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Nucleic Acids Res. 2016 Jun 2;44(10):e93. doi: 10.1093/nar/gkw111. Epub 2016 Feb 22.

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

Author information

1
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.
2
Center for Molecular Biology at Heidelberg University (ZMBH), German Cancer Research Center (DKFZ)-ZMBH-Alliance, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Ramat Aviv 69978, Israel.
3
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.
4
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.
5
Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, and Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.
6
Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Ramat Aviv 69978, Israel.
7
Center for Molecular Biology at Heidelberg University (ZMBH), German Cancer Research Center (DKFZ)-ZMBH-Alliance, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany Telomere Biology Group, Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany.
8
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 rainer.koenig@uni-jena.de.

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.

PMID:
26908654
PMCID:
PMC4889924
DOI:
10.1093/nar/gkw111
[Indexed for MEDLINE]
Free PMC Article

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