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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0001-8326-6178
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Motivation: Cancer is known to stem from multiple, independent mutations, the effects of which aggregate to drive the cell into a cancerous state. To understand the complex interplay between affected genes, their gene regulatory network (GRN) needs to be uncovered, to revealing detailed insights of regulatory mechanisms. We therefore decided to infer a reliable GRN from perturbation responses of 40 genes known or suspected to have a role in human cancers yet whose regulatory interactions are poorly known.

Results: siRNA knockdown experiments of each gene were done in a human squamous carcinoma cell line, after which the transcriptomic response was measured. From these data GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. The best GRN was shown to be significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes but subjected to double perturbations. It agrees with many known links in addition to predicting a large number of novel interactions, a subset of which were experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-164633OAI: oai:DiVA.org:su-164633DiVA, id: diva2:1279864
Available from: 2019-01-17 Created: 2019-01-17 Last updated: 2019-02-11Bibliographically approved
In thesis
1. Towards Reliable Gene Regulatory Network Inference
Open this publication in new window or tab >>Towards Reliable Gene Regulatory Network Inference
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Phenotypic traits are now known to stem from the interplay between genetic variables across many if not every level of biology. The field of gene regulatory network (GRN) inference is concerned with understanding the regulatory interactions between genes in a cell, in order to build a model that captures the behaviour of the system. Perturbation biology, whereby genes or RNAs are targeted and their activity altered, is of great value for the GRN field. By first systematically perturbing the system and then reading the system's reaction as a whole, we can feed this data into various methods to reverse engineer the key agents of change.

The initial study sets the groundwork for the rest, and deals with finding common ground among the sundry methods in order to compare and rank performance in an unbiased setting. The GeneSPIDER (GS) MATLAB package is an inference benchmarking platform whereby methods can be added via a wrapper for testing in competition with one another. Synthetic datasets and networks spanning a wide range of conditions can be created for this purpose. The evaluation of methods across various conditions in the benchmark therein demonstrates which properties influence the accuracy of which methods, and thus which are more suitable for use under given characterized condition.

The second study introduces a novel framework NestBoot for increasing inference accuracy within the GS environment by independent, nested bootstraps, \ie repeated inference trials. Under low to medium noise levels, this allows support to be gathered for links occurring most often while spurious links are discarded through comparison to an estimated null distribution of shuffled-links. While noise continues to plague every method, nested bootstrapping in this way is shown to increase the accuracy of several different methods.

The third study applies NestBoot on real data to infer a reliable GRN from an small interfering RNA (siRNA) perturbation dataset covering 40 genes known or suspected to have a role in human cancers. Methods were developed to benchmark the accuracy of an inferred GRN in the absence of a true known GRN, by assessing how well it fits the data compared to a null model of shuffled topologies. A network of high confidence was recovered containing many regulatory links known in the literature, as well as a slew of novel links.

The fourth study seeks to infer reliable networks on large scale, utilizing the high dimensional biological datasets of the LINCS L1000 project.  This dataset has too much noise for accurate GRN inference as a whole, hence we developed a method to select a  subset that is sufficiently informative to accurately infer GRNs. This is a first step in the direction of identifying probable submodules within a greater genome-scale GRN yet to be uncovered.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2019. p. 40
Keywords
GRN, network inference, biological systems
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-164642 (URN)978-91-7797-600-4 (ISBN)978-91-7797-601-1 (ISBN)
Public defence
2019-04-05, Air & Fire, SciLifeLab, Tomtebodavägen 23A, Solna, 14:00 (English)
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Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.

Available from: 2019-03-13 Created: 2019-01-17 Last updated: 2019-03-18Bibliographically approved

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