Accepted Manuscript. Title: Genomics and transcriptomics in drug discovery. Author: Joaquin Dopazo

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1 Title: Genomics and transcriptomics in drug discovery Author: Joaquin Dopazo PII: S (13) DOI: Reference: DRUDIS 1194 To appear in: Received date: Revised date: Accepted date: Please cite this article as: Dopazo, J., Genomics and transcriptomics in drug discovery, Drug Discovery Today (2013), This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

2 Genomics and transcriptomics in drug discovery Joaquin Dopazo 1,2,3 1 Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain 2 Functional Genomics Node, (INB) at CIPF, Valencia, 46012, Spain 3 CIBER de Enfermedades Raras (CIBERER), Valencia, 46012, Spain Corresponding author: Dopazo, J. (jdopazo@cipf.es) The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery. Introduction During the late nineties and the beginning of this century high-throughput hybridization-based technologies (microarrays) almost eclipsed any other technology, Page 1 of 26

3 including sequencing, as main sources of massive data for biomedical research, making possible the study of the transcriptome at an unprecedented resolution. Early on, the potential of transcriptomic analysis in clinics was evident [1] as demonstrated by numerous diagnostic and prognostic gene expression-based biomarkers used to take decisions on treatments [2]. Actually, in 2004, the US Food and Drug Administration (FDA) launched the Critical Path Initiative to foster the incorporation of advances in genomic methodologies into development, assessment, manufacture and use of medical products [3]. In particular, much interest was focused on the potential of gene expression microarrays in the development of predictive models [4]. With a few years of delay, microarray-based Genome Wide Association Studies (GWAS) started to shed light on genetic determinants of traits of clinical interest. By the time of the completion of the human genome sequence, in 2005, just a few genetic variants were known to be significantly associated to diseases. When the first exhaustive catalogue of GWAS was compiled, only three years later, more than 500 single nucleotide polymorphisms (SNPs) were associated to traits [5] and, by the time this review was written, such catalog had collected more than 1700 papers reporting 8800 SNPs significantly associated to more than 1700 traits. However, most of the associations in complex trait only explain a small proportion of its heritability leaving a large proportion of missing heritability that remains to be explained [6]. In a sort of technological retaliation, New Generation Sequencing (NGS) technologies emerged only a few years ago and have virtually taken over microarrays today. NGS allows whole genome or exome sequencing (WGS or WES, respectively) and can also be used for complete transcriptome sequencing (RNA-seq) at affordable prices. WES Page 2 of 26

4 has proven to be an excellent tool for discovering new genes in Mendelian disorders or de novo syndromes with high penetrance [7]. In complex disorders the ratio of success has been far lower and much more data seem to be needed to obtain similar results. For example, an enormous international effort has been necessary to produce a catalogue of somatic mutations in cancer [8]. The lessons learned after a decade of high-throughput genomic data production are that: (i) combinations of many gene expression profiles produce reasonably accurate predictors of traits; (ii) large GWAS allowed finding genes associated to traits but these explained a small fraction of the trait heritability and (iii) WES was successful in finding disease genes in a few monogenic or oligogenic traits but present limitations in more complex systems such as cancer and other common diseases. The reality that emerges behind these observations is that approaches based on combinations of genes explain traits better than gene-by-gene approaches. Although it is long known that genetic interactions affect heritability calculations [9], most human genetic studies of missing heritability have ignored this fact. It is increasingly evident today that most of the biological functionality of the cell arises from complex interactions between their molecular components [10]. Interpreting the consequences of the combined effect of gene activity over the cell functionality is a major concern in the analysis of genomic data, and is essential to understand how gene activity perturbations account for disease. Genome technologies open the door to the application of real system biology concepts to understand disease mechanisms and therefore suggest ways of therapeutic intervention. Page 3 of 26

