The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform

Viji M. Draviam - One of the best experts on this subject based on the ideXlab platform.

  • RESEARCH ARTICLE A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions
    2016
    Co-Authors: Francesco Iorio, Roshan L. Shrestha, Nicolas Levin, Viviane Boilot, J. Garnett, Julio Saez-rodriguez, Viji M. Draviam
    Abstract:

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed com-pound). This signature can be derived from data in existing databases, such as the connec-tivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the princi-pal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising Agent) as seed compound. Our method predicts that glipizide an

  • a semi supervised approach for refining transcriptional signatures of drug response and repositioning predictions
    PLOS ONE, 2015
    Co-Authors: Francesco Iorio, Roshan L. Shrestha, Nicolas Levin, Viviane Boilot, Mathew J Garnett, Julio Saezrodriguez, Viji M. Draviam
    Abstract:

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound). This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising Agent) as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells–consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

Francesco Iorio - One of the best experts on this subject based on the ideXlab platform.

  • RESEARCH ARTICLE A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions
    2016
    Co-Authors: Francesco Iorio, Roshan L. Shrestha, Nicolas Levin, Viviane Boilot, J. Garnett, Julio Saez-rodriguez, Viji M. Draviam
    Abstract:

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed com-pound). This signature can be derived from data in existing databases, such as the connec-tivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the princi-pal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising Agent) as seed compound. Our method predicts that glipizide an

  • a semi supervised approach for refining transcriptional signatures of drug response and repositioning predictions
    PLOS ONE, 2015
    Co-Authors: Francesco Iorio, Roshan L. Shrestha, Nicolas Levin, Viviane Boilot, Mathew J Garnett, Julio Saezrodriguez, Viji M. Draviam
    Abstract:

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound). This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising Agent) as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells–consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

John H. Miller - One of the best experts on this subject based on the ideXlab platform.

  • laulimalide and peloruside a inhibit mitosis of saccharomyces cerevisiae by preventing microtubule depolymerisation dependent steps in chromosome separation and nuclear positioning
    Molecular BioSystems, 2013
    Co-Authors: Heather A Best, James H Matthews, Rosemary W Heathcott, Reem Hanna, Dora C Leahy, Namal V Coorey, David S Bellows, Paul H Atkinson, John H. Miller
    Abstract:

    The activity and mechanism of action of two Microtubule-Stabilising Agents, laulimalide and peloruside A, were investigated in Saccharomyces cerevisiae. In contrast to paclitaxel, both compounds displayed growth inhibitory activity in yeast with wild type TUB2 and were susceptible to the yeast pleiotropic drug efflux pumps, as evidenced by the increased sensitivity of a pump transcription factor knockout strain, pdr1Δpdr3Δ. Laulimalide (IC50 = 3.7 μM) was 5-fold more potent than peloruside A (IC50 = 19 μM) in this knockout strain. Bud index assays and flow cytometry revealed a G2/M block as seen in mammalian cells subsequent to treatment with these compounds. Furthermore, peloruside A treatment caused an increase in the number of cells with polymerised spindle microtubules. These results indicate an anti-mitotic action of both compounds with tubulin the likely target. This conclusion was supported by laulimalide and peloruside chemogenomic profiling using a yeast deletion library in the pdr1Δpdr3Δ background. The chemogenomic profiles of these compounds indicate that, in contrast to microtubule destabilising Agents like nocodazole and benomyl, laulimalide and peloruside A inhibit mitotic processes that are reliant on microtubule depolymerisation, consistent with their ability to stabilise microtubules. Gene deletion strains hypersensitive to laulimalide and peloruside A represent possible targets for drugs that can synergize with microtubule stabilising Agent and be of potential use in combination therapy for the treatment of cancer or other diseases.

Roshan L. Shrestha - One of the best experts on this subject based on the ideXlab platform.

  • RESEARCH ARTICLE A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions
    2016
    Co-Authors: Francesco Iorio, Roshan L. Shrestha, Nicolas Levin, Viviane Boilot, J. Garnett, Julio Saez-rodriguez, Viji M. Draviam
    Abstract:

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed com-pound). This signature can be derived from data in existing databases, such as the connec-tivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the princi-pal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising Agent) as seed compound. Our method predicts that glipizide an

  • a semi supervised approach for refining transcriptional signatures of drug response and repositioning predictions
    PLOS ONE, 2015
    Co-Authors: Francesco Iorio, Roshan L. Shrestha, Nicolas Levin, Viviane Boilot, Mathew J Garnett, Julio Saezrodriguez, Viji M. Draviam
    Abstract:

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound). This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising Agent) as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells–consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

Viviane Boilot - One of the best experts on this subject based on the ideXlab platform.

  • RESEARCH ARTICLE A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions
    2016
    Co-Authors: Francesco Iorio, Roshan L. Shrestha, Nicolas Levin, Viviane Boilot, J. Garnett, Julio Saez-rodriguez, Viji M. Draviam
    Abstract:

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed com-pound). This signature can be derived from data in existing databases, such as the connec-tivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the princi-pal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising Agent) as seed compound. Our method predicts that glipizide an

  • a semi supervised approach for refining transcriptional signatures of drug response and repositioning predictions
    PLOS ONE, 2015
    Co-Authors: Francesco Iorio, Roshan L. Shrestha, Nicolas Levin, Viviane Boilot, Mathew J Garnett, Julio Saezrodriguez, Viji M. Draviam
    Abstract:

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound). This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising Agent) as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells–consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.