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Sample records for biomarker discovery platform

  1. Network-Based Protein Biomarker Discovery Platforms

    PubMed Central

    Kim, Minhyung

    2016-01-01

    The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms. PMID:27103885

  2. Highly multiplexed proteomic platform for biomarker discovery, diagnostics, and therapeutics.

    PubMed

    Mehan, Michael R; Ostroff, Rachel; Wilcox, Sheri K; Steele, Fintan; Schneider, Daniel; Jarvis, Thale C; Baird, Geoffrey S; Gold, Larry; Janjic, Nebojsa

    2013-01-01

    Progression from health to disease is accompanied by complex changes in protein expression in both the circulation and affected tissues. Large-scale comparative interrogation of the human proteome can offer insights into disease biology as well as lead to the discovery of new biomarkers for diagnostics, new targets for therapeutics, and can identify patients most likely to benefit from treatment. Although genomic studies provide an increasingly sharper understanding of basic biological and pathobiological processes, they ultimately only offer a prediction of relative disease risk, whereas proteins offer an immediate assessment of "real-time" health and disease status. We have recently developed a new proteomic technology, based on modified aptamers, for biomarker discovery that is capable of simultaneously measuring more than a thousand proteins from small volumes of biological samples such as plasma, tissues, or cells. Our technology is enabled by SOMAmers (Slow Off-rate Modified Aptamers), a new class of protein binding reagents that contain chemically modified nucleotides that greatly expand the physicochemical diversity of nucleic acid-based ligands. Such modifications introduce functional groups that are absent in natural nucleic acids but are often found in protein-protein, small molecule-protein, and antibody-antigen interactions. The use of these modifications expands the range of possible targets for SELEX (Systematic Evolution of Ligands by EXponential Enrichment), results in improved binding properties, and facilitates selection of SOMAmers with slow dissociation rates. Our assay works by transforming protein concentrations in a mixture into a corresponding DNA signature, which is then quantified on current commercial DNA microarray platforms. In essence, we take advantage of the dual nature of SOMAmers as both folded binding entities with defined shapes and unique nucleic acid sequences recognizable by specific hybridization probes. Currently, our assay

  3. Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

    SciTech Connect

    Eschrich, Steven; Zhang Hongling; Zhao Haiyan; Boulware, David; Lee, Ji-Hyun; Bloom, Gregory; Torres-Roca, Javier F.

    2009-10-01

    Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. Methods and Materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

  4. Biomarker discovery by CE-MS enables sequence analysis via MS/MS with platform-independent separation.

    PubMed

    Zürbig, Petra; Renfrow, Matthew B; Schiffer, Eric; Novak, Jan; Walden, Michael; Wittke, Stefan; Just, Ingo; Pelzing, Matthias; Neusüss, Christian; Theodorescu, Dan; Root, Karen E; Ross, Mark M; Mischak, Harald

    2006-06-01

    CE-MS is a successful proteomic platform for the definition of biomarkers in different body fluids. Besides the biomarker defining experimental parameters, CE migration time and molecular weight, especially biomarker's sequence identity is an indispensable cornerstone for deeper insights into the pathophysiological pathways of diseases or for made-to-measure therapeutic drug design. Therefore, this report presents a detailed discussion of different peptide sequencing platforms consisting of high performance separation method either coupled on-line or off-line to different MS/MS devices, such as MALDI-TOF-TOF, ESI-IT, ESI-QTOF and Fourier transform ion cyclotron resonance, for sequencing indicative peptides. This comparison demonstrates the unique feature of CE-MS technology to serve as a reliable basis for the assignment of peptide sequence data obtained using different separation MS/MS methods to the biomarker defining parameters, CE migration time and molecular weight. Discovery of potential biomarkers by CE-MS enables sequence analysis via MS/MS with platform-independent sample separation. This is due to the fact that the number of basic and neutral polar amino acids of biomarkers sequences distinctly correlates with their CE-MS migration time/molecular weight coordinates. This uniqueness facilitates the independent entry of different sequencing platforms for peptide sequencing of CE-MS-defined biomarkers from highly complex mixtures.

  5. Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

    PubMed Central

    Walsh, Christopher J.; Hu, Pingzhao; Batt, Jane; Dos Santos, Claudia C.

    2015-01-01

    The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers. PMID:27600230

  6. Stemina biomarker discovery.

    PubMed

    Cezar, Gabriela G; Donley, Elizabeth L R

    2008-09-01

    Stemina Biomarker Discovery was established in 2006 to commercialize technology developed by Dr Gabriela Cezar at the University of Wisconsin (WI, USA). Stemina's cell-based assays arise from the strategic convergence of two cutting edge technologies: metabolomics and human embryonic stem (hES) cells. Stemina analyzes the small molecules secreted by hES cells and differentiated cell types such as neural and heart cells derived from hES cells by liquid chromatography mass spectrometry at its state-of-the-art facilities in Madison, WI, USA. Stemina's first technology platform has identified a dynamic set of small molecules in the extracellular secretome of hES cells secreted in response to exposure to a library of known teratogens. Alterations to small molecules in the biochemical pathway(s) of hES cells are mapped in silico to identify biomarkers of toxicity for drug screening and development in an all human system. These small human molecules may then be translated in vivo as biomarkers of toxic response and disease.

  7. Cell Surface Profiling Using High-Throughput Flow Cytometry: A Platform for Biomarker Discovery and Analysis of Cellular Heterogeneity

    PubMed Central

    Gedye, Craig A.; Hussain, Ali; Paterson, Joshua; Smrke, Alannah; Saini, Harleen; Sirskyj, Danylo; Pereira, Keira; Lobo, Nazleen; Stewart, Jocelyn; Go, Christopher; Ho, Jenny; Medrano, Mauricio; Hyatt, Elzbieta; Yuan, Julie; Lauriault, Stevan; Kondratyev, Maria; van den Beucken, Twan; Jewett, Michael; Dirks, Peter; Guidos, Cynthia J.; Danska, Jayne; Wang, Jean; Wouters, Bradly; Neel, Benjamin; Rottapel, Robert; Ailles, Laurie E.

    2014-01-01

    Cell surface proteins have a wide range of biological functions, and are often used as lineage-specific markers. Antibodies that recognize cell surface antigens are widely used as research tools, diagnostic markers, and even therapeutic agents. The ability to obtain broad cell surface protein profiles would thus be of great value in a wide range of fields. There are however currently few available methods for high-throughput analysis of large numbers of cell surface proteins. We describe here a high-throughput flow cytometry (HT-FC) platform for rapid analysis of 363 cell surface antigens. Here we demonstrate that HT-FC provides reproducible results, and use the platform to identify cell surface antigens that are influenced by common cell preparation methods. We show that multiple populations within complex samples such as primary tumors can be simultaneously analyzed by co-staining of cells with lineage-specific antibodies, allowing unprecedented depth of analysis of heterogeneous cell populations. Furthermore, standard informatics methods can be used to visualize, cluster and downsample HT-FC data to reveal novel signatures and biomarkers. We show that the cell surface profile provides sufficient molecular information to classify samples from different cancers and tissue types into biologically relevant clusters using unsupervised hierarchical clustering. Finally, we describe the identification of a candidate lineage marker and its subsequent validation. In summary, HT-FC combines the advantages of a high-throughput screen with a detection method that is sensitive, quantitative, highly reproducible, and allows in-depth analysis of heterogeneous samples. The use of commercially available antibodies means that high quality reagents are immediately available for follow-up studies. HT-FC has a wide range of applications, including biomarker discovery, molecular classification of cancers, or identification of novel lineage specific or stem cell markers. PMID:25170899

  8. Platforms for antibiotic discovery.

    PubMed

    Lewis, Kim

    2013-05-01

    The spread of resistant bacteria, leading to untreatable infections, is a major public health threat but the pace of antibiotic discovery to combat these pathogens has slowed down. Most antibiotics were originally isolated by screening soil-derived actinomycetes during the golden era of antibiotic discovery in the 1940s to 1960s. However, diminishing returns from this discovery platform led to its collapse, and efforts to create a new platform based on target-focused screening of large libraries of synthetic compounds failed, in part owing to the lack of penetration of such compounds through the bacterial envelope. This article considers strategies to re-establish viable platforms for antibiotic discovery. These include investigating untapped natural product sources such as uncultured bacteria, establishing rules of compound penetration to enable the development of synthetic antibiotics, developing species-specific antibiotics and identifying prodrugs that have the potential to eradicate dormant persisters, which are often responsible for hard-to-treat infections.

  9. Mass spectrometry in biomarker applications: from untargeted discovery to targeted verification, and implications for platform convergence and clinical application

    SciTech Connect

    Smith, Richard D.

    2012-03-01

    It is really only in the last ten years that mass spectrometry (MS) has had a truly significant (but still small) impact on biomedical research. Much of this impact can be attributed to proteomics and its more basic applications. Early biomedical applications have included a number of efforts aimed at developing new biomarkers; however, the success of these endeavors to date have been quite modest - essentially confined to preclinical applications - and have often suffered from combinations of immature technology and hubris. Now that MS-based proteomics is reaching adolescence, it is appropriate to ask if and when biomarker-related applications will extend to the clinical realm, and what developments will be essential for this transition. Biomarker development can be described as a multistage process consisting of discovery, qualification, verification, research assay optimization, validation, and commercialization (1). From a MS perspective, it is possible to 'bin' measurements into 1 of 2 categories - those aimed at discovering potential protein biomarkers and those seeking to verify and validate biomarkers. Approaches in both categories generally involve digesting proteins (e.g., with trypsin) as a first step to yield peptides that can be effectively detected and identified with MS. Discovery-based approaches use broad 'unbiased' or 'undirected' measurements that attempt to cover as many proteins as possible in the hope of revealing promising biomarker candidates. A key challenge with this approach stems from the extremely large dynamic range (i.e., relative stoichiometry) of proteins of potential interest in biofluids such as plasma and the expectation that biomarker proteins of the greatest clinical value for many diseases may very well be present at low relative abundances (2). Protein concentrations in plasma extend from approximately 10{sup 10} pg/mL for albumin to approximately 10 pg/mL and below for interleukins and other cytokines.

