Knowledge integration for analyzing ChIP-seq

To capture the genomic profiles for histone modification, chromatin immunoprecipitation (ChIP) is combined with next generation sequencing, which is called ChIP-seq. However, enriched regions generated from the ChIP-seq data are only evaluated on the limited knowledge acquired from manually examining the relevant biological literature. This paper proposes a novel framework, which integrates multiple knowledge sources such as biological literature, Gene Ontology, and microarray data. In order to precisely analyze ChIP-seq data for histone modification, knowledge integration is based on a unified probabilistic model. The model is employed to re-rank the enriched regions generated from peak finding algorithms. Through filtering the reranked enriched regions using some predefined threshold, more reliable and precise results could be generated. The combination of the multiple knowledge sources with the peaking finding algorithm produces a new paradigm for ChIP-seq data analysis. © (2012) Trans Tech Publications, Switzerland.

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

A comprehensive and integrative reconstruction of evolutionary history for Anomura (Crustacea: Decapoda)

Background The infraorder Anomura has long captivated the attention of evolutionary biologists due to its impressive morphological diversity and ecological adaptations. To date, 2500 extant species have been described but phylogenetic relationships at high taxonomic levels remain unresolved. Here, we reconstruct the evolutionary history—phylogeny, divergence times, character evolution and diversification—of this speciose clade. For this purpose, we sequenced two mitochondrial (16S and 12S) and three nuclear (H3, 18S and 28S) markers for 19 of the 20 extant families, using traditional Sanger and next-generation 454 sequencing methods. Molecular data were combined with 156 morphological characters in order to estimate the largest anomuran phylogeny to date. The anomuran fossil record allowed us to incorporate 31 fossils for divergence time analyses. Results Our best phylogenetic hypothesis (morphological + molecular data) supports most anomuran superfamilies and families as monophyletic. However, three families and eleven genera are recovered as para- and polyphyletic. Divergence time analysis dates the origin of Anomura to the Late Permian ~259 (224–296) MYA with many of the present day families radiating during the Jurassic and Early Cretaceous. Ancestral state reconstruction suggests that carcinization occurred independently 3 times within the group. The invasion of freshwater and terrestrial environments both occurred between the Late Cretaceous and Tertiary. Diversification analyses found the speciation rate to be low across Anomura, and we identify 2 major changes in the tempo of diversification; the most significant at the base of a clade that includes the squat-lobster family Chirostylidae. Conclusions Our findings are compared against current classifications and previous hypotheses of anomuran relationships. Many families and genera appear to be poly- or paraphyletic suggesting a need for further taxonomic revisions at these levels. A divergence time analysis provides key insights into the origins of major lineages and events and the timing of morphological (body form) and ecological (habitat) transitions. Living anomuran biodiversity is the product of 2 major changes in the tempo of diversification; our initial insights suggest that the acquisition of a crab-like form did not act as a key innovation.

Towards the prediction of mutations in genomic sequences

Bio-systems are inherently complex information processing systems. Furthermore, physiological complexities of biological systems limit the formation of a hypothesis in terms of behavior and the ability to test hypothesis. More importantly the identification and classification of mutation in patients are centric topics in today's cancer research. Next generation sequencing (NGS) technologies can provide genome-wide coverage at a single nucleotide resolution and at reasonable speed and cost. The unprecedented molecular characterization provided by NGS offers the potential for an individualized approach to treatment. These advances in cancer genomics have enabled scientists to interrogate cancer-specific genomic variants and compare them with the normal variants in the same patient. Analysis of this data provides a catalog of somatic variants, present in tumor genome but not in the normal tissue DNA. In this dissertation, we present a new computational framework to the problem of predicting the number of mutations on a chromosome for a certain patient, which is a fundamental problem in clinical and research fields. We begin this dissertation with the development of a framework system that is capable of utilizing published data from a longitudinal study of patients with acute myeloid leukemia (AML), who's DNA from both normal as well as malignant tissues was subjected to NGS analysis at various points in time. By processing the sequencing data at the time of cancer diagnosis using the components of our framework, we tested it by predicting the genomic regions to be mutated at the time of relapse and, later, by comparing our results with the actual regions that showed mutations (discovered at relapse time). We demonstrate that this coupling of the algorithm pipeline can drastically improve the predictive abilities of searching a reliable molecular signature. Arguably, the most important result of our research is its superior performance to other methods like Radial Basis Function Network, Sequential Minimal Optimization, and Gaussian Process. In the final part of this dissertation, we present a detailed significance, stability and statistical analysis of our model. A performance comparison of the results are presented. This work clearly lays a good foundation for future research for other types of cancer.^

