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 A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Omid Oftadeh ◽  
Pierre Salvy ◽  
Maria Masid ◽  
Maxime Curvat ◽  
Ljubisa Miskovic ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Expression System ◽  
Biomass Composition ◽  
Industrial Biotechnology ◽  
Overflow Metabolism ◽  
Cellular Expression ◽  
A Genome ◽  
Wide Range ◽  
Eukaryotic Organisms ◽  
Genome Scale

AbstractEukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

Genome-wide indentification for genes involved in sodium dodecyl sulfate toxicity in Saccharomyces cerevisiae

2020 ◽  
Author(s):  
Chunlei Cao ◽  
Zhengfeng Cao ◽  
Peibin Yu ◽  
Yunying Zhao
Keyword(s):  
Sodium Dodecyl Sulfate ◽  
Single Gene ◽  
Sodium Dodecyl ◽  
Yeast Cells ◽  
Antioxidant Defenses ◽  
Cellular Transport ◽  
A Genome ◽  
Intracellular Ros ◽  
Dodecyl Sulfate ◽  
Yeast Genes

Abstract Background: Sodium dodecyl sulfate (SDS) is one of the most widely used anionic alkyl sulfate surfactants. Toxicological information on SDS is accumulating, however, mechanisms of SDS toxicity regulation remain poorly understood. In this study, the relationship between the SDS-sensitive mutants and their intracellular ROS levels has been investigated. Results: Through a genome-scale screen, we have identified 108 yeast single-gene deletion mutants that are sensitive to 0.03% SDS. These genes were predominantly related to the cellular processes of metabolism, cell cycle and DNA processing, cellular transport, transport facilities and transport routes, transcription and the protein with binding function or cofactor requirement (structural or catalytic). Measurement of the intracellular ROS (reactive oxygen species) levels of these SDS-sensitive mutants showed that about 79% of SDS-sensitive mutants accumulated significantly higher intracellular ROS levels than the wild-type cells under SDS stress. Moreover, SDS could generate oxidative damage and up-regulate several antioxidant defenses genes, and some of the SDS-sensitive genes were involved in this process. Conclusion: This study provides insight on yeast genes involved in SDS tolerance and the elevated intracellular ROS caused by SDS stress, which is a potential way to understand the detoxification mechanisms of SDS by yeast cells.

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 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 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 CRISPR interference guide library enables comprehensive phenotypic profiling in yeast

2020 ◽  
Author(s):  
Nicholas J. McGlincy ◽  
Zuriah A. Meacham ◽  
Kendra Swain ◽  
Ryan Muller ◽  
Rachel Baum ◽  
...  
Keyword(s):  
In Vitro Transcription ◽  
Essential Genes ◽  
Competitive Growth ◽  
Loss Of Function ◽  
Design Rules ◽  
Relative Growth ◽  
Growth Defects ◽  
A Genome ◽  
Genome Scale

CRISPR/Cas9-mediated transcriptional interference (CRISPRi) enables programmable gene knock-down, yielding interpretable loss-of-function phenotypes for nearly any gene. Effective, inducible CRISPRi has been demonstrated in budding yeast, but no genome-scale guide libraries have been reported. We present a comprehensive yeast CRISPRi library, based on empirical design rules, containing 10 distinct guides for most genes. Competitive growth after pooled transformation revealed strong fitness defects for most essential genes, verifying that the library provides comprehensive genome coverage. We used the relative growth defects caused by different guides targeting essential genes to further refine yeast CRISPRi design rules. In order to obtain more accurate and robust guide abundance measurements in pooled screens, we link guides with random nucleotide barcodes and carry out linear amplification by in vitro transcription. Taken together, we demonstrate a broadly useful platform for comprehensive, high-precision CRISPRi screening in yeast.

scholarly journals A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks

2019 ◽  
Author(s):  
Víctor A López-Agudelo ◽  
Emma Laing ◽  
Tom A Mendum ◽  
Andres Baena ◽  
Luis F Barrera ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Target ◽  
Metabolic Networks ◽  
Systematic Evaluation ◽  
Dynamic Interactions ◽  
Future Studies ◽  
A Genome ◽  
Powerful Approach ◽  
The Many ◽  
Genome Scale

AbstractThe metabolism of the causative agent of TB, Mycobacterium tuberculosis (Mtb) has recently re-emerged as an attractive drug target. A powerful approach to study Mtb metabolism is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of the many individual components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between Mtb genotype and phenotype. However, their utility is hampered by the existence of multiple models of varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.

scholarly journals A genome-scale CRISPR interference guide library enables comprehensive phenotypic profiling in yeast

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nicholas J. McGlincy ◽  
Zuriah A. Meacham ◽  
Kendra K. Reynaud ◽  
Ryan Muller ◽  
Rachel Baum ◽  
...  
Keyword(s):  
In Vitro Transcription ◽  
Essential Genes ◽  
Competitive Growth ◽  
Loss Of Function ◽  
Design Rules ◽  
Growth Defects ◽  
Genome Wide ◽  
A Genome ◽  
Genome Scale

Abstract Background CRISPR/Cas9-mediated transcriptional interference (CRISPRi) enables programmable gene knock-down, yielding loss-of-function phenotypes for nearly any gene. Effective, inducible CRISPRi has been demonstrated in budding yeast, and genome-scale guide libraries enable systematic, genome-wide genetic analysis. Results We present a comprehensive yeast CRISPRi library, based on empirical design rules, containing 10 distinct guides for most genes. Competitive growth after pooled transformation revealed strong fitness defects for most essential genes, verifying that the library provides comprehensive genome coverage. We used the relative growth defects caused by different guides targeting essential genes to further refine yeast CRISPRi design rules. In order to obtain more accurate and robust guide abundance measurements in pooled screens, we link guides with random nucleotide barcodes and carry out linear amplification by in vitro transcription. Conclusions Taken together, we demonstrate a broadly useful platform for comprehensive, high-precision CRISPRi screening in yeast.

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.

scholarly journals A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

2021 ◽  
Author(s):  
Omid Oftadeh ◽  
Pierre Salvy ◽  
Maria Masid ◽  
Maxime Curvat ◽  
Ljubisa Miskovic ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Expression System ◽  
Biomass Composition ◽  
Industrial Biotechnology ◽  
Overflow Metabolism ◽  
Cellular Expression ◽  
A Genome ◽  
Wide Range ◽  
Eukaryotic Organisms ◽  
Genome Scale

AbstractEukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the current most accurate models of biological systems include metabolism and expression (ME-models), and Expression and Thermodynamics FLux (ETFL) is one such formulation that efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To therefore adapt this ME-model for Saccharomyces cerevisiae, we herein developed yETFL. To do this, we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the predictive ability of yETFL to capture maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the extended ETFL formulation can be applied to ME-model development for a wide range of eukaryotic organisms. The utility of these ME-models can be extended into academic and industrial research.

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