The Neighborhood of the Spike Gene Is a Hotspot for Modular Intertypic Homologous and Nonhomologous Recombination in Coronavirus Genomes.

Coronaviruses (CoVs) have very large RNA viral genomes with a distinct genomic architecture of core and accessory open reading frames (ORFs). It is of utmost importance to understand their patterns and limits of homologous and nonhomologous recombination, because such events may affect the emergence of novel CoV strains, alter their host range, infection rate, tissue tropism pathogenicity, and their ability to escape vaccination programs. Intratypic recombination among closely related CoVs of the same subgenus has often been reported; however, the patterns and limits of genomic exchange between more distantly related CoV lineages (intertypic recombination) need further investigation. Here, we report computational/evolutionary analyses that clearly demonstrate a substantial ability for CoVs of different subgenera to recombine. Furthermore, we show that CoVs can obtain-through nonhomologous recombination-accessory ORFs from core ORFs, exchange accessory ORFs with different CoV genera, with other viruses (i.e., toroviruses, influenza C/D, reoviruses, rotaviruses, astroviruses) and even with hosts. Intriguingly, most of these radical events result from double crossovers surrounding the Spike ORF, thus highlighting both the instability and mobile nature of this genomic region. Although many such events have often occurred during the evolution of various CoVs, the genomic architecture of the relatively young SARS-CoV/SARS-CoV-2 lineage so far appears to be stable.

PomBase 2018: user-driven reimplementation of the fission yeast database provides rapid and intuitive access to diverse, interconnected information.

PomBase (www.pombase.org), the model organism database for the fission yeast Schizosaccharomyces pombe, has undergone a complete redevelopment, resulting in a more fully integrated, better-performing service. The new infrastructure supports daily data updates as well as fast, efficient querying and smoother navigation within and between pages. New pages for publications and genotypes provide routes to all data curated from a single source and to all phenotypes associated with a specific genotype, respectively. For ontology-based annotations, improved displays balance comprehensive data coverage with ease of use. The default view now uses ontology structure to provide a concise, non-redundant summary that can be expanded to reveal underlying details and metadata. The phenotype annotation display also offers filtering options to allow users to focus on specific areas of interest. An instance of the JBrowse genome browser has been integrated, facilitating loading of and intuitive access to, genome-scale datasets. Taken together, the new data and pages, along with improvements in annotation display and querying, allow users to probe connections among different types of data to form a comprehensive view of fission yeast biology. The new PomBase implementation also provides a rich set of modular, reusable tools that can be deployed to create new, or enhance existing, organism-specific databases.

Low complexity regions in the proteins of prokaryotes perform important functional roles and are highly conserved.

We provide the first high-throughput analysis of the properties and functional role of Low Complexity Regions (LCRs) in more than 1500 prokaryotic and phage proteomes. We observe that, contrary to a widespread belief based on older and sparse data, LCRs actually have a significant, persistent and highly conserved presence and role in many and diverse prokaryotes. Their specific amino acid content is linked to proteins with certain molecular functions, such as the binding of RNA, DNA, metal-ions and polysaccharides. In addition, LCRs have been repeatedly identified in very ancient, and usually highly expressed proteins of the translation machinery. At last, based on the amino acid content enriched in certain categories, we have developed a neural network web server to identify LCRs and accurately predict whether they can bind nucleic acids, metal-ions or are involved in chaperone functions. An evaluation of the tool showed that it is highly accurate for eukaryotic proteins as well.

Spinal motor neuron protein supersaturation patterns are associated with inclusion body formation in ALS.

Amyotrophic lateral sclerosis (ALS) is a heterogeneous degenerative motor neuron disease linked to numerous genetic mutations in apparently unrelated proteins. These proteins, including SOD1, TDP-43, and FUS, are highly aggregation-prone and form a variety of intracellular inclusion bodies that are characteristic of different neuropathological subtypes of the disease. Contained within these inclusions are a variety of proteins that do not share obvious characteristics other than coaggregation. However, recent evidence from other neurodegenerative disorders suggests that disease-affected biochemical pathways can be characterized by the presence of proteins that are supersaturated, with cellular concentrations significantly greater than their solubilities. Here, we show that the proteins that form inclusions of mutant SOD1, TDP-43, and FUS are not merely a subset of the native interaction partners of these three proteins, which are themselves supersaturated. To explain the presence of coaggregating proteins in inclusions in the brain and spinal cord, we observe that they have an average supersaturation even greater than the average supersaturation of the native interaction partners in motor neurons, but not when scores are generated from an average of other human tissues. These results suggest that inclusion bodies in various forms of ALS result from a set of proteins that are metastable in motor neurons, and thus prone to aggregation upon a disease-related progressive collapse of protein homeostasis in this specific setting.

