Conceptual Design of RNA-RNA Interaction Based Devices

AbstractA key goal of synthetic biology is to use biological molecules to create novel biological systems. Due to their role as transmitters in such systems, RNA molecules have gained much attention from synthetic biologists to design and construct novel RNA molecules with desirable functions and properties. In recent decades, the design of RNAs, however, has been limited to RNA architecture with primitive functions: aptamer and catalysis. To expand the paradigm of RNA-based design, we herein propose a conceptual design of RNA-RNA interaction based systems, considering domain-based structures of RNAs, as well as thermodynamic properties of RNA molecules and their interactions. Two evaluation scores, namely structural score (SS) and affinity score (AS), are used as criteria for selection of proper RNA sets. We employ this concept to design various RNA sets, each of which contains three RNA strands that altogether function like a comparator device. With these criteria, we show that four out of forty RNA sets would behave like a biological comparator since they have appropriate structure (SS=1) and proper interaction order (AS>1). The proposed scores are proven to be proper criteria for selection of RNA sets with required functions. This preliminary design offers an opportunity for synthetic biologists to expand the design of RNA sequence from a single strand to multiple strands that would behave in the same manner as enzymatic reactions

SYSTEMS BIOLOGY AND METABOLIC ENGINEERING OF ARTHROSPIRA CELL FACTORIES

AbstractArthrospira are attractive candidates to serve as cell factories for production of many valuable compounds useful for food, feed, fuel and pharmaceutical industries. In connection with the development of sustainable bioprocessing, it is a challenge to design and develop efficient Arthrospira cell factories which can certify effective conversion from the raw materials (i.e. CO2 and sun light) into desired products. With the current availability of the genome sequences and metabolic models of Arthrospira, the development of Arthrospira factories can now be accelerated by means of systems biology and the metabolic engineering approach. Here, we review recent research involving the use of Arthrospira cell factories for industrial applications, as well as the exploitation of systems biology and the metabolic engineering approach for studying Arthrospira. The current status of genomics and proteomics through the development of the genome-scale metabolic model of Arthrospira, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies are discussed. At the end, the perspective and future direction on Arthrospira cell factories for industrial biotechnology are presented

The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism

Background: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, ilN800 that includes a more rigorous and detailed descrition of lipid metabolism. Results: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by ilN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of ilN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets. Conclusions: Performing integrated analyses using ilN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states

Analysis and Practical Guideline of Constraint-Based Boolean Method in Genetic Network Inference

Boolean-based method, despite of its simplicity, would be a more attractive approach for inferring a network from high-throughput expression data if its effectiveness has not been limited by high false positive prediction. In this study, we explored factors that could simply be adjusted to improve the accuracy of inferring networks. Our work focused on the analysis of the effects of discretisation methods, biological constraints, and stringency of Boolean function assignment on the performance of Boolean network, including accuracy, precision, specificity and sensitivity, using three sets of microarray time-series data. The study showed that biological constraints have pivotal influence on the network performance over the other factors. It can reduce the variation in network performance resulting from the arbitrary selection of discretisation methods and stringency settings. We also presented the master Boolean network as an approach to establish the unique solution for Boolean analysis. The information acquired from the analysis was summarised and deployed as a general guideline for an efficient use of Boolean-based method in the network inference. In the end, we provided an example of the use of such a guideline in the study of Arabidopsis circadian clock genetic network from which much interesting biological information can be inferred

Exploring Components of the CO2-Concentrating Mechanism in Alkaliphilic Cyanobacteria Through Genome-Based Analysis

In cyanobacteria, the CO2-concentrating mechanism (CCM) is a vital biological process that provides effective photosynthetic CO2 fixation by elevating the CO2 level near the active site of Rubisco. This process enables the adaptation of cyanobacteria to various habitats, particularly in CO2-limited environments. Although CCM of freshwater and marine cyanobacteria are well studied, there is limited information on the CCM of cyanobacteria living under alkaline environments. Here, we aimed to explore the molecular components of CCM in 12 alkaliphilic cyanobacteria through genome-based analysis. These cyanobacteria included 6 moderate alkaliphiles; Pleurocapsa sp. PCC 7327, Synechococcus spp., Cyanobacterium spp., Spirulina subsalsa PCC 9445, and 6 strong alkaliphiles (i.e. Arthrospira spp.). The results showed that both groups belong to β-cyanobacteria based on β-carboxysome shell proteins with form 1B of Rubisco. They also contained standard genes, ccmKLMNO cluster, which is essential for β-carboxysome formation. Most strains did not have the high-affinity Na+/HCO3− symporter SbtA and the medium-affinity ATP-dependent HCO3− transporter BCT1. Specifically, all strong alkaliphiles appeared to lack BCT1. Beside the transport systems, carboxysomal β-CA, CcaA, was absent in all alkaliphiles, except for three moderate alkaliphiles: Pleurocapsa sp. PCC 7327, Cyanobacterium stranieri PCC 7202, and Spirulina subsalsa PCC 9445. Furthermore, comparative analysis of the CCM components among freshwater, marine, and alkaliphilic β-cyanobacteria revealed that the basic molecular components of the CCM in the alkaliphilic cyanobacteria seemed to share more degrees of similarity with freshwater than marine cyanobacteria. These findings provide a relationship between the CCM components of cyanobacteria and their habitats. Keywords: Inorganic carbon uptake, CO2-concentrating mechanism, Carbonic anhydrase, Carboxysomes, Alkaliphilic cyanobacteria, Genomic dat

iDoRNA: An Interacting Domain-based Tool for Designing RNA-RNA Interaction Systems

