Genome-wide assessment ofMycobacterium tuberculosisconditionally essential metabolic pathways

2019 ◽  
Author(s):  
Yusuke Minato ◽  
Daryl M Gohl ◽  
Joshua M. Thiede ◽  
Jeremy M. Chacón ◽  
William R. Harcombe ◽  
...  
Keyword(s):  
Functional Genomics ◽  
In Silico ◽  
Metabolic Pathways ◽  
Genomic Analysis ◽  
Essential Genes ◽  
Comparative Genomic ◽  
Cellular Functions ◽  
Flux Balance ◽  
Genome Wide ◽  
Balance Analysis

AbstractBetter understanding of essential cellular functions in pathogenic bacteria is important for the development of more effective antimicrobial agents. We performed a comprehensive identification of essential genes inMycobacterium tuberculosis, the major causative agent of tuberculosis, using a combination of transposon insertion sequencing (Tn-seq) and comparative genomic analysis. To identify conditional essential genes by Tn-seq, we used media with different nutrient composition. Although many conditional gene essentialities were affected by the presence of relevant nutrient sources, we also found that the essentiality of genes in a subset of metabolic pathways was unaffected by metabolites. Comparative genomic analysis revealed that not all essential genes identified by Tn-seq were fully conserved within theM. tuberculosiscomplex including some existing anti-tubercular drug target genes. In addition, we utilized an availableM. tuberculosisgenome-scale metabolic model, iSM810, to predictM. tuberculosisgene essentialityin silico. Comparing the sets of essential genes experimentally identified by Tn-seq to those predictedin silicoreveals the capabilities and limitations of gene essentiality predictions highlighting the complexity ofM. tuberculosisessential metabolic functions. This study provides a promising platform to study essential cellular functions inM. tuberculosis.Author SummaryMycobacterium tuberculosiscauses 10 million cases of tuberculosis (TB) each year resulting in over one million deaths. TB therapy is challenging because it requires a minimum of six months of treatment with multiple drugs. Protracted treatment times and the emergent spread of drug resistant TB necessitate the identification of novel targets for drug discovery to curb this global health threat. Essential functions, defined as those indispensable for growth and/or survival, are potential targets for new antimicrobial drugs. In this study, we aimed to define gene essentialities ofM. tuberculosison a genome-wide scale to comprehensively identify potential targets for drug discovery. We utilized a combination of experimental (functional genomics) andin silicoapproaches (comparative genomics and flux balance analysis). Our functional genomics approach identified sets of genes whose essentiality was affected by nutrient availability. Comparative genomics revealed that not all essential genes were fully conserved within theM. tuberculosiscomplex. Comparing sets of essential genes identified by functional genomics to those predicted by flux balance analysis highlighted gaps in current knowledge regardingM. tuberculosismetabolic capabilities. Thus, our study identifies numerous potential anti-tubercular drug targets and provides a comprehensive picture of the complexity ofM. tuberculosisessential cellular functions.

scholarly journals Genomewide Assessment ofMycobacterium tuberculosisConditionally Essential Metabolic Pathways

mSystems ◽  
2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Yusuke Minato ◽  
Daryl M. Gohl ◽  
Joshua M. Thiede ◽  
Jeremy M. Chacón ◽  
William R. Harcombe ◽  
...  
Keyword(s):  
Functional Genomics ◽  
In Silico ◽  
Metabolic Pathways ◽  
Genomic Analysis ◽  
Essential Genes ◽  
Comparative Genomic ◽  
Cellular Functions ◽  
Content Type ◽  
Flux Balance ◽  
Balance Analysis

