19 research outputs found

    Localisation and metabolic impact of overexpressed <i>Ntab</i>CYP82E5<i>v</i>2-GFP.

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    <p>(<b>a</b>) <i>Ntab</i>CYP82E5<i>v</i>2-GFP signal, (<b>b</b>) rhodamine-conjugated ER-tracker, (<b>c</b>) merged signal of both channels showing the tight colocalisation of the nicotine demethylase <i>Ntab</i>CYP82E5<i>v</i>2 GFP with the endoplasmic reticulum. <b>(d, e)</b> Alkaloid profiles measured after 3 days of culture in presence of 100 μM jasmonic acid either intracellularly <b>(d)</b> or secreted to the medium <b>(e)</b>. The levels of nornicotine were below detection limit (indicated by N.D.). Note the difference in scales between <b>(d)</b> and <b>(e)</b> (the level of secreted alkaloids is in some case around tenfold lower). For the alkaloid measurement, mean and SE are shown from six independent experimental series. Significant differences to the non-transformed WT cells assessed by a Student’s t-test are indicated by * (<i>P</i> < 0.05) or ** (<i>P</i> < 0.01), respectively.</p

    Phenotyping and alkaloid accumulation patterns for BY-2 cells overexpressing <i>Ntab</i>MPO1 compared to non-transformed BY-2 cells (WT).

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    <p>(<b>a)</b> cell viability (mean of n = 1000), <b>(b)</b> mitotic index (mean of n = 1000), <b>(c)</b> cell elongation as ratio of cell length over cell width at days 4 and 7 (mean of n = 500), <b>(d, e)</b> from the time course of cell density a cell cycle duration of 27.96 h for the non-transformed BY-2 cells <b>(d)</b> and of 32.9 h for BY-2 cells overexpressing <i>Ntab</i>MPO1 <b>(e)</b> can be inferred. All experimental data are derived from three independent experimental series; error bars = SE. <b>(f, g)</b> Alkaloid profiles measured after 3 days of culture in presence of 100 μM jasmonic acid either intracellularly <b>(f)</b> or secreted to the medium <b>(g)</b>. The levels of nornicotine were below detection limit (indicated by N.D.). Note the difference in scales between <b>(f)</b> and <b>(g)</b>. For the alkaloid measurement, mean and SE are shown from six independent experimental series. Significant differences to the non-transformed WT cells assessed by a Student’s t-test are indicated by * (<i>P</i> < 0.05) or ** (<i>P</i> < 0.01), respectively.</p

    Combination of Plant Metabolic Modules Yields Synthetic Synergies

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    <div><p>The great potential of pharmacologically active secondary plant metabolites is often limited by low yield and availability of the producing plant. Chemical synthesis of these complex compounds is often too expensive. Plant cell fermentation offers an alternative strategy to overcome these limitations. However, production in batch cell cultures remains often inefficient. One reason might be the fact that different cell types have to interact for metabolite maturation, which is poorly mimicked in suspension cell lines. Using alkaloid metabolism of tobacco, we explore an alternative strategy, where the metabolic interactions of different cell types in a plant tissue are technically mimicked based on different plant-cell based metabolic modules. In this study, we simulate the interaction found between the nicotine secreting cells of the root and the nicotine-converting cells of the senescent leaf, generating the target compound nornicotine in the model cell line tobacco BY-2. When the nicotine demethylase <i>Ntom</i>CYP82E4 was overexpressed in tobacco BY-2 cells, nornicotine synthesis was triggered, but only to a minor extent. However, we show here that we can improve the production of nornicotine in this cell line by feeding the precursor, nicotine. Engineering of another cell line overexpressing the key enzyme <i>Ntab</i>MPO1 allows to stimulate accumulation and secretion of this precursor. We show that the nornicotine production of <i>Ntom</i>CYP82E4 cells can be significantly stimulated by feeding conditioned medium from <i>Ntab</i>MPO1 overexpressors without any negative effect on the physiology of the cells. Co-cultivation of <i>Ntom</i>CYP82E4 with <i>Ntab</i>MPO1 stimulated nornicotine accumulation even further, demonstrating that the physical presence of cells was superior to just feeding the conditioned medium collected from the same cells. These results provide a proof of concept that combination of different metabolic modules can improve the productivity for target compounds in plant cell fermentation.</p></div

    Subcellular compartmentalisation of nicotine / nornicotine biosynthetic enzymes—modified from [13].

