PAVOOC: designing CRISPR sgRNAs using 3D protein structures and functional domain annotations.

SUMMARY: Single-guide RNAs (sgRNAs) targeting the same gene can significantly vary in terms of efficacy and specificity. PAVOOC (Prediction And Visualization of On- and Off-targets for CRISPR) is a web-based CRISPR sgRNA design tool that employs state of the art machine learning models to prioritize most effective candidate sgRNAs. In contrast to other tools, it maps sgRNAs to functional domains and protein structures and visualizes cut sites on corresponding protein crystal structures. Furthermore, PAVOOC supports homology-directed repair template generation for genome editing experiments and the visualization of the mutated amino acids in 3D. AVAILABILITY AND IMPLEMENTATION: PAVOOC is available under https://pavooc.me and accessible using modern browsers (Chrome/Chromium recommended). The source code is hosted at github.com/moritzschaefer/pavooc under the MIT License. The backend, including data processing steps, and the frontend are implemented in Python 3 and ReactJS, respectively. All components run in a simple Docker environment. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network.

BACKGROUND: Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. DESCRIPTION: We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface. CONCLUSIONS: Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com.

Phenotype mining for functional genomics and gene discovery.

In gene prediction, studying phenotypes is highly valuable for reducing the number of locus candidates in association studies and to aid disease gene candidate prioritization. This is due to the intrinsic nature of phenotypes to visibly reflect genetic activity, making them potentially one of the most useful data types for functional studies. However, systematic use of these data has begun only recently. 'Comparative phenomics' is the analysis of genotype-phenotype associations across species and experimental methods. This is an emerging research field of utmost importance for gene discovery and gene function annotation. In this chapter, we review the use of phenotype data in the biomedical field. We will give an overview of phenotype resources, focusing on PhenomicDB--a cross-species genotype-phenotype database--which is the largest available collection of phenotype descriptions across species and experimental methods. We report on its latest extension by which genotype-phenotype relationships can be viewed as graphical representations of similar phenotypes clustered together ('phenoclusters'), supplemented with information from protein-protein interactions and Gene Ontology terms. We show that such 'phenoclusters' represent a novel approach to group genes functionally and to predict novel gene functions with high precision. We explain how these data and methods can be used to supplement the results of gene discovery approaches. The aim of this chapter is to assist researchers interested in understanding how phenotype data can be used effectively in the gene discovery field.

Evaluation of activity and combination strategies with the microtubule-targeting drug sagopilone in breast cancer cell lines.

Sagopilone, a fully synthetic epothilone, is a microtubule-stabilizing agent optimized for high in vitro and in vivo activity against a broad range of tumor models, including those resistant to paclitaxel and other systemic treatments. Sagopilone development is accompanied by translational research studies to evaluate the molecular mode of action, to recognize mechanisms leading to resistance, to identify predictive response biomarkers, and to establish a rationale for combination with different therapies. Here, we profiled sagopilone activity in breast cancer cell lines. To analyze the mechanisms of mitotic arrest and apoptosis and to identify additional targets and biomarkers, an siRNA-based RNAi drug modifier screen interrogating 300 genes was performed in four cancer cell lines. Defects of the spindle assembly checkpoint (SAC) were identified to cause resistance against sagopilone-induced mitotic arrest and apoptosis. Potential biomarkers for resistance could therefore be functional defects like polymorphisms or mutations in the SAC, particularly in the central SAC kinase BUB1B. Moreover, chromosomal heterogeneity and polyploidy are also potential biomarkers of sagopilone resistance since they imply an increased tolerance for aberrant mitosis. RNAi screening further demonstrated that the sagopilone-induced mitotic arrest can be enhanced by concomitant inhibition of mitotic kinesins, thus suggesting a potential combination therapy of sagopilone with a KIF2C (MCAK) kinesin inhibitor. However, the combination of sagopilone and inhibition of the prophase kinesin KIF11 (EG5) is antagonistic, indicating that the kinesin inhibitor has to be highly specific to bring about the required therapeutic benefit.

Phenoclustering: online mining of cross-species phenotypes.

SUMMARY: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional analysis. This system is part of PhenomicDB, providing the world's largest cross-species phenotype data collection with a tool to mine its wealth of information. AVAILABILITY: Freely available at http://www.phenomicdb.de

ROCO kinase activity is controlled by internal GTPase function.

