Bacterial membrane vesicles (BMVs) are known to be critical communication tools in several pathophysiological processes between bacteria and host cells. Given this situation, BMVs for transporting and delivering exogenous therapeutic cargoes have been inspiring as promising platforms for developing smart drug delivery systems (SDDSs). In the first section of this review paper, starting with an introduction to pharmaceutical technology and nanotechnology, we delve into the design and classification of SDDSs. We discuss the characteristics of BMVs including their size, shape, charge, effective production and purification techniques, and the different methods used for cargo loading and drug encapsulation. We also shed light on the drug release mechanism, the design of BMVs as smart carriers, and recent remarkable findings on the potential of BMVs for anticancer and antimicrobial therapy. Furthermore, this review covers the safety of BMVs and the challenges that need to be overcome for clinical use. Finally, we discuss the recent advancements and prospects for BMVs as SDDSs and highlight their potential in revolutionizing the fields of nanomedicine and drug delivery. In conclusion, this review paper aims to provide a comprehensive overview of the state-of-the-art field of BMVs as SDDSs, encompassing their design, composition, fabrication, purification, and characterization, as well as the various strategies used for targeted delivery. Considering this information, the aim of this review is to provide researchers in the field with a comprehensive understanding of the current state of BMVs as SDDSs, enabling them to identify critical gaps and formulate new hypotheses to accelerate the progress of the field.
ABSTRACT Halomonas elongata thrives in hypersaline environments producing polyhydroxyalkanoates (PHAs) and osmoprotectants such as ectoine. Despite its biotechnological importance, several aspects of the dynamics of its metabolism remain elusive. Here, we construct and validate a genome‐scale metabolic network model for H. elongata 153B. Then, we investigate the flux distribution dynamics during optimal growth, ectoine, and PHA biosynthesis using statistical methods, and a pipeline based on shadow prices. Lastly, we use optimization algorithms to uncover novel engineering targets to increase PHA production. The resulting model ( i EB1239) includes 1534 metabolites, 2314 reactions, and 1239 genes. i EB1239 can reproduce growth on several carbon sources and predict growth on previously unreported ones. It also reproduces biochemical phenotypes related to Oad and Ppc gene functions in ectoine biosynthesis. A flux distribution analysis during optimal ectoine and PHA biosynthesis shows decreased energy production through oxidative phosphorylation. Furthermore, our analysis unveils a diverse spectrum of metabolic alterations that extend beyond mere flux changes to encompass heightened precursor production for ectoine and PHA synthesis. Crucially, these findings capture other metabolic changes linked to adaptation in hypersaline environments. Bottlenecks in the glycolysis and fatty acid metabolism pathways are identified, in addition to PhaC , which has been shown to increase PHA production when overexpressed. Overall, our pipeline demonstrates the potential of genome‐scale metabolic models in combination with statistical approaches to obtain insights into the metabolism of H. elongata . Our platform can be exploited for researching environmental adaptation, and for designing and optimizing metabolic engineering strategies for bioproduct synthesis.
Enterococci are commensals of the human intestinal tract. Their use as probiotics is supported by their ability to confer several health benefits and eliminate foodborne pathogens but is controversial due to the presence of virulence and antibiotic resistance traits. To use them as probiotics requires thorough research to establish their safety. Here, we sequenced the whole genome of a newly isolated Enterococcus durans MN187066 and used a suite of bioinformatics tools to analyze its beneficial probiotic traits as well as antimicrobial resistance and virulence genes. The whole genome had a length of 2 978 152 bp, and an average G + C content of 37.88%. The bopABCD genes involved in biofilm formation were annotated in the genome. However, further analysis showed that these genes are mostly helpful in strengthening their colonization and establishment in the gastrointestinal tract. Also, we identified secondary metabolite gene clusters and the bacteriocins Enterolysin A and Enterocin P. We also identified repUS15 and rep1 replicons and genes that were associated with antimicrobial resistance and virulence. Nevertheless, vancomycin resistance genes were not detected. Our results show that the Ent. durans strain MN187066 can be considered a nontoxigenic strain and produces beneficial metabolites that are critical for their success as probiotics.
Biosurfactants are biological surface-active agents produced naturally by some species of microbes. They have an amphiphilic character in nature because they have both hydrophilic and hydrophobic regions in their molecular structure. This allows them to cluster at interfaces of fluids that have different polarities, thereby reducing surface tension. This confers them a solubilizing property, which is important to many processes around us and in the industry. Biosurfactants are very promising, and they have drawn a lot of interest due to the fact that they are based on renewable resources, sustainable, and biologically degradable. Bio-based products have extended to market for many areas alongside current shifts in industrial processes from synthetic to more sustainable bio-based products. Besides, other properties as well as lower toxicity, higher foaming, high selectivity, and specific activity under extreme conditions such as temperature, pH, and salinity make it competitive with its current synthetic counterparts. Nowadays, biological surfactants are economically one of the most sought biotechnological compounds. There are numerous works on low-cost substrates for their production, but the strategies related to bioprocess optimization, coproduction, and yield amplification using molecular, nano-technological approaches have been studied less. Therefore, ineffective bioprocessing has occurred, and it has diminished the larger-scale production of these compounds. This chapter focuses on the recent improvements for biosurfactant production, its application fields, its profitable importance such as market share and challenges to commercialization, and the approaches that can be proposed to produce biosurfactants more economically.
Abstract Salt tolerant organisms are increasingly being used for the industrial production of high‐value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date. Advanced high throughput technologies such as whole‐genome sequencing allow in‐depth insight into the potential of organisms while at the frontiers of systems biology. At the same time, genome‐scale metabolic models (GEMs) enable phenotype predictions through a mechanistic representation of metabolism. Here, we sequence and analyze the genome of C. canadensis 85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux balance analysis and validate it against literature data on C. canadensis . We show that C. canadensis 85B is a metabolically versatile organism with many features for stress and osmotic adaptation. Pathways to produce ectoine and polyhydroxybutyrates were also predicted. The GEM reveals the ability to grow on several carbon sources in a minimal medium and reproduce osmoadaptation phenotypes. Overall, this study reveals insights from the genome of C. canadensis 85B, providing genomic data and a draft GEM that will serve as the first steps towards a better understanding of its metabolism, for novel applications in industrial biotechnology.
In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists' gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed.