Recovery and encapsulation of Dunaliella salina ß-carotene through a novel sustainable approach: Sequential application of an ionic liquid as naturally-derived solvent and emulsifier.

Dunaliella salina is a promising source of ß-carotene, widely employed in the food industry. This study aimed to evaluate the sequential application of the Ionic Liquid (IL) cholinium oleate as an extraction solvent for D. salina ß-carotene recovery and, sequentially, as emulsifier for emulsion-based products obtained therefrom. The IL was evaluated regarding its ability to permeabilize the cells and recover ß-carotene at different temperatures (25-65 °C) and IL concentrations (0-46%). The use of the IL as solvent greatly improved ß-carotene recovery (>84%). The IL already present in the obtained extracts loaded with recovered ß-carotene was sequentially used as emulsifier in the production of nanoemulsions (NE). NE presented a ß-carotene entrapment efficiency of 100% and were kinetically stable for 30 days and presented droplet size, size distribution, and ζ-potential of 220 nm, 0.21, and -67 mV, respectively. These results indicate that using IL sequential as solvent and emulsifier has potential applications in the food industry.

BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models.

As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

Static Black Binaries in de Sitter Space.

We construct the first four-dimensional multiple black hole solution of general relativity with a positive cosmological constant. The solution consists of two static black holes whose gravitational attraction is balanced by the cosmic expansion. These static binaries provide the first four-dimensional example of nonuniqueness in general relativity without matter.

Microalgae biomass as an alternative source of biocompounds: New insights and future perspectives of extraction methodologies.

Microalgae have characteristics that make them unique and full of potential. Their capacity to generate interesting bioactive molecules can add value to various industrial applications. However, most of these valuable compounds are intracellular, which makes their extraction a major bottleneck. Conventional extraction methodologies have some drawbacks, such as low eco-friendly character, high costs and energy demand, long treatment times, low selectivity and reduced extraction yields, as well as degradation of extracted compounds. The gaps found for these methods demonstrate that emergent approaches, such as ohmic heating, pulsed electric fields, ionic liquids, deep eutectic solvents, or high-pressure processing, show potential to overcome the current drawbacks in the release and extraction of added-value compounds from microalgae. These new processing techniques can potentially extract a variety of compounds, making the process more profitable and applicable to large scales. This review provides an overview of the most important and promising factors to consider in the extraction methodologies applied to microalgae. Additionally, it delivers broad knowledge of the present impact of these methods on biomass and its compounds, raising the possibility of applying them in an integrated manner within a biorefinery concept.

Metabolic reconstruction of the human pathogen Candida auris: using a cross-species approach for drug target prediction.

Candida auris is an emerging human pathogen, associated with antifungal drug resistance and hospital candidiasis outbreaks. In this work, we present iRV973, the first reconstructed Genome-scale metabolic model (GSMM) for C. auris. The model was manually curated and experimentally validated, being able to accurately predict the specific growth rate of C. auris and the utilization of several sole carbon and nitrogen sources. The model was compared to GSMMs available for other pathogenic Candida species and exploited as a platform for cross-species comparison, aiming the analysis of their metabolic features and the identification of potential new antifungal targets common to the most prevalent pathogenic Candida species. From a metabolic point of view, we were able to identify unique enzymes in C. auris in comparison with other Candida species, which may represent unique metabolic features. Additionally, 50 enzymes were identified as potential drug targets, given their essentiality in conditions mimicking human serum, common to all four different Candida models analysed. These enzymes represent interesting drug targets for antifungal therapy, including some known targets of antifungal agents used in clinical practice, but also new potential drug targets without any human homolog or drug association in Candida species.

The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber.

Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.

TranSyT, an innovative framework for identifying transport systems.

MOTIVATION: The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGE's release, software development for the automatic integration of transport reactions into models has stalled. RESULTS: Here, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase. AVAILABILITY AND IMPLEMENTATION: TranSyT web service: https://transyt.bio.di.uminho.pt/; GitHub for the tool: https://github.com/BioSystemsUM/transyt; GitHub with examples and instructions to run TranSyT: https://github.com/ecunha1996/transyt_paper.

Systematic assessment of template-based genome-scale metabolic models created with the BiGG Integration Tool.

