Etienne Z. Gnimpieba

Using logic programming for modeling the one-carbon metabolism network to study the impact of folate deficiency on methylation processes

Dynamical modeling is an accurate tool for describing the dynamic regulation of one-carbon metabolism (1CM) with emphasis on the alteration of DNA methylation and/or dUMP methylation into dTMP. Using logic programming we present a comprehensive and adaptative mathematical model to study the impact of folate deficiency, including folate transport and enzymes activities. 5-Methyltetrahydrofolate (5mTHF) uptake and DNA and dUMP methylation were studied by simulating nutritional 5mTHF deficiency and methylenetetrahydrofolate reductase (MTHFR) gene defects. Both conditions had distinct effects on 1CM metabolite synthesis. Simulating severe 5mTHF deficiency (25% of normal levels) modulated 11 metabolites. However, simulating a severe decrease in MTHFR activity (25% of normal activity) modulated another set of metabolites. Two oscillations of varying amplitude were observed at the steady state for DNA methylation with severe 5mTHF deficiency, and the dUMP/dTMP ratio reached a steady state after 2 h, compared to 2.5 h for 100% 5mTHF. MTHFR activity with 25% of V(max) resulted in an increased methylated DNA pool after half an hour. We observed a deviation earlier in the profile compared to 50% and 100% V(max). For dUMP methylation, the highest level was observed with 25%, suggesting a low rate of dUMP methylation into dTMP with 25% of MTHFR activity. In conclusion, using logic programming we were able to construct the 1CM for analyzing the dynamic system behavior. This model may be used to refine biological interpretations of data or as a tool that can provide new hypotheses for pathogenesis.

Therapeutic Targeting of PTK7 is Cytotoxic in Atypical Teratoid Rhabdoid Tumors

Novel discoveries involving the evaluation of potential therapeutics are based on newly identified molecular targets for atypical teratoid rhabdoid tumors (ATRT), which are the most common form of infantile brain tumors. Central nervous system ATRTs are rare, aggressive, and fast growing tumors of the brain and spinal cord and carry a very poor prognosis. Currently, the standard of care for ATRT patients is based on surgical resection followed by systemic chemotherapy and radiotherapy, which result in severe side effects. As protein tyrosine kinases have proven to be actionable targets that reduce tumor growth in a number of cancers, we examined how inhibiting tyrosine kinases affected ATRT tumor growth. Here, we examine the therapeutic efficacy of the broad-spectrum tyrosine kinase inhibitor vatalanib in the treatment of ATRT. Vatalanib significantly reduced the growth of ATRT tumor cell lines, both in two-dimensional cell culture and in three-dimensional cell culture using a spheroid model. As vatalanib had a remarkable effect on the growth of ATRT, we decided to use a transcriptomic approach to therapy by examining new actionable targets, such as tyrosine kinases. Next-generation RNA-sequencing and NanoString data analysis showed a significant increase in PTK7 RNA expression levels in ATRT tumors. Inhibition of PTK7 by siRNA treatment significantly decreases the viability of ATRT patient–derived tumor cell lines. Implications: These studies provide the groundwork for future preclinical in vivo studies aiming to investigate the efficacy of PTK7 inhibition on ATRT tumor growth. Mol Cancer Res; 15(8); 973–83. ©2017 AACR.

Life science data analysis workflow development using the bioextract server leveraging the iPlant collaborative cyberinfrastructure

In order to handle the vast quantities of biological data gener6ated by high‐throughput experimental technologies, the BioExtract Server (bioextract.org) has leveraged iPlant Collaborative (www.iplantcollaborative.org) functionality to help address big data storage and analysis issues in the bioinformatics field. The BioExtract Server is a Web‐based, workflow‐enabling system that offers researchers a flexible environment for analyzing genomic data. It provides researchers with the ability to save a series of BioExtract Server tasks (e.g., query a data source, save a data extract, and execute an analytic tool) as a workflow and the opportunity for researchers to share their data extracts, analytic tools, and workflows with collaborators. The iPlant Collaborative is a community of researchers, educators, and students working to enrich science through the development of cyberinfrastructure—the physical computing resources, collaborative environment, virtual machine resources, and interoperable analysis software and data services—that are essential components of modern biology. The iPlant AGAVE Advanced Programming Interface, developed through the iPlant Collaborative, is a hosted, Software‐as‐a‐Service resource providing access to a collection of high performance computing and cloud resources. Leveraging AGAVE, the BioExtract Server gives researchers easy access to multiple high performance computers and delivers computation and storage as dynamically allocated resources via the Internet.

