Utilization of synthetic biology approaches to enhance current plastic degradation processes is of crucial importance, as natural plastic degradation processes are very slow. For instance, the predicted lifetime of a polyethylene terephthalate (PET) bottle under ambient conditions ranges from 16 to 48 years.

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

The study assessed the antimicrobial and antioxidant activities of commonly used and commercially available essential oils as an alternative to synthetic preservatives. The plant sources were as follows: lavender (Lavandula angustifolia), tea tree (Melaleuca alternifolia), bergamot (Citrus bergamia) and peppermint (Mentha piperita). The antioxidant activity of essential oils was tested by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2´-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) methods. The microdilution broth susceptibility assay revealed that lavender and bergamot essential oils were more efficient in inhibiting the bacterial growth than other tested oils, with the minimum inhibitory concentration of 5 μg/mL. This study also reports the successful implementation of an electrostatic extrusion technique for encapsulating essential oils into alginate beads, which enables the essential oils to maintain their free radical scavenging ability over time.

Introduction: Nowadays, plant extracts are highly applied in food industries either as sources of bioactive components or as an alternative to artificial additives. Therefore, food manufacturers are focused on innovative products, which can satisfy consumers' requirements. Objectives: This study investigates the encapsulation of Origanum majorana, Achillea millefolium, Foeniculum vulgare, Juniperus communis and Anethum graveolens EOs in alginate capsules as a means of controlling the fast release of volatile constituents. Materials and Methods: The EOs were obtained via steam distillation. Sodium alginate was chosen as a carrier because of its biodegradable and biocompatible properties. The paper describes the simple dripping technique used for the preparation of the alginate microcapsules with EO cores, and a possible application of the microcapsules as a natural flavor additive. Results: Sensorial properties of the final product were subjectively analyzed and described. The changes of the taste and the flavour of candies in comparison with the control sample were significant. Nevertheless, the strong herbal odour was found as "uncommon in confectionary but pleasant. Conclusion: It has been investigated, that the sodium alginate encapsulated EOs have to be added as a final step of a recipe to save its antimicrobial and antioxidant potential. Further assays need to be performed to investigate the recipe, which includes the EO alginate microcapsules in order to get a high-quality final product that can be used for commercial purposes.

Poor recycling has accumulated millions of tons of plastic waste in terrestrial and marine environments. While biodegradation is a plausible route towards sustainable management of plastic waste, the global diversity of plastic-degrading enzymes remains poorly understood. Taking advantage of global environmental DNA sampling projects, here we construct HMM models from experimentally-verified enzymes and mine ocean and soil metagenomes to assess the global potential of microorganisms to degrade plastics. By controlling for false positives using gut microbiome data, we compile a catalogue of over 30,000 non-redundant enzyme homologues with the potential to degrade 10 different plastic types. While differences between the ocean and soil microbiomes likely reflect the base compositions of these environments, we find that ocean enzyme abundance might increase with depth as a response to plastic pollution and not merely taxonomic composition. By obtaining further pollution measurements, we reveal that the abundance of the uncovered enzymes in both ocean and soil habitats significantly correlates with marine and country-specific plastic pollution trends. Our study thus uncovers the earth microbiome’s potential to degrade plastics, providing evidence of a measurable effect of plastic pollution on the global microbial ecology as well as a useful resource for further applied research.

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