scholarly journals WheatNet: A genome-scale functional network for hexaploid bread wheat, Triticum aestivum

2017 ◽  
Author(s):  
Tak Lee ◽  
Sohyun Hwang ◽  
Chan Yeong Kim ◽  
Hongseok Shim ◽  
Hyojin Kim ◽  
...  
Keyword(s):  
Complex Traits ◽  
Gene Networks ◽  
Unique Feature ◽  
Single Gene ◽  
Functional Network ◽  
System Level ◽  
Edge Information ◽  
Integrated Network ◽  
A Genome ◽  
Genome Scale

Gene networks provide a system-level overview of genetic organizations and enable the dissection of functional modules underlying complex traits. Here we report the generation of WheatNet, the first genome-scale functional network for T. aestivum and a companion web server (www.inetbio.org/wheatnet). WheatNet was constructed by integrating 20 distinct genomics datasets, including 156,000 wheat-specific co-expression links mined from 1,929 microarray data. A unique feature of WheatNet is that each network node represents either a single gene or a group of genes. We computationally partitioned gene groups mimicking homeologous genes by clustering 99,386 wheat genes, resulting in 20,248 gene groups comprising 63,401 genes and 35,985 individual genes. Thus, WheatNet was constructed using 56,233 nodes, and the final integrated network has 20,230 nodes and 567,000 edges. The edge information of the integrated WheatNet and all 20 component networks are available for download.

scholarly journals Metabolic Network Analysis of Pseudomonas aeruginosa during Chronic Cystic Fibrosis Lung Infection

2010 ◽  
Vol 192 (20) ◽  
pp. 5534-5548 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Joanna B. Goldberg ◽  
Michael Hogardt ◽  
Jason A. Papin
Keyword(s):  
Cystic Fibrosis ◽  
Disease Progression ◽  
Time Course ◽  
Lung Infection ◽  
System Level ◽  
Metabolic Reconstruction ◽  
Metabolic Network Analysis ◽  
A Genome ◽  
Future Drug ◽  
Genome Scale

ABSTRACT System-level modeling is beginning to be used to decipher high throughput data in the context of disease. In this study, we present an integration of expression microarray data with a genome-scale metabolic reconstruction of P seudomonas aeruginosa in the context of a chronic cystic fibrosis (CF) lung infection. A genome-scale reconstruction of P. aeruginosa metabolism was tailored to represent the metabolic states of two clonally related lineages of P. aeruginosa isolated from the lungs of a CF patient at different points over a 44-month time course, giving a mechanistic glimpse into how the bacterial metabolism adapts over time in the CF lung. Metabolic capacities were analyzed to determine how tradeoffs between growth and other important cellular processes shift during disease progression. Genes whose knockouts were either significantly growth reducing or lethal in silico were also identified for each time point and serve as hypotheses for future drug targeting efforts specific to the stages of disease progression.

scholarly journals A Genome-Scale Metabolic Model of Thalassiosira pseudonana CCMP 1335 for a Systems-Level Understanding of Its Metabolism and Biotechnological Potential

2020 ◽  
Vol 8 (9) ◽  
pp. 1396
Author(s):  
Ahmad Ahmad ◽  
Archana Tiwari ◽  
Shireesh Srivastava
Keyword(s):  
Carbon Fixation ◽  
Electron Flow ◽  
Single Gene ◽  
Metabolic Model ◽  
Thalassiosira Pseudonana ◽  
Marine Diatom ◽  
Omega 3 ◽  
Biotechnological Potential ◽  
A Genome ◽  
Genome Scale

Thalassiosira pseudonana is a transformable and biotechnologically promising model diatom with an ability to synthesise nutraceuticals such as fucoxanthin and store a significant amount of polyglucans and lipids including omega-3 fatty acids. While it was the first diatom to be sequenced, a systems-level analysis of its metabolism has not been done yet. This work presents first comprehensive, compartmentalized, and functional genome-scale metabolic model of the marine diatom Thalassiosira pseudonana CCMP 1335, which we have termed iThaps987. The model includes 987 genes, 2477 reactions, and 2456 metabolites. Comparison with the model of another diatom Phaeodactylum tricornutum revealed presence of 183 unique enzymes (belonging primarily to amino acid, carbohydrate, and lipid metabolism) in iThaps987. Model simulations showed a typical C3-type photosynthetic carbon fixation and suggested a preference of violaxanthin–diadinoxanthin pathway over violaxanthin–neoxanthin pathway for the production of fucoxanthin. Linear electron flow was found be active and cyclic electron flow was inactive under normal phototrophic conditions (unlike green algae and plants), validating the model predictions with previous reports. Investigation of the model for the potential of Thalassiosira pseudonana CCMP 1335 to produce other industrially useful compounds suggest iso-butanol as a foreign compound that can be synthesized by a single-gene addition. This work provides novel insights about the metabolism and potential of the organism and will be helpful to further investigate its metabolism and devise metabolic engineering strategies for the production of various compounds.

