Jake Chen

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https://hdl.handle.net/1805/6688

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An integrated proteomics analysis of bone tissues in response to mechanical stimulation
(2010-07) Li, Jillian; Zhang, Fan; Chen, Jake Yue
Bone cells can sense physical forces and convert mechanical stimulation conditions into biochemical signals that lead to expression of mechanically sensitive genes and proteins. However, it is still poorly understood how genes and proteins in bone cells are orchestrated to respond to mechanical stimulations. In this research, we applied integrated proteomics, statistical, and network biology techniques to study proteome-level changes to bone tissue cells in response to two different conditions, normal loading and fatigue loading. We harvested ulna midshafts and isolated proteins from the control, loaded, and fatigue loaded Rats. Using a label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) experimental proteomics technique, we derived a comprehensive list of 1,058 proteins that are differentially expressed among normal loading, fatigue loading, and controls. By carefully developing protein selection filters and statistical models, we were able to identify 42 proteins representing 21 Rat genes that were significantly associated with bone cells' response to quantitative changes between normal loading and fatigue loading conditions. We further applied network biology techniques by building a fatigue loading activated protein-protein interaction subnetwork involving 9 of the human-homolog counterpart of the 21 rat genes in a large connected network component. Our study shows that the combination of decreased anti-apoptotic factor, Raf1, and increased pro-apoptotic factor, PDCD8, results in significant increase in the number of apoptotic osteocytes following fatigue loading. We believe controlling osteoblast differentiation/proliferation and osteocyte apoptosis could be promising directions for developing future therapeutic solutions for related bone diseases.
Resources for Translational Bioinformaticians
(Office of the Vice Chancellor for Research, 2012-04-13) Chen, Jake Yue
In this project researchers developed software that can help extract results from PubMed literature to a comprehensive connectivity map, developing information on the relationships among drugs, proteins, and diseases. The relationships mined from literature can be thoroughly curated with the tool's web-based online annotation graphical user interface. These comprehensive connectivity maps cover disease-specific information and will become a valuable resource for translational bioinformaticians.
TOWARDS A PATHWAY MODELING APPROACH TO ALZHEIMER’S DISEASE DRUG DISCOVERY
(Office of the Vice Chancellor for Research, 2012-04-13) Ibrahim, Sara; Capouch, Don; Chandorkar, Sujay; Chen, Jake Yue; Saykin, Andrew J.; Wu, Xiaogang; Huang, Hui
Network pharmacology has emerged as a new topic of study in recent years. Molecular connectivity maps between drugs and genes/proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic and toxicological profiles of drugs. To assess drug pharmacological effects, we assume that “ideal” drugs for a patient can treat or prevent the disease by modulating gene expression profiles of this patient to the similar level with those in healthy people. Starting from this hypothesis, we build comprehensive disease-gene-drug connectivity relationships with drug-protein directionality (inhibit/activate) information based on a computational connectivity maps (CMaps) platform. In this work, we develop a novel approach based on integrative pathway modeling. Using Alzheimer’s disease (AD) as an example, we identify and rank AD-related drugs/compounds with their overall drug-protein “connectivity map” profile. First, we retrieve AD-associated proteins through the CMaps platform by using “Alzheimer’s disease” as a query term. Second, we retrieve AD-related pathways by using those AD-associated proteins as input and searching in the Human Pathway Database (HPD) and the PubMed. Third, we integrate the AD-related pathways into unified pathway models, from which we categorize the pharmaceutical effects of candidate drugs on all AD-associated proteins as either “therapeutic” or “toxic” (Figure 1). Finally, we transform the integrated pathways into network models and rank drugs based on the network topological features of drug targets, drug-affecting genes/proteins, and curated AD-associated proteins. We demonstrate that our approach can help identify AD drug candidates with significant therapeutic potentials with small toxic side effects. The case study correlates very well with the existing pharmacology of AD drugs and highlights the significance of the CMaps platform. Ongoing studies towards this direction also have the potential of changing future process of AD drug development. 1Indiana University School of Medicine.
PAGER: constructing PAGs and new PAG-PAG relationships for network biology
(Oxford University Press, 2015-06-15) Yue, Zongliang; Kshirsagar, Madhura M.; Nguyen, Thanh; Suphavilai, Chayaporn; Neylon, Michael T.; Zhu, Liugen; Ratliff, Timothy; Chen, Jake Yue; Department of Computer & Information Science, School of Science
In this article, we described a new database framework to perform integrative "gene-set, network, and pathway analysis" (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene-gene relationships and two types of 3 174 323 PAG-PAG regulatory relationships-co-membership based and regulatory relationship based. To help users assess each PAG's biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG-PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/.