  10. Cancer biomarker discovery and validation

    PubMed Central

    Goossens, Nicolas; Nakagawa, Shigeki; Sun, Xiaochen; Hoshida, Yujin

    2015-01-01

    With the emergence of genomic profiling technologies and selective molecular targeted therapies, biomarkers play an increasingly important role in the clinical management of cancer patients. Single gene/protein or multi-gene “signature”-based assays have been introduced to measure specific molecular pathway deregulations that guide therapeutic decision-making as predictive biomarkers. Genome-based prognostic biomarkers are also available for several cancer types for potential incorporation into clinical prognostic staging systems or practice guidelines. However, there is still a large gap between initial biomarker discovery studies and their clinical translation due to the challenges in the process of cancer biomarker development. In this review we summarize the steps of biomarker development, highlight key issues in successful validation and implementation, and overview representative examples in the oncology field. We also discuss regulatory issues and future perspectives in the era of big data analysis and precision medicine. PMID:26213686

  11. Systems biology and biomarker discovery

    SciTech Connect

    Rodland, Karin D.

    2010-12-01

    Medical practitioners have always relied on surrogate markers of inaccessible biological processes to make their diagnosis, whether it was the pallor of shock, the flush of inflammation, or the jaundice of liver failure. Obviously, the current implementation of biomarkers for disease is far more sophisticated, relying on highly reproducible, quantitative measurements of molecules that are often mechanistically associated with the disease in question, as in glycated hemoglobin for the diagnosis of diabetes [1] or the presence of cardiac troponins in the blood for confirmation of myocardial infarcts [2]. In cancer, where the initial symptoms are often subtle and the consequences of delayed diagnosis often drastic for disease management, the impetus to discover readily accessible, reliable, and accurate biomarkers for early detection is compelling. Yet despite years of intense activity, the stable of clinically validated, cost-effective biomarkers for early detection of cancer is pathetically small and still dominated by a handful of markers (CA-125, CEA, PSA) first discovered decades ago. It is time, one could argue, for a fresh approach to the discovery and validation of disease biomarkers, one that takes full advantage of the revolution in genomic technologies and in the development of computational tools for the analysis of large complex datasets. This issue of Disease Markers is dedicated to one such new approach, loosely termed the 'Systems Biology of Biomarkers'. What sets the Systems Biology approach apart from other, more traditional approaches, is both the types of data used, and the tools used for data analysis - and both reflect the revolution in high throughput analytical methods and high throughput computing that has characterized the start of the twenty first century.

  12. Biological Networks for Cancer Candidate Biomarkers Discovery

    PubMed Central

    Yan, Wenying; Xue, Wenjin; Chen, Jiajia; Hu, Guang

    2016-01-01

    Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. PMID:27625573

  13. Computational biomarker pipeline from discovery to clinical implementation: plasma proteomic biomarkers for cardiac transplantation.

    PubMed

    Cohen Freue, Gabriela V; Meredith, Anna; Smith, Derek; Bergman, Axel; Sasaki, Mayu; Lam, Karen K Y; Hollander, Zsuzsanna; Opushneva, Nina; Takhar, Mandeep; Lin, David; Wilson-McManus, Janet; Balshaw, Robert; Keown, Paul A; Borchers, Christoph H; McManus, Bruce; Ng, Raymond T; McMaster, W Robert

    2013-04-01

    Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac

  14. Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation

    PubMed Central

    Cohen Freue, Gabriela V.; Meredith, Anna; Smith, Derek; Bergman, Axel; Sasaki, Mayu; Lam, Karen K. Y.; Hollander, Zsuzsanna; Opushneva, Nina; Takhar, Mandeep; Lin, David; Wilson-McManus, Janet; Balshaw, Robert; Keown, Paul A.; Borchers, Christoph H.; McManus, Bruce; Ng, Raymond T.; McMaster, W. Robert

    2013-01-01

    Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac

  15. Computational biology for cardiovascular biomarker discovery.

    PubMed

    Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

    2009-07-01

    Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using 'omic' information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of 'omic' data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of 'omic' approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

  16. Implications for powering biomarker discovery studies.

    PubMed

    Dibben, Sian M; Holt, Robert J; Davison, Timothy S; Wilson, Claire L; Taylor, Janet; Paul, Ian; McManus, Kieran; Kelly, Paul J; Proutski, Vitali; Harkin, D Paul; Kerr, Peter; Fennell, Dean A; James, Jacqueline A; Kennedy, Richard D

    2012-01-01

    This study examined variations in gene expression between FFPE blocks within tumors of individual patients. Microarray data were used to measure tumor heterogeneity within and between patients and disease states. Data were used to determine the number of samples needed to power biomarker discovery studies. Bias and variation in gene expression were assessed at the intrapatient and interpatient levels and between adenocarcinoma and squamous samples. A mixed-model analysis of variance was fitted to gene expression data and model signatures to assess the statistical significance of observed variations within and between samples and disease states. Sample size analysis, adjusted for sample heterogeneity, was used to determine the number of samples required to support biomarker discovery studies. Variation in gene expression was observed between blocks taken from a single patient. However, this variation was considerably less than differences between histological characteristics. This degree of block-to-block variation still permits biomarker discovery using either macrodissected tumors or whole FFPE sections, provided that intratumor heterogeneity is taken into account. Failure to consider intratumor heterogeneity may result in underpowered biomarker studies that may result in either the generation of longer gene signatures or the inability to identify a viable biomarker. Moreover, the results of this study indicate that a single biopsy sample is suitable for applying a biomarker in non-small-cell lung cancer.

  17. Statistical Aspects in Proteomic Biomarker Discovery.

    PubMed

    Jung, Klaus

    2016-01-01

    In the pursuit of a personalized medicine, i.e., the individual treatment of a patient, many medical decision problems are desired to be supported by biomarkers that can help to make a diagnosis, prediction, or prognosis. Proteomic biomarkers are of special interest since they can not only be detected in tissue samples but can also often be easily detected in diverse body fluids. Statistical methods play an important role in the discovery and validation of proteomic biomarkers. They are necessary in the planning of experiments, in the processing of raw signals, and in the final data analysis. This review provides an overview on the most frequent experimental settings including sample size considerations, and focuses on exploratory data analysis and classifier development.

  18. Targeted proteomic strategy for clinical biomarker discovery.

    PubMed

    Schiess, Ralph; Wollscheid, Bernd; Aebersold, Ruedi

    2009-02-01

    The high complexity and large dynamic range of blood plasma proteins currently prohibit the sensitive and high-throughput profiling of disease and control plasma proteome sample sets large enough to reliably detect disease indicating differences. To circumvent these technological limitations we describe here a new two-stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers. In an initial discovery phase N-linked glycoproteins with distinguishable expression patterns in primary normal and diseased tissue are detected and identified. In the second step the proteins identified in the initial phase are subjected to targeted MS analysis in plasma samples, using the highly sensitive and specific selected reaction monitoring (SRM) technology. Since glycosylated proteins, such as those secreted or shed from the cell surface are likely to reside and persist in blood, the two-stage strategy is focused on the quantification of tissue derived glycoproteins in plasma. The focus on the N-glycoproteome not only reduces the complexity of the analytes, but also targets an information-rich subproteome which is relevant for remote sensing of diseases in the plasma. The N-glycoprotein based biomarker discovery and validation workflow reviewed here allows for the robust identification of protein candidate panels that can finally be selectively monitored in the blood plasma at high sensitivity in a reliable, non-invasive and quantitative fashion.

  19. Advances in Lipidomics for Cancer Biomarkers Discovery

    PubMed Central

    Perrotti, Francesca; Rosa, Consuelo; Cicalini, Ilaria; Sacchetta, Paolo; Del Boccio, Piero; Genovesi, Domenico; Pieragostino, Damiana

    2016-01-01

    Lipids play critical functions in cellular survival, proliferation, interaction and death, since they are involved in chemical-energy storage, cellular signaling, cell membranes, and cell–cell interactions. These cellular processes are strongly related to carcinogenesis pathways, particularly to transformation, progression, and metastasis, suggesting the bioactive lipids are mediators of a number of oncogenic processes. The current review gives a synopsis of a lipidomic approach in tumor characterization; we provide an overview on potential lipid biomarkers in the oncology field and on the principal lipidomic methodologies applied. The novel lipidomic biomarkers are reviewed in an effort to underline their role in diagnosis, in prognostic characterization and in prediction of therapeutic outcomes. A lipidomic investigation through mass spectrometry highlights new insights on molecular mechanisms underlying cancer disease. This new understanding will promote clinical applications in drug discovery and personalized therapy. PMID:27916803

  20. Random glycopeptide bead libraries for seromic biomarker discovery.

    PubMed

    Kracun, Stjepan K; Cló, Emiliano; Clausen, Henrik; Levery, Steven B; Jensen, Knud J; Blixt, Ola

    2010-12-03

    Identification of disease-specific biomarkers is important to address early diagnosis and management of disease. Aberrant post-translational modifications (PTM) of proteins such as O-glycosylations (O-PTMs) are emerging as triggers of autoantibodies that can serve as sensitive biomarkers. Here we have developed a random glycopeptide bead library screening platform for detection of autoantibodies and other binding proteins. Libraries were build on biocompatible PEGA beads including a safety-catch C-terminal amide linker (SCAL) that allowed mild cleavage conditions (I(2)/NaBH(4) and TFA) for release of glycopeptides and sequence determination by ESI-Orbitrap-MS(n). As proof-of-principle, tumor -specific glycopeptide reporter epitopes were built-in into the libraries and were detected by tumor-specific monoclonal antibodies and autoantibodies from cancer patients. Sequenced and identified glycopeptides were resynthesized at the preparative scale by automated parallel peptide synthesis and printed on microarrays for validation and broader analysis with larger sets of sera. We further showed that chemical synthesis of the monosaccharide O-glycopeptide library (Tn-glycoform) could be diversified to other tumor glycoforms by on-bead enzymatic glycosylation reactions with recombinant glycosyltransferases. Hence, we have developed a high-throughput flexible platform for rapid discovery of O-glycopeptide biomarkers and the method has applicability in other types of assays such as lectin/antibody/enzyme specificity studies as well as investigation of other PTMs.

