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Genome stability is significantly influenced by the precise coordination of chromatin complexes that facilitate the loading and eviction of histones from chromatin during replication, transcription, and DNA repair processes. In this study, we investigate the role of the Arabidopsis H3 histone chaperones ANTI-SILENCING FUNCTION 1 (ASF1) and HISTONE REGULATOR A (HIRA) in the maintenance of telomeres and 45S rDNA loci, genomic sites that are particularly susceptible to changes in the chromatin structure. We find that both ASF1 and HIRA are essential for telomere length regulation, as telomeres are significantly shorter in asf1a1b and hira mutants. However, these shorter telomeres remain localized around the nucleolus and exhibit a comparable relative H3 occupancy to the wild type. In addition to regulating telomere length, ASF1 and HIRA contribute to silencing 45S rRNA genes and affect their copy number. Besides, ASF1 supports global heterochromatin maintenance. Our findings also indicate that ASF1 transiently binds to the TELOMERE REPEAT BINDING 1 protein and the N terminus of telomerase in vivo, suggesting a physical link between the ASF1 histone chaperone and the telomere maintenance machinery., (© 2024 The Author(s). The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

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., (Copyright © 2022 Sidak, Schwarzerová, Weckwerth and Waldherr.)

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.