Studio della correlazione fra la percentuale di cellule neoplastiche mutate in EGFR e la risposta a farmaci TKI nell'adenocarcinoma polmonare

Il gene EGFR e' un marcatore molecolare fondamentale per determinare la sensibilita' o meno ai farmaci inibitori delle tirosin-chinasi (TKI) in pazienti affetti da adenocarcinoma polmonare. Lo scopo del lavoro e' di determinare se la percentuale di cellule neoplastiche mutate in EGFR correli con la risposta ai farmaci TKI. Sono stati analizzati 18 casi; di ogni caso e' stato analizzato il gene EGFR (esoni 18, 19, 20 e 21) mediante Next Generation Sequencing (NGS). La quantita' di cellule neoplastiche presenti nell'area analizzata e' stata valutata da un patologo, su un vetrino colorato con Ematossilina-Eosina, e tale quantita' e' stata normalizzata alla percentuale di alleli mutati rilevata mediante NGS. E' stata rilevata una correlazione fra la percentuale di cellule neoplastiche mutate in EGFR e la risposta ai TKI, ed e' stato osservato che pazienti con una percentuale di cellule neoplastiche mutate al di sopra del 56% presentano una migliore "overall survival" rispetto ai pazienti con una percentuale inferiore. I dati suggeriscono che, oltre al valore predittivo, la definizione della percentuale di cellule neoplastiche mutate in EGFR potrebbe avere un valore prognostico.

Role of ABL Family Kinases in Breast Cancer

The ABL family of non-receptor tyrosine kinases, ABL1 (also known as c-ABL) and ABL2 (also known as Arg), links diverse extracellular stimuli to signaling pathways that control cell growth, survival, adhesion, migration and invasion. ABL tyrosine kinases play an oncogenic role in human leukemias. However, the role of ABL kinases in solid tumors including breast cancer progression and metastasis is just emerging.

To evaluate whether ABL family kinases are involved in breast cancer development and metastasis, we first analyzed genomic data from large-scale screen of breast cancer patients. We found that ABL kinases are up-regulated in invasive breast cancer patients and high expression of ABL kinases correlates with poor prognosis and early metastasis. Using xenograft mouse models combined with genetic and pharmacological approaches, we demonstrated that ABL kinases are required for regulating breast cancer progression and metastasis to the bone. Using next generation sequencing and bioinformatics analysis, we uncovered a critical role for ABL kinases in promoting multiple oncogenic pathways including TAZ and STAT5 signaling networks and the epithelial to mesenchymal transition (EMT). These findings revealed a role for ABL kinases in regulating breast cancer tumorigenesis and bone metastasis and provide a rationale for targeting breast tumors with ABL-specific inhibitors.

Kinome-wide CRISPR-Cas9 screen to identify kinases involved in Taxol resistance in breast cancer