  1. Integration of Proteomics, Bioinformatics, and Systems Biology in Traumatic Brain Injury Biomarker Discovery

    PubMed Central

    Guingab-Cagmat, J.D.; Cagmat, E.B.; Hayes, R.L.; Anagli, J.

    2013-01-01

    Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials for TBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy as TBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed. PMID:23750150

  2. Perspective: A Program to Improve Protein Biomarker Discovery for Cancer

    SciTech Connect

    Aebersold, Ruedi; Anderson, Leigh N.; Caprioli, Richard M.; Druker, Brian; Hartwell, L D.; Smith, Richard D.

    2005-06-01

    Biomarkers for cancer risk, early detection, prognosis, and therapeutic response promise to revolutionize cancer management. Protein biomarkers offer tremendous potential in this regard due to their great diversity and intimate involvement in physiology. An effective program to discover protein biomarkers using existing technology will require team science, an integrated informatics platform, identification and quantitation of candidate biomarkers in disease tissue, mouse models of disease, standardized reagents for analyzing candidate biomarkers in bodily fluids, and implementation of automation. Technology improvements for better fractionation of the proteome, selection of specific biomarkers from complex mixtures, and multiplexed assay of biomarkers would greatly enhance progress.

  3. Biomarker Gene Signature Discovery Integrating Network Knowledge

    PubMed Central

    Cun, Yupeng; Fröhlich, Holger

    2012-01-01

    Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches. PMID:24832044

  4. Biomarker Discovery by Novel Sensors Based on Nanoproteomics Approaches

    PubMed Central

    Dasilva, Noelia; Díez, Paula; Matarraz, Sergio; González-González, María; Paradinas, Sara; Orfao, Alberto; Fuentes, Manuel

    2012-01-01

    During the last years, proteomics has facilitated biomarker discovery by coupling high-throughput techniques with novel nanosensors. In the present review, we focus on the study of label-based and label-free detection systems, as well as nanotechnology approaches, indicating their advantages and applications in biomarker discovery. In addition, several disease biomarkers are shown in order to display the clinical importance of the improvement of sensitivity and selectivity by using nanoproteomics approaches as novel sensors. PMID:22438764

  5. Discovery of nutritional biomarkers: future directions based on omics technologies.

    PubMed

    Odriozola, Leticia; Corrales, Fernado J

    2015-07-01

    Understanding the interactions between food and human biology is of utmost importance to facilitate the development of more efficient nutritional interventions that might improve our wellness status and future health outcomes by reducing risk factors for non-transmittable chronic diseases, such as cardiovascular diseases, cancer, obesity and metabolic syndrome. Dissection of the molecular mechanisms that mediate the physiological effects of diets and bioactive compounds is one of the main goals of current nutritional investigation and the food industry as might lead to the discovery of novel biomarkers. It is widely recognized that the availability of robust nutritional biomarkers represents a bottleneck that delays the innovation process of the food industry. In this regard, omics sciences have opened up new avenues of research and opportunities in nutrition. Advances in mass spectrometry, nuclear magnetic resonance, next generation sequencing and microarray technologies allow massive genome, gene expression, proteomic and metabolomic profiling, obtaining a global and in-depth analysis of physiological/pathological scenarios. For this reason, omics platforms are most suitable for the discovery and characterization of novel nutritional markers that will define the nutritional status of both individuals and populations in the near future, and to identify the nutritional bioactive compounds responsible for the health outcomes.

  6. Metabolomics in cancer biomarker discovery: current trends and future perspectives.

    PubMed

    Armitage, Emily G; Barbas, Coral

    2014-01-01

    Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted.

  7. Biomarker discovery: success as a function of risk mitigation.

    PubMed

    Weiser, Stefan

    2016-01-01

    Protein biomarker discovery is a fascinating enterprise; however, success in terms of products for in vitro diagnostic use is sparse. New developments in mass spectrometry-based quantitative proteomics as discovery technology have opened up new avenues for this endeavor. In addition to choice of technology, sample properties, study design and validation strategy are potent pillars required for project success. The challenge for successful biomarker discovery can be described by a series of risks that need to be mitigated. This article intends to describe the major risks along with possible solutions.

  8. Novel droplet platforms for the detection of disease biomarkers.

    PubMed

    Zec, Helena; Shin, Dong Jin; Wang, Tza-Huei

    2014-09-01

    Personalized medicine - healthcare based on individual genetic variation - has the potential to transform the way healthcare is delivered to patients. The promise of personalized medicine has been predicated on the predictive and diagnostic power of genomic and proteomic biomarkers. Biomarker screening may help improve health outcomes, for example, by identifying individuals' susceptibility to diseases and predicting how patients will respond to drugs. Microfluidic droplet technology offers an exciting opportunity to revolutionize the accessibility of personalized medicine. A framework for the role of droplet microfluidics in biomarker detection can be based on two main themes. Emulsion-based microdroplet platforms can provide new ways to measure and detect biomolecules. In addition, microdroplet platforms facilitate high-throughput screening of biomarkers. Meanwhile, surface-based droplet platforms provide an opportunity to develop miniaturized diagnostic systems. These platforms may function as portable benchtop environments that dramatically shorten the transition of a benchtop assay into a point-of-care format.

  9. Unbiased approaches to biomarker discovery in neurodegenerative diseases

    PubMed Central

    Chen-Plotkin, Alice S.

    2014-01-01

    Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and frontotemporal dementia have several important features in common. They are progressive, they affect a relatively inaccessible organ, and we have no disease-modifying therapies for them. For these brain-based diseases, current diagnosis and evaluation of disease severity rely almost entirely on clinical examination, which may only be a rough approximation of disease state. Thus, the development of biomarkers – objective, relatively easily measured and precise indicators of pathogenic processes – could improve patient care and accelerate therapeutic discovery. Yet existing, rigorously tested neurodegenerative disease biomarkers are few, and even fewer biomarkers have translated into clinical use. To find new biomarkers for these diseases, an unbiased, high-throughput screening approach may be needed. In this review, I will describe the potential utility of such an approach to biomarker discovery, using Parkinson’s disease as a case example. PMID:25442938

  10. Biomarker Discovery in Neurodegenerative Diseases: A Proteomic Approach

    PubMed Central

    Shi, Min; Caudle, W. Michael; Zhang, Jing

    2010-01-01

    Biomarkers for neurodegenerative disorders are essential to facilitate disease diagnosis, ideally at early stages, monitor disease progression, and assess response to existing and future treatments. Application of proteomics to the human brain, cerebrospinal fluid and plasma has greatly hastened the unbiased and high-throughput searches for novel biomarkers. There are many steps critical to biomarker discovery, whether for neurodegenerative or other diseases, including sample preparation, protein/peptide separation and identification, as well as independent confirmation and validation. In this review we have summarized current proteomics technologies involved in discovery of biomarkers for neurodegenerative diseases, practical considerations and limitations of several major aspects, as well as the current status of candidate biomarkers revealed by proteomics for Alzheimer and Parkinson diseases. PMID:18938247

  11. PICan: An integromics framework for dynamic cancer biomarker discovery.

    PubMed

    McArt, Darragh G; Blayney, Jaine K; Boyle, David P; Irwin, Gareth W; Moran, Michael; Hutchinson, Ryan A; Bankhead, Peter; Kieran, Declan; Wang, Yinhai; Dunne, Philip D; Kennedy, Richard D; Mullan, Paul B; Harkin, D Paul; Catherwood, Mark A; James, Jacqueline A; Salto-Tellez, Manuel; Hamilton, Peter W

    2015-06-01

    Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

  12. Novel methodologies for biomarker discovery in atherosclerosis.

    PubMed

    Hoefer, Imo E; Steffens, Sabine; Ala-Korpela, Mika; Bäck, Magnus; Badimon, Lina; Bochaton-Piallat, Marie-Luce; Boulanger, Chantal M; Caligiuri, Giuseppina; Dimmeler, Stefanie; Egido, Jesus; Evans, Paul C; Guzik, Tomasz; Kwak, Brenda R; Landmesser, Ulf; Mayr, Manuel; Monaco, Claudia; Pasterkamp, Gerard; Tuñón, Jose; Weber, Christian

    2015-10-14

    Identification of subjects at increased risk for cardiovascular events plays a central role in the worldwide efforts to improve prevention, prediction, diagnosis, and prognosis of cardiovascular disease and to decrease the related costs. Despite their high predictive value on population level, traditional risk factors fail to fully predict individual risk. This position paper provides a summary of current vascular biomarkers other than the traditional risk factors with a special focus on the emerging -omics technologies. The definition of biomarkers and the identification and use of classical biomarkers are introduced, and we discuss the limitations of current biomarkers such as high sensitivity C-reactive protein (hsCRP) or N-terminal pro-brain natriuretic peptide (NT-proBNP). This is complemented by circulating plasma biomarkers, including high-density lipoprotein (HDL), and the conceptual shift from HDL cholesterol levels to HDL composition/function for cardiovascular risk assessment. Novel sources for plasma-derived markers include microparticles, microvesicles, and exosomes and their use for current omics-based analytics. Measurement of circulating micro-RNAs, short RNA sequences regulating gene expression, has attracted major interest in the search for novel biomarkers. Also, mass spectrometry and nuclear magnetic resonance spectroscopy have become key complementary technologies in the search for new biomarkers, such as proteomic searches or identification and quantification of small metabolites including lipids (metabolomics and lipidomics). In particular, pro-inflammatory lipid metabolites have gained much interest in the cardiovascular field. Our consensus statement concludes on leads and needs in biomarker research for the near future to improve individual cardiovascular risk prediction.

  13. The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

    PubMed Central

    Wang, Hui; Shi, Tujin; Qian, Wei-Jun; Liu, Tao; Kagan, Jacob; Srivastava, Sudhir; Smith, Richard D.; Rodland, Karin D.; Camp, David G.

