabolomic Predictions (SAPCDAMP), an open-source workflow available at https://github.com/JanaSchwarzerova/SAPCDAMP.