5 This review focuses on the impact of genomics methodologies on drug discovery and rational therapeutic strategies. The first section outlines the recent advancements in the knowledge of the human genome and the new challenges posed by the NGS technologies and the following sections discuss the changes that the availability of genomic data are causing in different aspects of the drug discovery process. Next generation sequencing: a new genomic scenario with new challenges In contrast to conventional Sanger sequencing, NGS technologies produce several orders of magnitude more sequence data at extremely cheaper prices per nucleotide sequenced [11]. The most commonly used platforms for WES are Illumina, and SOLiD. Other more recent platforms include Pacific Biosciences and Complete genomics. Pacific Biosciences coverage levels seems to be the least biased, followed by Illumina, according to recent comparative studies [12]. There are also benchtop versions of some of these technologies, such as 454 GS Junior (Roche), MiSeq (Illumina) and Ion Torrent PGM (Life Technologies). As general features, 454 generate the longest reads (now over 600 bases), while Illumina shows the highest throughputs (and IonTorrent among the benchtops). However, the MiSeq, the Ion Torrent PGM and 454 GS Junior are known to produce homopolymer-associated indel errors [13]. The information compiled along the few years of increasing availability of genomes and exomes has allowed pinpointing a scenario of much more variability than previously thought when only GWAS data was available. Thus, the result of international consortia such as the 1000 genomes project has revealed an enormous amount of variation at the genome level [14]. A total of 20 25,000 variants are commonly found in only the coding part of the genomes including variants predicted Page 4 of 26

6 to severely affect the function of human protein-coding genes, known as loss-offunction (LOF) [15]. In fact, recent genome sequencing projects have revealed an unexpectedly large number of these variants in the genomes of apparently healthy individuals. A conservative estimation suggests that there are at least 250 LOF variants per sequenced genome, 100 of them involved in known human diseases, and more than 30 in a homozygous state, which suggests a previously unnoticed level of variation with putative functional consequences [16]. A recent report indicates that such ratios of deleterious variation can also be extrapolated to other non-coding elements of the genome such as mirnas [17]. This high level of variation along with the pervasive presence of (apparently) deleterious mutations in healthy genomes adds extra complexity to the challenge of discovering genetic determinants of traits. The use of NGS technologies produce huge amounts of genomic data that have three main associated problems: (i) the technical problem of big data management and storage, (ii) the problem of data primary processing (process by which raw data is converted into comprehensive descriptions of variation of the genomes or transcriptomes) and (iii) the most complex problem of data interpretation, when variations at genomic or/and transcriptomic level have to be related to traits. Despite the fact that none of these problems has been satisfactorily solved it is true that there is a gradient of complexity. The two first problems have a strong technological component and new solutions that ease such processes are continuously arising. The cloud is increasingly more accessible and affordable and provides computation and storage at reasonable prices [18] as well as software for genomic data analysis as virtual machines [19 22]. However, data need to be transferred to the cloud and there are also important considerations regarding data security, which can constitute serious Page 5 of 26

7 obstacles for its use. Regarding data processing there are different algorithms for read mapping in DNA or transcripts and variant calling or differential expression comprehensively described elsewhere [23,24]. RNA-seq algorithms present comparatively lower efficiency in mapping and in quantifying transcripts [25], being still much room for improvement. All genomic data have a parallel intrinsic nature that is hardly used in the design of algorithms, which are mostly sequential. Therefore, new distributed computing solutions such as MapReduce and heterogeneous computational environments, in which conventional CPUs are merged with specialized accelerators such as graphics processing units (GPUs) or field-programmable gate array (FPGA) that can speedup calculations several orders of magnitude, will be soon common for genomic big data analysis. However, understanding the mechanisms by which some (among many) variants shape a determinate phenotype is, beyond a few obvious monogenic cases, a much more complex problem that depends on our yet incomplete knowledge of the organization of the biological system studied. It requires integrating different, large-scale data sets to construct models that can predict complex phenotypes such as disease or the response to a particular drug. The construction and use of models that include functional, regulatory and physical protein protein interaction information to predict the effect of variants and/or differences in gene expression can be computationally very demanding. Therefore, it is expected that, as the amount and the diversity of the data grow, modeling will become an increasingly important tool for studying and predicting the behavior of complex biological systems [26]. Figure 1 represents the concept of using biological information to build models by means of which the genomic data of the patients can be understood and used in clinic and drug discovery. Page 6 of 26

