Systems analysis of host-parasite interactions.

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug-resistant parasites necessitates that the research community take an active role in understanding host-parasite infection biology in order to develop improved therapeutics. Recent advances in next-generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host-parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high-throughput -omic data will undoubtedly generate extraordinary insight into host-parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host-parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome‐scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field

MAP Kinases Phosphorylate SPF45 and Regulate Its Alternative Splicing Function: Insights onto Phosphorylation-Dependent and -Independent Effects in Ovarian Cancer Cells

Alternative pre-mRNA splicing increases proteome diversity and is important in the behavior of cells in health and disease. The processes that regulate alternative premRNA splicing are diverse and include regulation of splicing factors by phosphorylation. Here we report that the pre-mRNA alternative splicing factor SPF45 is a novel substrate of ERK, JNK and p38 MAP kinases in ovarian cancer cells. Using mutational analysis and phospho-specific antibodies, we demonstrate that MAP kinases phosphorylate SPF45 on Thr71 and Ser222 and that phosphorylation in cells is induced by a number of extracellular stimuli including PMA, EGF, serum, H20 2 and UV. Exon 6 of the death receptor Fas/CD95 has been shown to undergo alternative splicing in the presence of SPF45. Using a Fas minigene assay, we show that ERK2 and p38 inhibit SPF45 alternative splicing activity towards Fas, dependent upon these two phosphorylation sites. Other than Fas, no other pre-mRNA targets of SPF45 have been reported in mammalian cells. We generated SKOV3 cells stably-overexpressing wild-type SPF45 or a phosphorylation site mutant and performed an exon and gene array analysis to identify novel SPF45 splicing targets and genes whose expression is changed downstream of SPF45 splicing activity, respectively. From this analysis, 139 potential splicing targets and over 150 genes with altered expression were identified. We focus on four genes for validation with emphasis on ErbB2 and fibronectin. We show that SPF45 downregulates cellular proliferation in SKOV3 cells in a phosphorylation-dependent manner. Furthermore, we demonstrate that SPF45 regulates fibronectin alternative splicing, enhances inclusion of FN-EDIIIA region, and affects cellular adhesion to fibronectin matrix. We also assess SPF45 binding to SFl and SF3b15S, essential components of the spliceosome, as well as the impact of Thr71 and Ser222 mutations on this interaction. Finally, we determine the effect of SPF45 overexpression in SKOV3 cells on their drug resistance profile. This study provides a link between MAP kinase signaling and splicing factors and identifies the role of this interaction in regulating molecular processes in ovarian cancer cells. Additionally, it provides the basis to investigate the role of SPF45 in ovarian cancer and to produce successful therapeutic interventions targeting SPF45-mediated effects

Small Molecules in Development for the Treatment of Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disease resulting from pathologically low levels of survival motor neuron (SMN) protein. The majority of mRNA from the SMN2 allele undergoes alternative splicing and excludes critical codons, causing an SMN protein deficiency. While there is currently no FDA-approved treatment for SMA, early therapeutic efforts have focused on testing repurposed drugs such as phenylbutyrate (2), valproic acid (3), riluzole (6), hydroxyurea (7), and albuterol (9), none of which has demonstrated clinical effectiveness. More recently, clinical trials have focused on novel small-molecule compounds identified from high-throughput screening and medicinal chemistry optimization such as olesoxime (11), CK-2127107, RG7800, LMI070, and RG3039 (17). In this paper, we review both repurposed drugs and small-molecule compounds discovered following medicinal chemistry optimization for the potential treatment of SMA

Cellular Targeting of Oligonucleotides by Conjugation with Small Molecules

Drug candidates derived from oligonucleotides (ON) are receiving increased attention that is supported by the clinical approval of several ON drugs. Such therapeutic ON are designed to alter the expression levels of specific disease-related proteins, e.g., by displaying antigene, antisense, and RNA interference mechanisms. However, the high polarity of the polyanionic ON and their relatively rapid nuclease-mediated cleavage represent two major pharmacokinetic hurdles for their application in vivo. This has led to a range of non-natural modifications of ON structures that are routinely applied in the design of therapeutic ON. The polyanionic architecture of ON often hampers their penetration of target cells or tissues, and ON usually show no inherent specificity for certain cell types. These limitations can be overcome by conjugation of ON with molecular entities mediating cellular ‘targeting’, i.e., enhanced accumulation at and/or penetration of a specific cell type. In this context, the use of small molecules as targeting units appears particularly attractive and promising. This review provides an overview of advances in the emerging field of cellular targeting of ON via their conjugation with small-molecule targeting structures

Examining the Relationship between Pre-Malignant Breast Lesions, Carcinogenesis and Tumor Evolution in the Mammary Epithelium Using an Agent-Based Model

