Improved pathway reconstruction from RNA interference screens by exploiting off-target effects

Pathway reconstruction has proven to be an indispensable tool for analyzing the molecular mechanisms of signal transduction underlying cell function. Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as RNA interference (RNAi). NEMs assume that the short interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, it has been shown that most siRNAs exhibit strong off-target effects, which further confound the data, resulting in unreliable reconstruction of networks by NEMs.; Here, we present an extension of NEMs called probabilistic combinatorial nested effects models (pc-NEMs), which capitalize on the ancillary siRNA off-target effects for network reconstruction from combinatorial gene knockdown data. Our model employs an adaptive simulated annealing search algorithm for simultaneous inference of network structure and error rates inherent to the data. Evaluation of pc-NEMs on simulated data with varying number of phenotypic effects and noise levels as well as real data demonstrates improved reconstruction compared to classical NEMs. Application to Bartonella henselae infection RNAi screening data yielded an eight node network largely in agreement with previous works, and revealed novel binary interactions of direct impact between established components.; The software used for the analysis is freely available as an R package at https://github.com/cbg-ethz/pcNEM.git.; Supplementary data are available at Bioinformatics online

Understanding How Genetic Mutations Collaborate with Genomic Instability in Cancer

Chromosomal instability is the process of mis-segregation for ongoing chromosomes, which leads to cells with an abnormal number of chromosomes, also known as an aneuploid state. Induced aneuploidy is detrimental during development and in primary cells but aneuploidy is also a hallmark of cancer cells. It is therefore believed that premalignant cells need to overcome aneuploidy-imposed stresses to become tumorigenic. Over the past decade, some aneuploidy-tolerating pathways have been identified through small-scale screens, which suggest that aneuploidy tolerance pathways can potentially be therapeutically exploited. However, to better understand the processes that lead to aneuploidy tolerance in cancer cells, large-scale and unbiased genetic screens are needed, both in euploid and aneuploid cancer models. In this review, we describe some of the currently known aneuploidy-tolerating hits, how large-scale genome-wide screens can broaden our knowledge on aneuploidy specific cancer driver genes, and how we can exploit the outcomes of these screens to improve future cancer therapy

High-order combination effects and biological robustness

Biological systems are robust, in that they can maintain stable phenotypes under varying conditions or attacks. Biological systems are also complex, being organized into many functional modules that communicate through interlocking pathways and feedback mechanisms. In these systems, robustness and complexity are linked because both qualities arise from the same underlying mechanisms. When perturbed by multiple attacks, such complex systems become fragile in both theoretical and experimental studies, and this fragility depends on the number of agents applied. We explore how this relationship can be used to study the functional robustness of a biological system using systematic high-order combination experiments. This presents a promising approach toward many biomedical and bioengineering challenges. For example, high-order experiments could determine the point of fragility for pathogenic bacteria and might help identify optimal treatments against multi-drug resistance. Such studies would also reinforce the growing appreciation that biological systems are best manipulated not by targeting a single protein, but by modulating the set of many nodes that can selectively control a system's functional state

Synthetic Lethality and Cancer - Penetrance as the Major Barrier.

Synthetic lethality has long been proposed as an approach for targeting genetic defects in tumours. Despite a decade of screening efforts, relatively few robust synthetic lethal targets have been identified. Improved genetic perturbation techniques, including CRISPR/Cas9 gene editing, have resulted in renewed enthusiasm for searching for synthetic lethal effects in cancer. An implicit assumption behind this enthusiasm is that the lack of reproducibly identified targets can be attributed to limitations of RNAi technologies. We argue here that a bigger hurdle is that most synthetic lethal interactions (SLIs) are not highly penetrant, in other words they are not robust to the extensive molecular heterogeneity seen in tumours. We outline strategies for identifying and prioritising SLIs that are most likely to be highly penetrant

Sleeping Beauty transposition: from biology to applications

Sleeping Beauty (SB) is the first synthetic DNA transposon that was shown to be active in a wide variety of species. Here, we review studies from the last two decades addressing both basic biology and applications of this transposon. We discuss how host-transposon interaction modulates transposition at different steps of the transposition reaction. We also discuss how the transposon was translated for gene delivery and gene discovery purposes. We critically review the system in clinical, pre-clinical and non-clinical settings as a non-viral gene delivery tool in comparison with viral technologies. We also discuss emerging SB-based hybrid vectors aimed at combining the attractive safety features of the transposon with effective viral delivery. The success of the SB-based technology can be fundamentally attributed to being able to insert fairly randomly into genomic regions that allow stable long-term expression of the delivered transgene cassette. SB has emerged as an efficient and economical toolkit for safe and efficient gene delivery for medical applications

