A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism

A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism

Identification of Attractive Drug Targets in Neglected-Disease Pathogens Using an In Silico Approach

In cell-based drug development, researchers attempt to create drugs that kill a pathogen without necessarily understanding the details of how the drugs work. In contrast, target-based drug development entails the search for compounds that act on a specific intracellular target—often a protein known or suspected to be required for survival of the pathogen. The latter approach to drug development has been facilitated greatly by the sequencing of many pathogen genomes and the incorporation of genome data into user-friendly databases. The present paper shows how the database TDRtargets.org can identify proteins that might be considered good drug targets for diseases such as African sleeping sickness, Chagas disease, parasitic worm infections, tuberculosis, and malaria. These proteins may score highly in searches of the database because they are dissimilar to human proteins, are structurally similar to other “druggable” proteins, have functions that are easy to measure, and/or fulfill other criteria. Researchers can use the lists of high-scoring proteins as a basis for deciding which potential drug targets to pursue experimentally

A Comparative Chemogenomics Strategy to Predict Potential Drug Targets in the Metazoan Pathogen, Schistosoma mansoni

Schistosomiasis is a prevalent and chronic helmintic disease in tropical regions. Treatment and control relies on chemotherapy with just one drug, praziquantel and this reliance is of concern should clinically relevant drug resistance emerge and spread. Therefore, to identify potential target proteins for new avenues of drug discovery we have taken a comparative chemogenomics approach utilizing the putative proteome of Schistosoma mansoni compared to the proteomes of two model organisms, the nematode, Caenorhabditis elegans and the fruitfly, Drosophila melanogaster. Using the genome comparison software Genlight, two separate in silico workflows were implemented to derive a set of parasite proteins for which gene disruption of the orthologs in both the model organisms yielded deleterious phenotypes (e.g., lethal, impairment of motility), i.e., are essential genes/proteins. Of the 67 and 68 sequences generated for each workflow, 63 were identical in both sets, leading to a final set of 72 parasite proteins. All but one of these were expressed in the relevant developmental stages of the parasite infecting humans. Subsequent in depth manual curation of the combined workflow output revealed 57 candidate proteins. Scrutiny of these for ‘druggable’ protein homologs in the literature identified 35 S. mansoni sequences, 18 of which were homologous to proteins with 3D structures including co-crystallized ligands that will allow further structure-based drug design studies. The comparative chemogenomics strategy presented generates a tractable set of S. mansoni proteins for experimental validation as drug targets against this insidious human pathogen

Helminth Genomics: The Implications for Human Health

More than two billion people (one-third of humanity) are infected with parasitic roundworms or flatworms, collectively known as helminth parasites. These infections cause diseases that are responsible for enormous levels of morbidity and mortality, delays in the physical development of children, loss of productivity among the workforce, and maintenance of poverty. Genomes of the major helminth species that affect humans, and many others of agricultural and veterinary significance, are now the subject of intensive genome sequencing and annotation. Draft genome sequences of the filarial worm Brugia malayi and two of the human schistosomes, Schistosoma japonicum and S. mansoni, are now available, among others. These genome data will provide the basis for a comprehensive understanding of the molecular mechanisms involved in helminth nutrition and metabolism, host-dependent development and maturation, immune evasion, and evolution. They are likely also to predict new potential vaccine candidates and drug targets. In this review, we present an overview of these efforts and emphasize the potential impact and importance of these new findings

Major prospects for exploring canine vector borne diseases and novel intervention methods using 'omic technologies

Canine vector-borne diseases (CVBDs) are of major socioeconomic importance worldwide. Although many studies have provided insights into CVBDs, there has been limited exploration of fundamental molecular aspects of most pathogens, their vectors, pathogen-host relationships and disease and drug resistance using advanced, 'omic technologies. The aim of the present article is to take a prospective view of the impact that next-generation, 'omics technologies could have, with an emphasis on describing the principles of transcriptomic/genomic sequencing as well as bioinformatic technologies and their implications in both fundamental and applied areas of CVBD research. Tackling key biological questions employing these technologies will provide a 'systems biology' context and could lead to radically new intervention and management strategies against CVBDs

MOTILE ORGANISMS DISPERSING AND TRACKING CHEMICAL SIGNALS

Ph.D

In silico identification of novel redox enzyme inhibitors for the development of antiparasitic and anticancer drugs

