scholarly journals GeneReg: A constraint-based approach for design of feasible metabolic engineering strategies at the gene level

2020 ◽  
Author(s):  
Zahra Razaghi-Moghadam ◽  
Zoran Nikoloski
Keyword(s):  
Gene Expression ◽  
Metabolic Engineering ◽  
Large Scale ◽  
Metabolic Networks ◽  
Tca Cycle ◽  
Growth Conditions ◽  
Knock Out ◽  
Cell Factories ◽  
Gene Level ◽  
Tca Cycle Intermediates

Abstract Motivation Large-scale metabolic models are widely used to design metabolic engineering strategies for diverse biotechnological applications. However, the existing computational approaches focus on alteration of reaction fluxes and often neglect the manipulations of gene expression to implement these strategies. Results Here we find that the association of genes with multiple reactions leads to infeasibility of engineering strategies at the flux level, since they require contradicting manipulations of gene expression. Moreover, we identify that all of the existing approaches to design gene knock-out strategies do not ensure that the resulting design may also require other gene alterations, such as up- or down-regulations, to match the desired flux distribution. To address these issues, we propose a constraint-based approach, termed GeneReg, that facilitates the design of feasible metabolic engineering strategies at the gene level and that is readily applicable to large-scale metabolic networks. We show that GeneReg can identify feasible strategies to overproduce ethanol in Escherichia coli and lactate in Saccharomyces cerevisiae, but overproduction of the TCA cycle intermediates is not feasible in five organisms used as cell factories under default growth conditions. Therefore, GeneReg points at the need to couple gene regulation and metabolism to design rational metabolic engineering strategies. Availability https://github.com/MonaRazaghi/GeneReg

scholarly journals Genetic Evidence for an Alternative Citrate-Dependent Biofilm Formation Pathway in Staphylococcus aureus That Is Dependent on Fibronectin Binding Proteins and the GraRS Two-Component Regulatory System

2008 ◽  
Vol 76 (6) ◽  
pp. 2469-2477 ◽  
Author(s):  
Robert M. Q. Shanks ◽  
Michael A. Meehl ◽  
Kimberly M. Brothers ◽  
Raquel M. Martinez ◽  
Niles P. Donegan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Staphylococcus Aureus ◽  
Biofilm Formation ◽  
Binding Proteins ◽  
Regulatory System ◽  
Tca Cycle ◽  
Formation Pathway ◽  
Two Component ◽  
Fibronectin Binding ◽  
Tca Cycle Intermediates

ABSTRACT We reported previously that low concentrations of sodium citrate strongly promote biofilm formation by Staphylococcus aureus laboratory strains and clinical isolates. Here, we show that citrate promotes biofilm formation via stimulating both cell-to-surface and cell-to-cell interactions. Citrate-stimulated biofilm formation is independent of the ica locus, and in fact, citrate represses polysaccharide adhesin production. We show that fibronectin binding proteins FnbA and FnbB and the global regulator SarA, which positively regulates fnbA and fnbB gene expression, are required for citrate's positive effects on biofilm formation, and citrate also stimulates fnbA and fnbB gene expression. Biofilm formation is also stimulated by several other tricarboxylic acid (TCA) cycle intermediates in an FnbA-dependent fashion. While aconitase contributes to biofilm formation in the absence of TCA cycle intermediates, it is not required for biofilm stimulation by these compounds. Furthermore, the GraRS two-component regulator and the GraRS-regulated efflux pump VraFG, identified for their roles in intermediate vancomycin resistance, are required for citrate-stimulated cell-to-cell interactions, but the GraRS regulatory system does not impact the expression of the fnbA and fnbB genes. Our data suggest that distinct genetic factors are required for the early steps in citrate-stimulated biofilm formation. Given the role of FnbA/FnbB and SarA in virulence in vivo and the lack of a role for ica-mediated biofilm formation in S. aureus catheter models of infection, we propose that the citrate-stimulated biofilm formation pathway may represent a clinically relevant pathway for the formation of these bacterial communities on medical implants.