Small guanosine triphosphatases (GTPases) have long been known to control the activities of downstream protein kinases. Some members of a rather new multidomain protein family contain not only a GTPase domain of the ROC (Ras of complex protein) subtype but also a protein kinase domain, and both domains seem to cooperate with each other in the same polypeptide. Data now show that the kinase activity of one of these ROCO proteins depends on whether guanosine diphosphate or guanosine triphosphate (GTP) is bound and that the activity is controlled by the adjacent GTPase, which suggests a novel mechanism of intrinsic control. This ROCO family member, leucine-rich repeat kinase 2 (LRRK2), is of special interest because mutations within both its protein kinase and its GTPase domains are associated with Parkinson's disease (PD). These mutations lead to abnormally enhanced protein kinase activity, which is believed to cause or at least contribute to neuronal damage. The crystal structure of the GTPase domain of LRRK2 has now been resolved and shows that the ROC GTPase domain is responsible for LRRK2 homodimerization in a surprising way. The structure not only offers insights into the molecular effects of some of the PD-associated mutations of LRRK2, but may also help to improve our understanding of the intrinsic control mechanism between a GTPase and a protein kinase within the same protein.

Mining phenotypes for gene function prediction.

BACKGROUND: Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships. RESULTS: We present results on a study where we use a large set of phenotype data - in textual form - to predict gene annotation. To this end, we use text clustering to group genes based on their phenotype descriptions. We show that these clusters correlate well with several indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We exploit these clusters for predicting gene function by carrying over annotations from well-annotated genes to other, less-characterized genes in the same cluster. For a subset of groups selected by applying objective criteria, we can predict GO-term annotations from the biological process sub-ontology with up to 72.6% precision and 16.7% recall, as evaluated by cross-validation. We manually verified some of these clusters and found them to exhibit high biological coherence, e.g. a group containing all available antennal Drosophila odorant receptors despite inconsistent GO-annotations. CONCLUSION: The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions. Thus, systematically analyzing these data on a large scale offers many possibilities for inferring functional annotation of genes. We show that text clustering can play an important role in this process.

Phylogenetic conservation of the androgen receptor AR45 variant form in placental mammals.

A cDNA coding for a tissue-specific AR45 variant form of the androgen receptor (AR) has recently been identified in humans, with highest expression levels found in heart. The deduced protein comprises the DNA-binding domain, hinge region and ligand-binding domain of the AR, but not the N-terminal domain which is replaced by a unique, short, seven amino-acid-long stretch. This sequence is encoded by the mutually exclusive exon 1B, located between exons 1 and 2 of the human AR gene. As transcript variants of the steroid receptor family have been shown to have important implications for hormone function, we set out to analyse the genomes of different organisms for potential AR45 expression. We found exon 1B to be conserved in the syntenic chromosomal region of non-human primates such as the chimpanzee Pan troglodytes, the orang-utan Pongo pygmaeus, the macaque Macaca mulatta and the marmoset Callithrix jacchus, and of the elephant Loxondonta africana, the pig Sus scrofa and the dog Canis familiaris. Quantification of AR45 transcript levels in heart, skeletal muscle and lung of Macaca fascicularis showed the heart to be the main organ of expression. A complete AR45 cDNA was furthermore isolated from the heart of this species. Comparative analysis of the identified AR45 exon 1B regions and of the deduced amino acids revealed a high conservation among species. The four N-terminal residues were identical in all eight species, whereas a few changes were seen in the other three residues in the marmoset, elephant and pig. In contrast, we observed more divergence in the mouse Mus musculus and rat Rattus norvegicus syntenic regions. Here a stop codon was found downstream of the potential start codon in the putatively deduced protein sequence and it can be inferred that no protein corresponding to AR45 exists in these two species. The existence of AR45 in different placental mammals with the exception of mouse and rat suggests a disappearance in rodents late in evolution, before the separation of the mouse and rat lineages, about 16 million years ago. In view of the potential function of AR45 as a regulator of AR function, and considering the multiple roles of androgens in normal physiology and in several diseases, these findings have important implications with regard to subtle differences in the action of the male sexual hormone in various organisms.

PhenomicDB: a new cross-species genotype/phenotype resource.

Phenotypes are an important subject of biomedical research for which many repositories have already been created. Most of these databases are either dedicated to a single species or to a single disease of interest. With the advent of technologies to generate phenotypes in a high-throughput manner, not only is the volume of phenotype data growing fast but also the need to organize these data in more useful ways. We have created PhenomicDB (freely available at http://www.phenomicdb.de), a multi-species genotype/phenotype database, which shows phenotypes associated with their corresponding genes and grouped by gene orthologies across a variety of species. We have enhanced PhenomicDB recently by additionally incorporating quantitative and descriptive RNA interference (RNAi) screening data, by enabling the usage of phenotype ontology terms and by providing information on assays and cell lines. We envision that integration of classical phenotypes with high-throughput data will bring new momentum and insights to our understanding. Modern analysis tools under development may help exploiting this wealth of information to transform it into knowledge and, eventually, into novel therapeutic approaches.

