Detecting horizontal gene transfer among microbiota: an innovative pipeline for identifying co-shared genes within the mobilome through advanced comparative analysis.

Horizontal gene transfer (HGT) is a key driver in the evolution of bacterial genomes. The acquisition of genes mediated by HGT may enable bacteria to adapt to ever-changing environmental conditions. Long-term application of antibiotics in intensive agriculture is associated with the dissemination of antibiotic resistance genes among bacteria with the consequences causing public health concern. Commensal farm-animal-associated gut microbiota are considered the reservoir of the resistance genes. Therefore, in this study, we identified known and not-yet characterized mobilized genes originating from chicken and porcine fecal samples using our innovative pipeline followed by network analysis to provide appropriate visualization to support proper interpretation.

Metabolome plasticity in 241 Arabidopsis thaliana accessions reveals evolutionary cold adaptation processes.

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.

Interpretable machine learning methods for predictions in systems biology from omics data.

Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by "omics" experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design "interpretable" models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: "What is interpretability?" We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.

The First Insight into Polyhydroxyalkanoates Accumulation in Multi-Extremophilic Rubrobacter xylanophilus and Rubrobacter spartanus.

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.