8 Drug discovery: disease genes, target identification and diagnostic In spite of the enormous investment in research and development made by the pharmaceutical industry, an amazingly high number of drugs continue to fail in clinical development, mainly due to lack of efficacy in phase 2 trials [27]. Many compounds demonstrate to be active against the intended target without presenting any toxicity problem, but they do not improve the primary clinical indication. It is increasingly apparent now that this lack of efficacy comes from a failure in selecting the correct target rather than from a chemical failure. The origins of this problem lie on the simplistic ways in which potential drug targets for complex diseases are identified, and strongly suggest that approaches conceptually more innovative to identify causal relationships between molecular entities and disease are needed. At present, NGS technology is starting to have a major role in target identification. In addition to WGS or WES, gene expression can also enormously help in the identification of genes and pathways relevant in disease pathology, which can inform the selection of new targets for intervention (Figure 2) [28]. However, in spite of the success in identifying mutations in genes underlying rare Mendelian disorders, WES or WGS of families or case-controls frequently does not reveal causative mutations in many cases, especially in complex diseases. A recognized bottleneck in target identification is the interpretation of gene variants found by WES or WGS and their effect on human health. This interpretation critically relies on the available knowledge to identify the damaged functionality, which constitutes, at the end, the disease mechanism. This requires sophisticated computational tools that integrate genomic data [29] as well as models that relate Page 7 of 26

9 biological components of the cell among them according to the known network of functional, regulatory and physical interaction known in order to make sense of experimental genomic data in the context of the biological system studied [30,31]. Different methodologies for gene prioritization based on common functionality have been used to help in the determination of disease genes [32]. However, additional conceptual and methodological developments of such computational tools that allow a better integration of different genomic data types across the multiple levels of organization that are characteristic of human physiology and disease are still needed [31]. In addition to target identification, the knowledge of disease genes allows new and much more precise applications of targeted resequencing for diagnostic purposes which can be used to take therapeutic decisions (Figure 2) [33]. Adverse drug effects Adverse drug effects are associated to more than 2 million hospitalizations and approximately 100,000 deaths in the US [34] having a major impact on health care. Understanding the basis of adverse drug effects implies not only elucidating the molecular mechanisms and biological effects of a drug, but most importantly predicting the risk for an adverse effect with respect to the personal disposition of the patient. Again, predictive biomarkers for drug therapeutic effects do not exist for more than 90% of drugs currently in the market. This is mainly due to the fact that monogenic pharmacogenetic traits are mostly unable to explain the variations in a complex phenotype such as drug response [35]. The use of genomic information to guide clinical decisions is becoming a major driving force in personalized medicine, Page 8 of 26

10 which can be defined as a comprehensive, prospective approach to prevent, diagnose, and treat disease by using each patient s unique clinical, genomic, and environmental information [36]. Pharmacogenomics is becoming a key component of the concept of personalized medicine. The increasing availability of personal genome sequences allows an immediate translation of the knowledge on variation associated to drug response (about 300 SNPs reported association to pharmacogenomics traits). This information, however, must be completed with other non-genetic factors that confer variability, such as epigenomic or various clinical and environmental data to have predictive models of personalized drug response. Again, the proper management and exploitation of this genomic and environmental data requires computational tools and mathematical models that allows us to interpret them in the context of the biological system studied [30,31] to provide predictive capability of drug response. Of course, there are barriers to the direct translation of clinical outcomes predictions due to a multitude of factors influencing drug response, including compliance and psychological factors. Despite this, it is expectable that optimized personalized treatments will experience a revolutionary advancement in the coming years [37]. Rational therapies An extreme case of patient-specific response to treatments is cancer. An increasing corpus of evidences suggests that cancers are not clonal cell populations but rather a dynamic ensemble of subpopulations with distinct mutations [38]. Thus, given the complex relationships among proteins in the underlying network, different tumors can develop similar phenotypes by acquiring mutations in different proteins [39] or two different mutations individually innocuous can develop a tumor when they appear in combination, even in two neighboring cells [40]. Thus, inter-patient heterogeneity can Page 9 of 26

11 be caused by different mutations in the same or in different proteins that affect the underlying signaling network in a similar way and, consequently, produces almost indistinguishable phenotypic outcome. Making use of genomic technologies, personalized cancer network biology aims to propose a rational therapy by treating each tumor with the best combination of drugs specifically tailored for it [41]. For the application of a rational therapy tumor-specific cell lines and xenograft models should be developed after diagnosis. The tumor should be sequenced and combinations of drugs based on the results observed should be tried in the cell-lines and xenograft models. The successful combinations will then be used for the treatment of the patient (Figure 1, bottom) [41]. In the field of infectious diseases, drug failures are due to the rise of resistant strains that rapidly propagate through the pathogen s population. Actually, the continuous emergence of resistant strains constitutes another well-known threat for the whole health system. There are examples of successful use of genome sequencing in microbial species to identify the determinants drug resistance in virus [42] or bacteria [43], which is critical to take the proper therapeutic decisions. This higher ratio of success comes from the fact that monogenic (or oligogenic) traits are more common in simpler microbial systems. Drug repositioning Blockbusters, drugs developed for the biggest possible populations (e.g. diabetes, among others), were integral to the classical strategy of pharmaceutical companies for drug development. However, the heterogeneity in drug responses makes more and more evident that producing a new blockbuster is more than a hard task. New Page 10 of 26