    2016-01-01

    Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC–MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC–MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC–MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives. PMID:26581546

  14. Integrative system biology strategies for disease biomarker discovery.

    PubMed

    Zhang, Haiyuan; Hu, Hao; Deng, Cao; Chun, Yeona; Zhou, Shengtao; Huang, Fuqiang; Zhou, Qin

    2012-05-01

    Biomarkers are currently widely used to diagnose diseases, monitor treatments, and evaluate potential drug candidates. Research of differential Omics accelerate the advancements of biomarkers' discovery. By extracting biological knowledge from the 'omics' through integration, integrative system biology creates predictive models of cells, organs, biochemical processes and complete organisms, in addition to identifying human disease biomarkers. Recent development in high-throughput methods enables analysis of genome, transcriptome, proteome, and metabolome at an unprecedented scale, thus contributing to the deluge of experimental data in numerous public databases. Several integrative system biology approaches have been developed and applied to the discovery of disease biomarkers from databases. In this review, we highlight several of these approaches and identify future steps in the context of the field of integrative system biology.

  15. Biomarkers for Bone Tumors: Discovery from Genomics and Proteomics Studies and Their Challenges

    PubMed Central

    Wan-Ibrahim, Wan I; Singh, Vivek A; Hashim, Onn H; Abdul-Rahman, Puteri S

    2015-01-01

    Diagnosis of bone tumor currently relies on imaging and biopsy, and hence, the need to find less invasive ways for its accurate detection. More recently, numerous promising deoxyribonucleic acid (DNA) and protein biomarkers with significant prognostic, diagnostic and/or predictive abilities for various types of bone tumors have been identified from genomics and proteomics studies. This article reviewed the putative biomarkers for the more common types of bone tumors (that is, osteosarcoma, Ewing sarcoma, chondrosarcoma [malignant] and giant cell tumor [benign]) that were unveiled from the studies. The benefits and drawbacks of these biomarkers, as well as the technology platforms involved in the research, were also discussed. Challenges faced in the biomarker discovery studies and the problems in their translation from the bench to the clinical settings were also addressed. PMID:26581086

  16. Soluble biomarkers development in osteoarthritis: from discovery to personalized medicine

    PubMed Central

    Henrotin, Yves; Sanchez, Christelle; Cornet, Anne; Van de Put, Joachim; Douette, Pierre; Gharbi, Myriam

    2015-01-01

    Abstract Context: Specific soluble biomarkers could be a precious tool for diagnosis, prognosis and personalized management of osteoarthritic (OA) patients. Objective: To describe the path of soluble biomarker development from discovery to clinical qualification and regulatory adoption toward OA-related biomarker qualification. Methods and results: This review summarizes current guidance on the use of biomarkers in OA in clinical trials and their utility at five stages, including preclinical development and phase 1 to phase 4 trials. It also presents all the available regulatory requirements. Conclusions: The path through the adoption of a specific soluble biomarker for OA is steep but is worth the challenge due to the benefit that it can provide. PMID:26954785

  17. Human saliva proteome analysis and disease biomarker discovery.

    PubMed

    Hu, Shen; Loo, Joseph A; Wong, David T

    2007-08-01

    Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.

  18. Proteomic Approaches in Biomarker Discovery: New Perspectives in Cancer Diagnostics

    PubMed Central

    Kocevar, Nina; Komel, Radovan

    2014-01-01

    Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies. PMID:24550697

  19. Biomarker discovery for neuroendocrine cervical cancer.

    PubMed

    Lin, Li-Hsun; Chang, Shing-Jyh; Hu, Ren-Yu; Lin, Meng-Wei; Lin, Szu-Ting; Huang, Shun-Hong; Lyu, Ping-Chiang; Chou, Hsiu-Chuan; Lai, Zih-Yin; Chuang, Yung-Jen; Chan, Hong-Lin

    2014-07-01

    Neuroendocrine cervical cancer is an aggressive but rare form of cervical cancer. The majority of neuroendocrine cervical cancer patients present with advanced-stage diseases. However, the limited numbers of neuroendocrine tumor markers are insufficient for clinical purposes. Thus, we used a proteomic approach combining lysine labeling 2D-DIGE and MALDI-TOF MS to investigate the biomarkers for neuroendocrine cervical cancer. By analyzing the global proteome alteration between the neuroendocrine cervical cancer line (HM-1) and non-neuroendocrine cervical cancer lines (CaSki cells, ME-180 cells, and Hela cells), we identified 82 proteins exhibiting marked changes between HM-1 and CaSki cells, and between ME-180 and Hela cells. Several proteins involved in protein folding, cytoskeleton, transcription control, signal transduction, glycolysis, and redox regulation exhibited significant changes in abundance. Proteomic and immunoblot analyses indicated respective 49.88-fold and 25-fold increased levels of transgelin in HM-1 cells compared with that in other non-neuroendocrine cervical cancer cell lines, implying that transgelin is a biomarker for neuroendocrine cervical cancer. In summary, we used a comprehensive neuroendocrine/non-neuroendocrine cervical cancer model based proteomic approach for identifying neuroendocrine cervical cancer markers, which might contribute to the prognosis and diagnosis of neuroendocrine cervical cancer.

  20. Cancer Biomarker Discovery: The Entropic Hallmark

    PubMed Central

    Berretta, Regina; Moscato, Pablo

    2010-01-01

    Background It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-througput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the

  1. Multiplatform Biomarker Discovery for Bladder Cancer Recurrence Diagnosis

    PubMed Central

    De Paoli, Marine; Gogalic, Selma; Sauer, Ursula; Preininger, Claudia; Pandha, Hardev; Simpson, Guy; Horvath, Andras

    2016-01-01

    Purpose. Nonmuscle invasive bladder cancer (BCa) has a high recurrence rate requiring lifelong surveillance. Urinary biomarkers are promising as simple alternatives to cystoscopy for the diagnosis of recurrent bladder cancer. However, no single marker can achieve the required accuracy. The purpose of this study was to select a multiparameter panel, comprising urinary biomarkers and clinical parameters, for BCa recurrence diagnosis. Experimental Design. Candidate biomarkers were measured in urine samples of BCa patients with recurrence and BCa patients without recurrence. A multiplatform strategy was used for marker quantification comprising a multiplexed microarray and an automated platform for ELISA analysis. A multivariate statistical analysis combined the results from both platforms with the collected clinical data. Results. The best performing combination of biomarkers and clinical parameters achieved an AUC value of 0.91, showing better performance than individual parameters. This panel comprises six biomarkers (cadherin-1, IL-8, ErbB2, IL-6, EN2, and VEGF-A) and three clinical parameters (number of past recurrences, number of BCG therapies, and stage at time of diagnosis). Conclusions. The multiparameter panel could be a useful noninvasive tool for BCa surveillance and potentially impact the clinical management of this disease. Validation of results in an independent cohort is warranted. PMID:27660385

  2. State of the Art in Tumor Antigen and Biomarker Discovery

    PubMed Central

    Even-Desrumeaux, Klervi; Baty, Daniel; Chames, Patrick

    2011-01-01

    Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology. PMID:24212823

  3. Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery

    PubMed Central

    Gold, Larry; Ayers, Deborah; Bertino, Jennifer; Bock, Christopher; Bock, Ashley; Brody, Edward N.; Carter, Jeff; Dalby, Andrew B.; Eaton, Bruce E.; Fitzwater, Tim; Flather, Dylan; Forbes, Ashley; Foreman, Trudi; Fowler, Cate; Gawande, Bharat; Goss, Meredith; Gunn, Magda; Gupta, Shashi; Halladay, Dennis; Heil, Jim; Heilig, Joe; Hicke, Brian; Husar, Gregory; Janjic, Nebojsa; Jarvis, Thale; Jennings, Susan; Katilius, Evaldas; Keeney, Tracy R.; Kim, Nancy; Koch, Tad H.; Kraemer, Stephan; Kroiss, Luke; Le, Ngan; Levine, Daniel; Lindsey, Wes; Lollo, Bridget; Mayfield, Wes; Mehan, Mike; Mehler, Robert; Nelson, Sally K.; Nelson, Michele; Nieuwlandt, Dan; Nikrad, Malti; Ochsner, Urs; Ostroff, Rachel M.; Otis, Matt; Parker, Thomas; Pietrasiewicz, Steve; Resnicow, Daniel I.; Rohloff, John; Sanders, Glenn; Sattin, Sarah; Schneider, Daniel; Singer, Britta; Stanton, Martin; Sterkel, Alana; Stewart, Alex; Stratford, Suzanne; Vaught, Jonathan D.; Vrkljan, Mike; Walker, Jeffrey J.; Watrobka, Mike; Waugh, Sheela; Weiss, Allison; Wilcox, Sheri K.; Wolfson, Alexey; Wolk, Steven K.; Zhang, Chi; Zichi, Dom

    2010-01-01

    Background The interrogation of proteomes (“proteomics”) in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine. Methodology/Principal Findings We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (∼100 fM–1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states. Conclusions/Significance We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of

  4. Discovery of novel biomarkers and phenotypes by semantic technologies

    PubMed Central

    2013-01-01

    Background Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents. Results This paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex semantic engine, without prior human manipulations such as parsing. Recall and precision against established, but different benchmarks lie in ranges up to 30% and 50% respectively. Retrieval of known entities missed by other traditional approaches could be demonstrated. Finally, the InfoCodex semantic engine was shown to discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates with a high potential, although noticeable noise (uninteresting or obvious terms) was generated. Conclusions The reported approach of employing autonomous self-organising semantic engines to aid biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential to impact, conservatively, a faster alternative to vocabulary processes dependent on humans having to read and analyze all the texts. More optimistically, it could impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early recognition of dead ends and adverse reactions. PMID:23402646

  5. Detection of biomarkers using recombinant antibodies coupled to nanostructured platforms

    PubMed Central

    Kierny, Michael R.; Cunningham, Thomas D.; Kay, Brian K.