scholarly journals Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity

2014 ◽  
Vol 11 (94) ◽  
pp. 20130908 ◽  
Author(s):  
Beatriz Valcárcel ◽  
Timothy M. D. Ebbels ◽  
Antti J. Kangas ◽  
Pasi Soininen ◽  
Paul Elliot ◽  
...  
Keyword(s):  
Network Analysis ◽  
Genetic Variants ◽  
Complex Traits ◽  
Association Studies ◽  
System Level ◽  
Genome Wide Association Studies ◽  
Metabolomics Data ◽  
Integrated Network ◽  
Molecular Associations ◽  
Genome Wide

Current studies of phenotype diversity by genome-wide association studies (GWAS) are mainly focused on identifying genetic variants that influence level changes of individual traits without considering additional alterations at the system-level. However, in addition to level alterations of single phenotypes, differences in association between phenotype levels are observed across different physiological states. Such differences in molecular correlations between states can potentially reveal information about the system state beyond that reported by changes in mean levels alone. In this study, we describe a novel methodological approach, which we refer to as genome metabolome integrated network analysis (GEMINi) consisting of a combination of correlation network analysis and genome-wide correlation study. The proposed methodology exploits differences in molecular associations to uncover genetic variants involved in phenotype variation. We test the performance of the GEMINi approach in a simulation study and illustrate its use in the context of obesity and detailed quantitative metabolomics data on systemic metabolism. Application of GEMINi revealed a set of metabolic associations which differ between normal and obese individuals. While no significant associations were found between genetic variants and body mass index using a standard GWAS approach, further investigation of the identified differences in metabolic association revealed a number of loci, several of which have been previously implicated with obesity-related processes. This study highlights the advantage of using molecular associations as an alternative phenotype when studying the genetic basis of complex traits and diseases.

scholarly journals WheatNet: a Genome-Scale Functional Network for Hexaploid Bread Wheat, Triticum aestivum

2017 ◽  
Vol 10 (8) ◽  
pp. 1133-1136 ◽  
Author(s):  
Tak Lee ◽  
Sohyun Hwang ◽  
Chan Yeong Kim ◽  
Hongseok Shim ◽  
Hyojin Kim ◽  
...  
Keyword(s):  
Triticum Aestivum ◽  
Bread Wheat ◽  
Functional Network ◽  
A Genome ◽  
Genome Scale

scholarly journals Reprogramming cell fate with a genome-scale library of artificial transcription factors

2016 ◽  
Vol 113 (51) ◽  
pp. E8257-E8266 ◽  
Author(s):  
Asuka Eguchi ◽  
Matthew J. Wleklinski ◽  
Mackenzie C. Spurgat ◽  
Evan A. Heiderscheit ◽  
Anna S. Kropornicka ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Cell Fate ◽  
High Throughput Screening ◽  
Gene Networks ◽  
Transcriptional Profiling ◽  
Interaction Domain ◽  
Cell Fate Decisions ◽  
Artificial Transcription Factors ◽  
A Genome ◽  
Genome Scale

Artificial transcription factors (ATFs) are precision-tailored molecules designed to bind DNA and regulate transcription in a preprogrammed manner. Libraries of ATFs enable the high-throughput screening of gene networks that trigger cell fate decisions or phenotypic changes. We developed a genome-scale library of ATFs that display an engineered interaction domain (ID) to enable cooperative assembly and synergistic gene expression at targeted sites. We used this ATF library to screen for key regulators of the pluripotency network and discovered three combinations of ATFs capable of inducing pluripotency without exogenous expression ofOct4(POU domain, class 5, TF 1). Cognate site identification, global transcriptional profiling, and identification of ATF binding sites reveal that the ATFs do not directly targetOct4; instead, they target distinct nodes that converge to stimulate the endogenous pluripotency network. This forward genetic approach enables cell type conversions without a priori knowledge of potential key regulators and reveals unanticipated gene network dynamics that drive cell fate choices.

scholarly journals Systems biology of the metabolism of Mycobacterium tuberculosis

2010 ◽  
Vol 38 (5) ◽  
pp. 1286-1289 ◽  
Author(s):  
Dany J.V. Beste ◽  
Johnjoe McFadden
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Experimental Studies ◽  
Public Health Problem ◽  
System Level ◽  
Computational Systems Biology ◽  
A Genome ◽  
Approaches To Study ◽  
Genome Scale ◽  
Computational Systems

Despite decades of research, many aspects of the biology of Mycobacterium tuberculosis remain unclear, and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking the metabolism of M. tuberculosis and pathogenesis has renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes, whereas recent advances in computational systems biology and high-throughput experimental technologies now allows metabolism to be studied on a genome scale. In the present article, we discuss the progress being made in applying system-level approaches to study the metabolism of this important pathogen.

scholarly journals TomatoNet: A Genome-wide Co-functional Network for Unveiling Complex Traits of Tomato, a Model Crop for Fleshy Fruits

