Predicting drug-target interactions through integrative analysis of chemogenetic assays in yeast

Peer reviewe

Systems Genetics of DNA Damage Tolerance – Cisplatin, RAD5 & CRISPR-mediated Nonsense

DNA sequence information is constantly threatened by damage. In the clinic, intentional DNA damage is often used to treat cancer. Cisplatin, a first-line chemotherapy used to treat millions of patients, functions specifically by generating physical links within DNA strands, blocking DNA replication, and killing dividing cells. To maintain genome integrity, organisms have evolved the capacity to repair, respond, or otherwise resist change to the DNA sequence through a network of genetically encoded DNA damage tolerance pathways. In chapter 1, I present advances in experimental design and current progress for a systems genetics approach, using Saccharomyces cerevisiae, to reveal relationships between cisplatin tolerance pathways. Additionally, recent efforts to sequence thousands of cancer genomes have revealed recurrent genetic changes that cause overexpression of specific cisplatin tolerance genes. In chapter 2, I present a submitted manuscript that models overexpression of an essential cisplatin tolerance gene. This study uses a systems genetics approach to reveal the genetic pathways that are essential for tolerating this perturbation, which ultimately led to mechanistic insights for this gene. Convenient genome engineering in Saccharomyces has made this organism an ideal model to develop systems genetics concepts and approaches. In chapter 3, I present a published manuscript that demonstrates a new approach to disrupting genes by making site-specific nonsense mutations. Importantly, this approach does not require cytotoxic double-strand DNA breaks and is applicable to many model organisms for disrupting almost any gene, which may advance systems genetics into new model organisms. Systems genetics provides a framework for determining how DNA damage tolerance pathways act together to maintain cellular fitness and genome integrity. Such insights may one day help clinicians predict which cancers will respond to treatment, potentially sparing patients from unnecessary chemotherapy

'Big data' approaches for novel anti-cancer drug discovery

Introduction: The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we review how recent advances in platform technologies and the increasing availability of biological ‘big data’ are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. We then discuss how these discoveries may be amenable to therapeutic interventions. Areas covered: We discuss the current approaches that use ‘big data’ to identify cancer drivers. These approaches include genomic sequencing, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. We review how big data is being used to assess the tractability of potential drug targets and how systems biology is being utilised to identify novel drug targets. We finish the review with an overview of available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs

A Bioinformatics Approach to Synthetic Lethal Interactions in Cancer with Gene Expression Data

Introduction Synthetic lethal genetic interactions are re-emerging as an important concept in the post-genomics era due to their potential for use in precision medicine against cancers. Synthetic lethal drug design exploits the functional redundancy of genes disrupted in cancers (including tumour suppressors) to develop specific treatments against them. CDH1, which encodes E-cadherin, is a tumour supressor gene with loss of function in breast and stomach cancers. Experimental screens have identified candidate synthetic lethal interactions with CDH1, which can be further supported with bioinformatics analysis. Furthermore, gene expression data enables investigation of synthetic lethal pathways and the structure of synthetic lethal genes. Methods A computational methodology, the Synthetic Lethal Prediction Tool (SLIPT) was developed to detect synthetic lethal interactions in gene expression data. The application of this methodology is demonstrated on interactions with CDH1 in breast and stomach cancer data from The Cancer Genome Atlas (TCGA) project. Synthetic lethal genes and pathways were further investigated with unsupervised clustering, gene set over-representation analysis, metagenes, and permutation resampling. In particular, analyses focused on comparing SLIPT gene candidates to an experimental short interfering RNA (siRNA) screen. Network analysis methods were applied to the most supported pathways to test for pathway structure between synthetic lethal candidates. Simulation and modelling was used to assess the statistical performance of SLIPT, including simulated data with correlation structures from graph structures. Results Many candidate synthetic lethal partners of CDH1 were detected in TCGA breast cancer. These genes clustered into several distinct groups, with distinct biological functions and elevated expression in different clinical subtypes. While the number of genes detected by both SLIPT and siRNA was not significant, these contained significantly enriched pathways. In particular, G αi signalling, cytoplasmic microfibres, and extracellular fibrin clotting were robustly supported by both approaches, which is consistent with the known cytoskeletal and cell signalling roles of E-cadherin. Many of these pathways were replicated in stomach cancer data. The pathways supported only by SLIPT included regulation of immune signalling and translation, which were not expected to be detected in an isogenic cell line model but are still candidates for further investigation. Synthetic lethal candidates detected by SLIPT and siRNA were compared within the graph structures of the candidate synthetic lethal pathways. SLIPT genes had lower centrality and were consistently upstream of siRNA candidates, specifically in the G αi signalling pathway. A statistical model of synthetic lethality was used to simulate gene expression data with known synthetic lethal partners for a gene. The SLIPT methodology had high statistical performance when detecting few synthetic lethal partners, which diminished with more synthetic lethal partners or lower sample size. The SLIPT methodology performed better than Pearson correlation or the χ 2 -test. In particular, it performed well with high specificity for datasets containing thousands of genes, or genes positively correlated with the query gene (as expected to occur in gene expression data). SLIPT was robust across correlation structures, including those derived from complex pathway structures, and often distinguished synthetic lethal genes from those positively or negatively correlated with them. Thus this thesis has developed, evaluated, and applied a bioinformatics approach for the discovery of synthetic lethal genes from gene expression data. This approach has been demonstrated to detect biologically informative and clinically relevant candidate synthetic lethal partners for CDH1 in breast and stomach cancers

Current challenges of research on filamentous fungi in relation to human welfare and a sustainable bio-economy: a white paper.

The EUROFUNG network is a virtual centre of multidisciplinary expertise in the field of fungal biotechnology. The first academic-industry Think Tank was hosted by EUROFUNG to summarise the state of the art and future challenges in fungal biology and biotechnology in the coming decade. Currently, fungal cell factories are important for bulk manufacturing of organic acids, proteins, enzymes, secondary metabolites and active pharmaceutical ingredients in white and red biotechnology. In contrast, fungal pathogens of humans kill more people than malaria or tuberculosis. Fungi are significantly impacting on global food security, damaging global crop production, causing disease in domesticated animals, and spoiling an estimated 10 % of harvested crops. A number of challenges now need to be addressed to improve our strategies to control fungal pathogenicity and to optimise the use of fungi as sources for novel compounds and as cell factories for large scale manufacture of bio-based products. This white paper reports on the discussions of the Think Tank meeting and the suggestions made for moving fungal bio(techno)logy forward

Saturation of the Human Phenome

The phenome is the complete set of phenotypes resulting from genetic variation in populations of an organism. Saturation of a phenome implies the identification and phenotypic description of mutations in all genes in an organism, potentially constrained to those encoding proteins. The human genome is believed to contain 20-25,000 protein coding genes, but only a small fraction of these have documented mutant phenotypes, thus the human phenome is far from complete. In model organisms, genetic saturation entails the identification of multiple mutant alleles of a gene or locus, allowing a consistent description of mutational phenotypes for that gene. Saturation of several model organisms has been attempted, usually by targeting annotated coding genes with insertional transposons (Drosophila melanogaster, Mus musculus) or by sequence directed deletion (Saccharomyces cerevisiae) or using libraries of antisense oligonucleotide probes injected directly into animals (Caenorhabditis elegans, Danio rerio). This paper reviews the general state of the human phenome, and discusses theoretical and practical considerations toward a saturation analysis in humans. Throughout, emphasis is placed on high penetrance genetic variation, of the kind typically asociated with monogenic versus complex traits