Mahesh Visvanathan

Feasibility of MicroRNAs as Biomarkers for Barrett's Esophagus Progression: A Pilot Cross-Sectional, Phase 2 Biomarker Study

OBJECTIVES:Risk stratification of Barretts esophagus (BE) using biomarkers remains an important goal. We evaluated feasibility and clinical accuracy of novel microRNA (miRNA) biomarkers for prediction of BE dysplasia.METHODS:Paired fresh-frozen and hematoxylin/eosin specimens from a prospective tissue repository where only biopsies with the lesion of interest (i.e., intestinal metaplasia (IM) or high-grade dysplasia (HGD)/esophageal adenocarcinoma (EAC)) occupying >50% of biopsy area were included. Tissue miRNA expression was determined by microarrays and validated by quantitative reverse transcription-PCR (qRT-PCR). Three groups were compared—group A, IM tissues from BE patients without dysplasia; group B, IM tissues from group C patients; and group C, dysplastic tissues from BE patients with HGD/EAC.RESULTS:Overall, 22 BE patients, 11 with and without dysplasia (mean age 64±8.2 and 63±11.6 years, respectively, all Caucasian males) were evaluated. Nine miRNAs were identified by high-throughout analysis (miR-15b, -21, -192, -205, 486-5p, -584, -1246, let-7a, and -7d) and qRT-PCR confirmed expression of miR-15b, -21, 486-5p, and let-7a. Two of 4 miRNAs (miR-145 and -203, but not -196a and -375) previously described in BE patients also exhibited differential expression. Sensitivity and specificity of miRNAs for HGD/EAC were miR-15b: 87 and 80%, miR-21: 93 and 70%, miR-203: 87 and 90%, miR-486-5p: 82 and 55%, and miR-let-7a: 88 and 70%. MiRNA-15b, -21, and -203 exhibited field effects (i.e., groups A and B tissues while histologically similar yet exhibited different miRNA expression).CONCLUSIONS:This pilot study demonstrates feasibility of miRNAs to discriminate BE patients with and without dysplasia with reasonable clinical accuracy. However, the specific miRNAs need to be evaluated further in future prospective trials.

LipidomeDB Data Calculation Environment: Online Processing of Direct-Infusion Mass Spectral Data for Lipid Profiles

LipidomeDB Data Calculation Environment (DCE) is a web application to quantify complex lipids by processing data acquired after direct infusion of a lipid-containing biological extract, to which a cocktail of internal standards has been added, into an electrospray source of a triple quadrupole mass spectrometer. LipidomeDB DCE is located on the public Internet at http://lipidome.bcf.ku.edu:9000/Lipidomics. LipidomeDB DCE supports targeted analyses; analyte information can be entered, or pre-formulated lists of typical plant or animal polar lipid analytes can be selected. LipidomeDB DCE performs isotopic deconvolution and quantification in comparison to internal standard spectral peaks. Multiple precursor or neutral loss spectra from up to 35 samples may be processed simultaneously with data input as Excel files and output as tables viewable on the web and exportable in Excel. The pre-formulated compound lists and web access, used with direct-infusion mass spectrometry, provide a simple approach to lipidomic analysis, particularly for new users.

A filter-based feature selection approach for identifying potential biomarkers for lung cancer

