scholarly journals Reconciling high-throughput gene essentiality data with metabolic network reconstructions

2018 ◽  
Author(s):  
Anna S. Blazier ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
High Throughput ◽  
Large Scale ◽  
Essential Genes ◽  
Growth And Survival ◽  
Gene Essentiality ◽  
Model Driven ◽  
Functional Explanations ◽  
Genome Scale

AbstractThe identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.Author SummaryWith the rise of antibiotic resistance, there is a growing need to discover new therapeutic targets to treat bacterial infections. One attractive strategy is to target genes that are essential for growth and survival. Essential genes can be identified with transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification and analysis of essential genes. We performed a large-scale comparison of multiple gene essentiality screens of the microbial pathogen Pseudomonas aeruginosa. We implemented a computational model-driven approach to provide functional explanations for essentiality and reconcile differences between screens. The integration of computational modeling with high-throughput experimental screens may enable the identification of drug targets with high-confidence and provide greater understanding for the development of novel therapeutic strategies.

scholarly journals Genome-scale Reconstruction of Metabolic Network inBacillus subtilisBased on High-throughput Phenotyping and Gene Essentiality Data

2007 ◽  
Vol 282 (39) ◽  
pp. 28791-28799 ◽  
Author(s):  
You-Kwan Oh ◽  
Bernhard O. Palsson ◽  
Sung M. Park ◽  
Christophe H. Schilling ◽  
Radhakrishnan Mahadevan
Keyword(s):  
Metabolic Network ◽  
High Throughput ◽  
Gene Essentiality ◽  
High Throughput Phenotyping ◽  
Genome Scale

scholarly journals Managing Uncertainty in Metabolic Network Structure and Improving Predictions Using EnsembleFBA

2016 ◽  
Author(s):  
Matthew B. Biggs ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Small Molecule ◽  
Essential Genes ◽  
Model Organisms ◽  
Network Structures ◽  
Major Barrier ◽  
Metabolic Systems ◽  
Species Specific ◽  
Genome Scale

AbstractGenome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel ensemble approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs and is compatible with many automated reconstruction approaches. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contribute disproportionately to the set of predicted essential reactions in a way that is unique to each Streptococcus species. These species-specific network structures lead to species-specific outcomes from small molecule interactions. Through these analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.Author SummaryMetabolism is the driving force behind all biological activity. Genome-scale metabolic network reconstructions (GENREs) are representations of metabolic systems that can be analyzed mathematically to make predictions about how a biochemical system will behave as well as to design biochemical systems with new properties. GENREs have traditionally been reconstructed manually, which can require extensive time and effort. Recent software solutions automate the process (drastically reducing the required effort) but the resulting GENREs are of lower quality and produce less reliable predictions than the manually-curated versions. We present a novel method (“EnsembleFBA”) which overcomes uncertainties involved in automated reconstruction by pooling many different draft GENREs together into an ensemble. We tested EnsembleFBA by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. We found that when predicting growth or essential genes, ensembles of GENREs achieved much better precision or captured many more essential genes than any of the individual GENREs within the ensemble. By improving the predictions that can be made with automatically-generated GENREs, we open the door to studying systems which would otherwise be infeasible.

scholarly journals Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

2008 ◽  
Vol 190 (8) ◽  
pp. 2790-2803 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Jacek Puchałka ◽  
Kimberly E. Fryer ◽  
Vítor A. P. Martins dos Santos ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Opportunistic Pathogen ◽  
Scale Model ◽  
Modeling Framework ◽  
Major Life ◽  
Metabolic Versatility ◽  
A Genome ◽  
Scale Properties ◽  
Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

scholarly journals State-of-the-Art Genetic Modalities to Engineer Cyanobacteria for Sustainable Biosynthesis of Biofuel and Fine-Chemicals to Meet Bio–Economy Challenges

Life ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 54 ◽  
Author(s):  
Aqib Zafar Khan ◽  
Muhammad Bilal ◽  
Shahid Mehmood ◽  
Ashutosh Sharma ◽  
Hafiz M. N. Iqbal
Keyword(s):  
High Throughput ◽  
Metabolic Flux Analysis ◽  
Large Scale ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
Fine Chemicals ◽  
Carbon Regulation ◽  
Regulation Mechanisms ◽  
Large Scale Cultivation ◽  
Genome Scale