scholarly journals Structure and Complexity of a Bacterial Transcriptome

2009 ◽  
Vol 191 (10) ◽  
pp. 3203-3211 ◽  
Author(s):  
Karla D. Passalacqua ◽  
Anjana Varadarajan ◽  
Brian D. Ondov ◽  
David T. Okou ◽  
Michael E. Zwick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bacterial Cell ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Transcript Abundance ◽  
Growth Conditions ◽  
A Genome ◽  
Nucleotide Resolution ◽  
Bacterial Transcriptome

ABSTRACT Although gene expression has been studied in bacteria for decades, many aspects of the bacterial transcriptome remain poorly understood. Transcript structure, operon linkages, and information on absolute abundance all provide valuable insights into gene function and regulation, but none has ever been determined on a genome-wide scale for any bacterium. Indeed, these aspects of the prokaryotic transcriptome have been explored on a large scale in only a few instances, and consequently little is known about the absolute composition of the mRNA population within a bacterial cell. Here we report the use of a high-throughput sequencing-based approach in assembling the first comprehensive, single-nucleotide resolution view of a bacterial transcriptome. We sampled the Bacillus anthracis transcriptome under a variety of growth conditions and showed that the data provide an accurate and high-resolution map of transcript start sites and operon structure throughout the genome. Further, the sequence data identified previously nonannotated regions with significant transcriptional activity and enhanced the accuracy of existing genome annotations. Finally, our data provide estimates of absolute transcript abundance and suggest that there is significant transcriptional heterogeneity within a clonal, synchronized bacterial population. Overall, our results offer an unprecedented view of gene expression and regulation in a bacterial cell.

scholarly journals Modular, synthetic chromosomes as new tools for large scale engineering of metabolism

2021 ◽  
Author(s):  
Eline Postma ◽  
Else-Jasmijn Hassing ◽  
Venda Mangkusaputra ◽  
Jordi Geelhoed ◽  
Pilar de la Torre ◽  
...  
Keyword(s):  
Copy Number ◽  
Large Scale ◽  
Metabolic Networks ◽  
De Novo ◽  
Microbial Cell ◽  
Cell Factory ◽  
Microbial Cell Factory ◽  
Microbial Cell Factories ◽  
Cell Factories

The construction of powerful cell factories requires intensive genetic engineering for the addition of new functionalities and the remodeling of native pathways and processes. The present study demonstrates the feasibility of extensive genome reprogramming using modular, specialized de novo-assembled neochromosomes in yeast. The in vivo assembly of linear and circular neochromosomes, carrying 20 native and 21 heterologous genes, enabled the first de novo production in a microbial cell factory of anthocyanins, plant compounds with a broad range pharmacological properties. Turned into exclusive expression platforms for heterologous and essential metabolic routes, the neochromosomes mimic native chromosomes regarding mitotic and genetic stability, copy number, harmlessness for the host and editability by CRISPR/Cas9. This study paves the way for future microbial cell factories with modular genomes in which core metabolic networks, localized on satellite, specialized neochromosomes can be swapped for alternative configurations and serve as landing pads for the addition of functionalities.

scholarly journals Clustering analysis of tumor metabolic networks

2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Ichcha Manipur ◽  
Ilaria Granata ◽  
Lucia Maddalena ◽  
Mario R. Guarracino
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Protein Interactions ◽  
Biological Networks ◽  
Clustering Analysis ◽  
Large Scale ◽  
Metabolic Networks ◽  
Computational Time ◽  
Expression Data ◽  
Metabolic Models

Abstract Background Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity. Results We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions. Conclusions We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

scholarly journals Trajectory inference across multiple conditions with condiments: differential topology, progression, differentiation, and expression.