BackgroundLung cancer is the leading cause of death from cancer in the world and its treatment is dependant on the type and stage of cancer detected in the patient. Molecular biomarkers that can characterize the cancer phenotype are thus a key tool in planning a therapeutic response. A common protocol for identifying such biomarkers is to employ genomic microarray analysis to find genes that show differential expression according to disease state or type. Data-mining techniques such as feature selection are often used to isolate, from among a large manifold of genes with differential expression, those specific genes whose differential expression patterns are of optimal value in phenotypic differentiation. One such technique, Biomarker Identifier (BMI), has been developed to identify features with the ability to distinguish between two data groups of interest, which is thus highly applicable for such studies.ResultsMicroarray data with validated genes was used to evaluate the utility of BMI in identifying markers for lung cancer. This data set contains a set of 129 gene expression profiles from large-airway epithelial cells (60 samples from smokers with lung cancer and 69 from smokers without lung cancer) and 7 genes from this data have been confirmed to be differentially expressed by quantitative PCR. Using this data set, BMI was compared with various well-known feature selection methods and was found to be more successful than other methods in finding useful genes to classify cancerous samples. Also it is evident that genes selected by BMI (given the same number of genes and classification algorithms) showed better discriminative power than those from the original study. After pathway analysis on the selected genes by BMI, we have been able to correlate the selected genes with well-known cancer-related pathways.ConclusionsOur results show that BMI can be used to analyze microarray data and to find useful genes for classifying samples. Pathway analysis suggests that BMI is successful in identifying biomarker-quality cancer-related genes from the data.

Discovery and Validation of Barrett's Esophagus MicroRNA Transcriptome by Next Generation Sequencing

Objective Barretts esophagus (BE) is transition from squamous to columnar mucosa as a result of gastroesophageal reflux disease (GERD). The role of microRNA during this transition has not been systematically studied. Design For initial screening, total RNA from 5 GERD and 6 BE patients was size fractionated. RNA <70 nucleotides was subjected to SOLiD 3 library preparation and next generation sequencing (NGS). Bioinformatics analysis was performed using R package “DEseq”. A p value<0.05 adjusted for a false discovery rate of 5% was considered significant. NGS-identified miRNA were validated using qRT-PCR in an independent group of 40 GERD and 27 BE patients. MicroRNA expression of human BE tissues was also compared with three BE cell lines. Results NGS detected 19.6 million raw reads per sample. 53.1% of filtered reads mapped to miRBase version 18. NGS analysis followed by qRT-PCR validation found 10 differentially expressed miRNA; several are novel (-708-5p, -944, -224-5p and -3065-5p). Up- or down- regulation predicted by NGS was matched by qRT-PCR in every case. Human BE tissues and BE cell lines showed a high degree of concordance (70–80%) in miRNA expression. Prediction analysis identified targets that mapped to developmental signaling pathways such as TGFβ and Notch and inflammatory pathways such as toll-like receptor signaling and TGFβ. Cluster analysis found similarly regulated (up or down) miRNA to share common targets suggesting coordination between miRNA. Conclusion Using highly sensitive next-generation sequencing, we have performed a comprehensive genome wide analysis of microRNA in BE and GERD patients. Differentially expressed miRNA between BE and GERD have been further validated. Expression of miRNA between BE human tissues and BE cell lines are highly correlated. These miRNA should be studied in biological models to further understand BE development.

Lipidomic analysis of plant membrane lipids by direct infusion tandem mass spectrometry.

Plant phospholipids and glycolipids can be analyzed by direct infusion electrospray ionization triple-quadrupole mass spectrometry. A biological extract is introduced in solvent by continuous infusion into the mass spectrometers electrospray ionization source, where ions are produced from the lipids. For analysis of membrane lipids, a series of precursor and neutral loss scans, each specific for lipids containing a common head group, are obtained sequentially. The mass spectral data are processed and combined, using the Web application LipidomeDB Data Calculation Environment, to create a lipid profile.

Cluster validation: An integrative method for cluster analysis

Clustering is a widely used to discover underlying patterns and groups in data and there is a need to validate the quality of clusters generated by the numerous clustering algorithms in use. The need for cluster validitation arises from the fundamental definition of unsupervised learning. As clustering is an unsupervised learning process, the prediction of correct number of clusters is a hurdle which can be cleared by using cluster validity indices to assess the quality of the clusters. We have developed a tool for cluster validation as a part of GOAPhAR, a web based tool that integrates from disparate sources, information regarding gene annotations, protein annotations, identifiers associated with probe sets, functional pathways, protein interactions, gene Ontology and publicly available microarray datasets. Our cluster validity tool calculates three indices to indicate clustering quality viz. the Silhouette, Dunns and Davies-Bouldin indices and outputs them to the user. The values of these indices can be used to judge the quality of clustering and to optimize the process of selecting an appropriate clustering algorithm and number of clusters. The tool is freely available at http://bioinformatics.kumc.edu/goaphar/