Die Methoden der Bio- und der Chemoinformatik sind heutzutage aus der modernen Wirkstoffforschung nicht mehr wegzudenken. Dies belegen zahlreiche Erfolgsbeispiele wie Gleevec oder Tamiflu, bei deren Entwicklung solche in silico-Methoden maßgeblich beteiligt waren. Da die dabei durchgeführten Berechnungen sehr ressourcenintensiv sind, werden leistungsfähige Computer benötigt. Neben den Grid-Computing-Systemen bieten Linux-Cluster eine probate Möglichkeit, die benötigten Ressourcen zur Verfügung zu stellen. Im Rahmen der vorliegenden Arbeit wurde eine Linux-Cluster-Umgebung für virtuelles Hochdurchsatz-Screening validiert und das Setup optimiert. Mit diesem System konnten im Anschluss sowohl Literaturdaten reproduziert, als auch neue bioaktive Enzym-Inhibitoren identifiziert werden. Darüber hinaus konnte eine bemerkenswerte Geschwindigkeitssteigerung bei gleicher Qualität der Ergebnisse gegenüber der bisher verwendeten Hardware erzielt werden. Tropische Infektionskrankheiten wie Malaria, Trypanosomiasis und Lei¬sh¬¬maniose, welche durch Protozoen der Gattungen Plasmodium sp., Trypanosoma sp. und Leishmania sp. hervorgerufen werden, haben massive Auswirkungen auf die Gesundheit des Menschen sowie auf den Wohlstand vieler Länder. Sie fordern pro Jahr mehrere Millionen Todesopfer. Da viele der zurzeit zugelassenen Medikamente für die Betroffenen nicht erschwinglich sind und sie zudem aufgrund von stärker werdenden Resistenzen immer mehr an Wirkung verlieren, ist die Entwicklung neuer Medikamente zur Behandlung dieser Krankheiten dringend notwendig. Die Tatsache, dass die o. g. Protozoen sehr empfindlich gegenüber oxidativem Stress sind, macht die Hauptkomponenten ihrer antioxidativen Netzwerke zu hervorragenden Angriffspunkten für Chemotherapeutika. Dabei sind auch die antioxidativen Netzwerke der Wirtsorganismen von besonderem Interesse, da eine Inhibierung derselben ebenfalls zu erhöhtem oxidativen Stress für den Parasiten führt. Die Hauptkomponenten dieser Netzwerke sind u. a. die Glutathion-Reduktase und die Trypanothion-Reduktase. Ein weiteres Enzym, welches kein Bestandteil dieser Netzwerke ist, aber dennoch für die Entstehung von oxidativem Stress sorgen kann, ist die Liponamid-Dehydrogenase. Um weitere Vorstufen für die Entwicklung neuer Medikamente gegen Malaria, Trypanosomiasis und Leishmaniose bereit zu stellen, wurden in dieser Arbeit mit in silico-Methoden neue Inhibitoren dieser Enzyme id¬en¬tifiziert. Hierzu wurde ein Großteil der aus der Literatur bekannten Inhibitoren in einer Datenbank erfasst und aus diesen Liganden Pharmakophor-Modelle abgeleitet. Mit Hilfe dieser Modelle wurden im Anschluss virtuelle Substanzbibliotheken dahingehend gefiltert, dass die resultierenden Substanzen eine hohe Wahrscheinlichkeit haben, mit dem Zielprotein zu interagieren. Die so erhaltenen Moleküle wurden nun unter Verwendung eines zuvor validierten und optimierten Docking-Setups in das Zielprotein eingepasst und die chemischen Interaktionen zwischen Protein und Ligand bewertet. Eine Auswahl an Substanzen konnte dann mittels in vitro-Tests auf ihre biologische Wirksamkeit überprüft werden. Dabei zeigte sich, dass die Aktivität einiger Inhibitoren mit den besten aus der Literatur bekannten Substanzen vergleichbar war. Anhand dieser Daten war es möglich Struktur-Aktivitäts-Beziehungen abzuleiten, die deutliche Hinweise für die Optimierung der identifizierten Inhibitoren liefern. Bei allen hier durchgeführten Docking-Experimenten konnte eine gute Korrelation zwischen den vorgeschlagenen Bindungsmodi und den gemessenen biologischen Aktivitäten der Inhibitoren beobachtet werden, was eine weitere Bestätigung für die Funktionalität der hier angewendeten Methode liefert. Die vorliegenden Ergebnisse stellen eine wertvolle Grundlage für die zukünftige Entwicklung neuer Wirkstoffe gegen Malaria, Trypanosomiasis und Leishmaniose dar.Bio- and Chemoinformatic methods are key technologies within the drug discovery process. Such in silico techniques, for example, contributed significantly to the development of Gleevec and Tamiflu, two well-known and successful drugs. Due to the fact that these approaches generally require a lot of computing power, grid-computing systems or Linux clusters are used to fulfil these needs. One part of this dissertation dealt with the validation and optimisation of a Linux cluster for virtual high-throughput screening purposes. Using this optimised setup it was possible to reproduce literature data and to identify new bioactive enzyme inhibitors. Compared to the hardware used till now, a significant speed increase could be observed providing consistent results. Tropical infectious diseases, e.g. Malaria, Trypanosomiasis and Leishmaniasis which are caused by the protozoan parasites Plasmodium sp., Trypanosoma sp. and Leishmania sp. represent an immense social and economic burden leading to millions of deaths annually. Because most of the marketed drugs are too expensive and are subject to efficacy losses due to increasing resistance problems, there is a very strong need for the development of new achievable and resistance-breaking drugs for the treatment of these diseases. The above mentioned protozoa are very sensitive towards oxidative stress. Therefore, the main antioxidant defense systems of these parasites - consisting of Glutathione reductase and Trypanothione reductase - are attractive target proteins for chemotherapeutics. In this context the antioxidant defense systems of the host organisms are considered as promising targets as well, because an inhibition of these proteins leads to an increase of oxidative stress for the parasite. The structurally closely related Lipoamide dehydrogenase, although not a part of the antioxidant defense systems, may contribute to the generation of oxidative stress, either. In order to provide further precursor molecules for the development of new drugs against Malaria, Trypanosomiasis and Leishmaniasis, in silico methods were used to identify new inhibitors of these enzymes. Based on known inhibitors pharmacophore models were generated which could be used to filter a virtual compound library. This step led to an increased interaction probability between the resulting compounds and the target proteins. Afterwards the molecules were fitted into the target proteins and the chemical interactions were scored using a validated and optimised docking setup. With the help of in vitro assays the biological activity of a selection of docked and scored compounds could be determined leading to substances which showed activities comparable to the best inhibitors known from literature. These data enabled the investigation of structure-activity-relationships providing clear hints for further chemical optimisation of the identified inhibitors. Within all docking experiments a good correlation between the proposed binding modes and the biological activities could be observed thereby confirming that these in silico techniques represent important methods for the identification of new enzyme inhibitors. The results in hand establish a valuable basis for the future development of drugs against Malaria, Trypanosomiasis and Leishmaniasis