SLDR: a computational technique to identify novel genetic regulatory relationships
(Springer (Biomed Central Ltd.), 2014) Yue, Zongliang; Wan, Ping; Huang, Hui; Xie, Zhan; Chen, Jake Yue; Department of BioHealth Informatics, School of Informatics and Computing
We developed a new computational technique called Step-Level Differential Response (SLDR) to identify genetic regulatory relationships. Our technique takes advantages of functional genomics data for the same species under different perturbation conditions, therefore complementary to current popular computational techniques. It can particularly identify "rare" activation/inhibition relationship events that can be difficult to find in experimental results. In SLDR, we model each candidate target gene as being controlled by N binary-state regulators that lead to ≤2N observable states ("step-levels") for the target. We applied SLDR to the study of the GEO microarray data set GSE25644, which consists of 158 different mutant S. cerevisiae gene expressional profiles. For each target gene t, we first clustered ordered samples into various clusters, each approximating an observable step-level of t to screen out the "de-centric" target. Then, we ordered each gene x as a candidate regulator and aligned t to x for the purpose of examining the step-level correlations between low expression set of x (Ro) and high expression set of x (Rh) from the regulator x to t, by finding max f(t, x): |Ro-Rh| over all candidate × in the genome for each t. We therefore obtained activation and inhibitions events from different combinations of Ro and Rh. Furthermore, we developed criteria for filtering out less-confident regulators, estimated the number of regulators for each target t, and evaluated identified top-ranking regulator-target relationship. Our results can be cross-validated with the Yeast Fitness database. SLDR is also computationally efficient with o(N²) complexity. In summary, we believe SLDR can be applied to the mining of functional genomics big data for future network biology and network medicine applications.
MicroRNA Expression Profiling of Human Respiratory Epithelium Affected by Invasive Candida Infection
(Public Library of Science, 2015) Muhammad, Syed Aun; Fatima, Nighat; Syed, Nawazish-I.-Husain; Wu, Xiaogang; Yang, X. Frank; Chen, Jake Yue; IU School of Informatics and Computing
Invasive candidiasis is potentially life-threatening systemic fungal infection caused by Candida albicans (C. albicans). Candida enters the blood stream and disseminate throughout the body and it is often observed in hospitalized patients, immunocompromised individuals or those with chronic diseases. This infection is opportunistic and risk starts with the colonization of C. albicans on mucocutaneous surfaces and respiratory epithelium. MicroRNAs (miRNAs) are small non-coding RNAs which are involved in the regulation of virtually every cellular process. They regulate and control the levels of mRNA stability and post-transcriptional gene expression. Aberrant expression of miRNAs has been associated in many disease states, and miRNA-based therapies are in progress. In this study, we investigated possible variations of miRNA expression profiles of respiratory epithelial cells infected by invasive Candida species. For this purpose, respiratory epithelial tissues of infected individuals from hospital laboratory were accessed before their treatment. Invasive Candida infection was confirmed by isolation of Candia albicans from the blood cultures of the same infected individuals. The purity of epithelial tissues was assessed by flow cytometry (FACSCalibur cytometer; BD Biosciences, Heidelberg, Germany) using statin antibody (S-44). TaqMan quantitative real-time PCR (in a TaqMan Low Density Array format) was used for miRNA expression profiling. MiRNAs investigated, the levels of expression of 55 miRNA were significantly altered in infected tissues. Some miRNAs showed dramatic increase (miR-16-1) or decrease of expression (miR-17-3p) as compared to control. Gene ontology enrichment analysis of these miRNA-targeted genes suggests that Candidal infection affect many important biological pathways. In summary, disturbance in miRNA expression levels indicated the change in cascade of pathological processes and the regulation of respiratory epithelial functions following invasive Candidal infection. These findings contribute to our understanding of host cell response to Candidal systemic infections.