4SC-202 as a potential treatment for the pediatric brain tumor medulloblastoma

This project involves an examination of the effect of the small molecule inhibitor 4SC-202 on the growth of the pediatric brain cancer medulloblastoma. The small molecule inhibitor 4SC-202 significantly inhibits the viability of the pediatric desmoplastic cerebellar human medulloblastoma cell line DAOY, with an IC50 = 58.1 nM, but does not affect the viability of noncancerous neural stem cells (NSC). 4SC-202 exposure inhibits hedgehog expression in the DAOY cell line. Furthermore, microarray analysis of human medulloblastoma patient tumors indicate significant upregulation of key targets in the Hedgehog signaling pathway and Protein Tyrosine Kinase (PTK7).

Automatic biosystems comparison using semantic and name similarity

With the growth of bio-systems model development, automatic approaches are needed to support systems biologists in model similarity evaluation. Several algorithms have been proposed, but they lack efficiency. We have developed an efficient, intuitive approach using name and semantic similarity checking. Individual components in two given SBML models are compared by their names using ParaABioS (a heuristic Parallelizable Algorithm for Similarity Based Biosystems Comparison) and by their meaning using annotated URI (Unified Resource Identifier). We developed a tool SMBLcompare, an implementation of this approach for automatic bio-systems model comparison in SBML format. This implementation has been embedded into a web portal for small biosystems comparison and also integrated into the Bioextract Server (bioextract.org) in order to be able to use within workflows designed to address escience challenges. SBMLcompare has been successfully used on FOCM (Folate One Carbon Metabolite) models and two genome-scale yeast metabolic models iND750, iFF708. The similarity result showed a significant improvement compared to existing related work (over 10%).

Heuristic parallelizable algorithm for similarity based biosystems comparison

Biosystem comparison plays a major role in system biology. Similar biosystems are identified based on the similarity of species naming. Since the species naming does not follow a standard nomenclature, similarity is not easy to formalize. A single metabolite can have different name strings that vary slightly in pattern. Several algorithms have been designed to find similarity between two species using different measures. However, these algorithms failed to achieve good performance in biological species similarity checking due to failure to account for important facts about biological name analysis. We developed ParaABioS, a heuristic, intuitive algorithm for biosystem similarity evaluation. This algorithm integrates sub-name analysis and a symbol management strategy that conserves species name specifications. ParaABioS provides similarity checking between two names that consumes the time O(k!) where k is the number of sub names in worst case. It is implemented in Java and parallelized on a Texas Advanced Computing Center TACC high performance computing (HPC) server and accessed through the iPlant Collaborative Foundation API in order to compare large models. It is available online and also through the BioExtract Server workflow management system (WMS).

RNA-seq gene and transcript expression analysis using the BioExtract server and iPlant collaborative

Background: The development of Next Generation Sequencing (NGS) technology provides great opportunities to study gene expression, gene spliced transcripts, post-transcriptional changes, and gene fusion mutations/SNPs. The large amount of data being generated from these approaches presents many challenges. For example, how can we manage and analyze these vast datasets in order to extract new knowledge. Aims: This paper provides an integrated, adaptable, and scalable scenario to guide researchers through a complex, data analysis process using the iPlant Collaborative AGAVE RESTful API through the BioExtract Server. In 3 modules, we show how a High Performance Cluster (HPC) can be leveraged in a Workflow Management System (WMS) by following simple analytic steps. Results: A workflow has been developed in the BioExtract Server to analyze RNA-Seq data. The running of this workflow on a 21.6GB dataset provides reliable gene and transcript expression results. The BioExtract Servers results compared to an existing manual workflow on the same dataset shows ≈800% improvement in execution time (from ≈18h to ≈2h10min). Additionally, there are several qualitative improvements such as; automation, reproducibility, sharability, and scalability. (Note: the performance was not compared to the workflow installed at Galaxy, https://usegalaxy.org/, due to extensive wait times on their public site.) Our workflow execution provides analysis results from input datasets and reveals a 0.05 fold discovery rate (FDR) showing that 342 genes, 228 isoforms, 270 TSS, 47 CDS and 23 promoters are significantly differentially expressed. Conclusion: Having the ability to easily create and execute workflows leveraging the robust iPlant cyberinfrastructure to analyze NGS data represents one more steps in eScience initiative improvement. It improves, considerably, the ability of life science researchers to apply NGS tools. However, enhancements to this approach remains important as everyday improvements in HPC and WMS technology, techniques, and software continues. Our coming challenge will consist to follow that evolution in order to minimize the gap between researchers and these powerful resources. Availability: Tools used here are freely available on referenced link. Additional data analysis from our workflow execution is available on demand. Our workflow is available on MyExperiment under creative commons (cc) license (http://www.myexperiment.org/workflows/3895.html?version=1).