    2012-01-01

    The utility of biomarker detection in tomorrow's personalized health care field will mean early and accurate diagnosis of many types of human physiological conditions and diseases. In the search for biomarkers, recombinant affinity reagents can be generated to candidate proteins or post-translational modifications that differ qualitatively or quantitatively between normal and diseased tissues. The use of display technologies, such as phage-display, allows for manageable selection and optimization of affinity reagents for use in biomarker detection. Here we review the use of recombinant antibody fragments, such as scFvs and Fabs, which can be affinity-selected from phage-display libraries, to bind with both high specificity and affinity to biomarkers of cancer, such as Human Epidermal growth factor Receptor 2 (HER2) and Carcinoembryonic antigen (CEA). We discuss how these recombinant antibodies can be fabricated into nanostructures, such as carbon nanotubes, nanowires, and quantum dots, for the purpose of enhancing detection of biomarkers at low concentrations (pg/mL) within complex mixtures such as serum or tissue extracts. Other sensing technologies, which take advantage of ‘Surface Enhanced Raman Scattering’ (gold nanoshells), frequency changes in piezoelectric crystals (quartz crystal microbalance), or electrical current generation and sensing during electrochemical reactions (electrochemical detection), can effectively provide multiplexed platforms for detection of cancer and injury biomarkers. Such devices may soon replace the traditional time consuming ELISAs and Western blots, and deliver rapid, point-of-care diagnostics to market. PMID:22833780

  6. Proteomics for discovery of candidate colorectal cancer biomarkers

    PubMed Central

    Álvarez-Chaver, Paula; Otero-Estévez, Olalla; Páez de la Cadena, María; Rodríguez-Berrocal, Francisco J; Martínez-Zorzano, Vicenta S

    2014-01-01

    Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in Europe and other Western countries, mainly due to the lack of well-validated clinically useful biomarkers with enough sensitivity and specificity to detect this disease at early stages. Although it is well known that the pathogenesis of CRC is a progressive accumulation of mutations in multiple genes, much less is known at the proteome level. Therefore, in the last years many proteomic studies have been conducted to find new candidate protein biomarkers for diagnosis, prognosis and as therapeutic targets for this malignancy, as well as to elucidate the molecular mechanisms of colorectal carcinogenesis. An important advantage of the proteomic approaches is the capacity to look for multiple differentially expressed proteins in a single study. This review provides an overview of the recent reports describing the different proteomic tools used for the discovery of new protein markers for CRC such as two-dimensional electrophoresis methods, quantitative mass spectrometry-based techniques or protein microarrays. Additionally, we will also focus on the diverse biological samples used for CRC biomarker discovery such as tissue, serum and faeces, besides cell lines and murine models, discussing their advantages and disadvantages, and summarize the most frequently identified candidate CRC markers. PMID:24744574

  7. Molecular biology tools: proteomics techniques in biomarker discovery.

    PubMed

    Lottspeich, Friedrich; Kellermann, Josef; Keidel, Eva-Maria

    2010-01-01

    Despite worldwide efforts biomarker discovery by plasma proteomics was not successful so far. Several reasons for this failure are obvious. Mainly, proteome diversity is remarkable between different individuals and is caused by genetic, environmental and life style parameters. To recognize disease related proteins that could serve as potential biomarkers is only feasible by investigating a non realizable large number of patients. Furthermore, plasma proteomics comprises enormous technical hurdles for quantitative analysis. High reproducibility of blood sampling in clinical routine is hard to achieve. Quantitative proteome analysis has to struggle with the complexity of millions of protein species comprising typical plasma proteins, cellular leakage proteins and antibodies and concentration differences of more than 1011 between high and low abundant proteins. Therefore, no successful quantitative and comprehensive plasma proteome analysis is reported so far. A novel proteomics strategy is proposed for biomarker discovery in plasma. Instead of comparing the plasma proteome of different individuals it is recommended to analyze the proteomes of different time points of a single individual during the development of a disease. This strategy is realized by the use of plasma of the Bavarian Red Cross Blood Bank, were three million samples are stored under standardized conditions. To achieve reliable data the isotope coded protein labelling proteomics technology was used.

  8. Challenges for red blood cell biomarker discovery through proteomics.

    PubMed

    Barasa, Benjamin; Slijper, Monique

    2014-05-01

    Red blood cells are rather unique body cells, since they have lost all organelles when mature, which results in lack of potential to replace proteins that have lost their function. They maintain only a few pathways for obtaining energy and reducing power for the key functions they need to fulfill. This makes RBCs highly sensitive to any aberration. If so, these RBCs are quickly removed from circulation, but if the RBC levels reduce extremely fast, this results in hemolytic anemia. Several causes of HA exist, and proteome analysis is the most straightforward way to obtain deeper insight into RBC functioning under the stress of disease. This should result in discovery of biomarkers, typical for each source of anemia. In this review, several challenges to generate in-depth RBC proteomes are described, like to obtain pure RBCs, to overcome the wide dynamic range in protein expression, and to establish which of the identified/quantified proteins are active in RBCs. The final challenge is to acquire and validate suited biomarkers unique for the changes that occur for each of the clinical questions; in red blood cell aging (also important for transfusion medicine), for thalassemias or sickle cell disease. Biomarkers for other hemolytic anemias that are caused by dysfunction of RBC membrane proteins (the RBC membrane defects) or RBC cytosolic proteins (the enzymopathies) are sometimes even harder to discover, in particular for the patients with RBC rare diseases with unknown cause. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

  9. Biomarker Discovery in Gulf War Veterans: Development of a War Illness Diagnostic Panel

    DTIC Science & Technology

    2015-10-01

    Award Number: W81XWH-12-1-0382 TITLE: Biomarker Discovery in Gulf War Veterans: Development of a War Illness Diagnostic Panel PRINCIPAL...SUBTITLE Biomarker Discovery in Gulf War Veterans: Development of a War Illness Diagnostic Panel 5a. CONTRACT NUMBER W81XWH-12-1-0382 5b. GRANT NUMBER...b. ABSTRACT U c. THIS PAGE U UU 11 19b. TELEPHONE NUMBER (include area code) Biomarker Discovery in Gulf War Veterans: Development of a Gulf War

  10. Metabolomics-Based Discovery of Diagnostic Biomarkers for Onchocerciasis

    PubMed Central

    Denery, Judith R.; Nunes, Ashlee A. K.; Hixon, Mark S.; Dickerson, Tobin J.; Janda, Kim D.

    2010-01-01

    Background Development of robust, sensitive, and reproducible diagnostic tests for understanding the epidemiology of neglected tropical diseases is an integral aspect of the success of worldwide control and elimination programs. In the treatment of onchocerciasis, clinical diagnostics that can function in an elimination scenario are non-existent and desperately needed. Due to its sensitivity and quantitative reproducibility, liquid chromatography-mass spectrometry (LC-MS) based metabolomics is a powerful approach to this problem. Methodology/Principal Findings Analysis of an African sample set comprised of 73 serum and plasma samples revealed a set of 14 biomarkers that showed excellent discrimination between Onchocerca volvulus–positive and negative individuals by multivariate statistical analysis. Application of this biomarker set to an additional sample set from onchocerciasis endemic areas where long-term ivermectin treatment has been successful revealed that the biomarker set may also distinguish individuals with worms of compromised viability from those with active infection. Machine learning extended the utility of the biomarker set from a complex multivariate analysis to a binary format applicable for adaptation to a field-based diagnostic, validating the use of complex data mining tools applied to infectious disease biomarker discovery and diagnostic development. Conclusions/Significance An LC-MS metabolomics-based diagnostic has the potential to monitor the progression of onchocerciasis in both endemic and non-endemic geographic areas, as well as provide an essential tool to multinational programs in the ongoing fight against this neglected tropical disease. Ultimately this technology can be expanded for the diagnosis of other filarial and/or neglected tropical diseases. PMID:20957145

  11. Proteomic Technologies for the Discovery of Type 1 Diabetes Biomarkers

    PubMed Central

    Zhi, Wenbo; Purohit, Sharad; Carey, Colleen; Wang, Meiyao; She, Jin-Xiong

    2010-01-01

    In this review, we discuss several important issues concerning the discovery of protein biomarkers for complex human diseases, with a focus on type 1 diabetes. Serum or plasma is the first choice of specimen due to its richness in biological information and relatively easy availability. It is a challenging task to comprehensively characterize the serum/plasma proteome because of the large dynamic range of protein concentration. Therefore, sample pretreatment is required in order to explore the low- to medium-abundance proteins contained in serum/plasma. In this regard, enrichment of low-abundance proteins using random hexapeptide library beads has distinct advantages over the traditional immune-depletion methods, including higher efficiency, higher binding capacity, and lower cost. In-depth mining of serum/plasma proteome using different separation techniques have also been evaluated and are discussed in this review. Overall, the shotgun proteomics—multidimensional separation of digested peptides followed by mass spectrometry analysis—is highly efficient and therefore has become a preferred method for protein biomarker discovery. PMID:20663466

  12. Targeted discovery and validation of plasma biomarkers of Parkinson's disease.

    PubMed

    Pan, Catherine; Zhou, Yong; Dator, Romel; Ginghina, Carmen; Zhao, Yanchun; Movius, James; Peskind, Elaine; Zabetian, Cyrus P; Quinn, Joseph; Galasko, Douglas; Stewart, Tessandra; Shi, Min; Zhang, Jing

    2014-11-07

    Despite extensive research, an unmet need remains for protein biomarkers of Parkinson's disease (PD) in peripheral body fluids, especially blood, which is easily accessible clinically. The discovery of such biomarkers is challenging, however, due to the enormous complexity and huge dynamic range of human blood proteins, which are derived from nearly all organ systems, with those originating specifically from the central nervous system (CNS) being exceptionally low in abundance. In this investigation of a relatively large cohort (∼300 subjects), selected reaction monitoring (SRM) assays (a targeted approach) were used to probe plasma peptides derived from glycoproteins previously found to be altered in the CNS based on PD diagnosis or severity. Next, the detected peptides were interrogated for their diagnostic sensitivity and specificity as well as the correlation with PD severity, as determined by the Unified Parkinson's Disease Rating Scale (UPDRS). The results revealed that 12 of the 50 candidate glycopeptides were reliably and consistently identified in plasma samples, with three of them displaying significant differences among diagnostic groups. A combination of four peptides (derived from PRNP, HSPG2, MEGF8, and NCAM1) provided an overall area under curve (AUC) of 0.753 (sensitivity: 90.4%; specificity: 50.0%). Additionally, combining two peptides (derived from MEGF8 and ICAM1) yielded significant correlation with PD severity, that is, UPDRS (r = 0.293, p = 0.004). The significance of these results is at least two-fold: (1) it is possible to use a targeted approach to identify otherwise very difficult to detect CNS related biomarkers in peripheral blood and (2) the novel biomarkers, if validated in independent cohorts, can be employed to assist with clinical diagnosis of PD as well as monitoring disease progression.

