Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast

Autoren

Alexandra M Poos
André Maicher
Anna K Dieckmann
Marcus Oswald
Roland Eils
Martin Kupiec
Brian Luke
Rainer König

DOI

10.1093/nar/gkw111

eISSN

1362-4962

ISSN

0305-1048

Ausgabe der Veröffentlichung

10

Zeitschrift

Nucleic Acids Research

Sprache

en

Online publication date

2016

Paginierung

e93 - e93

Datum der Veröffentlichung

2016

Status

Published

Herausgeber

Oxford University Press (OUP)

Herausgeber URL

http://dx.doi.org/10.1093/nar/gkw111

Datum der Datenerfassung

2022

Titel

Mixed Integer Linear Programming based machine learning approach identifies<i>regulators</i>of telomerase in yeast

Ausgabe der Zeitschrift

44

Datenquelle: Crossref

Abstract

Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.

Addresses

Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Germany Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knöll Institute (HKI) Jena, Beutenbergstrasse 11a, 07745 Jena, Germany Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.

Autoren

Alexandra M Poos
André Maicher
Anna K Dieckmann
Marcus Oswald
Roland Eils
Martin Kupiec
Brian Luke
Rainer König

DOI

10.1093/nar/gkw111

eISSN

1362-4962

Externe Identifier

PubMed Identifier: 26908654
PubMed Central ID: PMC4889924

Open access

true

ISSN

0305-1048

Ausgabe der Veröffentlichung

10

Zeitschrift

Nucleic acids research

Schlüsselwörter

Saccharomyces cerevisiae
Telomerase
Saccharomyces cerevisiae Proteins
Nuclear Proteins
Histones
Repressor Proteins
Reproducibility of Results
Gene Expression Regulation, Fungal
Mutation
Software
Programming, Linear
Gene Regulatory Networks
Sirtuin 2
Mediator Complex
Machine Learning

Sprache

eng

Medium

Print-Electronic

Online publication date

2016

Open access status

Open Access

Paginierung

e93

Datum der Veröffentlichung

2016

Status

Published

Publisher licence

CC BY-NC

Datum der Datenerfassung

2016

Titel

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

Sub types

research-article
Journal Article

Ausgabe der Zeitschrift

44

Files

https://academic.oup.com/nar/article-pdf/44/10/e93/19694800/gkw111.pdf https://europepmc.org/articles/PMC4889924?pdf=render

Datenquelle: Europe PubMed Central

Abstract

Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.

Date of acceptance

2016

Autoren

Alexandra M Poos
André Maicher
Anna K Dieckmann
Marcus Oswald
Roland Eils
Martin Kupiec
Brian Luke
Rainer König

Autoren-URL

https://www.ncbi.nlm.nih.gov/pubmed/26908654

DOI

10.1093/nar/gkw111

eISSN

1362-4962

Externe Identifier

PubMed Central ID: PMC4889924

Ausgabe der Veröffentlichung

10

Zeitschrift

Nucleic Acids Res

Schlüsselwörter

Gene Expression Regulation, Fungal
Gene Regulatory Networks
Histones
Machine Learning
Mediator Complex
Mutation
Nuclear Proteins
Programming, Linear
Repressor Proteins
Reproducibility of Results
Saccharomyces cerevisiae
Saccharomyces cerevisiae Proteins
Sirtuin 2
Software
Telomerase

Sprache

eng

Country

England

Paginierung

e93

PII

gkw111

Datum der Veröffentlichung

2016

Status

Published

Datum, an dem der Datensatz öffentlich gemacht wurde

2017

Titel

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

Sub types

Journal Article

Ausgabe der Zeitschrift

44

Datenquelle: PubMed

Abstract

Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.

Autoren

Alexandra M Poos
André Maicher
Anna K Dieckmann
Marcus Oswald
Roland Eils
Martin Kupiec
Brian Luke
Rainer König

DOI

10.1093/nar/gkw111

Zeitschrift

Nucleic acids research

Notes

keywords: Gene Expression Regulation, Fungal;Gene Regulatory Networks;Histones/genetics/metabolism;Machine Learning;Mediator Complex/genetics;Mutation;Nuclear Proteins/genetics;Programming, Linear;Repressor Proteins/genetics;Reproducibility of Results;Saccharomyces cerevisiae Proteins/genetics;Saccharomyces cerevisiae/genetics;Sirtuin 2/genetics;Software;Telomerase/genetics

Artikelnummer

10

Paginierung

e93 - e93

Datum der Veröffentlichung

2016

Datum der Datenerfassung

2023

Titel

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast

Sub types

article

Ausgabe der Zeitschrift

44

Datenquelle: Manual

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast

Files

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