12 strategies are more oriented to take into account this reality and, instead of targeting huge populations, they are targeting either genotypically well-characterized subpopulations of patients of a disease or rare diseases, which drives the research focus to figure out the molecular pathway by which the disease works and how to intervene on it. Under this rationale, drugs can be used in other diseases affected by the same pathway. This involves a conceptually different way to understand a therapeutic target, not as a single, isolated molecule but rather as a component of a more complex functional module. Thus, drug repositioning, or finding new uses for existing drugs, increases its importance as a fundamental part of drug discovery strategies. In this scenario, the availability of genomic data and the possibility of modeling them in the context of the network of functional interactions in the cell become crucial for efficient drug repositioning. There are two main strategies for suggesting new uses for drugs: those based on the study of the properties and molecular interactions of the drug and those based on the perspective of the disease or the pathology [44]. When enough molecular knowledge is available the first strategy is preferable. Thus, if 3D structures of molecules and targets are available then molecular docking methods can be used for drug repositioning by predicting physical interactions between them [45]. However, high quality information on 3D structures is often not available. Also, different measurements of molecular structural or chemical similarity or pharmacophore descriptors have been used to suggest, for example, novel targets for metabotropic glutamate receptor antagonists [46]. Another more promising approach consists on the use of gene expression profiles either characteristic of the disease or of other similar drugs. The connectivity map project [47] currently contains a collection of gene expression profiles for more than 1700 compounds in different Page 11 of 26

13 cancer cell lines. Such transcriptomic data has been used to cluster drugs into coherent groups using similarity scores for expression profiles. The resulting drug clusters contained compounds with similar mechanisms of action, which often shared targets and pathways [48]. Available information on biological networks can be used also for drug repositioning. Thus, the integration of a large set of molecular disease profiles with the network of human protein interactions was used to infer modules composed by proteins with common functions and sub-networks shared among many diseases [49]. Network-based approaches provide a promising approach to find causal genes by modeling and comparing complex molecular disease states [50]. Another quite original approach is based on the comparison of drug side effects, given that they encode the physiological consequence of the biological activity of a drug compound. The comparison of side effect profiles provides the basis to relate drugs to other drugs or diseases, even in cases where their underlying pharmacological mechanisms are unknown [51]. Metagenomics: part of the environment has a genome While much attention has been paid to the genetic component of the phenotype, the environment has been often considered a sort of uncontrolled, external factor. However, a neglected although critical constituent of xenobiotic metabolism is mediated by the trillions of microorganisms inhabiting our gastrointestinal tract [52]. It is known that the gut microbiota secrete enzymes able to degrade carbohydrate and proteins, conferring them a metabolic potential to affect drug stability. Actually, it has been reported that more than 30 commercially available drugs are substrates for gut bacterial enzymes and many more are expected to be discovered [53]. In fact the metabolism of therapeutic drugs can indirectly be affected by the gut microbiota in Page 12 of 26

14 different ways. Microbes can secrete metabolites that are substrates for host enzymes that process a given drug, thus attenuating their activity. By doing so the gut microbiota may indirectly affect the desired pharmacological action, potentially prolonging the time spent in circulation or increasing drug toxicity (Figure 3B) [54]. Obviously, these interactions are reciprocal, and exposure to xenobiotics, especially antibiotics, can affect the structure of the microbiota populations. Actually, the bacteriotherapy, which attempts to modulate the microbiota through antibiotics and probiotics or directly by transplantation of a complete microbiota into a recipient, is a promising clinical area [55]. Recently, microbiome transplantation has been successfully used to treat recurrent Clostridium difficile infection, restoring a normal and stable microbiota [56]. The success of this strategy demonstrates that, if a global change in the microbiota can restore the normal functionality of the gut, targeted interventions can have similar or even better results. Thus, microbiota computational models of the global microbiota metabolism [57] could be used to directly enhance or reduce particular microbiota activities by modulating or changing the population structure (Figure 3) [58]. Such modeling framework could be used for designing novel microbiomes or reconstructing stable communities with some desired metabolic activity by mixing and matching available species at certain relative abundances. Accordingly, designed microbiomes can provide an interesting therapeutic alternative for treating numerous diseases such as obesity, diabetes, inflammatory bowel disease, diarrhea and acute gastroenteritis [59]. Page 13 of 26