Genome-scale metabolic models (GEMs) are essential tools for in silico phenotype prediction and strain optimisation. The most straightforward GEMs reconstruction approach uses published models as templates to generate the initial draft, requiring further curation. Such an approach is used by BiGG Integration Tool (BIT), available for merlin users. This tool uses models from BiGG Models database as templates for the draft models. Moreover, BIT allows the selection between different template combinations. The main objective of this study is to assess the draft models generated using this tool and compare them BIT, comparing these to CarveMe models, both of which use the BiGG database, and curated models. For this, three organisms were selected, namely Streptococcus thermophilus, Xylella fastidiosa and Mycobacterium tuberculosis. The models' variability was assessed using reactions and genes' metabolic functions. This study concluded that models generated with BIT for each organism were differentiated, despite sharing a significant portion of metabolic functions. Furthermore, the template seems to influence the content of the models, though to a lower extent. When comparing each draft with curated models, BIT had better performances than CarveMe in all metrics. Hence, BIT can be considered a fast and reliable alternative for draft reconstruction for bacteria models.

merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models.

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms' metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models' reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

A randomised control trial protocol of MuST for vascular access cannulation in hemodialysis patients (MuST Study): contributions for a safe nursing intervention.

BACKGROUND: The vascular access preservation and the maintenance of a complication-free fistula remains an Achilles' heel of hemodialysis in chronic kidney patients due to its substantial contribution to the morbidity and mortality. Systematic studies in the area of examining cannulation practices, achieving complication-free cannulation, and strategies to improve fistula survival are needed. For this reason, we consider it essential to create and investigate new methodologies for approaching fistula in patients on regular HD. The Multiple Single Cannulation Technique (MuST) is based on the association between the rope-ladder (RL) using the arteriovenous vessel through progressive rotation, and the buttonhole (BH) since there are three specific cannulation sites for each cannulation day during the week. The MuST is simple to implement and seems to be a very promising technique in terms of patient safety. Previous studies already showed an arteriovenous fistula survival similar to RL but significantly higher than BH. METHODS: This MuST study is a multicenter, prospective, non-blind, parallel-group, randomized controlled trial with the intervention group submitted to MuST and a control group undergoing the rope-ladder, up to 100 subjects for each group. Patients will be randomized 1:1 to one of two cannulation technique (CT), and the follow-up period of this study will be 12 months. Primary outcome is to evaluate the arteriovenous fistula survival rate at 12 months determined by the percentage of fistulas in use from the beginning of the study to the date of the first clinical intervention by angioplasty or vascular surgery, to maintain or restore patency (unassisted patency). Secondary outcome is to evaluate arteriovenous fistula survival rate at 12 month determined by the percentage of fistulas in use from the study start to the date of access abandonment due to dysfunction, patient abandonment, or death, treatment change modality or study end. We will also evaluate the assisted primary patency and include the following secondary outcomes associated with the cannulation technique: Infection, Hematoma, Aneurysm development, and pain. DISCUSSION: The study will investigate whether fistula survival can be improved when using cannulation by MuST compared to the RL. MuST study will provide important information on fistula survival when cannulated by MuST but also information related to its use in fistulas previously cannulated by other CTs. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT05081648 registered on 18 October 2021.

Exploring synergies between plant metabolic modelling and machine learning.

As plants produce an enormous diversity of metabolites to help them adapt to the environment, the study of plant metabolism is of utmost importance to understand different plant phenotypes. Omics data have been generated at an unprecedented rate for several organisms, including plants, and are widely used to study the central dogma of molecular biology, connecting the genome to phenotypes. Constraint-based modelling (CBM) methods, working over genome-scale metabolic models (GSMMs), have been crucial for organising and analysing omics data by integrating them with biochemical knowledge. In 2009, the first plant GSMM was reconstructed and, since then, several advances have been made, including the creation of context- and multi-tissue models that have supported the study of plant metabolism. Nevertheless, plant metabolic modelling remains very challenging. In parallel, as omics datasets are complex and heterogeneous, machine learning (ML) models have been applied in their interpretation to foster knowledge discovery. Recently, the first studies combining both CBM and ML approaches have emerged and have shown promising results. Here, we present the major advances in plant metabolic modelling and review the main CBM-ML hybrid studies. Finally, we discuss the application of machine learning to address the unique challenges of plant metabolic modelling.