Breast cancer is the most frequently diagnosed cancer in women, accounting for over 25% of cancer diagnoses and 13% of cancer-related deaths in Canadian women. There are many types of therapies for treatment or management of breast cancer, with chemotherapy being one of the most widely used. Taxol (paclitaxel) is one of the most extensively used chemotherapeutic agents for treating cancers of the breast and numerous other sites. Taxol stabilizes microtubules during mitosis, causing the cell cycle to arrest until eventually the cell undergoes apoptosis. Although Taxol has had significant benefits in many patients, response rates range from only 25-69%, and over half of Taxol-treated patients eventually acquire resistance to the drug. Drug resistance remains one of the greatest barriers to effective cancer treatment, yet little has been discerned regarding resistance to Taxol, despite its widespread clinical use. Kinases are known to be heavily involved in cancer development and progression, and several kinases have been linked to resistance of Taxol and other chemotherapeutic agents. However, a systematic screen for kinases regulating Taxol resistance is lacking. Thus, in this study, a set of kinome-wide screens was conducted to interrogate the involvement of kinases in the Taxol response. Positive-selection and negative-selection CRISPR-Cas9 screens were conducted, whereby a pooled library of 5070 sgRNAs targeted 507 kinase-encoding genes in MCF-7 breast cancer cells that were Taxol-sensitive (WT) or Taxol-resistant (TxR) which were then treated with Taxol. Next generation sequencing (NGS) was performed on cells that survived Taxol treatment, allowing identification and quantitation of sgRNAs. STK38, Blk, FASTK and Nek3 stand out as potentially critical kinases for Taxol-induced apoptosis to occur. Furthermore, kinases CDKL1 and FRK may have a role in Taxol resistance. Further validation of these candidate kinases will provide novel pre-clinical data about potential predictive biomarkers or therapeutic targets for breast cancer patients in the future.

Genomic consequences of mating system evolution in the Pacific coastal dune endemic, Abronia umbellata (Nyctaginaceae)

The advent of next-generation sequencing has significantly reduced the cost of obtaining large-scale genetic resources, opening the door for genomic studies of non-model but ecologically interesting species. The shift in mating system, from outcrossing to selfing, has occurred thousands of times in angiosperms and is accompanied by profound changes in the population genetics and ecology of a species. A large body of work has been devoted to understanding why the shift occurs and the impact of the shift on the genetics of the resulting selfing populations, however, the causes and consequences of the transition to selfing involve a complicated interaction of genetic and demographic factors which are difficult to untangle. Abronia umbellata is a Pacific coastal dune endemic which displays a striking shift in mating system across its geographic range, with large-flowered outcrossing populations south of San Francisco and small-flowered selfing populations to the north. Abronia umbellata is an attractive model system for the study of mating system transitions because the shift appears to be recent and therefore less obscured by post-shift processes, it has a near one-dimensional geographic range which simplifies analysis and interpretation, and demographic data has been collected for many of the populations. In this study, we generated transcriptome-level data for 12 plants including individuals from both subspecies, along with a resequencing study of 48 individuals from populations across the range. The genetic analysis revealed a recent transition to selfing involving a drastic reduction in genetic diversity in the selfing lineage, potentially indicative of a recent population bottleneck and a transition to selfing due to reproductive assurance. Interestingly, the genetic structure of the populations was not coincident with the current subspecies demarcation, and two large-flowered populations were classified with the selfing subspecies, suggesting a potential need for re-evaluation of the current subspecies classification. Our finding of low diversity in selfing populations may also have implications for the conservation value of the threatened selfing subspecies.

Application of microarrays (phylochips) for analysis of community diversity by species identification

Abstract Molecular probe-based methods (Fluorescent in-situ hybridisation or FISH, Next Generation Sequencing or NGS) have proved successful in improving both the efficiency and accuracy of the identification of microorganisms, especially those that lack distinct morphological features, such as picoplankton. However, FISH methods have the major drawback that they can only identify one or just a few species at a time because of the reduced number of available fluorochromes that can be added to the probe. Although the length of sequence that can be obtained is continually improving, NGS still requires a great deal of handling time, its analysis time is still months and with a PCR step it will always be sensitive to natural enzyme inhibitors. With the use of DNA microarrays, it is possible to identify large numbers of taxa on a single-glass slide, the so-called phylochip, which can be semi-quantitative. This review details the major steps in probe design, design and production of a phylochip and validation of the array. Finally, major microarray studies in the phytoplankton community are reviewed to demonstrate the scope of the method.