RNA-RNA interactions play a crucial role in gene regulation in living organisms. They have gained increasing interest in the field of synthetic biology because of their potential applications in medicine and biotechnology. However, few novel regulators based on RNA-RNA interactions with desired structures and functions have been developed due to the challenges of developing design tools. Recently, we proposed a novel tool, called iDoDe, for designing RNA-RNA interacting sequences by first decomposing RNA structures into interacting domains and then designing each domain using a stochastic algorithm. However, iDoDe did not provide an optimal solution because it still lacks a mechanism to optimize the design. In this work, we have further developed the tool by incorporating a genetic algorithm (GA) to find an RNA solution with maximized structural similarity and minimized hybridized RNA energy, and renamed the tool iDoRNA. A set of suitable parameters for the genetic algorithm were determined and found to be a weighting factor of 0.7, a crossover rate of 0.9, a mutation rate of 0.1, and the number of individuals per population set to 8. We demonstrated the performance of iDoRNA in comparison with iDoDe by using six RNA-RNA interaction models. It was found that iDoRNA could efficiently generate all models of interacting RNAs with far more accuracy and required far less computational time than iDoDe. Moreover, we compared the design performance of our tool against existing design tools using forty-four RNA-RNA interaction models. The results showed that the performance of iDoRNA is better than RiboMaker when considering the ensemble defect, the fitness score and computation time usage. However, it appears that iDoRNA is outperformed by NUPACK and RNAiFold 2.0 when considering the ensemble defect. Nevertheless, iDoRNA can still be an useful alternative tool for designing novel RNA-RNA interactions in synthetic biology research. The source code of iDoRNA can be downloaded from the site http://synbio.sbi.kmutt.ac.th

Metabolic traits specific for lipid-overproducing strain of Mucor circinelloides WJ11 identified by genome-scale modeling approach

The genome-scale metabolic model of a lipid-overproducing strain of Mucor circinelloides WJ11 was developed. The model (iNI1159) contained 1,159 genes, 648 EC numbers, 1,537 metabolites, and 1,355 metabolic reactions, which were localized in different compartments of the cell. Using flux balance analysis (FBA), the iNI1159 model was validated by predicting the specific growth rate. The metabolic traits investigated by phenotypic phase plane analysis (PhPP) showed a relationship between the nutrient uptake rate, cell growth, and the triacylglycerol production rate, demonstrating the strength of the model. A putative set of metabolic reactions affecting the lipid-accumulation process was identified when the metabolic flux distributions under nitrogen-limited conditions were altered by performing fast flux variability analysis (fastFVA) and relative flux change. Comparative analysis of the metabolic models of the lipid-overproducing strain WJ11 (iNI1159) and the reference strain CBS277.49 (iWV1213) using both fastFVA and coordinate hit-and-run with rounding (CHRR) showed that the flux distributions between these two models were significantly different. Notably, a higher flux distribution through lipid metabolisms such as lanosterol, zymosterol, glycerolipid and fatty acids biosynthesis in iNI1159 was observed, leading to an increased lipid production when compared to iWV1213. In contrast, iWV1213 exhibited a higher flux distribution across carbohydrate and amino acid metabolisms and thus generated a high flux for biomass production. This study demonstrated that iNI1159 is an effective predictive tool for the pathway engineering of oleaginous strains for the production of diversified oleochemicals with industrial relevance

An Improved Genome-Scale Metabolic Model of <i>Arthrospira platensis</i> C1 (<i>i</i>AK888) and Its Application in Glycogen Overproduction

Glycogen-enriched biomass of Arthrospira platensis has increasingly gained attention as a source for bioethanol production. To study the metabolic capabilities of glycogen production in A. platensis C1, a genome-scale metabolic model (GEM) could be a useful tool for predicting cellular behavior and suggesting strategies for glycogen overproduction. New experimentally validated GEM of A. platensis C1 namely iAK888, which has improved metabolic coverage and functionality was employed in this research. The iAK888 is a fully functional compartmentalized GEM consisting of 888 genes, 1,096 reactions, and 994 metabolites. This model was demonstrated to reasonably predict growth and glycogen fluxes under different growth conditions. In addition, iAK888 was further employed to predict the effect of deficiencies of NO3&#8722;, PO43&#8722;, or SO42&#8722; on the growth and glycogen production in A. platensis C1. The simulation results showed that these nutrient limitations led to a decrease in growth flux and an increase in glycogen flux. The experiment of A. platensis C1 confirmed the enhancement of glycogen fluxes after the cells being transferred from normal Zarrouk&#8217;s medium to either NO3&#8722;, PO43&#8722;, or SO42&#8722;-free Zarrouk&#8217;s media. Therefore, iAK888 could be served as a predictive model for glycogen overproduction and a valuable multidisciplinary tool for further studies of this important academic and industrial organism