2021 ◽  
Author(s):  
Hector Roux de Bézieux ◽  
Koen Van den Berge ◽  
Kelly Street ◽  
Sandrine Dudoit
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cell Types ◽  
Discrete Groups ◽  
Dimensional Representation ◽  
Knock Out ◽  
Stem Cell Populations ◽  
Cell Trajectories ◽  
Gene Expression Levels ◽  
Multiple Conditions

Abstract In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present a method for the estimation and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

scholarly journals Trajectory inference across multiple conditions with condiments: differential topology, progression, differentiation, and expression

2021 ◽  
Author(s):  
Hector Roux de Bézieux ◽  
Koen Van den Berge ◽  
Kelly Street ◽  
Sandrine Dudoit
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cell Types ◽  
Discrete Groups ◽  
Dimensional Representation ◽  
Knock Out ◽  
Stem Cell Populations ◽  
Cell Trajectories ◽  
Gene Expression Levels ◽  
Multiple Conditions

AbstractIn single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-seq dataset.While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations.In this manuscript, we present a method for the estimation and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

scholarly journals Advanced Metabolic Engineering Approaches and Renewable Energy to Improve Environmental Benefits of Algal Biofuels: LCA of Large-Scale Biobutanol Production with Cyanobacteria Synechocystis PCC6803

2021 ◽  
Author(s):  
Daniela Villacreses-Freire ◽  
Franziska Ketzer ◽  
Christine Rösch
Keyword(s):  
Renewable Energy ◽  
Metabolic Engineering ◽  
Large Scale ◽  
Positive Impact ◽  
Continuous Production ◽  
Genetically Engineered ◽  
Environmental Benefits ◽  
Novel Technologies ◽  
Synechocystis Pcc6803 ◽  
Cell Factories

Abstract Microalgae have the potential to serve as sustainable biocatalysts for direct sun-to-bioproduct approaches, e.g. for the synthesis of biofuels. Genetic engineered mutants with a higher photon conversion efficiency of sunlight into biofuels of interest are capable to serve as powerful green cell factories. These advanced metabolic engineering approaches are expected to have environmental benefits, especially for climate protection.The Life Cycle Assessment (LCA) on these novel technologies for the continuous production of algal biobutanol are based on unique and until now unpublished data from a pilot plant. Applying an upscaling approach the environmental impacts of algal biobutanol production at large-scale (20 ha plant) are presented for different scenarios. The results of the prospective LCA show that the higher productivity of the genetically engineered cyanobacteria Synechocystis PCC6803 and its specific feature of discharging the product biobutanol into the medium has a positive impact on the environment. However, electricity demand required for algae cultivation and product harvest overcompensates this advantage. The scenario calculations show that a positive climate gas balance can only be achieved if renewable energy is used.These results indicate the importance of genetic engineering and the energy transition for a fully renewable electricity supply to take full advantage of their environmental potential. Besides, the importance of applying upscaling approaches in LCA for a fairer comparison with mature reference technologies is demonstrated.

scholarly journals Global Transcription Analysis of Krebs Tricarboxylic Acid Cycle Mutants Reveals an Alternating Pattern of Gene Expression and Effects on Hypoxic and Oxidative Genes

2003 ◽  
Vol 14 (3) ◽  
pp. 958-972 ◽  
Author(s):  
Mark T. McCammon ◽  
Charles B. Epstein ◽  
Beata Przybyla-Zawislak ◽  
Lee McAlister-Henn ◽  
Ronald A. Butow
Keyword(s):  
Gene Expression ◽  
Metabolic Networks ◽  
Cell Function ◽  
Citrate Synthase ◽  
Tricarboxylic Acid Cycle ◽  
Tca Cycle ◽  
Tricarboxylic Acid ◽  
Transcription Analysis ◽  
Cycle Enzyme ◽  
Ketoglutarate Dehydrogenase

