Chemogenomics and biotechnology

Pathogenomics and Management of Fusarium Diseases in Plants

There is an urgency to supplant the heavy reliance on chemical control of Fusarium diseases in different economically important, staple food crops due to development of resistance in the pathogen population, the high cost of production to the risk-averse grower, and the concomitant environmental impacts. Pathogenomics has enabled (i) the creation of genetic inventories which identify those putative genes, regulators, and effectors that are associated with virulence, pathogenicity, and primary and secondary metabolism; (ii) comparison of such genes among related pathogens; (iii) identification of potential genetic targets for chemical control; and (iv) better characterization of the complex dynamics of host–microbe interactions that lead to disease. This type of genomic data serves to inform host-induced gene silencing (HIGS) technology for targeted disruption of transcription of select genes for the control of Fusarium diseases. This review discusses the various repositories and browser access points for comparison of genomic data, the strategies for identification and selection of pathogenicity- and virulence-associated genes and effectors in different Fusarium species, HIGS and successful Fusarium disease control trials with a consideration of loss of RNAi, off-target effects, and future challenges in applying HIGS for management of Fusarium diseases.

Current computational methods for predicting protein interactions of natural products

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.

Chemogenetic interactions in human cancer cells

Chemogenetic profiling enables the identification of genes that enhance or suppress the phenotypic effect of chemical compounds. Using this approach in cancer therapies could improve our ability to predict the response of specific tumor genotypes to chemotherapeutic agents, thus accelerating the development of personalized drug therapy. In the not so distant past, this strategy was only applied in model organisms because there was no feasible technology to thoroughly exploit desired genetic mutations and their impact on drug efficacy in human cells. Today, with the advent of CRISPR gene-editing technology and its application to pooled library screens in mammalian cells, chemogenetic screens are performed directly in human cell lines with high sensitivity and specificity. Chemogenetic profiling provides insights into drug mechanism-of-action, genetic vulnerabilities, and resistance mechanisms, all of which will help to accurately deliver the right drug to the right target in the right patient while minimizing side effects.

Getting the 'MOST' out of crop improvement

Unraveling the function of genes affecting agronomic traits is accelerating due to progress in DNA sequencing and other high-throughput genomic approaches. Characterized genes can be exploited by plant breeders by using either marker-aided selection (MAS) or transgenic procedures. Here, we propose a third 'outlet', 'molecular strengthening' (MOST), as alternative option for exploiting detailed molecular understanding of trait expression, which is comparable to the pharmaceutical treatment of human diseases. MOST treatments can be used to enhance yield stability. Alternatively, they can be used to control traits temporally, such as flowering time to facilitate crosses for plant breeders. We also discuss the essence for developing MOST treatments, their prospects, and limitations.

Whole-genome sequencing reveals that mutations in myosin-5 confer resistance to the fungicide phenamacril in Fusarium graminearum

To determine the mechanism of resistance to the fungicide phenamacril (JS399-19) in Fusarium graminearum, the causal agent of Fusarium head blight, we sequenced and annotated the genome of the resistant strain YP-1 (generated by treating the F. graminearum reference strain PH-1 with phenamacril). Of 1.4 million total reads from an Illumina-based paired-end sequencing assay, 92.80% were aligned to the F. graminearum reference genome. Compared with strain PH-1, strain YP-1 contained 1,989 single-nucleotide polymorphisms that led to amino acid mutations in 132 genes. We sequenced 22 functional annotated genes of another F. graminearum sensitive strain (strain 2021) and corresponding resistant strains. The only mutation common to all of the resistant mutants occurred in the gene encoding myosin-5 (point mutations at codon 216, 217, 418, 420, or 786). To confirm whether the mutations in myosin-5 confer resistance to phenamacril, we exchanged the myosin-5 locus between the sensitive strain 2021 and the resistant strain Y2021A by homologous double exchange. The transformed mutants with a copy of the resistant fragment exhibited resistance to phenamacril, and the transformed mutant with a copy of the sensitive fragment exhibited sensitivity to phenamacril. These results indicate that mutations in myosin-5 confers resistance to phenamacril in F. graminearum.