Predictive and Personalized Medicine with Systems Biology Solutions
(Office of the Vice Chancellor for Research, 2011-04-08) Wu, Xiaogang; Chen, Jake Yue
Systems biology refers to the use of systems engineering and systems science techniques to the understanding of biological systems. At Indiana Center for Systems Biology and Personalized Medicine (ICSBPM), we are particularly interested in developing systems biology techniques that can help shorten the gaps between basic biomedical research and clinical applications of genome sciences toward predictive and personalized medicine. In the past several years, ICSBPM has developed many critical informatics resources for the systems biology and personalized medicine community. The database and software tools that we developed have promoted systems biology and personalized medicine research communities at the national scale. These tools include: HPD, an integrated human pathway database and analysis tool (Chowbina et al., in BMC Bioinformatics 2009, 10(S11): S5); HAPPI, a human annotated and predicted protein interaction database (Chen et al., in BMC Genomics 2009, 10(S1):S16); HIP2, a Database of Healthy Human Individual's Integrated Plasma Proteome (Saha et al., in BMC Medical Genomics 2008, 1(1):12); PEPPI, a Peptidomic Database of Protein Isoforms (Zhou et al., in BMC bioinformatics 2010, 11(S6), S7); ProteoLens, a multi-scale network visualization and data mining tool (Huan et al., in BMC bioinformatics 2008, 9(S9):S5); GeneTerrain, a visual exploration tool for network-organized expression panel biomarker development (You et al., in Information Visualization 2010, 9(1)), and C-Maps, comprehensive molecular connectivity maps between disease-specific proteins and drugs (Li et al., in PLoS Computational Biology, 5(7), e1000450). These tools has been demonstrated to help improve tumor classifications, understand cancer biological systems at the systems scale, tackle biomarker discovery challenges, and facilitate clinical adoption of predictive models developed from computational techniques. We hope that our experience and resources can cement collaborative translational medicine research towards predictive and personalized medicine applications.
DMAP: a connectivity map database to enable identification of novel drug repositioning candidates
(BioMed Central, 2015-09-25) Huang, Hui; Nguyen, Thanh; Ibrahim, Sara; Shantharam, Sandeep; Yue, Zongliang; Chen, Jake Yue; Department of Computer & Information Science, School of Science
BACKGROUND: Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning. METHODS: To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects. RESULTS: In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps CONCLUSIONS: Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.
Computational Biomarker Discovery: From Systems Biology to Predictive and Personalized Medicine Applications
(Office of the Vice Chancellor for Research, 2010-04-09) Chen, Jake Yue; Wu, Xiaogang; Zhang, Fan; Pandey, Ragini; Huang, Hui; Huan, Tianxiao
With the advent of Genome-based Medicine, there is an escalating need for discovering how the modifications of biological molecules, either individually or as an ensemble, can be uniquely associated with human physiological states. This knowledge could lead to breakthroughs in the development of clinical tests known as "biomarker tests" to assess disease risks, early onset, prognosis, and treatment outcome predictions. Therefore, development of molecular biomarkers is a key agenda in the next 5-10 years to take full advantage of the human genome to improve human well-beings. However, the complexity of human biological systems and imperfect instrumentations of high-throughput biological instruments/results have created significant hurdles in biomarker development. Only recently did computational methods become an important player of the research topic, which has seen conventional molecular biomarkers development both extremely long and cost-ineffective. At Indiana Center for Systems Biology and Personalized Medicine, we are developing several computational systems biology strategies to address these challenges. We will show examples of how we approach the problem using a variety of computational techniques, including data mining, algorithm development to take into account of biological contexts, biological knowledge integration, and information visualization. Finally, we outline how research in this direction to derive more robust molecular biomarkers may lead to predictive and personalized medicine. Indiana Center for Systems Biology and Personalized Medicine (CSBPM) was founded in 2007 as an IUPUI signature center by Dr. Jake Chen and his colleagues in the Indiana University School of Informatics, School of Medicine, and School of Science. CSBPM is the only research center in the State of Indiana with the primary goal of pursuing predictive and personalized medicine. CSBPM currently consists of eleven faculty members from the School of Medicine, School of Science, School of Engineering, School of Informatics, and Indiana University Simon Cancer Center. The primary mission of the center is to foster the development and use of systems biology and computational modeling techniques to address challenges in future genome-based medicine. The ultimate goal of the center is to shorten the discovery-to-practice gap between integrative ―Omics‖ biology studies—including genomics, transcriptomics, proteomics, and metabolomics—and predictive and personalized medicine applications.
Pathway and network analysis in proteomics
(Elsevier, 2014-12-07) Wu, Xiaogang; Hasan, Mohammad Al; Chen, Jake Yue; Department of BioHealth Informatics, School of Informatics and Computing
Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.

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