Application of microarrays (phylochips) for analysis of community diversity by species identification

Abstract Molecular probe-based methods (Fluorescent in-situ hybridisation or FISH, Next Generation Sequencing or NGS) have proved successful in improving both the efficiency and accuracy of the identification of microorganisms, especially those that lack distinct morphological features, such as picoplankton. However, FISH methods have the major drawback that they can only identify one or just a few species at a time because of the reduced number of available fluorochromes that can be added to the probe. Although the length of sequence that can be obtained is continually improving, NGS still requires a great deal of handling time, its analysis time is still months and with a PCR step it will always be sensitive to natural enzyme inhibitors. With the use of DNA microarrays, it is possible to identify large numbers of taxa on a single-glass slide, the so-called phylochip, which can be semi-quantitative. This review details the major steps in probe design, design and production of a phylochip and validation of the array. Finally, major microarray studies in the phytoplankton community are reviewed to demonstrate the scope of the method.

Superinfection exclusion and the long-term survival of honey bees in Varroa-infested colonies

Over the past 50 years, many millions of European honey bee (Apis mellifera) colonies have died as the ectoparasitic mite, Varroa destructor, has spread around the world. Subsequent studies have indicated that the mite’s association with a group of RNA viral pathogens (Deformed Wing Virus, DWV) correlates with colony death. Here, we propose a phenomenon known as superinfection exclusion that provides an explanation of how certain A. mellifera populations have survived, despite Varroa infestation and high DWV loads. Next-generation sequencing has shown that a non-lethal DWV variant ‘type B’ has become established in these colonies and that the lethal ‘type A’ DWV variant fails to persist in the bee population. We propose that this novel stable host-pathogen relationship prevents the accumulation of lethal variants, suggesting that this interaction could be exploited for the development of an effective treatment that minimises colony losses in the future.

Superinfection exclusion and the long-term survival of honey bees in Varroa-infested colonies

Over the past 50 years, many millions of European honey bee (Apis mellifera) colonies have died as the ectoparasitic mite, Varroa destructor, has spread around the world. Subsequent studies have indicated that the mite’s association with a group of RNA viral pathogens (Deformed Wing Virus, DWV) correlates with colony death. Here, we propose a phenomenon known as superinfection exclusion that provides an explanation of how certain A. mellifera populations have survived, despite Varroa infestation and high DWV loads. Next-generation sequencing has shown that a non-lethal DWV variant ‘type B’ has become established in these colonies and that the lethal ‘type A’ DWV variant fails to persist in the bee population. We propose that this novel stable host-pathogen relationship prevents the accumulation of lethal variants, suggesting that this interaction could be exploited for the development of an effective treatment that minimises colony losses in the future.

Diversity in a honey bee pathogen: first report of a third master variant of the Deformed Wing Virus quasispecies

Treatment of emerging RNA viruses is hampered by the high mutation and replication rates that enable these viruses to operate as a quasispecies. Declining honey bee populations have been attributed to the ectoparasitic mite Varroa destructor and its affiliation with Deformed Wing Virus (DWV). In the current study we use next-generation sequencing to investigate the DWV quasispecies in an apiary known to suffer from overwintering colony losses. We show that the DWV species complex is made up of three master variants. Our results indicate that a new DWV Type C variant is distinct from the previously described types A and B, but together they form a distinct clade compared with other members of the Iflaviridae. The molecular clock estimation predicts that Type C diverged from the other variants ~319 years ago. The discovery of a new master variant of DWV has important implications for the positive identification of the true pathogen within global honey bee populations.

Diversity in a honey bee pathogen: first report of a third master variant of the Deformed Wing Virus quasispecies

Treatment of emerging RNA viruses is hampered by the high mutation and replication rates that enable these viruses to operate as a quasispecies. Declining honey bee populations have been attributed to the ectoparasitic mite Varroa destructor and its affiliation with Deformed Wing Virus (DWV). In the current study we use next-generation sequencing to investigate the DWV quasispecies in an apiary known to suffer from overwintering colony losses. We show that the DWV species complex is made up of three master variants. Our results indicate that a new DWV Type C variant is distinct from the previously described types A and B, but together they form a distinct clade compared with other members of the Iflaviridae. The molecular clock estimation predicts that Type C diverged from the other variants ~319 years ago. The discovery of a new master variant of DWV has important implications for the positive identification of the true pathogen within global honey bee populations.