LRRK1 protein kinase activity is stimulated upon binding of GTP to its Roc domain.

Human leucine-rich repeat kinase 1 (LRRK1) is a multi-domain protein of unknown function belonging to the ROCO family of complex proteins. Here, we report the molecular characterization of human LRRK1 and show, for the first time, that LRRK1 is both a functional protein kinase and a GDP/GTP-binding protein. Binding of GTP to LRRK1 is specific, requires the GTPase-like Roc domain, and leads to a stimulation of LRRK1 kinase activity. LRRK1 is the first example of a GTP-regulated protein kinase harboring both the kinase effector domain and the GTP-binding regulatory domain. Hence, we propose a model in which LRRK1 cycles between a GTP-bound active and a GDP-bound inactive state. Moreover, we mutated LRRK1 to mimic mutations previously identified in LRRK2/dardarin, the only human paralogue of LRRK1, that have been linked to autosomal-dominant parkinsonism. We demonstrate that three of four mutations analyzed significantly downregulate LRRK1 kinase activity. Ultimately, the results presented for LRRK1 may contribute to the elucidation of LRRK2's role in the pathogenesis of Parkinson's disease.

PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics.

UNLABELLED: We have created PhenomicDB, a multi-species genotype/phenotype database by merging public genotype/phenotype data from a wide range of model organisms and Homo sapiens. Until now these data were available in distinct organism-specific databases (e.g. WormBase, OMIM, FlyBase and MGI). We compiled this wealth of data into a single integrated resource by coarse-grained semantic mapping of the phenotypic data fields, by including common gene indices (NCBI Gene), and by the use of associated orthology relationships. With its use-case-oriented user interface, PhenomicDB allows scientists to compare and browse known phenotypes for a given gene or a set of genes from different organisms simultaneously. AVAILABILITY: PhenomicDB has been implemented at Schering AG as described below. A PhenomicDB implementation differing in some technical details has been set up for the public at Metalife AG http://www.phenomicDB.de SUPPLEMENTARY INFORMATION: database model, semantic mapping table.

TT virus-derived apoptosis-inducing protein induces apoptosis preferentially in hepatocellular carcinoma-derived cells.

TT virus (TTV) is widespread among the global population. Its pathogenic nature is still unclear but TTV seems to be more prevalent in cases of hepatitis than in healthy individuals. TTV harbours similarities to chicken anaemia virus (CAV). Here, the apoptotic potential of a putative TTV-derived 105 aa protein and of the main apoptosis-inducing agent of CAV, Apoptin, is compared. As the putative protein induced apoptosis in various human hepatocellular carcinoma (HCC) cell lines, it was named TTV-derived apoptosis-inducing protein (TAIP). The apoptotic activity of TAIP in HCC lines was comparable with that of Apoptin. Conversely, unlike Apoptin, TAIP induced only low-level apoptosis in several non-HCC human cancer cell lines. The data suggest that TAIP acts in a different way to Apoptin as it is selective to a certain degree for HCC lines. This activity of TAIP, coupled with the heterogeneity of TTV isolates, may help to explain the variable reports of TTV pathogenicity.

OTEX, an androgen-regulated human member of the paired-like class of homeobox genes.

paired genes emerged early in evolution and code for homeobox transcription factors, having fundamental roles in various biological processes. We identified a novel human member of the paired-like class, which we named OTEX. A phylogenetic analysis revealed that OTEX belonged to the recently defined PEPP subfamily of paired-like homeobox genes. It was organized into three introns and, like the other PEPP genes, it was mapped to chromosome X. Its transcripts were detected mainly in the ovary, testis and epididymis, but also in the prostate and mammary gland. In the PC-3/ARwt prostate cell line, OTEX expression was stimulated dramatically following androgen treatment. Immunofluorescence studies revealed an exclusively nuclear localization of the OTEX protein. Mutation of the RARCRRHQRE amino acid sequence present at the C-terminus of the OTEX homeodomain resulted in a mainly cytoplasmic localization, indicating that this motif harboured the nuclear localization signal. No inherent transactivation function was seen for OTEX using the one-hybrid assay, and no homodimer formation was observed in the two-hybrid assay, suggesting that additional partners were needed for this activity. Taken together, the data show that OTEX represents a novel, androgen-regulated, paired-like homeobox protein, with possibly an important role in human reproduction.