In recent years, metabolic engineering of microorganisms has attained much research interest to produce biofuels and industrially pertinent chemicals. Owing to the relatively fast growth rate, genetic malleability, and carbon neutral production process, cyanobacteria has been recognized as a specialized microorganism with a significant biotechnological perspective. Metabolically engineering cyanobacterial strains have shown great potential for the photosynthetic production of an array of valuable native or non-native chemicals and metabolites with profound agricultural and pharmaceutical significance using CO2 as a building block. In recent years, substantial improvements in developing and introducing novel and efficient genetic tools such as genome-scale modeling, high throughput omics analyses, synthetic/system biology tools, metabolic flux analysis and clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease (CRISPR/cas) systems have been made for engineering cyanobacterial strains. Use of these tools and technologies has led to a greater understanding of the host metabolism, as well as endogenous and heterologous carbon regulation mechanisms which consequently results in the expansion of maximum productive ability and biochemical diversity. This review summarizes recent advances in engineering cyanobacteria to produce biofuel and industrially relevant fine chemicals of high interest. Moreover, the development and applications of cutting-edge toolboxes such as the CRISPR-cas9 system, synthetic biology, high-throughput “omics”, and metabolic flux analysis to engineer cyanobacteria for large-scale cultivation are also discussed.

Graph methods for the investigation of metabolic networks in parasitology

Parasitology ◽  
2010 ◽  
Vol 137 (9) ◽  
pp. 1393-1407 ◽  
Author(s):  
LUDOVIC COTTRET ◽  
FABIEN JOURDAN
Keyword(s):  
Metabolic Network ◽  
Large Scale ◽  
Metabolic Networks ◽  
Graph Model ◽  
New Drugs ◽  
Mathematical Methods ◽  
Graph Models ◽  
Metabolic Network Reconstruction ◽  
Network Analyses ◽  
Genome Scale

SUMMARYRecently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

scholarly journals Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

2015 ◽  
Vol 167 (4) ◽  
pp. 1685-1698 ◽  
Author(s):  
Taehyong Kim ◽  
Kate Dreher ◽  
Ricardo Nilo-Poyanco ◽  
Insuk Lee ◽  
Oliver Fiehn ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Large Scale ◽  
A Genome ◽  
Genome Scale ◽  
Gene Perturbations

scholarly journals An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Dawson D. Payne ◽  
Alina Renz ◽  
Laura J. Dunphy ◽  
Taylor Lewis ◽  
Andreas Dräger ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Infection Prevention ◽  
Network Reconstruction ◽  
The Body ◽  
Carbon Source Utilization ◽  
Metabolic Network Reconstruction ◽  
Propionate Metabolism ◽  
Context Specific ◽  
Genome Scale

AbstractMucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.

scholarly journals Up, down, and out: optimized libraries for CRISPRa, CRISPRi, and CRISPR-knockout genetic screens

2018 ◽  
Author(s):  
Kendall R Sanson ◽  
Ruth E Hanna ◽  
Mudra Hegde ◽  
Katherine F Donovan ◽  
Christine Strand ◽  
...  
Keyword(s):  
Gene Function ◽  
Large Scale ◽  
Essential Genes ◽  
Open Reading Frames ◽  
Genetic Screens ◽  
Genome Wide ◽  
Comparable Performance ◽  
Modulation Of Gene Expression ◽  
Genome Scale ◽  
Level Performance

ABSTRACTAdvances in CRISPR-Cas9 technology have enabled the flexible modulation of gene expression at large scale. In particular, the creation of genome-wide libraries for CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi), and CRISPR activation (CRISPRa) has allowed gene function to be systematically interrogated. Here, we evaluate numerous CRISPRko libraries and show that our recently-described CRISPRko library (Brunello) is more effective than previously published libraries at distinguishing essential and non-essential genes, providing approximately the same perturbation-level performance improvement over GeCKO libraries as GeCKO provided over RNAi. Additionally, we developed genome-wide libraries for CRISPRi (Dolcetto) and CRISPRa (Calabrese). Negative selection screens showed that Dolcetto substantially outperforms existing CRISPRi libraries with fewer sgRNAs per gene and achieves comparable performance to CRISPRko in the detection of gold-standard essential genes. We also conducted positive selection CRISPRa screens and show that Calabrese outperforms the SAM library approach at detecting vemurafenib resistance genes. We further compare CRISPRa to genome-scale libraries of open reading frames (ORFs). Together, these libraries represent a suite of genome-wide tools to efficiently interrogate gene function with multiple modalities.tracr

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