Modelling aptamers with nucleic acid mimics (NAM): From sequence to three-dimensional docking.

Aptamers are single-stranded oligonucleotides, formerly evolved by Systematic Evolution of Ligands by EXponential enrichment (SELEX), that fold into functional three-dimensional structures. Such conformation is crucial for aptamers' ability to bind to a target with high affinity and specificity. Unnatural nucleotides have been used to develop nucleic acid mimic (NAM) aptamers with increased performance, such as biological stability. Prior knowledge of aptamer-target interactions is critical for applying post-SELEX modifications with unnatural nucleotides since it can affect aptamers' structure and performance. Here, we describe an easy-to-apply in silico workflow using free available software / web servers to predict the tertiary conformation of NAM, DNA and RNA aptamers, as well as the docking with the target molecule. Representative 2'-O-methyl (2'OMe), locked nucleic acid (LNA), DNA and RNA aptamers, with experimental data deposited in Protein Data Bank, were selected to validate the workflow. All aptamers' tertiary structure and docking models were successfully predicted with good structural similarity to the experimental data. Thus, this workflow will boost the development of aptamers, particularly NAM aptamers, by assisting in the rational modification of specific nucleotides and avoiding trial-and-error approaches.

A Genome-Scale Metabolic Model for the Human Pathogen Candida Parapsilosis and Early Identification of Putative Novel Antifungal Drug Targets.

Candida parapsilosis is an emerging human pathogen whose incidence is rising worldwide, while an increasing number of clinical isolates display resistance to first-line antifungals, demanding alternative therapeutics. Genome-Scale Metabolic Models (GSMMs) have emerged as a powerful in silico tool for understanding pathogenesis due to their systems view of metabolism, but also to their drug target predictive capacity. This study presents the construction of the first validated GSMM for C. parapsilosis-iDC1003-comprising 1003 genes, 1804 reactions, and 1278 metabolites across four compartments and an intercompartment. In silico growth parameters, as well as predicted utilisation of several metabolites as sole carbon or nitrogen sources, were experimentally validated. Finally, iDC1003 was exploited as a platform for predicting 147 essential enzymes in mimicked host conditions, in which 56 are also predicted to be essential in C. albicans and C. glabrata. These promising drug targets include, besides those already used as targets for clinical antifungals, several others that seem to be entirely new and worthy of further scrutiny. The obtained results strengthen the notion that GSMMs are promising platforms for drug target discovery and guide the design of novel antifungal therapies.

OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes.

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96% of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. We evaluated the identification of putative synergistic species interactions using the sequenced genomes of an independent study that had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy-to-use workflow that allows for rapid functional annotation based on a user-curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

Insights into the genome architecture and evolution of Shiga toxin encoding bacteriophages of Escherichia coli.

BACKGROUND: A total of 179 Shiga toxin-producing Escherichia coli (STEC) complete genomes were analyzed in terms of serotypes, prophage coding regions, and stx gene variants and their distribution. We further examined the genetic diversity of Stx-converting phage genomes (Stx phages), focusing on the lysis-lysogeny decision and lytic cassettes. RESULTS: We show that most STEC isolates belong to non-O157 serotypes (73 %), regardless the sources and geographical regions. While the majority of STEC genomes contain a single stx gene (61 %), strains containing two (35 %), three (3 %) and four (1 %) stx genes were also found, being stx2 the most prevalent gene variant. Their location is exclusively found in intact prophage regions, indicating that they are phage-borne. We further demonstrate that Stx phages can be grouped into four clusters (A, B, C and D), three subclusters (A1, A2 and A3) and one singleton, based on their shared gene content. This cluster distribution is in good agreement with their predicted virion morphologies. Stx phage genomes are highly diverse with a vast number of 1,838 gene phamilies (phams) of related sequences (of which 677 are orphams i.e. unique genes) and, although having high mosaicism, they are generally organized into three major transcripts. While the mechanisms that guide lysis-lysogeny decision are complex, there is a strong selective pressure to maintain the stx genes location close to the lytic cassette composed of predicted SAR-endolysin and pin-holin lytic proteins. The evolution of STEC Stx phages seems to be strongly related to acquiring genetic material, probably from horizontal gene transfer events. CONCLUSIONS: This work provides novel insights on the genetic structure of Stx phages, showing a high genetic diversity throughout the genomes, where the various lysis-lysogeny regulatory systems are in contrast with an uncommon, but conserved, lytic system always adjacent to stx genes.