Advancing CRISPR technologies to engineer yeast metabolism

The advent of genetic engineering tools has initiated an era of manipulating microorganisms for the production of valuable compounds for our society. Precise engineering of these microbes commonly requires introducing genetic modifications such as gene deletion, overexpression, and accurate regulation in order to enhance the production of the compound of interest. In this context, the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 technology, adapted from the prokaryotic adaptive immune system, has revolutionized our ability to manipulate a broad range of living organisms. In contrast to other methods, this technology works like a molecular pair of scissors (Cas9) which is guided by a programmable RNA (gRNA) molecule binding at a specific location in the DNA. The programmability and time-efficiency offered by this technology have in the recent years been successfully exploited in rewiring the metabolic network to enhance the production of metabolites used in various areas of industrial biotechnology. \ua0In this thesis, I present several studies applying the technological diversity provided by CRISPR in the context of building efficient yeast cell factories for the production of oleochemicals -sustainable substitutes for plant derived lipids. Since oleochemicals derive from lipid products, the main engineering strategies presented essentially focus on fatty acid metabolism and its precursors. First, we exploited CRISPR/Cas9 endonuclease capacity to extensively remodel yeast lipid metabolism. We showed that the disruption of several metabolic fluxes allows to overcome the main limiting steps in fatty acid biosynthesis and favors the production of free fatty acids and triacylglycerols, two important precursors for the production of oleochemicals. Second, we harnessed the ability to precisely regulate genes using the catalytically deactivated form of the Cas9 protein (dCas9) coupled to transcription factors for fine-tuning the expression of genes involved in lipid biogenesis. Additionally, we proposed a framework for dCas9-based applications based on computational techniques for predicting key genes potentially favoring the production of yeast endogenous metabolites. Finally, we expanded the CRISPR repertoire by building new tools to accelerate yeast cell factory design. We exploited a Type I CRISPR-associated endoribonuclease for multiplex genome engineering and transcriptional regulation via processing an RNA transcript into multiple gRNAs, and we developed a computational tool for designing gRNAs targeting multiple loci at once. In summary, the work presented in this thesis provides various ways to efficiently engineer yeast metabolism by exploiting the diversity of CRISPR technologies, as well as new tools to the community for future engineering strategies

Characterizing and exploiting the endocytic pathway for macromulecular delivery

Macromolecular drugs with cytosolic or nuclear targets often exhibit low therapeutic activity due, at least in part, to their inability to escape endosomal compartments following cellular internalization. There exist a range of strategies to address this inefficient delivery by attempting to bolster the endosomal escape of the therapeutic cargo. Many of these delivery strategies apply to a broad range of molecular species, including proteins and nucleic acids. Two strategies of particular interest involve the use of extracellular vesicles (EVs) or endosomolytic small molecule compounds (SMCs). EVs are nanoscale, membrane-bound particles produced by all cell types and present in all bodily fluids. As a biological nanoparticulate species, EVs are inherently capable of delivering the material they contain to cells. Further, EVs can be modified through recombinant protein-based engineering strategies which can bestow a range of functional utilities such as fusogenicity, preferential cargo loading, and molecular targeting. However, the use of EVs as a scalable therapeutic modality is hampered by an inability to reliably mass-produce a homogenous population of these nanoparticles in vitro. SMCs, on the other hand, are easily synthesized at scale and can function in a stochastic manner dependent on an appropriate co-dosing strategy with their complementary therapeutic cargo. However, the mechanisms underlying SMC-mediated macromolecular delivery can be difficult to elucidate due to a lack of high-resolution characterization techniques. In this thesis, two issues - one underpinning each strategy - are investigated. First, the effects of culture media composition on the production of proteinloaded EVs in vitro are explored, with the ultimate aim of increasing EV output while characterizing the cellular biology driving the EV production. Certain serum components can differentially affect EV biogenesis by influencing ceramide-dependent EV biogenesis. In the second project, a functional screen of a novel family of SMCs is conducted to identify several chemical analogs in this family that demonstrate endosomolytic activity. Thereafter, superresolution and real-time microscopic assays are employed to determine the mechanism and consequence of the novel compounds during their co-treatment with a splice-switching oligonucleotide (SSO). SSOs are clinically relevant small-RNA therapeutics that alter the production of splice variants for a given genetic transcript. The novel SMCs bolster SSO activity by disrupting the structure of endosomes in a manner dependent on the acidification of the endosomal compartments, suggesting the SMCs display a buffering capacity at certain concentrations. The findings herein strengthen the potential of each delivery strategy as a therapeutically relevant approach to functionally delivering macromolecular cargo to cells

Regulation of MEK Signaling and Inhibitor Sensitivity in Melanoma

Melanoma, the deadliest form of skin cancer, is characterized by aberrant hyperactivation of the ERK mitogen-activated protein kinase signaling pathway. Genetic lesions in the core components of the RAS-RAF-MEK-ERK protein kinase cascade as well as its upstream regulators are key features of melanoma progression and drug resistance. MEK, the central kinase within the cascade, is constitutively activated by many upstream oncogenic events and is an important drug target. MEK inhibition in combination with BRAF inhibition is the standard of care for treating BRAFV600E melanoma. However, not all BRAFV600E melanomas respond to these inhibitors, and those that do respond eventually acquire resistance. To better understand mechanisms of MEK inhibitor susceptibility and MEK regulation in BRAFV600E melanoma, I performed a loss-of-function screen to identify kinases and phosphatases that modulate sensitivity to two clinical MEK1/2 inhibitors. In this screen, I identified PPP6C, the catalytic subunit of protein phosphatase 6 (PP6), as a factor promoting sensitivity to MEK inhibition. I established PPP6C as a major MEK phosphatase in cells exhibiting oncogenic ERK pathway activation. Recruitment of MEK to PPP6C occurs through an interaction with its associated regulatory subunits. Loss of PPP6C causes hyperphosphorylation of MEK at both activating and crosstalk phosphorylation sites, promoting signaling through the ERK pathway and decreasing sensitivity to the growth inhibitory effects of MEK inhibitors. Consistent with its role in regulating ERK signaling, PPP6C is frequently mutated in melanoma, as is MEK1. I found that recurrent melanoma-associated PPP6C mutations cause MEK hyperphosphorylation and ERK signaling hyperactivation when expressed in cells. Recurrent MEK1 mutations all promote MEK1 kinase activity but are activated by different mechanisms of action. The elevated MEK activity associated with PPP6C mutations or MEK1 mutations suggests that they promote disease by a common mechanism: activating the core oncogenic pathway driving melanoma. Collectively, our studies identify novel modulators of susceptibility to ERK pathway targeted cancer therapies, including PPP6C, a key negative regulator of ERK signaling, and cancer-associated mutations that influence ERK signaling activation