Functional profiling of human fungal pathogen genomes

Fungal infections are challenging to diagnose and often difficult to treat, with only a handful of drug classes existing. Understanding the molecular mechanisms by which pathogenic fungi cause human disease is imperative. Here, we discuss how the development and use of genome-scale genetic resources, such as whole-genome knockout collections, can address this unmet need. Using work in Saccharomcyes cerevisiae as a guide, studies of Cryptococcus neoformans and Candida albicans have shown how the challenges of large-scale gene deletion can be overcome, and how such collections can be effectively used to obtain insights into mechanisms of pathogenesis. We conclude that, with concerted efforts, full genome-wide functional analysis of human fungal pathogen genomes is within reach.

Drug target central

BACKGROUND

One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets.

OBJECTIVE

Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system. We consider how compiling the drug target universe requires integration across several resources such as competitor intelligence and pharmacological activity databases, as well as input from techniques such as text-mining. In particular, we address data standards and the complexities of representing targets in a structured ontology as well as opportunities for future development.

CONCLUSION

Drug target-orientated databases address important areas of drug discovery such as chemogenomics, drug/candidate repurposing and business intelligence. As research in industry and academia drives continued expansion of the druggable genome, it is crucial that such systems be maintained to provide an accurate picture of the landscape. This power of this information stretches beyond drug discovery and into the wider scientific community where small molecule tool compounds can enable the dissection of complex cellular pathways.

An algorithm for chemical genomic profiling that minimizes batch effects: bucket evaluations

Background

Chemical genomics is an interdisciplinary field that combines small molecule perturbation with traditional genomics to understand gene function and to study the mode(s) of drug action. A benefit of chemical genomic screens is their breadth; each screen can capture the sensitivity of comprehensive collections of mutants or, in the case of mammalian cells, gene knock-downs, simultaneously. As with other large-scale experimental platforms, to compare and contrast such profiles, e.g. for clustering known compounds with uncharacterized compounds, a robust means to compare a large cohort of profiles is required. Existing methods for correlating different chemical profiles include diverse statistical discriminant analysis-based methods and specific gene filtering or normalization methods. Though powerful, none are ideal because they typically require one to define the disrupting effects, commonly known as batch effects, to detect true signal from experimental variation. These effects are not always known, and they can mask true biological differences. We present a method, Bucket Evaluations (BE) that surmounts many of these problems and is extensible to other datasets such as those obtained via gene expression profiling and which is platform independent.

Results

We designed an algorithm to analyse chemogenomic profiles to identify potential targets of known drugs and new chemical compounds. We used levelled rank comparisons to identify drugs/compounds with similar profiles that minimizes batch effects and avoids the requirement of pre-defining the disrupting effects. This algorithm was also tested on gene expression microarray data and high throughput sequencing chemogenomic screens and found the method is applicable to a variety of dataset types.

Conclusions

BE, along with various correlation methods on a collection of datasets proved to be highly accurate for locating similarity between experiments. BE is a non-parametric correlation approach, which is suitable for locating correlations in somewhat perturbed datasets such as chemical genomic profiles. We created software and a user interface for using BE, which is publically available.

Yeast toxicogenomics: genome-wide responses to chemical stresses with impact in environmental health, pharmacology, and biotechnology

The emerging transdisciplinary field of Toxicogenomics aims to study the cell response to a given toxicant at the genome, transcriptome, proteome, and metabolome levels. This approach is expected to provide earlier and more sensitive biomarkers of toxicological responses and help in the delineation of regulatory risk assessment. The use of model organisms to gather such genomic information, through the exploitation of Omics and Bioinformatics approaches and tools, together with more focused molecular and cellular biology studies are rapidly increasing our understanding and providing an integrative view on how cells interact with their environment. The use of the model eukaryote Saccharomyces cerevisiae in the field of Toxicogenomics is discussed in this review. Despite the limitations intrinsic to the use of such a simple single cell experimental model, S. cerevisiae appears to be very useful as a first screening tool, limiting the use of animal models. Moreover, it is also one of the most interesting systems to obtain a truly global understanding of the toxicological response and resistance mechanisms, being in the frontline of systems biology research and developments. The impact of the knowledge gathered in the yeast model, through the use of Toxicogenomics approaches, is highlighted here by its use in prediction of toxicological outcomes of exposure to pesticides and pharmaceutical drugs, but also by its impact in biotechnology, namely in the development of more robust crops and in the improvement of yeast strains as cell factories.