15 Discussion The increasing use of genomic technologies is radically changing the scenario in biomedical research and is starting to have a major impact on drug discovery [60]. The unexpectedly high level of variability across individuals recently discovered [16,17] along with the fact that many traits of clinical interest (disease outcome, response to drugs, among others) depend on a network of complex relationships among proteins [61], explains the limitations of conventional biomarkers. Instead, computational tools and mathematical models are becoming more and more used to detect causal genes based on systems biology concepts as well as to design rational therapies tailored to individual patients [41]. Actually, a comprehensive model of the drug proteins network should include the human microbiome too. Also, data integration will be key in this new scenario because not only genomic data but many derivatives, such as copy number variations and other structural variations as well as alterations such as epigenetic modifications are likely to have roles of increasing importance in disease, especially after the recently reported results of the ENCODE project which suggest a functional role for a vast portion of the genome [62]. A cultural change is taking place in the pharmaceutical companies in the way they focus the drug discovery process. The realization of the enormous extent of genetic, epigenetic, microbiotic, among others, variability makes it difficult to keep the concept of blockbuster drugs. Drugs tailored to segmented patients populations or even personalized treatments will become more common in the near future. Also drug repositioning is likely to have an increasingly important role in drug development and health care. Page 14 of 26

16 Acknowledgements This work is supported by grants from projects BIO from the Spanish Ministry of Science and Innovation and PROMETEO/2010/001 from the GVA-FEDER. The CIBER de Enfermedades Raras is an initiative of the Instituto de Salud Carlos III (ISCIII), MINECO. References 1 Sotiriou, C. and Piccart, M.J. (2007) Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat. Rev. Cancer 7, van t Veer, L.J. and Bernards, R. (2008) Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 452, Coons, S.J. (2009) The FDA s critical path initiative: a brief introduction. Clin. Ther. 31, Shi, L. et al. (2010) The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol. 28, Hindorff, L.A. et al. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, Manolio, T.A. et al. (2009) Finding the missing heritability of complex diseases. Nature 461, Bamshad, M.J. et al. (2011) Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 12, Page 15 of 26

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22 60 Woollard, P.M. et al. (2011) The application of next-generation sequencing technologies to drug discovery and development. Drug Discov. Today 16, Barabasi, A.L. and Oltvai, Z.N. (2004) Network biology: understanding the cell s functional organization. Nat. Rev. Genet. 5, Dunham, I. et al. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489, Figure Legends Figure 1. Biological knowledge on the network of functional regulatory and physical interactions (right) allows the construction of mathematical models and computational tools (center) that can be used to interpret genomic and other omics data obtained from individuals (left). Such models can help in the discovery of therapeutic targets and diagnostic biomarkers for personalized medicine. Models can be used as well to suggest drugs active in pathways discovered to be affected in patients. Personalized therapies are thus possible by deriving cell cultures from the cancer and trying drugs on them, whose success is further tested in xenograft models. Figure 2. Genomic technologies can be applied at different phases of the drug discovery process and are contributing to its acceleration and, in some aspects to its conceptual change. Page 21 of 26

23 Figure 3. Microbita community and its interaction with xenobiotics. (a) Microbiota metabolism can be understood as an ecosystem community model of interacting pathways. Using the appropriate modeling framework the type and magnitude of the interactions within the community can be predicted. Thus, competitive or collaborative behavior between species can be inferred. (b) The gut microbiota interacts with xenobiotics producing biotransformations such as activations of inactive compounds or inactivation of active compounds or even transformation in toxic byproducts. In turn, many xenobiotics can have an antibiotic effect. Prebiotics can be used to promote the growth of certain microbial species and probiotics (microbial species) can directly be introduced in the microbiota community. Page 22 of 26

24 Accepted Manuscrip Page 23 of 26 Figure 1

25 Accepted Manuscrip Page 24 of 26 Figure 2

26 Accepted Manuscrip Page 25 of 26 Figure 3

27 *Highlights (for review) Highlights I revise the advantages and new challenges posed by new sequencing technologies. Gene disease discovery and target discovery are easier using genomic approaches. Biomarkers for adverse drug effects can be determined using genomic data. Population heterogeneity leads to pathway drugs instead of blockbusters. The gut microbiota has a strong impact in drug effects. Page 26 of 26