ABSTRACTA better understanding of essential cellular functions in pathogenic bacteria is important for the development of more effective antimicrobial agents. We performed a comprehensive identification of essential genes inMycobacterium tuberculosis, the major causative agent of tuberculosis, using a combination of transposon insertion sequencing (Tn-seq) and comparative genomic analysis. To identify conditionally essential genes by Tn-seq, we used media with different nutrient compositions. Although many conditional gene essentialities were affected by the presence of relevant nutrient sources, we also found that the essentiality of genes in a subset of metabolic pathways was unaffected by metabolite availability. Comparative genomic analysis revealed that not all essential genes identified by Tn-seq were fully conserved within theM. tuberculosiscomplex, including some existing antitubercular drug target genes. In addition, we utilized an availableM. tuberculosisgenome-scale metabolic model, iSM810, to predictM. tuberculosisgene essentialityin silico. Comparing the sets of essential genes experimentally identified by Tn-seq to those predictedin silicoreveals the capabilities and limitations of gene essentiality predictions, highlighting the complexity ofM. tuberculosisessential metabolic functions. This study provides a promising platform to study essential cellular functions inM. tuberculosis.IMPORTANCEMycobacterium tuberculosiscauses 10 million cases of tuberculosis (TB), resulting in over 1 million deaths each year. TB therapy is challenging because it requires a minimum of 6 months of treatment with multiple drugs. Protracted treatment times and the emergent spread of drug-resistantM. tuberculosisnecessitate the identification of novel targets for drug discovery to curb this global health threat. Essential functions, defined as those indispensable for growth and/or survival, are potential targets for new antimicrobial drugs. In this study, we aimed to define gene essentialities ofM. tuberculosison a genomewide scale to comprehensively identify potential targets for drug discovery. We utilized a combination of experimental (functional genomics) andin silicoapproaches (comparative genomics and flux balance analysis). Our functional genomics approach identified sets of genes whose essentiality was affected by nutrient availability. Comparative genomics revealed that not all essential genes were fully conserved within theM. tuberculosiscomplex. Comparing sets of essential genes identified by functional genomics to those predicted by flux balance analysis highlighted gaps in current knowledge regardingM. tuberculosismetabolic capabilities. Thus, our study identifies numerous potential antitubercular drug targets and provides a comprehensive picture of the complexity ofM. tuberculosisessential cellular functions.

scholarly journals Metabolic engineering design to enhance (R,R)-2,3-butanediol production from glycerol in Bacillus subtilis based on flux balance analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Nunthaphan Vikromvarasiri ◽  
Tomokazu Shirai ◽  
Akihiko Kondo
Keyword(s):  
Flux Balance Analysis ◽  
In Silico ◽  
Metabolic Pathways ◽  
In Silico Analysis ◽  
Metabolic Model ◽  
Fermentation Products ◽  
Flux Balance ◽  
Balance Analysis ◽  
Chemical Products ◽  
Genome Scale

Abstract Background Glycerol is a desirable alternative substrate for 2,3-butanediol (2,3-BD) production for sustainable development in biotechnological industries and non-food competitive feedstock. B. subtilis, a “generally recognized as safe” organism that is highly tolerant to fermentation products, is an ideal platform microorganism to engineer the pathways for the production of valuable bio-based chemicals, but it has never been engineered to improve 2,3-BD production from glycerol. In this study, we aimed to enhance 2,3-BD production from glycerol in B. subtilis through in silico analysis. Genome-scale metabolic model (GSM) simulations was used to design and develop the metabolic pathways of B. subtilis. Flux balance analysis (FBA) simulation was used to evaluate the effects of step-by-step gene knockouts to improve 2,3-BD production from glycerol in B. subtilis. Results B. subtilis was bioengineered to enhance 2,3-BD production from glycerol using FBA in a published GSM model of B. subtilis, iYO844. Four genes, ackA, pta, lctE, and mmgA, were knocked out step by step, and the effects thereof on 2,3-BD production were evaluated. While knockout of ackA and pta had no effect on 2,3-BD production, lctE knockout led to a substantial increase in 2,3-BD production. Moreover, 2,3-BD production was improved by mmgA knockout, which had never been investigated. In addition, comparisons between in silico simulations and fermentation profiles of all B. subtilis strains are presented in this study. Conclusions The strategy developed in this study, using in silico FBA combined with experimental validation, can be used to optimize metabolic pathways for enhanced 2,3-BD production from glycerol. It is expected to provide a novel platform for the bioengineering of strains to enhance the bioconversion of glycerol into other highly valuable chemical products.

scholarly journals In silico Analysis of Phylogeny and Virulence Genes in STEC Strains Associated with Outbreaks

2021 ◽  
Author(s):  
Mehmet Demirci ◽  
Akın Yiğin ◽  
Fadile Yıldız Zeyrek
Keyword(s):  
Foodborne Pathogens ◽  
In Silico ◽  
Virulence Genes ◽  
Genomic Analysis ◽  
In Silico Analysis ◽  
Comparative Genomic ◽  
Phylogeny Analysis ◽  
In Silico Study ◽  
The World ◽  
Different Parts