  13. Urine metabolomics analysis for kidney cancer detection and biomarker discovery.

    PubMed

    Kim, Kyoungmi; Aronov, Pavel; Zakharkin, Stanislav O; Anderson, Danielle; Perroud, Bertrand; Thompson, Ian M; Weiss, Robert H

    2009-03-01

    Renal cell carcinoma (RCC) accounts for 11,000 deaths per year in the United States. When detected early, generally serendipitously by imaging conducted for other reasons, long term survival is generally excellent. When detected with symptoms, prognosis is poor. Under these circumstances, a screening biomarker has the potential for substantial public health benefit. The purpose of this study was to evaluate the utility of urine metabolomics analysis for metabolomic profiling, identification of biomarkers, and ultimately for devising a urine screening test for RCC. Fifty urine samples were obtained from RCC and control patients from two institutions, and in a separate study, urine samples were taken from 13 normal individuals. Hydrophilic interaction chromatography-mass spectrometry was performed to identify small molecule metabolites present in each sample. Cluster analysis, principal components analysis, linear discriminant analysis, differential analysis, and variance component analysis were used to analyze the data. Previous work is extended to confirm the effectiveness of urine metabolomics analysis using a larger and more diverse patient cohort. It is now shown that the utility of this technique is dependent on the site of urine collection and that there exist substantial sources of variation of the urinary metabolomic profile, although group variation is sufficient to yield viable biomarkers. Surprisingly there is a small degree of variation in the urinary metabolomic profile in normal patients due to time since the last meal, and there is little difference in the urinary metabolomic profile in a cohort of pre- and postnephrectomy (partial or radical) renal cell carcinoma patients, suggesting that metabolic changes associated with RCC persist after removal of the primary tumor. After further investigations relating to the discovery and identity of individual biomarkers and attenuation of residual sources of variation, our work shows that urine metabolomics

  14. Oblique view of the Orbiter Discovery from an elevated platform ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    Oblique view of the Orbiter Discovery from an elevated platform in the Vehicle Assembly Building at NASA's Kennedy Space Center. Note the Forward Reaction Control System (RCS) Module from the forward section and the Orbiter Maneuvering System (OMS)/RCS pods from the aft section have been removed. Ground support equipment called Strongbacks are attached to the payload bay doors and the Flight Deck windows have been covered by ground support equipment. Also note the scale figure standing by the Orbiter. - Space Transportation System, Orbiter Discovery (OV-103), Lyndon B. Johnson Space Center, 2101 NASA Parkway, Houston, Harris County, TX

  15. Novel avenues of drug discovery and biomarkers for diabetes mellitus.

    PubMed

    Maiese, Kenneth; Chong, Zhao Zhong; Shang, Yan Chen; Hou, Jinling

    2011-02-01

    Globally, developed nations spend a significant amount of their resources on health care initiatives that poorly translate into increased population life expectancy. As an example, the United States devotes 16% of its gross domestic product to health care, the highest level in the world, but falls behind other nations that enjoy greater individual life expectancy. These observations point to the need for pioneering avenues of drug discovery to increase life span with controlled costs. In particular, innovative drug development for metabolic disorders such as diabetes mellitus becomes increasingly critical given that the number of diabetic people will increase exponentially over the next 20 years. This article discusses the elucidation and targeting of novel cellular pathways that are intimately tied to oxidative stress in diabetes mellitus for new treatment strategies. Pathways that involve wingless, β-nicotinamide adenine dinucleotide (NAD(+)) precursors, and cytokines govern complex biological pathways that determine both cell survival and longevity during diabetes mellitus and its complications. Furthermore, the role of these entities as biomarkers for disease can further enhance their utility irrespective of their treatment potential. Greater understanding of the intricacies of these unique cellular mechanisms will shape future drug discovery for diabetes mellitus to provide focused clinical care with limited or absent long-term complications.

  16. Multifunctional Core–Shell Nanoparticles: Discovery of Previously Invisible Biomarkers

    PubMed Central

    2011-01-01

    Many low-abundance biomarkers for early detection of cancer and other diseases are invisible to mass spectrometry because they exist in body fluids in very low concentrations, are masked by high-abundance proteins such as albumin and immunoglobulins, and are very labile. To overcome these barriers, we created porous, buoyant, core–shell hydrogel nanoparticles containing novel high affinity reactive chemical baits for protein and peptide harvesting, concentration, and preservation in body fluids. Poly(N-isopropylacrylamide-co-acrylic acid) nanoparticles were functionalized with amino-containing dyes via zero-length cross-linking amidation reactions. Nanoparticles functionalized in the core with 17 different (12 chemically novel) molecular baits showed preferential high affinities (KD < 10–11 M) for specific low-abundance protein analytes. A poly(N-isopropylacrylamide-co-vinylsulfonic acid) shell was added to the core particles. This shell chemistry selectively prevented unwanted entry of all size peptides derived from albumin without hindering the penetration of non-albumin small proteins and peptides. Proteins and peptides entered the core to be captured with high affinity by baits immobilized in the core. Nanoparticles effectively protected interleukin-6 from enzymatic degradation in sweat and increased the effective detection sensitivity of human growth hormone in human urine using multiple reaction monitoring analysis. Used in whole blood as a one-step, in-solution preprocessing step, the nanoparticles greatly enriched the concentration of low-molecular weight proteins and peptides while excluding albumin and other proteins above 30 kDa; this achieved a 10,000-fold effective amplification of the analyte concentration, enabling mass spectrometry (MS) discovery of candidate biomarkers that were previously undetectable. PMID:21999289

  17. Multifunctional core-shell nanoparticles: discovery of previously invisible biomarkers.

    PubMed

    Tamburro, Davide; Fredolini, Claudia; Espina, Virginia; Douglas, Temple A; Ranganathan, Adarsh; Ilag, Leopold; Zhou, Weidong; Russo, Paul; Espina, Benjamin H; Muto, Giovanni; Petricoin, Emanuel F; Liotta, Lance A; Luchini, Alessandra

    2011-11-30

    Many low-abundance biomarkers for early detection of cancer and other diseases are invisible to mass spectrometry because they exist in body fluids in very low concentrations, are masked by high-abundance proteins such as albumin and immunoglobulins, and are very labile. To overcome these barriers, we created porous, buoyant, core-shell hydrogel nanoparticles containing novel high affinity reactive chemical baits for protein and peptide harvesting, concentration, and preservation in body fluids. Poly(N-isopropylacrylamide-co-acrylic acid) nanoparticles were functionalized with amino-containing dyes via zero-length cross-linking amidation reactions. Nanoparticles functionalized in the core with 17 different (12 chemically novel) molecular baits showed preferential high affinities (K(D) < 10(-11) M) for specific low-abundance protein analytes. A poly(N-isopropylacrylamide-co-vinylsulfonic acid) shell was added to the core particles. This shell chemistry selectively prevented unwanted entry of all size peptides derived from albumin without hindering the penetration of non-albumin small proteins and peptides. Proteins and peptides entered the core to be captured with high affinity by baits immobilized in the core. Nanoparticles effectively protected interleukin-6 from enzymatic degradation in sweat and increased the effective detection sensitivity of human growth hormone in human urine using multiple reaction monitoring analysis. Used in whole blood as a one-step, in-solution preprocessing step, the nanoparticles greatly enriched the concentration of low-molecular weight proteins and peptides while excluding albumin and other proteins above 30 kDa; this achieved a 10,000-fold effective amplification of the analyte concentration, enabling mass spectrometry (MS) discovery of candidate biomarkers that were previously undetectable.

  18. A platform for the discovery of new macrolide antibiotics

    NASA Astrophysics Data System (ADS)

    Seiple, Ian B.; Zhang, Ziyang; Jakubec, Pavol; Langlois-Mercier, Audrey; Wright, Peter M.; Hog, Daniel T.; Yabu, Kazuo; Allu, Senkara Rao; Fukuzaki, Takehiro; Carlsen, Peter N.; Kitamura, Yoshiaki; Zhou, Xiang; Condakes, Matthew L.; Szczypiński, Filip T.; Green, William D.; Myers, Andrew G.

    2016-05-01

    The chemical modification of structurally complex fermentation products, a process known as semisynthesis, has been an important tool in the discovery and manufacture of antibiotics for the treatment of various infectious diseases. However, many of the therapeutics obtained in this way are no longer effective, because bacterial resistance to these compounds has developed. Here we present a practical, fully synthetic route to macrolide antibiotics by the convergent assembly of simple chemical building blocks, enabling the synthesis of diverse structures not accessible by traditional semisynthetic approaches. More than 300 new macrolide antibiotic candidates, as well as the clinical candidate solithromycin, have been synthesized using our convergent approach. Evaluation of these compounds against a panel of pathogenic bacteria revealed that the majority of these structures had antibiotic activity, some efficacious against strains resistant to macrolides in current use. The chemistry we describe here provides a platform for the discovery of new macrolide antibiotics and may also serve as the basis for their manufacture.

  19. A platform for the discovery of new macrolide antibiotics.

    PubMed

    Seiple, Ian B; Zhang, Ziyang; Jakubec, Pavol; Langlois-Mercier, Audrey; Wright, Peter M; Hog, Daniel T; Yabu, Kazuo; Allu, Senkara Rao; Fukuzaki, Takehiro; Carlsen, Peter N; Kitamura, Yoshiaki; Zhou, Xiang; Condakes, Matthew L; Szczypiński, Filip T; Green, William D; Myers, Andrew G

    2016-05-19

    The chemical modification of structurally complex fermentation products, a process known as semisynthesis, has been an important tool in the discovery and manufacture of antibiotics for the treatment of various infectious diseases. However, many of the therapeutics obtained in this way are no longer effective, because bacterial resistance to these compounds has developed. Here we present a practical, fully synthetic route to macrolide antibiotics by the convergent assembly of simple chemical building blocks, enabling the synthesis of diverse structures not accessible by traditional semisynthetic approaches. More than 300 new macrolide antibiotic candidates, as well as the clinical candidate solithromycin, have been synthesized using our convergent approach. Evaluation of these compounds against a panel of pathogenic bacteria revealed that the majority of these structures had antibiotic activity, some efficacious against strains resistant to macrolides in current use. The chemistry we describe here provides a platform for the discovery of new macrolide antibiotics and may also serve as the basis for their manufacture.