High-energy guanine nucleotides as a signal capable of linking growth to cellular energy status via the control of gene transcription.

This mini-review considers the idea that guanylate nucleotide energy charge acts as an integrative signal for the regulation of gene expression in eukaryotic cells and discusses possible routes for that signal's transduction. Gene expression is intimately linked with cell nutrition and diverse signaling systems serve to coordinate the synthesis of proteins required for growth and proliferation with the prevailing cellular nutritional status. Using short pathways for the inducible and futile consumption of ATP or GTP in engineered cells of Saccharomyces cerevisiae, we have recently shown that GTP levels can also play a role in determining how genes act to respond to changes in cellular energy supply. This review aims to interpret the importance of GTP as an integrative signal in the context of an increasing body of evidence indicating the spatio-temporal complexity of cellular de novo purine nucleotide biosynthesis.

Transcriptional regulation of the genes involved in protein metabolism and processing in Saccharomyces cerevisiae.

Topological analysis of large networks, which focus on a specific biological process or on related biological processes, where functional coherence exists among the interacting members, may provide a wealth of insight into cellular functionality. This work presents an unbiased systems approach to analyze genetic, transcriptional regulatory and physical interaction networks of yeast genes possessing such functional coherence to gain novel biological insight. The present analysis identified only a few transcriptional regulators amongst a large gene cohort associated with the protein metabolism and processing in yeast. These transcription factors are not functionally required for the maintenance of these tasks in growing cells. Rather, they are involved in rewiring gene transcription in response to such major challenges as starvation, hypoxia, DNA damage, heat shock or the accumulation of unfolded proteins. Indeed, only a subset of these proteins were captured empirically in the nuclear-enriched fraction of non-stressed yeast cells, suggesting that the transcriptional regulation of protein metabolism and processing in yeast is primarily concerned with maintaining cellular robustness in the face of threat by either internal or external stressors.

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements.

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor-utilising enzymes and transporters through constraint-based modelling. The first eukaryotic genome-scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron-sulphur (Fe-S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron-recruiting enzyme at a rate of 3.02 × 10 -11 mmol·(g biomass) -1 ·h -1 .

Enhancing the functionality of a microscale bioreactor system as an industrial process development tool for mammalian perfusion culture.

Without a scale-down model for perfusion, high resource demand makes cell line screening or process development challenging, therefore, potentially successful cell lines or perfusion processes are unrealized and their ability untapped. We present here the refunctioning of a high-capacity microscale system that is typically used in fed-batch process development to allow perfusion operation utilizing in situ gravity settling and automated sampling. In this low resource setting, which involved routine perturbations in mixing, pH and dissolved oxygen concentrations, the specific productivity and the maximum cell concentration were higher than 3.0 × 106 mg/cell/day and 7 × 10 7 cells/ml, respectively, across replicate microscale perfusion runs conducted at one vessel volume exchange per day. A comparative analysis was conducted at bench scale with vessels operated in perfusion mode utilizing a cell retention device. Neither specific productivity nor product quality indicated by product aggregation (6%) was significantly different across scales 19 days after inoculation, thus demonstrating this setup to be a suitable and reliable platform for evaluating the performance of cell lines and the effect of process parameters, relevant to perfusion mode of culturing.

TAMMiCol: Tool for analysis of the morphology of microbial colonies.

Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 µM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding.

Chronological Lifespan in Yeast Is Dependent on the Accumulation of Storage Carbohydrates Mediated by Yak1, Mck1 and Rim15 Kinases.

Upon starvation for glucose or any other macronutrient, yeast cells exit from the mitotic cell cycle and acquire a set of characteristics that are specific to quiescent cells to ensure longevity. Little is known about the molecular determinants that orchestrate quiescence entry and lifespan extension. Using starvation-specific gene reporters, we screened a subset of the yeast deletion library representing the genes encoding 'signaling' proteins. Apart from the previously characterised Rim15, Mck1 and Yak1 kinases, the SNF1/AMPK complex, the cell wall integrity pathway and a number of cell cycle regulators were shown to be necessary for proper quiescence establishment and for extension of chronological lifespan (CLS), suggesting that entry into quiescence requires the integration of starvation signals transmitted via multiple signaling pathways. The CLS of these signaling mutants, and those of the single, double and triple mutants of RIM15, YAK1 and MCK1 correlates well with the amount of storage carbohydrates but poorly with transition-phase cell cycle status. Combined removal of the glycogen and trehalose biosynthetic genes, especially GSY2 and TPS1, nearly abolishes the accumulation of storage carbohydrates and severely reduces CLS. Concurrent overexpression of GSY2 and TSL1 or supplementation of trehalose to the growth medium ameliorates the severe CLS defects displayed by the signaling mutants (rim15Δyak1Δ or rim15Δmck1Δ). Furthermore, we reveal that the levels of intracellular reactive oxygen species are cooperatively controlled by Yak1, Rim15 and Mck1, and the three kinases mediate the TOR1-regulated accumulation of storage carbohydrates and CLS extension. Our data support the hypothesis that metabolic reprogramming to accumulate energy stores and the activation of anti-oxidant defence systems are coordinated by Yak1, Rim15 and Mck1 kinases to ensure quiescence entry and lifespan extension in yeast.

