@article{11341,
  abstract     = {Intragenic regions that are removed during maturation of the RNA transcript—introns—are universally present in the nuclear genomes of eukaryotes1. The budding yeast, an otherwise intron-poor species, preserves two sets of ribosomal protein genes that differ primarily in their introns2,3. Although studies have shed light on the role of ribosomal protein introns under stress and starvation4,5,6, understanding the contribution of introns to ribosome regulation remains challenging. Here, by combining isogrowth profiling7 with single-cell protein measurements8, we show that introns can mediate inducible phenotypic heterogeneity that confers a clear fitness advantage. Osmotic stress leads to bimodal expression of the small ribosomal subunit protein Rps22B, which is mediated by an intron in the 5′ untranslated region of its transcript. The two resulting yeast subpopulations differ in their ability to cope with starvation. Low levels of Rps22B protein result in prolonged survival under sustained starvation, whereas high levels of Rps22B enable cells to grow faster after transient starvation. Furthermore, yeasts growing at high concentrations of sugar, similar to those in ripe grapes, exhibit bimodal expression of Rps22B when approaching the stationary phase. Differential intron-mediated regulation of ribosomal protein genes thus provides a way to diversify the population when starvation threatens in natural environments. Our findings reveal a role for introns in inducing phenotypic heterogeneity in changing environments, and suggest that duplicated ribosomal protein genes in yeast contribute to resolving the evolutionary conflict between precise expression control and environmental responsiveness9.},
  author       = {Lukacisin, Martin and Espinosa-Cantú, Adriana and Bollenbach, Mark Tobias},
  issn         = {1476-4687},
  journal      = {Nature},
  pages        = {113--118},
  publisher    = {Springer Nature},
  title        = {{Intron-mediated induction of phenotypic heterogeneity}},
  doi          = {10.1038/s41586-022-04633-0},
  volume       = {605},
  year         = {2022},
}

@article{7026,
  abstract     = {Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper’s transparent peer review process is included in the Supplemental Information.},
  author       = {Lukacisin, Martin and Bollenbach, Tobias},
  issn         = {2405-4712},
  journal      = {Cell Systems},
  number       = {5},
  pages        = {423--433.e1--e3},
  publisher    = {Cell Press},
  title        = {{Emergent gene expression responses to drug combinations predict higher-order drug interactions}},
  doi          = {10.1016/j.cels.2019.10.004},
  volume       = {9},
  year         = {2019},
}

@phdthesis{6392,
  abstract     = {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.  
In 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.

In 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.

In 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.

This 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. },
  author       = {Lukacisin, Martin},
  isbn         = {978-3-99078-001-5},
  issn         = {2663-337X},
  pages        = {103},
  publisher    = {IST Austria},
  title        = {{Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory}},
  doi          = {10.15479/AT:ISTA:6392},
  year         = {2019},
}

@misc{5563,
  abstract     = {MATLAB code and processed datasets available for reproducing the results in: 
Lukačišin, M.*, Landon, M.*, Jajoo, R*. (2016) Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.
*equal contributions},
  author       = {Lukacisin, Martin},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{MATLAB analysis code for 'Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast'}},
  doi          = {10.15479/AT:ISTA:64},
  year         = {2017},
}

@article{1029,
  abstract     = {RNA Polymerase II pauses and backtracks during transcription, with many consequences for gene expression and cellular physiology. Here, we show that the energy required to melt double-stranded nucleic acids in the transcription bubble predicts pausing in Saccharomyces cerevisiae far more accurately than nucleosome roadblocks do. In addition, the same energy difference also determines when the RNA polymerase backtracks instead of continuing to move forward. This data-driven model corroborates—in a genome wide and quantitative manner—previous evidence that sequence-dependent thermodynamic features of nucleic acids influence both transcriptional pausing and backtracking.},
  author       = {Lukacisin, Martin and Landon, Matthieu and Jajoo, Rishi},
  issn         = {19326203},
  journal      = {PLoS One},
  number       = {3},
  publisher    = {Public Library of Science},
  title        = {{Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast}},
  doi          = {10.1371/journal.pone.0174066},
  volume       = {12},
  year         = {2017},
}

@misc{5556,
  abstract     = {MATLAB code and processed datasets available for reproducing the results in: 
Lukačišin, M.*, Landon, M.*, Jajoo, R*. (2016) Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.
*equal contributions},
  author       = {Lukacisin, Martin and Landon, Matthieu and Jajoo, Rishi},
  keywords     = {transcription, pausing, backtracking, polymerase, RNA, NET-seq, nucleosome, basepairing},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{MATLAB analysis code for 'Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast'}},
  doi          = {10.15479/AT:ISTA:45},
  year         = {2016},
}