Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes

Adem Y. Dawed; Ashfaq Ali; Kaixin Zhou; Ewan R. Pearson; Paul W. Franks

doi:10.1007/s00125-017-4404-2

Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes

Adem Y. Dawed (Lead / Corresponding author), Ashfaq Ali, Kaixin Zhou, Ewan R. Pearson, Paul W. Franks

Population Health and Genomics

Research output: Contribution to journal › Article › peer-review

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Abstract

Aims/hypothesis: There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence.

Methods: We (1) built a database of published data on gene-metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS).

Results: From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10(-04)) and G6PC (p = 4.77 × 10(-04)).

Conclusions/interpretation: We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene-drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene-metformin interactions.

Original language	English
Pages (from-to)	2231-2239
Number of pages	8
Journal	Diabetologia
Volume	60
Issue number	11
Early online date	25 Aug 2017
DOIs	https://doi.org/10.1007/s00125-017-4404-2
Publication status	Published - Nov 2017

Keywords

G6PC
Gene-set enrichment
Metformin
SLC2A4
Text-mining
Type 2 diabetes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s00125-017-4404-2Licence: CC BY

Final Published Version
© The Author(s) 2017 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Final published version, 500 KBLicence: CC BY

DIRECT: Diabetes Research on Patient Stratification (joint with 25 other partners)
Colhoun, H. (Investigator), Houston, G. (Investigator), Morris, A. (Investigator), Palmer, C. (Investigator) & Pearson, E. (Investigator)
COMMISSION OF THE EUROPEAN COMMUNITIES
1/02/12 → 28/02/27
Project: Research

Cite this

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title = "Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes",

abstract = "Aims/hypothesis: There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence.Methods: We (1) built a database of published data on gene-metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS).Results: From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10(-04)) and G6PC (p = 4.77 × 10(-04)).Conclusions/interpretation: We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene-drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene-metformin interactions.",

keywords = "G6PC, Gene-set enrichment, Metformin, SLC2A4, Text-mining, Type 2 diabetes",

author = "Dawed, {Adem Y.} and Ashfaq Ali and Kaixin Zhou and Pearson, {Ewan R.} and Franks, {Paul W.}",

note = "Funding This work was supported in part by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contribution from the European Union{\textquoteright}s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution.",

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doi = "10.1007/s00125-017-4404-2",

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T1 - Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes

AU - Dawed, Adem Y.

AU - Ali, Ashfaq

AU - Zhou, Kaixin

AU - Pearson, Ewan R.

AU - Franks, Paul W.

N1 - Funding This work was supported in part by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution.

PY - 2017/11

Y1 - 2017/11

N2 - Aims/hypothesis: There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence.Methods: We (1) built a database of published data on gene-metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS).Results: From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10(-04)) and G6PC (p = 4.77 × 10(-04)).Conclusions/interpretation: We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene-drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene-metformin interactions.

AB - Aims/hypothesis: There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence.Methods: We (1) built a database of published data on gene-metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS).Results: From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10(-04)) and G6PC (p = 4.77 × 10(-04)).Conclusions/interpretation: We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene-drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene-metformin interactions.

KW - G6PC

KW - Gene-set enrichment

KW - Metformin

KW - SLC2A4

KW - Text-mining

KW - Type 2 diabetes

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DO - 10.1007/s00125-017-4404-2

M3 - Article

C2 - 28842730

SN - 0012-186X

VL - 60

SP - 2231

EP - 2239

JO - Diabetologia

JF - Diabetologia

IS - 11

ER -

Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes

Abstract

Keywords

UN SDGs

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Projects

DIRECT: Diabetes Research on Patient Stratification (joint with 25 other partners)

Cite this