Evolutionary and Biochemical Aspects of Chemical Stress Resistance in Saccharomyces cerevisiae

Large-scale chemical genetics screens (chemogenomics) in yeast have been widely used to find drug targets, understand the mechanism-of-action of compounds, and unravel the biochemistry of drug resistance. Chemogenomics is based on the comparison of growth of gene deletants in the presence and absence of a chemical substance. Such studies showed that more than 90% of the yeast genes are required for growth in the presence of at least one chemical. Analysis of these data, using computational approaches, has revealed non-trivial features of the natural chemical tolerance systems. As a result two non-overlapping sets of genes are seen to respectively impart robustness and evolvability in the context of natural chemical resistance. The former is composed of multidrug-resistance genes, whereas the latter comprises genes sharing chemical genetic profiles with many others. Recent publications showing the potential applications chemogenomics in studying the pharmacological basis of various drugs are discussed, as well as the expansion of chemogenomics to other organisms. Finally, integration of chemogenomics with sensitive sequence analysis and ubiquitination/phosphorylation data led to the discovery of a new conserved domain and important post-translational modification pathways involved in stress resistance.

A genome-wide screen identifies yeast genes required for protection against or enhanced cytotoxicity of the antimalarial drug quinine

Quinine is used in the treatment of Plasmodium falciparum severe malaria. However, both the drug's mode of action and mechanisms of resistance are still poorly understood and subject to debate. In an effort to clarify these questions, we used the yeast Saccharomyces cerevisiae as a model for pharmacological studies with quinine. Following on a previous work that examined the yeast genomic expression program in response to quinine, we now explore a genome-wide screen for altered susceptibility to quinine using the EUROSCARF collection of yeast deletion strains. We identified 279 quinine-susceptible strains, among which 112 conferred a hyper-susceptibility phenotype. The expression of these genes, mainly involved in carbohydrate metabolism, iron uptake and ion homeostasis functions, is required for quinine resistance in yeast. Sixty-two genes whose deletion leads to increased quinine resistance were also identified in this screen, including several genes encoding ribosome protein subunits. These well-known potential drug targets in Plasmodium are associated with quinine action for the first time in this study. The suggested involvement of phosphate signaling and transport in quinine tolerance was also studied, and activation of phosphate starvation-responsive genes was observed under a mild-induced quinine stress. Finally, P. falciparum homology searches were performed for a selected group of 41 genes. Thirty-two encoded proteins possess homologs in the parasite, including subunits of a parasitic vacuolar H(+)-ATPase complex, ion and phosphate importers, and several ribosome protein subunits, suggesting that the results obtained in yeast are good candidates to be transposed and explored in a P. falciparum context.

Molecular characterization of propolis-induced cell death in Saccharomyces cerevisiae

Propolis, a natural product of plant resins, is used by the bees to seal holes in their honeycombs and protect the hive entrance. However, propolis has also been used in folk medicine for centuries. Here, we apply the power of Saccharomyces cerevisiae as a model organism for studies of genetics, cell biology, and genomics to determine how propolis affects fungi at the cellular level. Propolis is able to induce an apoptosis cell death response. However, increased exposure to propolis provides a corresponding increase in the necrosis response. We showed that cytochrome c but not endonuclease G (Nuc1p) is involved in propolis-mediated cell death in S. cerevisiae. We also observed that the metacaspase YCA1 gene is important for propolis-mediated cell death. To elucidate the gene functions that may be required for propolis sensitivity in eukaryotes, the full collection of about 4,800 haploid S. cerevisiae deletion strains was screened for propolis sensitivity. We were able to identify 138 deletion strains that have different degrees of propolis sensitivity compared to the corresponding wild-type strains. Systems biology revealed enrichment for genes involved in the mitochondrial electron transport chain, vacuolar acidification, negative regulation of transcription from RNA polymerase II promoter, regulation of macroautophagy associated with protein targeting to vacuoles, and cellular response to starvation. Validation studies indicated that propolis sensitivity is dependent on the mitochondrial function and that vacuolar acidification and autophagy are important for yeast cell death caused by propolis.

