61 research outputs found

    Data_Sheet_2_Modeling the Metabolic State of Mycobacterium tuberculosis Upon Infection.XLSX

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    <p>Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the ‚Äúmetabolic objective‚ÄĚ of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.</p

    Genes differentially expressed in HB27+P compared to HB27.

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    <p>*: Fold-change increase in RNA levels in HB27+P compared to HB27. For all changes <i>P</i><0.02.</p><p>**: Function predicted based on domains and similarity to other genes.</p><p>Genes differentially expressed in HB27+P compared to HB27.</p

    Effects of <i>Tt</i>Ago on plasmid DNA and plasmid encoded RNA.

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    <p>A, Schematic representation of the <i>Escherichia coli-T</i>. <i>thermophilus</i> shuttle vector pMKPnqosGFP. Ori/MCS indicates the <i>E</i>. <i>coli</i> origin of replication (Ori) and a multiple cloning site (MCS). Note that cloning of this plasmid resulted in insertion of (incomplete) TTC1921 and TTHV050 genes. B, Relative plasmid content of <i>T</i>. <i>thermophilus</i> strains HB27 and HB27őĒ<i>ago</i> transformed with pMKPnqosGFP. Plasmid content was calculated from the complete DNA isolated from biological triplicates at an OD<sub>600 nm</sub> of 0.5. C, Gene expression of plasmid encoded genes. Expression values are given in Fragments Per Kilobase of exon per Million fragments mapped (FPKM).</p

    Data_Sheet_3_Modeling the Metabolic State of Mycobacterium tuberculosis Upon Infection.DOCX

    No full text
    <p>Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the ‚Äúmetabolic objective‚ÄĚ of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.</p

    Table_2_Modeling the Metabolic State of Mycobacterium tuberculosis Upon Infection.DOCX

    No full text
    <p>Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the ‚Äúmetabolic objective‚ÄĚ of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.</p

    Data_Sheet_1_Modeling the Metabolic State of Mycobacterium tuberculosis Upon Infection.ZIP

    No full text
    <p>Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the ‚Äúmetabolic objective‚ÄĚ of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.</p

    Scatterplot of A = 1/a versus the promiscuity ŌÄ for every domain.

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    <p>Domains with smaller slope <i>a</i>, and thus larger <i>A</i>, tend to be absolutely promiscuous. The intercept gives a measure of the degree of promiscuity: the larger the <i>p</i>, the smaller the promiscuity.</p

    Two domains IPR000421 and IPR000115 (Panels A and B, respectively) exemplify the effect of sample size on the domain promiscuity definition.

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    <p>Domain A shows a decrease in promiscuity depending on the number of strains used for calculation. Domain B has a stable promiscuity, independent of the number of strains used for calculation. Panel C shows a graphical illustration of the concept of absolute promiscuity. In this cartoon, domain A is absolutely promiscuous as it has always the same neighbouring partners, which is not the case of domain B.</p
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