2017 ◽  
Vol 10 (4) ◽  
pp. 652-655 ◽  
Author(s):  
Hyojin Kim ◽  
Bong Suk Kim ◽  
Jung Eun Shim ◽  
Sohyun Hwang ◽  
Sunmo Yang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Functional Network ◽  
Fleshy Fruits ◽  
Genome Wide ◽  
A Genome ◽  
Model Crop

scholarly journals Metabolic modeling ofStreptococcus mutansreveals complex nutrient requirements of an oral pathogen

2018 ◽  
Author(s):  
Kenan Jijakli ◽  
Paul A. Jensen
Keyword(s):  
Single Gene ◽  
Metabolic Model ◽  
Tooth Decay ◽  
Nutrient Requirements ◽  
Positive Bacterium ◽  
Gene Deletions ◽  
A Genome ◽  
Removal Experiments ◽  
Acidic Conditions ◽  
Genome Scale

AbstractStreptococcus mutansis a Gram positive bacterium that thrives under acidic conditions and is a primary cause of tooth decay (dental caries). To better understand the metabolism ofS. mutanson a systematic level, we manually constructed a genome-scale metabolic model of theS. mutanstype strain UA159. The model, called iSMU, contains 656 reactions involving 514 metabolites and the products of 488 genes.We interrogatedS. mutans’ nutrient requirements using model simulations and nutrient removal experiments in defined media. The iSMU model matched experimental results in greater than 90% of the conditions tested. We also simulated effects of single gene deletions. The model’s predictions agreed with 78.1% and 84.4% of the gene essentiality predictions from two experimental datasets. Our manually curated model is more accurate thanS. mutansmodels generated from automated reconstruction pipelines. We believe the iSMU model is an important resource for understanding how metabolism enables the cariogenicity ofS. mutans.

scholarly journals A genome-scale yeast library with inducible expression of individual genes

2020 ◽  
Author(s):  
Yuko Arita ◽  
Griffin Kim ◽  
Zhijian Li ◽  
Helena Friesen ◽  
Gina Turco ◽  
...  
Keyword(s):  
Single Gene ◽  
Expression System ◽  
Essential Genes ◽  
Dynamic Changes ◽  
A Genome ◽  
Powerful Approach ◽  
Molecular Barcode ◽  
Yeast Genes ◽  
Genome Scale ◽  
Lower Expression

AbstractThe ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships, including instances of feedback control. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which 5,687 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains Synthetic Genetic Array (SGA) screening markers and a unique molecular barcode, enabling high-throughput yeast genetics. We characterized YETI using quantitative growth phenotyping and pooled BAR-seq screens, and we used a YETI allele to characterize the regulon of ROF1, showing that it is a transcriptional repressor. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from other eukaryotic and prokaryotic systems shows that low native expression is a critical variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3EB42, that gives lower expression than Z3EV, a feature enabling both conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

scholarly journals Genome-Scale Metabolic Reconstruction and in Silico Perturbation Analysis of the Polar Diatom Fragilariopsis cylindrus Predicts High Metabolic Robustness

Biology ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 30
Author(s):  
Michel Lavoie ◽  
Blanche Saint-Béat ◽  
Jan Strauss ◽  
Sébastien Guérin ◽  
Antoine Allard ◽  
...  
Keyword(s):  
Growth Rate ◽  
Single Gene ◽  
Scale Model ◽  
Metabolic Reconstruction ◽  
Model Parameters ◽  
Alternative Pathways ◽  
A Genome ◽  
Fragilariopsis Cylindrus ◽  
Metabolic Robustness ◽  
Genome Scale

Diatoms are major primary producers in polar environments where they can actively grow under extremely variable conditions. Integrative modeling using a genome-scale model (GSM) is a powerful approach to decipher the complex interactions between components of diatom metabolism and can provide insights into metabolic mechanisms underlying their evolutionary success in polar ecosystems. We developed the first GSM for a polar diatom, Fragilariopsis cylindrus, which enabled us to study its metabolic robustness using sensitivity analysis. We find that the predicted growth rate was robust to changes in all model parameters (i.e., cell biochemical composition) except the carbon uptake rate. Constraints on total cellular carbon buffer the effect of changes in the input parameters on reaction fluxes and growth rate. We also show that single reaction deletion of 20% to 32% of active (nonzero flux) reactions and single gene deletion of 44% to 55% of genes associated with active reactions affected the growth rate, as well as the production fluxes of total protein, lipid, carbohydrate, DNA, RNA, and pigments by less than 1%, which was due to the activation of compensatory reactions (e.g., analogous enzymes and alternative pathways) with more highly connected metabolites involved in the reactions that were robust to deletion. Interestingly, including highly divergent alleles unique for F. cylindrus increased its metabolic robustness to cellular perturbations even more. Overall, our results underscore the high robustness of metabolism in F. cylindrus, a feature that likely helps to maintain cell homeostasis under polar conditions.

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