---
_id: '6392'
abstract:
- lang: eng
text: "The regulation of gene expression is one of the most fundamental processes
in living systems. In recent years, thanks to advances in sequencing technology
and automation, it has become possible to study gene expression quantitatively,
genome-wide and in high-throughput. This leads to the possibility of exploring
changes in gene expression in the context of many external perturbations and their
combinations, and thus of characterising the basic principles governing gene regulation.
In this thesis, I present quantitative experimental approaches to studying transcriptional
and protein level changes in response to combinatorial drug treatment, as well
as a theoretical data-driven approach to analysing thermodynamic principles guiding
transcription of protein coding genes. \r\nIn the first part of this work, I
present a novel methodological framework for quantifying gene expression changes
in drug combinations, termed isogrowth profiling. External perturbations through
small molecule drugs influence the growth rate of the cell, leading to wide-ranging
changes in cellular physiology and gene expression. This confounds the gene expression
changes specifically elicited by the particular drug. Combinatorial perturbations,
owing to the increased stress they exert, influence the growth rate even more
strongly and hence suffer the convolution problem to a greater extent when measuring
gene expression changes. Isogrowth profiling is a way to experimentally abstract
non-specific, growth rate related changes, by performing the measurement using
varying ratios of two drugs at such concentrations that the overall inhibition
rate is constant. Using a robotic setup for automated high-throughput re-dilution
culture of Saccharomyces cerevisiae, the budding yeast, I investigate all pairwise
interactions of four small molecule drugs through sequencing RNA along a growth
isobole. Through principal component analysis, I demonstrate here that isogrowth
profiling can uncover drug-specific as well as drug-interaction-specific gene
expression changes. I show that drug-interaction-specific gene expression changes
can be used for prediction of higher-order drug interactions. I propose a simplified
generalised framework of isogrowth profiling, with few measurements needed for
each drug pair, enabling the broad application of isogrowth profiling to high-throughput
screening of inhibitors of cellular growth and beyond. Such high-throughput screenings
of gene expression changes specific to pairwise drug interactions will be instrumental
for predicting the higher-order interactions of the drugs.\r\n\r\nIn the second
part of this work, I extend isogrowth profiling to single-cell measurements of
gene expression, characterising population heterogeneity in the budding yeast
in response to combinatorial drug perturbation while controlling for non-specific
growth rate effects. Through flow cytometry of strains with protein products fused
to green fluorescent protein, I discover multiple proteins with bi-modally distributed
expression levels in the population in response to drug treatment. I characterize
more closely the effect of an ionic stressor, lithium chloride, and find that
it inhibits the splicing of mRNA, most strongly affecting ribosomal protein transcripts
and leading to a bi-stable behaviour of a small ribosomal subunit protein Rps22B.
Time-lapse microscopy of a microfluidic culture system revealed that the induced
Rps22B heterogeneity leads to preferential survival of Rps22B-low cells after
long starvation, but to preferential proliferation of Rps22B-high cells after
short starvation. Overall, this suggests that yeast cells might use splicing of
ribosomal genes for bet-hedging in fluctuating environments. I give specific examples
of how further exploration of cellular heterogeneity in yeast in response to external
perturbation has the potential to reveal yet-undiscovered gene regulation circuitry.\r\n\r\nIn
the last part of this thesis, a re-analysis of a published sequencing dataset
of nascent elongating transcripts is used to characterise the thermodynamic constraints
for RNA polymerase II (RNAP) elongation. Population-level data on RNAP position
throughout the transcribed genome with single nucleotide resolution are used to
infer the sequence specific thermodynamic determinants of RNAP pausing and backtracking.
This analysis reveals that the basepairing strength of the eight nucleotide-long
RNA:DNA duplex relative to the basepairing strength of the same sequence when
in DNA:DNA duplex, and the change in this quantity during RNA polymerase movement,
is the key determinant of RNAP pausing. This is true for RNAP pausing while elongating,
but also of RNAP pausing while backtracking and of the backtracking length. The
quantitative dependence of RNAP pausing on basepairing energetics is used to infer
the increase in pausing due to transcriptional mismatches, leading to a hypothesis
that pervasive RNA polymerase II pausing is due to basepairing energetics, as
an evolutionary cost for increased RNA polymerase II fidelity.\r\n\r\nThis work
advances our understanding of the general principles governing gene expression,
with the goal of making computational predictions of single-cell gene expression
responses to combinatorial perturbations based on the individual perturbations
possible. This ability would substantially facilitate the design of drug combination
treatments and, in the long term, lead to our increased ability to more generally
design targeted manipulations to any biological system. "
acknowledged_ssus:
- _id: LifeSc
- _id: M-Shop
- _id: Bio
alternative_title:
- IST Austria Thesis
author:
- first_name: Martin
full_name: Lukacisin, Martin
id: 298FFE8C-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisin
orcid: 0000-0001-6549-4177
citation:
ama: Lukacisin M. Quantitative investigation of gene expression principles through
combinatorial drug perturbation and theory. 2019. doi:10.15479/AT:ISTA:6392
apa: Lukacisin, M. (2019). Quantitative investigation of gene expression principles
through combinatorial drug perturbation and theory. IST Austria. https://doi.org/10.15479/AT:ISTA:6392
chicago: Lukacisin, Martin. “Quantitative Investigation of Gene Expression Principles
through Combinatorial Drug Perturbation and Theory.” IST Austria, 2019. https://doi.org/10.15479/AT:ISTA:6392.
ieee: M. Lukacisin, “Quantitative investigation of gene expression principles through
combinatorial drug perturbation and theory,” IST Austria, 2019.
ista: Lukacisin M. 2019. Quantitative investigation of gene expression principles
through combinatorial drug perturbation and theory. IST Austria.
mla: Lukacisin, Martin. Quantitative Investigation of Gene Expression Principles
through Combinatorial Drug Perturbation and Theory. IST Austria, 2019, doi:10.15479/AT:ISTA:6392.
short: M. Lukacisin, Quantitative Investigation of Gene Expression Principles through
Combinatorial Drug Perturbation and Theory, IST Austria, 2019.
date_created: 2019-05-09T19:53:00Z
date_published: 2019-05-09T00:00:00Z
date_updated: 2023-09-22T09:19:41Z
day: '09'
ddc:
- '570'
department:
- _id: ToBo
doi: 10.15479/AT:ISTA:6392
extern: '1'
file:
- access_level: closed
checksum: 829bda074444857c7935171237bb7c0c
content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
creator: mlukacisin
date_created: 2019-05-10T13:51:49Z
date_updated: 2020-07-14T12:47:29Z
embargo_to: open_access
file_id: '6409'
file_name: Thesis_Draft_v3.4Final.docx
file_size: 43740796
relation: hidden
- access_level: open_access
checksum: 56cb5e97f5f8fc41692401b53832d8e0
content_type: application/pdf
creator: mlukacisin
date_created: 2019-05-10T14:13:42Z
date_updated: 2021-02-11T11:17:16Z
embargo: 2020-04-17
file_id: '6410'
file_name: Thesis_Draft_v3.4FinalA.pdf
file_size: 35228388
relation: main_file
file_date_updated: 2021-02-11T11:17:16Z
has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '103'
publication_identifier:
isbn:
- 978-3-99078-001-5
issn:
- 2663-337X
publication_status: published
publisher: IST Austria
related_material:
record:
- id: '1029'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Mark Tobias
full_name: Bollenbach, Mark Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
title: Quantitative investigation of gene expression principles through combinatorial
drug perturbation and theory
type: dissertation
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...