Metaheuristic approaches in biopharmaceutical process development data analysis.

There is a growing interest in mining and handling of big data, which has been rapidly accumulating in the repositories of bioprocess industries. Biopharmaceutical industries are no exception; the implementation of advanced process control strategies based on multivariate monitoring techniques in biopharmaceutical production gave rise to the generation of large amounts of data. Real-time measurements of critical quality and performance attributes collected during production can be highly useful to understand and model biopharmaceutical processes. Data mining can facilitate the extraction of meaningful relationships pertaining to these bioprocesses, and predict the performance of future cultures. This review evaluates the suitability of various metaheuristic methods available for data pre-processing, which would involve the handling of missing data, the visualisation of the data, and dimension reduction; and for data processing, which would focus on modelling of the data and the optimisation of these models in the context of biopharmaceutical process development. The advantages and the associated challenges of employing different methodologies in pre-processing and processing of the data are discussed. In light of these evaluations, a summary guideline is proposed for handling and analysis of the data generated in biopharmaceutical process development.

A heuristic approach to handling missing data in biologics manufacturing databases.

The biologics sector has amassed a wealth of data in the past three decades, in line with the bioprocess development and manufacturing guidelines, and analysis of these data with precision is expected to reveal behavioural patterns in cell populations that can be used for making predictions on how future culture processes might behave. The historical bioprocessing data likely comprise experiments conducted using different cell lines, to produce different products and may be years apart; the situation causing inter-batch variability and missing data points to human- and instrument-associated technical oversights. These unavoidable complications necessitate the introduction of a pre-processing step prior to data mining. This study investigated the efficiency of mean imputation and multivariate regression for filling in the missing information in historical bio-manufacturing datasets, and evaluated their performance by symbolic regression models and Bayesian non-parametric models in subsequent data processing. Mean substitution was shown to be a simple and efficient imputation method for relatively smooth, non-dynamical datasets, and regression imputation was effective whilst maintaining the existing standard deviation and shape of the distribution in dynamical datasets with less than 30% missing data. The nature of the missing information, whether Missing Completely At Random, Missing At Random or Missing Not At Random, emerged as the key feature for selecting the imputation method.

Flexible Nets: a modeling formalism for dynamic systems with uncertain parameters.

The modeling of dynamic systems is frequently hampered by a limited knowledge of the system to be modeled and by the difficulty of acquiring accurate data. This often results in a number of uncertain system parameters that are hard to incorporate into a mathematical model. Thus, there is a need for modeling formalisms that can accommodate all available data, even if uncertain, in order to employ them and build useful models. This paper shows how the Flexible Nets (FNs) formalism can be exploited to handle uncertain parameters while offering attractive analysis possibilities. FNs are composed of two nets, an event net and an intensity net, that model the relation between the state and the processes of the system. While the event net captures how the state of the system is updated by the processes in the system, the intensity net models how the speed of such processes is determined by the state of the system. Uncertain parameters are accounted for by sets of inequalities associated with both the event net and the intensity net. FNs are not only demonstrated to be a valuable formalism to cope with system uncertainties, but also to be capable of modeling different system features, such as resource allocation and control actions, in a facile manner.

Antiplasmodial and trypanocidal activity of violacein and deoxyviolacein produced from synthetic operons.

BACKGROUND: Violacein is a deep violet compound that is produced by a number of bacterial species. It is synthesized from tryptophan by a pathway that involves the sequential action of 5 different enzymes (encoded by genes vioA to vioE). Violacein has antibacterial, antiparasitic, and antiviral activities, and also has the potential of inducing apoptosis in certain cancer cells. RESULTS: Here, we describe the construction of a series of plasmids harboring the complete or partial violacein biosynthesis operon and their use to enable production of violacein and deoxyviolacein in E.coli. We performed in vitro assays to determine the biological activity of these compounds against Plasmodium, Trypanosoma, and mammalian cells. We found that, while deoxyviolacein has a lower activity against parasites than violacein, its toxicity to mammalian cells is insignificant compared to that of violacein. CONCLUSIONS: We constructed E. coli strains capable of producing biologically active violacein and related compounds, and propose that deoxyviolacein might be a useful starting compound for the development of antiparasite drugs.

Process development for the continuous production of heterologous proteins by the industrial yeast, Komagataella phaffii.