BioExtract Server, a Web-based workflow enabling system, leveraging iPlant collaborative resources

In order to handle the vast quantities of biological data generated by high-throughput experimental technologies, the BioExtract Server (bioextract.org) has leveraged iPlant Collaborative (www.iplantcollaborative.org) functionality to help address big data storage and analysis issues in the bioinformatics field. The BioExtract Server is a Web-based, workflow-enabling system that offers researchers a flexible environment for analyzing genomic data. It provides researchers with the ability to save a series of BioExtract Server tasks (e.g. query a data source, save a data extract, and execute an analytic tool) as a workflow and the opportunity for researchers to share their data extracts, analytic tools and workflows with collaborators. The iPlant Collaborative is a community of researchers, educators, and students working to enrich science through the development of cyberinfrastructure - the physical computing resources, collaborative environment, virtual machine resources, and interoperable analysis software and data services - that are essential components of modern biology. The iPlant Agave API (Agave), developed through the iPlant Collaborative, is a hosted, Software-as-a-Service resource providing access to a collection of High Performance Computing (HPC) and Cloud resources [6]. Leveraging Agave, the BioExtract Server gives researchers easy access to multiple high performance computers and delivers computation and storage as dynamically allocated resources via the Internet.

Bio-TDS: bioscience query tool discovery system

Abstract Bioinformatics and computational biology play a critical role in bioscience and biomedical research. As researchers design their experimental projects, one major challenge is to find the most relevant bioinformatics toolkits that will lead to new knowledge discovery from their data. The Bio-TDS (Bioscience Query Tool Discovery Systems, http://biotds.org/) has been developed to assist researchers in retrieving the most applicable analytic tools by allowing them to formulate their questions as free text. The Bio-TDS is a flexible retrieval system that affords users from multiple bioscience domains (e.g. genomic, proteomic, bio-imaging) the ability to query over 15 000 analytic tool descriptions integrated from well-established, community repositories. One of the primary components of the Bio-TDS is the ontology and natural language processing workflow for annotation, curation, query processing, and evaluation. The Bio-TDS’s scientific impact was evaluated using sample questions posed by researchers retrieved from Biostars, a site focusing on biological data analysis. The Bio-TDS was compared to five similar bioscience analytic tool retrieval systems with the Bio-TDS outperforming the others in terms of relevance and completeness. The Bio-TDS offers researchers the capacity to associate their bioscience question with the most relevant computational toolsets required for the data analysis in their knowledge discovery process.

NanoStringBioNet: Integrated R framework for bioscience knowledge discovery from NanoString nCounter data

The NanoString nCounter Analysis System is a medium-throughput gene expression quantification technique that is becoming increasingly popular in the fields of immunology and oncology due to its ease of use and sensitivity, particularly in the analysis of formalin-fixed paraffin embedded samples. Despite the growing interest in NanoString, systematic analysis frameworks for the reproducible analysis of nCounter data remain limited. NanoStringBioNet is a pair of R packages that form a semi-automatic, open source framework for integrative and systematic knowledge discovery from nCounter datasets. Using the NSData module, NanoStringBioNet preprocesses a raw NanoString dataset and stores it in Biobase format for sharability. Subsequently, the NSFunc module performs downstream analyses such as enrichment and network inference of stable differentially expressed gene clusters by leveraging existing data analysis tools and custom script.