Novel therapies in childhood heart failure: today and tomorrow

Heart failure is an important cause of morbidity and mortality in individuals of all ages. The many-faceted nature of the clinical heart failure syndrome has historically frustrated attempts to develop an overarching explanative theory. However, much useful information has been gained by basic and clinical investigation, even though a comprehensive understanding of heart failure has been elusive. Heart failure is a growing problem, in both adult and pediatric populations, for which standard medical therapy, as of 2010, can have positive effects, but these are usually limited and progressively diminish with time in most patients. If we want curative or near-curative therapy that will return patients to a normal state of health at a feasible cost, much better diagnostic and therapeutic technologies need to be developed. This review addresses the vexing group of heart failure etiologies that include cardiomyopathies and other ventricular dysfunctions of various types, for which current therapy is only modestly effective. Although there are many unique aspects to heart failure in patients with pediatric and congenital heart disease, many of the innovative approaches that are being developed for the care of adults with heart failure will be applicable to heart failure in childhood.

Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action

We present a cross-species chemogenomic screening platform using libraries of haploid deletion mutants from two yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We screened a set of compounds of known and unknown mode of action (MoA) and derived quantitative drug scores (or D-scores), identifying mutants that are either sensitive or resistant to particular compounds. We found that compound-functional module relationships are more conserved than individual compound-gene interactions between these two species. Furthermore, we observed that combining data from both species allows for more accurate prediction of MoA. Finally, using this platform, we identified a novel small molecule that acts as a DNA damaging agent and demonstrate that its MoA is conserved in human cells.

Robustness and evolvability in natural chemical resistance: identification of novel systems properties, biochemical mechanisms and regulatory interactions

A vast amount of data on the natural resistance of Saccharomyces cerevisiae to a diverse array of chemicals has been generated over the past decade (chemical genetics). We endeavored to use this data to better characterize the "systems" level properties of this phenomenon. By collating data from over 30 different genome-scale studies on growth of gene deletion mutants in presence of diverse chemicals, we assembled the largest currently available gene-chemical network. We also derived a second gene-gene network that links genes with significantly overlapping chemical-genetic profiles. We analyzed properties of these networks and investigated their significance by overlaying various sources of information, such as presence of TATA boxes in their promoters (which typically correlate with transcriptional noise), association with TFIID or SAGA, and propensity to function as phenotypic capacitors. We further combined these networks with ubiquitin and protein kinase-substrate networks to understand chemical tolerance in the context of major post-translational regulatory processes. Hubs in the gene-chemical network (multidrug resistance genes) are notably enriched for phenotypic capacitors (buffers against phenotypic variation), suggesting the generality of these players in buffering mechanistically unrelated deleterious forces impinging on the cell. More strikingly, analysis of the gene-gene network derived from the gene-chemical network uncovered another set of genes that appear to function in providing chemical tolerance in a cooperative manner. These appear to be enriched in lineage-specific and rapidly diverging members that also show a corresponding tendency for SAGA-dependent regulation, evolutionary divergence and noisy expression patterns. This set represents a previously underappreciated component of the chemical response that enables cells to explore alternative survival strategies. Thus, systems robustness and evolvability are simultaneously active as general forces in tolerating environmental variation. We also recover the actual genes involved in the above-discussed network properties and predict the biochemistry of their products. Certain key components of the ubiquitin system (e.g. Rcy1, Wss1 and Ubp16), peroxisome recycling (e.g. Irs4) and phosphorylation cascades (e.g. NPR1, MCK1 and HOG) are major participants and regulators of chemical resistance. We also show that a major sub-network boosting mitochondrial protein synthesis is important for exploration of alternative survival strategies under chemical stress. Further, we find evidence that cellular exploration of survival strategies under chemical stress and secondary metabolism draw from a common pool of biochemical players (e.g. acetyltransferases and a novel NTN hydrolase).

PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach

In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule’s aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen. PharmMapper is available at http://59.78.96.61/pharmmapper.

High-content, image-based screening for drug targets in yeast

Background

Drug discovery and development are predicated on elucidation of the potential mechanisms of action and cellular targets of candidate chemical compounds. Recent advances in high-content imaging techniques allow simultaneous analysis of a range of cellular events. In this study, we propose a novel strategy to identify drug targets by combining genetic screening and high-content imaging in yeast.

Methodology

In this approach, we infer the cellular functions affected by candidate drugs by comparing morphologic changes induced by the compounds with the phenotypes of yeast mutants.

Conclusions

Using this method and four well-characterized reagents, we successfully identified previously known target genes of the compounds as well as other genes involved with functionally related cellular pathways. This is the first demonstration of a genetic high-content assay that can be used to identify drug targets based on morphologic phenotypes of a reference mutant panel.

CYSTM, a novel cysteine-rich transmembrane module with a role in stress tolerance across eukaryotes

Using sensitive sequence profile analysis, we identify a hitherto uncharacterized cysteine-rich, transmembrane (TM) module, CYSTM, found in a wide range of tail-anchored membrane proteins across eukaryotes. This superfamily includes Schizosaccharomyces Uvi15, Arabidopsis PCC1, Digtaria CDT1 and Saccharomyces proteins YDL012C and YDR210W, which have all been implicated in resistance/response to stress or pathogens. Based on the pattern of conserved cysteines and data from different chemical genetics studies, we suggest that CYSTM proteins might have critical role in responding to deleterious compounds at the plasma membrane via chelation or redox-based mechanisms. Thus, CYSTM proteins are likely to be part of a novel cellular protective mechanism that is widely active in eukaryotes, including humans.

Contact:aravind@ncbi.nih.gov

Supplementary Information:Supplementary data are available at Bioinformatics online.

Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors

Systematic identification of protein-drug interaction networks is crucial to correlate complex modes of drug action to clinical indications. We introduce a novel computational strategy to identify protein-ligand binding profiles on a genome-wide scale and apply it to elucidating the molecular mechanisms associated with the adverse drug effects of Cholesteryl Ester Transfer Protein (CETP) inhibitors. CETP inhibitors are a new class of preventive therapies for the treatment of cardiovascular disease. However, clinical studies indicated that one CETP inhibitor, Torcetrapib, has deadly off-target effects as a result of hypertension, and hence it has been withdrawn from phase III clinical trials. We have identified a panel of off-targets for Torcetrapib and other CETP inhibitors from the human structural genome and map those targets to biological pathways via the literature. The predicted protein-ligand network is consistent with experimental results from multiple sources and reveals that the side-effect of CETP inhibitors is modulated through the combinatorial control of multiple interconnected pathways. Given that combinatorial control is a common phenomenon observed in many biological processes, our findings suggest that adverse drug effects might be minimized by fine-tuning multiple off-target interactions using single or multiple therapies. This work extends the scope of chemogenomics approaches and exemplifies the role that systems biology has in the future of drug discovery.

Both the cost to launch a new drug and the attrition rate during the late stage of the drug discovery and development process are increasing. Torcetrapib is a case in point, having been withdrawn from phase III clinical trials after 15 years of development and an estimated cost of US $800 M. Torcetrapib represents a new class of therapies for the treatment of cardiovascular disease; however, clinical studies indicated that Torcetrapib has deadly side-effects as a result of hypertension. To understand the origins of these adverse drug reactions from Torcetrapib and other related drugs undergoing clinical trials, we introduce a systematic strategy to identify off-targets in the human structural proteome and investigate the roles of these off-targets in impacting human physiology and pathology using biochemical pathway analysis. Our findings suggest that potential side-effects of a new drug can be identified at an early stage of the development cycle and be minimized by fine-tuning multiple off-target interactions. The hope is that this can reduce both the cost of drug development and the mortality rates during clinical trials.

Target discovery from data mining approaches

Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.