  20. Towards reproducible MRM based biomarker discovery using dried blood spots.

    PubMed

    Ozcan, Sureyya; Cooper, Jason D; Lago, Santiago G; Kenny, Diarmuid; Rustogi, Nitin; Stocki, Pawel; Bahn, Sabine

    2017-03-27

    There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation (r = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies.

  1. Towards reproducible MRM based biomarker discovery using dried blood spots

    PubMed Central

    Ozcan, Sureyya; Cooper, Jason D.; Lago, Santiago G.; Kenny, Diarmuid; Rustogi, Nitin; Stocki, Pawel; Bahn, Sabine

    2017-01-01

    There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation (r = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies. PMID:28345601

  2. Spatially resolved probing of electrochemical reactions via energy discovery platforms

    SciTech Connect

    Ding, Jilai; Strelcov, Evgheni; Kalinin, Sergei V.; Bassiri-Gharb, Nazanin

    2015-06-01

    The electrochemical reactivity of solid surfaces underpins functionality of a broad spectrum of materials and devices ranging from energy storage and conversion, to sensors and catalytic devices. The surface electrochemistry is, however, a complex process, controlled by the interplay of charge generation, field-controlled and diffusion-controlled transport. Here we explore the fundamental mechanisms of electrochemical reactivity on nanocrystalline ceria, using the synergy of nanofabricated devices and time-resolved Kelvin probe force microscopy (tr-KPFM), an approach we refer to as energy discovery platform. Through tr-KPFM, the surface potential mapping in both the space and time domains and current variation over time are obtained, enabling analysis of local ionic and electronic transport and their dynamic behavior on the 10 ms to 10 s scale. Based on their different responses in the time domain, conduction mechanisms can be separated and identified in a variety of environmental conditions, such as humidity and temperature. The theoretical modeling of ion transport through finite element method allows for creation of a minimal model consistent with observed phenomena, and establishing of the dynamic characteristics of the process, including mobility and diffusivity of charged species. Furthermore, the future potential of the energy discovery platforms is also discussed.

  3. Tissue constructs: platforms for basic research and drug discovery

    PubMed Central

    Elson, Elliot L.; Genin, Guy M.

    2016-01-01

    The functions, form and mechanical properties of cells are inextricably linked to their extracellular environment. Cells from solid tissues change fundamentally when, isolated from this environment, they are cultured on rigid two-dimensional substrata. These changes limit the significance of mechanical measurements on cells in two-dimensional culture and motivate the development of constructs with cells embedded in three-dimensional matrices that mimic the natural tissue. While measurements of cell mechanics are difficult in natural tissues, they have proven effective in engineered tissue constructs, especially constructs that emphasize specific cell types and their functions, e.g. engineered heart tissues. Tissue constructs developed as models of disease also have been useful as platforms for drug discovery. Underlying the use of tissue constructs as platforms for basic research and drug discovery is integration of multiscale biomaterials measurement and computational modelling to dissect the distinguishable mechanical responses separately of cells and extracellular matrix from measurements on tissue constructs and to quantify the effects of drug treatment on these responses. These methods and their application are the main subjects of this review. PMID:26855763

  4. Approach to Cerebrospinal Fluid (CSF) Biomarker Discovery and Evaluation in HIV Infection

    SciTech Connect

    Price, Richard W.; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E.; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena S.; Smith, Richard D.; Jacobs, Jon M.; Brown, Joseph N.; Gisslen, Magnus

    2013-12-13

    Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previouslydefined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

  5. Statistical spectroscopic tools for biomarker discovery and systems medicine.

    PubMed

    Robinette, Steven L; Lindon, John C; Nicholson, Jeremy K

    2013-06-04

    Metabolic profiling based on comparative, statistical analysis of NMR spectroscopic and mass spectrometric data from complex biological samples has contributed to increased understanding of the role of small molecules in affecting and indicating biological processes. To enable this research, the development of statistical spectroscopy has been marked by early beginnings in applying pattern recognition to nuclear magnetic resonance data and the introduction of statistical total correlation spectroscopy (STOCSY) as a tool for biomarker identification in the past decade. Extensions of statistical spectroscopy now compose a family of related tools used for compound identification, data preprocessing, and metabolic pathway analysis. In this Perspective, we review the theory and current state of research in statistical spectroscopy and discuss the growing applications of these tools to medicine and systems biology. We also provide perspectives on how recent institutional initiatives are providing new platforms for the development and application of statistical spectroscopy tools and driving the development of integrated "systems medicine" approaches in which clinical decision making is supported by statistical and computational analysis of metabolic, phenotypic, and physiological data.

  6. GWATCH: a web platform for automated gene association discovery analysis

    PubMed Central

    2014-01-01

    Background As genome-wide sequence analyses for complex human disease determinants are expanding, it is increasingly necessary to develop strategies to promote discovery and validation of potential disease-gene associations. Findings Here we present a dynamic web-based platform – GWATCH – that automates and facilitates four steps in genetic epidemiological discovery: 1) Rapid gene association search and discovery analysis of large genome-wide datasets; 2) Expanded visual display of gene associations for genome-wide variants (SNPs, indels, CNVs), including Manhattan plots, 2D and 3D snapshots of any gene region, and a dynamic genome browser illustrating gene association chromosomal regions; 3) Real-time validation/replication of candidate or putative genes suggested from other sources, limiting Bonferroni genome-wide association study (GWAS) penalties; 4) Open data release and sharing by eliminating privacy constraints (The National Human Genome Research Institute (NHGRI) Institutional Review Board (IRB), informed consent, The Health Insurance Portability and Accountability Act (HIPAA) of 1996 etc.) on unabridged results, which allows for open access comparative and meta-analysis. Conclusions GWATCH is suitable for both GWAS and whole genome sequence association datasets. We illustrate the utility of GWATCH with three large genome-wide association studies for HIV-AIDS resistance genes screened in large multicenter cohorts; however, association datasets from any study can be uploaded and analyzed by GWATCH. PMID:25374661

  7. Biomarker Discovery in Animal Health and Disease: The Application of Post-Genomic Technologies

    PubMed Central

    Moore, Rowan E.; Kirwan, Jennifer; Doherty, Mary K.; Whitfield, Phillip D.

    2007-01-01

    Summary: The causes of many important diseases in animals are complex and multifactorial, which present unique challenges. Biomarkers indicate the presence or extent of a biological process, which is directly linked to the clinical manifestations and outcome of a particular disease. Identifying biomarkers or biomarker profiles will be an important step towards disease characterization and management of disease in animals. The emergence of post-genomic technologies has led to the development of strategies aimed at identifying specific and sensitive biomarkers from the thousands of molecules present in a tissue or biological fluid. This review will summarize the current developments in biomarker discovery and will focus on the role of transcriptomics, proteomics and metabolomics in biomarker discovery for animal health and disease. PMID:19662203

  8. Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery

    PubMed Central

    Wachter, Astrid; Bernhardt, Stephan; Beissbarth, Tim; Korf, Ulrike

    2015-01-01

    Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article. PMID:27600238

  9. Biomarker Discovery in Subclinical Mycobacterial Infections of Cattle

    PubMed Central

    Janagama, Harish K.; Widdel, Andrea; Vulchanova, Lucy; Stabel, Judith R.; Waters, W. Ray; Palmer, Mitchell V.; Sreevatsan, Srinand

    2009-01-01

    Background Bovine tuberculosis is a highly prevalent infectious disease of cattle worldwide; however, infection in the United States is limited to 0.01% of dairy herds. Thus detection of bovine TB is confounded by high background infection with M. avium subsp. paratuberculosis. The present study addresses variations in the circulating peptidome based on the pathogenesis of two biologically similar mycobacterial diseases of cattle. Methodology/Principal Findings We hypothesized that serum proteomes of animals in response to either M. bovis or M. paratuberculosis infection will display several commonalities and differences. Sera prospectively collected from animals experimentally infected with either M. bovis or M. paratuberculosis were analyzed using high-resolution proteomics approaches. iTRAQ, a liquid chromatography and tandem mass spectrometry approach, was used to simultaneously identify and quantify peptides from multiple infections and contemporaneous uninfected control groups. Four comparisons were performed: 1) M. bovis infection versus uninfected controls, 2) M. bovis versus M. paratuberculosis infection, 3) early, and 4) advanced M. paratuberculosis infection versus uninfected controls. One hundred and ten differentially elevated proteins (P≤0.05) were identified. Vitamin D binding protein precursor (DBP), alpha-1 acid glycoprotein, alpha-1B glycoprotein, fetuin, and serine proteinase inhibitor were identified in both infections. Transthyretin, retinol binding proteins, and cathelicidin were identified exclusively in M. paratuberculosis infection, while the serum levels of alpha-1-microglobulin/bikunin precursor (AMBP) protein, alpha-1 acid glycoprotein, fetuin, and alpha-1B glycoprotein were elevated exclusively in M. bovis infected animals. Conclusions/Significance The discovery of these biomarkers has significant impact on the elucidation of pathogenesis of two mycobacterial diseases at the cellular and the molecular level and can be applied in the

  10. The use of time-resolved fluorescence in gel-based proteomics for improved biomarker discovery

    NASA Astrophysics Data System (ADS)

    Sandberg, AnnSofi; Buschmann, Volker; Kapusta, Peter; Erdmann, Rainer; Wheelock, Åsa M.