Objective: Shiga toxin-producing E. coli (STEC) strains are important foodborne pathogens. Significant outbreaks with STEC strains can be encountered, even if the geography, time or resources were different. The aim of our in silico study was to compare the virulance factors and phylogeny of STEC strains such as EDL933 and Sakai, which have been identified as an agent in important outbreaks in different parts of the world and whole genomic data were in open databases. Method: Genomic NCBI data of eight strains were included in our study, including seven different STEC strains associated with significant epidemics in different parts of the world, and one supershedder strain obtained from cattle feces. Results: According to phylogeny analysis, the most similar strain to EDL933 strain was TW14588, with 96.4% similarity. The most distant similarity was Sakai strains with 79.2%. According to the virulence genes analysis; the presence of 333 genes that constitute virulence factors under nine headings were detected. In the first STEC origin, EDL933, 45% of all virulence genes were found to be active. Adherence genes such as Ecp, Elf, Hcp and toxin genes such as clyA were active in all strains except stx genes. Conclusion: In our in silico study of comparative genomic analysis of STEC strains which are associated with outbreaks, it was determined that STEC strains used different virulence genes besides the stx gene. Indeed, they used certain virulence genes, even their sources, time and locations were different, in the pathogenesis

scholarly journals Genome assembly of the JD17 soybean provides a new reference genome for Comparative genomics

2021 ◽  
Author(s):  
Xinxin Yi ◽  
Jing Liu ◽  
Shengcai Chen ◽  
Hao Wu ◽  
Min Liu ◽  
...  
Keyword(s):  
Nitrogen Fixation ◽  
Genome Assembly ◽  
Reference Genome ◽  
De Novo ◽  
Genomic Analysis ◽  
Comparative Genomic ◽  
High Quality ◽  
Genome Wide ◽  
A Genome ◽  
Cultivated Soybean

Cultivated soybean (Glycine max) is an important source for protein and oil. Many elite cultivars with different traits have been developed for different conditions. Each soybean strain has its own genetic diversity, and the availability of more high-quality soybean genomes can enhance comparative genomic analysis for identifying genetic underpinnings for its unique traits. In this study, we constructed a high-quality de novo assembly of an elite soybean cultivar Jidou 17 (JD17) with chromsome contiguity and high accuracy. We annotated 52,840 gene models and reconstructed 74,054 high-quality full-length transcripts. We performed a genome-wide comparative analysis based on the reference genome of JD17 with three published soybeans (WM82, ZH13 and W05) , which identified five large inversions and two large translocations specific to JD17, 20,984 - 46,912 PAVs spanning 13.1 - 46.9 Mb in size, and 5 - 53 large PAV clusters larger than 500kb. 1,695,741 - 3,664,629 SNPs and 446,689 - 800,489 Indels were identified and annotated between JD17 and them. Symbiotic nitrogen fixation (SNF) genes were identified and the effects from these variants were further evaluated. It was found that the coding sequences of 9 nitrogen fixation-related genes were greatly affected. The high-quality genome assembly of JD17 can serve as a valuable reference for soybean functional genomics research.

scholarly journals Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

2020 ◽  
Author(s):  
Claudio Tomi-Andrino ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Philippe Soucaille ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Flux Analysis ◽  
In Silico ◽  
Physicochemical Parameters ◽  
Metabolic Flux ◽  
Living Systems ◽  
Flux Analysis ◽  
Flux Balance ◽  
Metabolic Fluxes ◽  
Balance Analysis

AbstractMetabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.Author summaryBiotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

Using Bees Hill Flux Balance Analysis (BHFBA) for in silico Microbial Strain Optimization

2013 ◽  
pp. 375-384 ◽  
Author(s):  
Yee Wen Choon ◽  
Mohd Saberi Bin Mohamad ◽  
Safaai Deris ◽  
Rosli Md. Illias ◽  
Lian En Chai ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
In Silico ◽  
Strain Optimization ◽  
Flux Balance ◽  
Balance Analysis ◽  
Microbial Strain

scholarly journals FastMM: an efficient toolbox for personalized constraint-based metabolic modeling

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Gong-Hua Li ◽  
Shaoxing Dai ◽  
Feifei Han ◽  
Wenxing Li ◽  
Jingfei Huang ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Large Scale ◽  
Computing Time ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Flux Balance ◽  
Mcmc Sampling ◽  
Genome Wide ◽  
Balance Analysis ◽  
User Friendly

Abstract Background Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. Results Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). Conclusion FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

scholarly journals Spirulina-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis

Cells ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 2097
Author(s):  
Supatcha Lertampaiporn ◽  
Jittisak Senachak ◽  
Wassana Taenkaew ◽  
Chiraphan Khannapho ◽  
Apiradee Hongsthong
Keyword(s):  
Flux Balance Analysis ◽  
Growth Temperature ◽  
In Silico ◽  
Critical Role ◽  
High Growth ◽  
Optimal Growth ◽  
Response Mechanism ◽  
Ppi Network ◽  
Flux Balance ◽  
Balance Analysis

This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of Spirulina under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of Spirulina to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein–protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The Spirulina, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.

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