To understand the many roles of the Krebs tricarboxylic acid (TCA) cycle in cell function, we used DNA microarrays to examine gene expression in response to TCA cycle dysfunction. mRNA was analyzed from yeast strains harboring defects in each of 15 genes that encode subunits of the eight TCA cycle enzymes. The expression of >400 genes changed at least threefold in response to TCA cycle dysfunction. Many genes displayed a common response to TCA cycle dysfunction indicative of a shift away from oxidative metabolism. Another set of genes displayed a pairwise, alternating pattern of expression in response to contiguous TCA cycle enzyme defects: expression was elevated in aconitase and isocitrate dehydrogenase mutants, diminished in α-ketoglutarate dehydrogenase and succinyl-CoA ligase mutants, elevated again in succinate dehydrogenase and fumarase mutants, and diminished again in malate dehydrogenase and citrate synthase mutants. This pattern correlated with previously defined TCA cycle growth–enhancing mutations and suggested a novel metabolic signaling pathway monitoring TCA cycle function. Expression of hypoxic/anaerobic genes was elevated in α-ketoglutarate dehydrogenase mutants, whereas expression of oxidative genes was diminished, consistent with a heme signaling defect caused by inadequate levels of the heme precursor, succinyl-CoA. These studies have revealed extensive responses to changes in TCA cycle function and have uncovered new and unexpected metabolic networks that are wired into the TCA cycle.

scholarly journals Programmable gene regulation for metabolic engineering using decoy transcription factor binding sites

2020 ◽  
Author(s):  
Tiebin Wang ◽  
Nathan Tague ◽  
Stephen A Whelan ◽  
Mary J Dunlop
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Metabolic Engineering ◽  
Dna Sequences ◽  
Binding Sites ◽  
Metabolic Flux ◽  
Fold Increase ◽  
Arginine Biosynthesis ◽  
Knock Out ◽  
Transcription Factor Decoy

Abstract Transcription factor decoy binding sites are short DNA sequences that can titrate a transcription factor away from its natural binding site, therefore regulating gene expression. In this study, we harness synthetic transcription factor decoy systems to regulate gene expression for metabolic pathways in Escherichia coli. We show that transcription factor decoys can effectively regulate expression of native and heterologous genes. Tunability of the decoy can be engineered via changes in copy number or modifications to the DNA decoy site sequence. Using arginine biosynthesis as a showcase, we observed a 16-fold increase in arginine production when we introduced the decoy system to steer metabolic flux towards increased arginine biosynthesis, with negligible growth differences compared to the wild type strain. The decoy-based production strain retains high genetic integrity; in contrast to a gene knock-out approach where mutations were common, we detected no mutations in the production system using the decoy-based strain. We further show that transcription factor decoys are amenable to multiplexed library screening by demonstrating enhanced tolerance to pinene with a combinatorial decoy library. Our study shows that transcription factor decoy binding sites are a powerful and compact tool for metabolic engineering.

scholarly journals Deletion of Many Yeast Introns Reveals a Minority of Genes that Require Splicing for Function

2008 ◽  
Vol 19 (5) ◽  
pp. 1932-1941 ◽  
Author(s):  
Julie Parenteau ◽  
Mathieu Durand ◽  
Steeve Véronneau ◽  
Andrée-Anne Lacombe ◽  
Geneviève Morin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cell Function ◽  
Normal Growth ◽  
Growth Conditions ◽  
Heterologous Promoter ◽  
Growth Defects ◽  
Free Model ◽  
Higher Eukaryotes ◽  
Severe Growth

Splicing regulates gene expression and contributes to proteomic diversity in higher eukaryotes. However, in yeast only 283 of the 6000 genes contain introns and their impact on cell function is not clear. To assess the contribution of introns to cell function, we initiated large-scale intron deletions in yeast with the ultimate goal of creating an intron-free model eukaryote. We show that about one-third of yeast introns are not essential for growth. Only three intron deletions caused severe growth defects, but normal growth was restored in all cases by expressing the intronless mRNA from a heterologous promoter. Twenty percent of the intron deletions caused minor phenotypes under different growth conditions. Strikingly, the combined deletion of all introns from the 15 cytoskeleton-related genes did not affect growth or strain fitness. Together, our results show that although the presence of introns may optimize gene expression and provide benefit under stress, a majority of introns could be removed with minor consequences on growth under laboratory conditions, supporting the view that many introns could be phased out of Saccharomyces cerevisiae without blocking cell growth.

Sign in / Sign up

Export Citation Format

Share Document