ABSTRACT
BACKGROUND: Microbial biopolymers such as poly-3-hydroxybutyrate (PHB) are emerging as promising alternatives for sustainable production of biodegradable bioplastics. Their promise is heightened by the potential utilisation of photosynthetic organisms, thus exploiting sunlight and carbon dioxide as source of energy and carbon, respectively. The cyanobacterium Synechocystis sp. B12 is an attractive candidate for its superior ability to accumulate high amounts of PHB as well as for its high-light tolerance, which makes it extremely suitable for large-scale cultivation. Beyond its practical applications, B12 serves as an intriguing model for unravelling the molecular mechanisms behind PHB accumulation. RESULTS: Through a multifaceted approach, integrating physiological, genomic and transcriptomic analyses, this work identified genes involved in the upregulation of chlorophyll biosynthesis and phycobilisome degradation as the possible candidates providing Synechocystis sp. B12 an advantage in growth under high-light conditions. Gene expression differences in pentose phosphate pathway and acetyl-CoA metabolism were instead recognised as mainly responsible for the increased Synechocystis sp. B12 PHB production during nitrogen starvation. In both response to strong illumination and PHB accumulation, Synechocystis sp. B12 showed a metabolic modulation similar but more pronounced than the reference strain, yielding in better performances. CONCLUSIONS: Our findings shed light on the molecular mechanisms of PHB biosynthesis, providing valuable insights for optimising the use of Synechocystis in economically viable and sustainable PHB production. In addition, this work supplies crucial knowledge about the metabolic processes involved in production and accumulation of these molecules, which can be seminal for the application to other microorganisms as well.
ABSTRACT
Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on time-consuming in vitro tests. While bioinformatic tools for the identification of bioactive peptides are available, they are focused on specific functional classes and have not been systematically tested on realistic settings. To tackle this problem, bioactive peptide sequences and functions were here gathered from a variety of databases to generate a unified collection of bioactive peptides from microbial fermentation. This collection was organized into nine functional classes including some previously studied and some unexplored such as immunomodulatory, opioid and cardiovascular peptides. Upon assessing their sequence properties, four alternative encoding methods were tested in combination with a multitude of machine learning algorithms, from basic classifiers like logistic regression to advanced algorithms like BERT. Tests on a total of 171 models showed that, while some functions are intrinsically easier to detect, no single combination of classifiers and encoders worked universally well for all classes. For this reason, we unified all the best individual models for each class and generated CICERON (Classification of bIoaCtive pEptides fRom micrObial fermeNtation), a classification tool for the functional classification of peptides. State-of-the-art classifiers were found to underperform on our realistic benchmark dataset compared to the models included in CICERON. Altogether, our work provides a tool for real-world peptide classification and can serve as a benchmark for future model development.