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    <p>A622: isoflavone reductase-like protein; ADC: arginine decarboxylase; BBL: berberine bridge enzyme-like; MPO: N-methylputrescine oxidase; NND: nicotine <i>N</i>-demethylase; ODC: ornithine decarboxylase; PMT: putrescine methyltransferase; QPT: quinolinate phosphoribosyltransferase. Genes that have been overexpressed in the present study are shown in green.</p

    Profile of intracellular alkaloids accumulated in the <i>Ntom</i>CYP82E4 overexpressor in response to conditioned medium.

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    <p>Stimulation of intracellular alkaloid accumulation by conditioned medium collected from non-transformed wild type (M<sub>1</sub>) or cells overexpressing MPO1 (M<sub>2</sub>). Unconditioned medium (M<sub>0</sub>) was used as negative control. Alkaloid synthesis was elicited by 100 μM of jasmonic acid for three days. Error bars indicate SE, from three independent experimental series. Significant differences to the non-transformed WT cells assessed by a Student’s t-test are indicated by * (<i>P</i> < 0.05) or ** (<i>P</i> < 0.01), respectively.</p

    Nicotine and nornicotine contents in senescent leaves of different <i>Nicotiana</i> species and metabolic impact of overexpressed <i>Nom</i>CYP82E4-GFP.

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    <p><b>(a)</b> The low ratio of nornicotine to nicotine in <i>N</i>. <i>tabacum</i> and the high level of nornicotine compared to nicotine in <i>N</i>. <i>tomentosiformis</i> are evident. Error bars represent SE from three independent experiments. <b>(b, c)</b> Alkaloid profiles measured after 3 days of culture in presence of 100 μM jasmonic acid either intracellularly <b>(b)</b> or secreted to the medium <b>(c)</b>. The levels of nornicotine below detection limit is indicated by non-detectable (N.D.). Note the difference in scales between <b>(b)</b> and <b>(c)</b> (the level of secreted alkaloids is in some case around tenfold lower). For the alkaloid measurement, mean and SE are shown from six independent experimental series. Significant differences to the non-transformed WT cells assessed by a Student’s t-test are indicated by * (<i>P</i> < 0.05) or ** (<i>P</i> < 0.01), respectively.</p

    Alkaloid accumulation in non-transformed BY-2 cells (WT) compared to cells overexpressing <i>Ntab</i>CYP82E5<i>v</i>2 and <i>Ntom</i>CYP82E4, respectively after feeding with 15 ÎĽg<sup>.</sup>ml<sup>-1</sup> of pure nicotine (Nic).

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    <p>The level of nornicotine below detection limit is indicated by non-detectable (N.D.). Error bars represent SE from 3 independent experimental series. Significant differences in nornicotine production to the WT treated with nicotine and also between two CYP overexpressing cell lines in a Student’s t-test are indicated by two (P < 0.01) asterisks.</p

    Table_3_In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection.XLSX

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    ObjectiveBovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.MethodsRNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein–protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).ResultsAs result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.ConclusionThe present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.</p

    Table_5_In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection.XLSX

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    ObjectiveBovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.MethodsRNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein–protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).ResultsAs result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.ConclusionThe present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.</p

    Table_11_In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection.XLSX

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    ObjectiveBovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.MethodsRNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein–protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).ResultsAs result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.ConclusionThe present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.</p
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