The current trend in industrial biotechnology is to move from batch or fed-batch fermentations to continuous operations. The success of this transition will require the development of genetically stable production strains, the use of strong constitutive promoters, and the development of new medium formulations that allow an appropriate balance between cell growth and product formation. We identified genes that showed high expression in Komagataella phaffii during different steady-state conditions and explored the utility of promoters of these genes (Chr1-4_0586 and FragB_0052) in optimizing the expression of two different r-proteins, human lysozyme (HuLy), and the anti-idiotypic antibody fragment, Fab-3H6, in comparison with the widely used glyceraldehyde-3-phosphate dehydrogenase promoter. Our results showed that the promoter strength was highly dependent on the cultivation conditions and thus constructs should be tested under a range of conditions to determine both the best performing clone and the ideal promoter for the expression of the protein of interest. An important benefit of continuous production is that it facilitates the use of the genome-scale metabolic models in the design of strains and cultivation media. In silico flux distributions showed that production of either protein increased the flux through aromatic amino acid biosynthesis. Tyrosine supplementation increased the productivity for both proteins, whereas tryptophan addition did not cause any significant change and, phenylalanine addition increased the expression of HuLy but decreased that of Fab-3H6. These results showed that a genome-scale metabolic model can be used to assess the metabolic burden imposed by the synthesis of a specific r-protein and then this information can be used to tailor a cultivation medium to increase production.

Saccharomyces cerevisiae adapted to grow in the presence of low-dose rapamycin exhibit altered amino acid metabolism.

BACKGROUND: Rapamycin is a potent inhibitor of the highly conserved TOR kinase, the nutrient-sensitive controller of growth and aging. It has been utilised as a chemotherapeutic agent due to its anti-proliferative properties and as an immunosuppressive drug, and is also known to extend lifespan in a range of eukaryotes from yeast to mammals. However, the mechanisms through which eukaryotic cells adapt to sustained exposure to rapamycin have not yet been thoroughly investigated. METHODS: Here, S. cerevisiae response to long-term rapamycin exposure was investigated by identifying the physiological, transcriptomic and metabolic differences observed for yeast populations inoculated into low-dose rapamycin-containing environment. The effect of oxygen availability and acidity of extracellular environment on this response was further deliberated by controlling or monitoring the dissolved oxygen level and pH of the culture. RESULTS: Yeast populations grown in the presence of rapamycin reached higher cell densities complemented by an increase in their chronological lifespan, and these physiological adaptations were associated with a rewiring of the amino acid metabolism, particularly that of arginine. The ability to synthesise amino acids emerges as the key factor leading to the major mechanistic differences between mammalian and microbial TOR signalling pathways in relation to nutrient recognition. CONCLUSION: Oxygen levels and extracellular acidity of the culture were observed to conjointly affect yeast populations, virtually acting as coupled physiological effectors; cells were best adapted when maximal oxygenation of the culture was maintained in slightly acidic pH, any deviation necessitated more extensive readjustment to additional stress factors.

The Yeast GSK-3 Homologue Mck1 Is a Key Controller of Quiescence Entry and Chronological Lifespan.

Upon starvation for glucose or any other core nutrient, yeast cells exit from the mitotic cell cycle and acquire a set of G0-specific characteristics to ensure long-term survival. It is not well understood whether or how cell cycle progression is coordinated with the acquisition of different G0-related features during the transition to stationary phase (SP). Here, we identify the yeast GSK-3 homologue Mck1 as a key regulator of G0 entry and reveal that Mck1 acts in parallel to Rim15 to activate starvation-induced gene expression, the acquisition of stress resistance, the accumulation of storage carbohydrates, the ability of early SP cells to exit from quiescence, and their chronological lifespan. FACS and microscopy imaging analyses indicate that Mck1 promotes mother-daughter cell separation and together with Rim15, modulates cell size. This indicates that the two kinases coordinate the transition-phase cell cycle, cell size and the acquisition of different G0-specific features. Epistasis experiments place MCK1, like RIM15, downstream of RAS2 in antagonising cell growth and activating stress resistance and glycogen accumulation. Remarkably, in the ras2∆ cells, deletion of MCK1 and RIM15 together, compared to removal of either of them alone, compromises respiratory growth and enhances heat tolerance and glycogen accumulation. Our data indicate that the nutrient sensor Ras2 may prevent the acquisition of G0-specific features via at least two pathways. One involves the negative regulation of the effectors of G0 entry such as Mck1 and Rim15, while the other likely to involve its functions in promoting respiratory growth, a phenotype also contributed by Mck1 and Rim15.

Erratum: CamOptimus: a tool for exploiting complex adaptive evolution to optimise experiments and processes in biotechnology.

[This corrects the article DOI: 10.1099/mic.0.000477.].

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology.

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).