Introduction: Breast cancer, the product of numerous rare mutational events that occur over an extended time period, presents numerous challenges to investigators interested in studying the transformation from normal breast epithelium to malignancy using traditional laboratory methods, particularly with respect to characterizing transitional and pre-malignant states. Dynamic computational modeling can provide insight into these pathophysiological dynamics, and as such we use a previously validated agent-based computational model of the mammary epithelium (the DEABM) to investigate the probabilistic mechanisms by which normal populations of ductal cells could transform into states replicating features of both pre-malignant breast lesions and a diverse set of breast cancer subtypes. Methods: The DEABM consists of simulated cellular populations governed by algorithms based on accepted and previously published cellular mechanisms. Cells respond to hormones, undergo mitosis, apoptosis and cellular differentiation. Heritable mutations to 12 genes prominently implicated in breast cancer are acquired via a probabilistic mechanism. 3000 simulations of the 40-year period of menstrual cycling were run in wild-type (WT) and BRCA1-mutated groups. Simulations were analyzed by development of hyperplastic states, incidence of malignancy, hormone receptor and HER-2 status, frequency of mutation to particular genes, and whether mutations were early events in carcinogenesis. Results: Cancer incidence in WT (2.6%) and BRCA1-mutated (45.9%) populations closely matched published epidemiologic rates. Hormone receptor expression profiles in both WT and BRCA groups also closely matched epidemiologic data. Hyperplastic populations carried more mutations than normal populations and mutations were similar to early mutations found in ER+ tumors (telomerase, E-cadherin, TGFB, RUNX3, p Conclusions: The DEABM generates diverse tumors that express tumor markers consistent with epidemiologic data. The DEABM also generates non-invasive, hyperplastic populations, analogous to atypia or ductal carcinoma in situ (DCIS), via mutations to genes known to be present in hyperplastic lesions and as early mutations in breast cancers. The results demonstrate that agent-based models are well-suited to studying tumor evolution through stages of carcinogenesis and have the potential to be used to develop prevention and treatment strategies.</p

Mechanistic and Statistical Models to Understand CXCL12/CXCR4/CXCR7 in Breast Cancer.

Signaling via the CXCL12/CXCR4 axis is instrumental to the metastasis of more than 20 cancers, yet blocking the pathway alone has not been effective as cancer therapy. Since cancer progression results from a complex network of interdependent biological events, preventing metastasis cannot be understood by studying only one gene or protein at a time. In this thesis, we employed mathematical and statistical models to examine complexity in the CXCL12/CXCR4/CXCR7 signaling axis. First, we performed a comprehensive analysis of CXCL12 isoform expression in breast cancer. This is the first study to correlate the expression levels of all six CXCL12 isoforms to cancer survival outcomes. Second, to understand mechanisms of physiological gradient formation, we built a hybrid agent-based model of cancer cell chemotaxis that links molecular scale events to chemokine gradient shaping and sensing. Third, to understand how co-expression of CXCR7 may alter CXCR4 signaling, we constructed a mechanistic model of CXCR4/CXCR7 receptor dynamics and signaling with an emphasis on shared signaling components. Themes arising from this work include the importance of non-specific binding of ligand to surfaces, receptor desensitization, gradient sensing, and compensatory effects resulting from the competition of shared signaling components.PhDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111458/1/seiwon_1.pd

GRID AND CLOUD COMPUTING FOR E-SCIENCE APPLICATIONS

eScience fields which include areas such as spatial data, electromagnetic,bioinformatics, energy, social sciences, simulation, physical science have on the course of recent years a significant development regarding the complexity of algorithms and applications for data analysis. Information data has also evolved with an explosion in term of data volume and datasets for the scientific community. This has led researchers to identify new necessity regarding tools analysis, applications, by a profound change in computing infrastructures utilization. The field of eScience is constantly evolving through the creation of ever more growing scientific community who have a real needs in availability in computational resources ever more powerful calculations. Another important issue is the ability to be able to share results, this is why cloud technology through virtualization can be an important help for the scientist community for giving a flexible and scalable IT infrastructure depending on necessities. Indeed, cloud computing allows for the provision of computing resources, storage in an easy configurable way and adaptable in functions of real needs. Researchers often do not have all the computing capacities to meet their needs, so cloud technology and cloud models as Private, Public and Hybrid is an enable technology for having a guarantee of service availability, scalability and flexibility. The transition from traditional infrastructure to new virtualized with distributed models allows researchers to have access to an environment extremely flexible allowing an optimization of the use of hardware for having more available resources. However, the computational needs on e-Science have a direct effect regarding the way that applications are developed. The approach of writing algorithm and applications is still too tied to a model centered on a workstation for example. The vast majority of researchers conducts the writing process of their applications on their laptop or workstation in a limited context of computing power, storage and in a non-distributed wa

GRID AND CLOUD COMPUTING FOR E-SCIENCE APPLICATIONS

eScience fields which include areas such as spatial data, electromagnetic,bioinformatics, energy, social sciences, simulation, physical science have on the course of recent years a significant development regarding the complexity of algorithms and applications for data analysis. Information data has also evolved with an explosion in term of data volume and datasets for the scientific community. This has led researchers to identify new necessity regarding tools analysis, applications, by a profound change in computing infrastructures utilization. The field of eScience is constantly evolving through the creation of ever more growing scientific community who have a real needs in availability in computational resources ever more powerful calculations. Another important issue is the ability to be able to share results, this is why cloud technology through virtualization can be an important help for the scientist community for giving a flexible and scalable IT infrastructure depending on necessities. Indeed, cloud computing allows for the provision of computing resources, storage in an easy configurable way and adaptable in functions of real needs. Researchers often do not have all the computing capacities to meet their needs, so cloud technology and cloud models as Private, Public and Hybrid is an enable technology for having a guarantee of service availability, scalability and flexibility. The transition from traditional infrastructure to new virtualized with distributed models allows researchers to have access to an environment extremely flexible allowing an optimization of the use of hardware for having more available resources. However, the computational needs on e-Science have a direct effect regarding the way that applications are developed. The approach of writing algorithm and applications is still too tied to a model centered on a workstation for example. The vast majority of researchers conducts the writing process of their applications on their laptop or workstation in a limited context of computing power, storage and in a non-distributed way