A kinetic model of the central carbon metabolism for acrylic acid production in Escherichia coli.

Acrylic acid is a value-added chemical used in industry to produce diapers, coatings, paints, and adhesives, among many others. Due to its economic importance, there is currently a need for new and sustainable ways to synthesise it. Recently, the focus has been laid in the use of Escherichia coli to express the full bio-based pathway using 3-hydroxypropionate as an intermediary through three distinct pathways (glycerol, malonyl-CoA, and ß-alanine). Hence, the goals of this work were to use COPASI software to assess which of the three pathways has a higher potential for industrial-scale production, from either glucose or glycerol, and identify potential targets to improve the biosynthetic pathways yields. When compared to the available literature, the models developed during this work successfully predict the production of 3-hydroxypropionate, using glycerol as carbon source in the glycerol pathway, and using glucose as a carbon source in the malonyl-CoA and ß-alanine pathways. Finally, this work allowed to identify four potential over-expression targets (glycerol-3-phosphate dehydrogenase (G3pD), acetyl-CoA carboxylase (AccC), aspartate aminotransferase (AspAT), and aspartate carboxylase (AspC)) that should, theoretically, result in higher AA yields.

Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction.

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.

A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation.

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

A Tailspike with Exopolysaccharide Depolymerase Activity from a New Providencia stuartii Phage Makes Multidrug-Resistant Bacteria Susceptible to Serum-Mediated Killing.

Providencia stuartii is emerging as a significant drug-resistant nosocomial pathogen, which encourages the search for alternative therapies. Here, we have isolated Providencia stuartii phage Stuart, a novel podovirus infecting multidrug-resistant hospital isolates of this bacterium. Phage Stuart is a proposed member of a new Autographivirinae subfamily genus, with a 41,218-bp genome, direct 345-bp repeats at virion DNA ends, and limited sequence similarity of proteins to proteins in databases. Twelve out of the 52 predicted Stuart proteins are virion components. We found one to be a tailspike with depolymerase activity. The tailspike could form a highly thermostable oligomeric ß-structure migrating close to the expected trimer in a nondenaturing gel. It appeared to be essential for the infection of three out of four P. stuartii hosts infected by phage Stuart. Moreover, it degraded the exopolysaccharide of relevant phage Stuart hosts, making the bacteria susceptible to serum killing. Prolonged exposure of a sensitive host to the tailspike did not cause the emergence of bacteria resistant to the phage or to serum killing, opposite to the prolonged exposure to the phage. This indicates that phage tail-associated depolymerases are attractive antivirulence agents that could complement the immune system in the fight with P. stuartiiIMPORTANCE The pace at which multidrug-resistant strains emerge has been alarming. P. stuartii is an infrequent but relevant drug-resistant nosocomial pathogen causing local to systemic life-threatening infections. We propose an alternative approach to fight this bacterium based on the properties of phage tailspikes with depolymerase activity that degrade the surface bacterial polymers, making the bacteria susceptible to the immune system. Unlike antibiotics, phage tailspikes have narrow and specific substrate spectra, and by acting as antivirulent but not bactericidal agents they do not cause the selection of resistant bacteria.

SamPler - a novel method for selecting parameters for gene functional annotation routines.

BACKGROUND: As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins. RESULTS: Here SamPler, a novel strategy to select parameters for gene functional annotation routines is presented. This semi-automated method is based on the manual curation of a randomly selected set of genes/proteins. Then, in a multi-dimensional array, this sample is used to assess the automatic annotations for all possible combinations of the algorithm's parameters. These assessments allow creating an array of confusion matrices, for which several metrics are calculated (accuracy, precision and negative predictive value) and used to reach optimal values for the parameters. CONCLUSIONS: The potential of this methodology is demonstrated with four genome functional annotations performed in merlin, an in-house user-friendly computational framework for genome-scale metabolic annotation and model reconstruction. For that, SamPler was implemented as a new plugin for the merlin tool.