Systems Biology Approach for Mapping TNFα-NFκB Mathematical Model to a Protein Interaction Map

We investigated different mathematical models concerning signaling pathways and built a new pathway model for TNF� -NF B signaling using an integrative analytical approach. This integrative approach consists of a knowledgebase, model designing/visualization and simulation environments. In particular, our new TNF� -NF B signaling pathway model was developed based on literature studies and the use of ordinary differential equations and a detailed protein-protein interaction connectivity map within this approach. Using the most detailed mathematical model as a base model, three new proteins -- TRAF1, FLIP, and MEKK3 -- were identified and included in our new model. Our results show that this integrative approach offers the most detailed and consistent mathematical description for TNF� - NF B signaling and further increases the understanding of TNF� -NF B signaling pathway. This tool can be downloaded through the following link (http://sourceforge.net/projects/dmsp11/).

DMSP – Database for Modeling Signaling Pathways - Combining Biological and Mathematical Modeling Knowledge for Pathways

OBJECTIVES Presently, the protein interaction information concerning different signaling pathways is available in a qualitative manner in different online protein interaction databases. The challenge here is to derive a quantitative way of modeling signaling pathways from qualitative way of modeling signaling pathways from a qualitative level. To address this issue we developed a database that includes mathematical modeling knowledge and biological knowledge about different signaling pathways. METHODS The database is part of an integrative environment that includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning pathways. The system is designed as a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. RESULTS DMSP--Database for Modeling Signaling Pathways incorporates biological datasets from online databases like BIND, DIP, PIP, and SPiD. The modeling knowledge that has been incorporated is based on a literature study. Pathway models can be designed, visualized and simulated based on the knowledge stored in the DMSP. The user can download the whole dataset and build pathway models using the knowledge stored in our database. As an example, the TNFalpha pathway model was implemented and tested using this approach. CONCLUSION DMSP is an initial step towards the aim of combining modeling and biological knowledge concerning signaling pathways. It helps in understanding pathways in a qualitative manner from a qualitative level. Simulation results enable the interpretation of a biological system from a quantitative and system-theoretic point of view.

GlycomicsDB - A Data Integration Platform for Glycans and their Strucutres

Glycomics is a discipline of biology that deals with the structure and function of glycans (or carbohydrates). Analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are having a significant impact on the field of glycomics. However, effective progress in glycomics research requires collaboration between laboratories to share experimental data, structural information of glycans, and simulation results. Herein we report the development of a web-based data management system that can incorporate large volumes of data from disparate sources and organize them into a uniform format for users to store and access. This system enables participating laboratories to set up a shared data repository which members of interdisciplinary teams can access. The system is able to manage and share raw MS data and structural information of glycans. The database is available at http://www.glycomics.bcf.ku.edu

Integrative Approach for Combining TNFα-NFκB Mathematical Model to a Protein Interaction Connectivity Map

We have investigated different mathematical models for signaling pathways and built a new pathway model for TNFα-NFκB signaling using an integrative analytical approach. This integrative approach consists of a knowledgebase, model designing/visualization and simulation environments. In particular, our new TNFα-NFκB signaling pathway model was developed based on literature studies and the use of ordinary differential equations and a detailed protein-protein interaction connectivity map within this approach. Using the most detailed mathematical model as a base model, three new relevant proteins --- TRAF1, FLIP, and MEKK3 --- were identified and included in our new model. Our results show that this integrative approach offers the most detailed and consistent mathematical description for TNFα-NFκB signaling and further increases the understanding of TNFα-NFκB signaling pathway.