    2010-02-01

    This paper describes a new platform for quantitative intact proteomics, entitled Cumulative Time-resolved Emission 2-Dimensional Gel Electrophoresis (CuTEDGE). The CuTEDGE technology utilizes differences in fluorescent lifetimes to subtract the confounding background fluorescence during in-gel detection and quantification of proteins, resulting in a drastic improvement in both sensitivity and dynamic range compared to existing technology. The platform is primarily designed for image acquisition in 2-dimensional gel electrophoresis (2-DE), but is also applicable to 1-dimensional gel electrophoresis (1-DE), and proteins electroblotted to membranes. In a set of proof-of-principle measurements, we have evaluated the performance of the novel technology using the MicroTime 100 instrument (PicoQuant GmbH) in conjunction with the CyDye minimal labeling fluorochromes (GE Healthcare, Uppsala, Sweden) to perform differential gel electrophoresis (DIGE) analyses. The results indicate that the CuTEDGE technology provides an improvement in the dynamic range and sensitivity of detection of 3 orders of magnitude as compared to current state-of-the-art image acquisition instrumentation available for 2-DE (Typhoon 9410, GE Healthcare). Given the potential dynamic range of 7-8 orders of magnitude and sensitivities in the attomol range, the described invention represents a technological leap in detection of low abundance cellular proteins, which is desperately needed in the field of biomarker discovery.

  11. Secretome analysis using a hollow fiber culture system for cancer biomarker discovery.

    PubMed

    Chiu, Kuo-Hsun; Chang, Ying-Hua; Liao, Pao-Chi

    2013-11-01

    Secreted proteins, collectively referred to as the secretome, were suggested as valuable biomarkers in disease diagnosis and prognosis. However, some secreted proteins from cell cultures are difficult to detect because of their intrinsically low abundance; they are frequently masked by the released proteins from lysed cells and the substantial amounts of serum proteins used in culture medium. The hollow fiber culture (HFC) system is a commercially available system composed of small fibers sealed in a cartridge shell; cells grow on the outside of the fiber. Recently, because this system can help cells grow at a high density, it has been developed and applied in a novel analytical platform for cell secretome collection in cancer biomarker discovery. This article focuses on the advantages of the HFC system, including the effectiveness of the system for collection of secretomes, and reviews the process of cell secretome collection by the HFC system and proteomic approaches to discover cancer biomarkers. The HFC system not only provides a high-density three-dimensional (3D) cell culture system to mimic tumor growth conditions in vivo but can also accommodate numerous cells in a small volume, allowing secreted proteins to be accumulated and concentrated. In addition, cell lysis rates can be greatly reduced, decreasing the amount of contamination by abundant cytosolic proteins from lysed cells. Therefore, the HFC system is useful for preparing a wide range of proteins from cell secretomes and provides an effective method for collecting higher amounts of secreted proteins from cancer cells. This article is part of a Special Issue entitled: An Updated Secretome.

  12. From SOMAmer-Based Biomarker Discovery to Diagnostic and Clinical Applications: A SOMAmer-Based, Streamlined Multiplex Proteomic Assay

    PubMed Central

    Kraemer, Stephan; Vaught, Jonathan D.; Bock, Christopher; Gold, Larry; Katilius, Evaldas; Keeney, Tracy R.; Kim, Nancy; Saccomano, Nicholas A.; Wilcox, Sheri K.; Zichi, Dom; Sanders, Glenn M.

    2011-01-01

    Recently, we reported a SOMAmer-based, highly multiplexed assay for the purpose of biomarker identification. To enable seamless transition from highly multiplexed biomarker discovery assays to a format suitable and convenient for diagnostic and life-science applications, we developed a streamlined, plate-based version of the assay. The plate-based version of the assay is robust, sensitive (sub-picomolar), rapid, can be highly multiplexed (upwards of 60 analytes), and fully automated. We demonstrate that quantification by microarray-based hybridization, Luminex bead-based methods, and qPCR are each compatible with our platform, further expanding the breadth of proteomic applications for a wide user community. PMID:22022604

  13. Biomarker discovery and validation: technologies and integrative approaches.

    PubMed

    Ilyin, Sergey E; Belkowski, Stanley M; Plata-Salamán, Carlos R

    2004-08-01

    The emerging field of biomarkers has applications in the diagnosis, staging, prognosis and monitoring of disease progression, as well as in the monitoring of clinical responses to a therapeutic intervention and the development and delivery of personalized treatments to reduce attrition in clinical trials. Moreover, biomarkers have a positive impact on health economics. The word "biomarker" has been used extensively across therapeutic areas and many disciplines, and its nature takes into consideration clinical, physiological, biochemical, developmental, morphological and molecular measures. In drug trials, biomarkers have been proposed for use in efficacy determination and patient population stratification, in deducing pharmacokinetic-pharmacodynamic relationships and in safety monitoring. The interfacing and integration of different technologies for data collection and analysis are pivotal to biomarker identification, characterization, validation and application. "Integrative functional informatics" represents a novel direction in such technology integration.

  14. Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data

    PubMed Central

    Grissa, Dhouha; Pétéra, Mélanie; Brandolini, Marion; Napoli, Amedeo; Comte, Blandine; Pujos-Guillot, Estelle

    2016-01-01

    Untargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive and complex data that need appropriate analyses to extract the biologically meaningful information. Despite various tools available, it is still a challenge to handle such large and noisy datasets with limited number of individuals without risking overfitting. Moreover, when the objective is focused on the identification of early predictive markers of clinical outcome, few years before occurrence, it becomes essential to use the appropriate algorithms and workflow to be able to discover subtle effects among this large amount of data. In this context, this work consists in studying a workflow describing the general feature selection process, using knowledge discovery and data mining methodologies to propose advanced solutions for predictive biomarker discovery. The strategy was focused on evaluating a combination of numeric-symbolic approaches for feature selection with the objective of obtaining the best combination of metabolites producing an effective and accurate predictive model. Relying first on numerical approaches, and especially on machine learning methods (SVM-RFE, RF, RF-RFE) and on univariate statistical analyses (ANOVA), a comparative study was performed on an original metabolomic dataset and reduced subsets. As resampling method, LOOCV was applied to minimize the risk of overfitting. The best k-features obtained with different scores of importance from the combination of these different approaches were compared and allowed determining the variable stabilities using Formal Concept Analysis. The results revealed the interest of RF-Gini combined with ANOVA for feature selection as these two complementary methods allowed selecting the 48

  15. Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data.

    PubMed

    Grissa, Dhouha; Pétéra, Mélanie; Brandolini, Marion; Napoli, Amedeo; Comte, Blandine; Pujos-Guillot, Estelle

    2016-01-01

    Untargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive and complex data that need appropriate analyses to extract the biologically meaningful information. Despite various tools available, it is still a challenge to handle such large and noisy datasets with limited number of individuals without risking overfitting. Moreover, when the objective is focused on the identification of early predictive markers of clinical outcome, few years before occurrence, it becomes essential to use the appropriate algorithms and workflow to be able to discover subtle effects among this large amount of data. In this context, this work consists in studying a workflow describing the general feature selection process, using knowledge discovery and data mining methodologies to propose advanced solutions for predictive biomarker discovery. The strategy was focused on evaluating a combination of numeric-symbolic approaches for feature selection with the objective of obtaining the best combination of metabolites producing an effective and accurate predictive model. Relying first on numerical approaches, and especially on machine learning methods (SVM-RFE, RF, RF-RFE) and on univariate statistical analyses (ANOVA), a comparative study was performed on an original metabolomic dataset and reduced subsets. As resampling method, LOOCV was applied to minimize the risk of overfitting. The best k-features obtained with different scores of importance from the combination of these different approaches were compared and allowed determining the variable stabilities using Formal Concept Analysis. The results revealed the interest of RF-Gini combined with ANOVA for feature selection as these two complementary methods allowed selecting the 48

  16. Biomarker discovery in transplantation--proteomic adventure or mission impossible?

    PubMed

    Kienzl-Wagner, Katrin; Pratschke, Johann; Brandacher, Gerald

    2013-04-01

    Optimal management of transplanted organs requires specific and sensitive biomarkers for immunologic graft monitoring and subsequently patient tailored treatment. Proteomic science has emerged as an attractive tool in clinical biomarker research generating massive amounts of proteomic-driven data. However, critical interpretation of these data requires basic knowledge of proteomic principles and technology. This review provides an overview of proteomic approaches along with their advantages and limitations. Furthermore, this article summarizes the current status of biomarker achievements in the different areas of solid organ transplantation and discusses the hurdles that have precluded routine clinical application of these promising markers so far.

  17. Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

    SciTech Connect

    McDermott, Jason E.; Wang, Jing; Mitchell, Hugh D.; Webb-Robertson, Bobbie-Jo M.; Hafen, Ryan P.; Ramey, John A.; Rodland, Karin D.

    2013-01-01

    The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities both for purely statistical and expert knowledge-based approaches and would benefit from improved integration of the two. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to biomarker discovery and characterization are key to future success in the biomarker field. We will describe our recommendations of possible approaches to this problem including metrics for the evaluation of biomarkers.

  18. Approach to cerebrospinal fluid (CSF) biomarker discovery and evaluation in HIV infection.

    PubMed

    Price, Richard W; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena; Smith, Richard D; Jacobs, Jon M; Brown, Joseph N; Gisslen, Magnus

    2013-12-01

    Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previously-defined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

  19. The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

    SciTech Connect

    Wang, Hui; Shi, Tujin; Qian, Wei-Jun; Liu, Tao; Kagan, Jacob; Srivastava, Sudhir; Smith, Richard D.; Rodland, Karin D.; Camp, David G.

    2015-12-04

    Mass spectrometry-based proteomics has become an indispensable tool in biomedical research with broad applications ranging from fundamental biology, systems biology, and biomarker discovery. Recent advances in LC-MS have made it become a major technology in clinical applications, especially in cancer biomarker discovery and verification. To overcome the challenges associated with the analysis of clinical samples, such as extremely wide dynamic range of protein concentrations in biofluids and the need to perform high throughput and accurate quantification, significant efforts have been devoted to improve the overall performance of LC-MS bases clinical proteomics. In this review, we summarize the recent advances in LC-MS in the aspect of cancer biomarker discovery and quantification, and discuss its potentials, limitations, and future perspectives.

  20. Platform for Plasmodium vivax vaccine discovery and development

    PubMed Central

    Valencia/, Sócrates Herrera; Rodríguez, Diana Carolina; Acero, Diana Lucía; Ocampo, Vanessa; Arévalo-Herrera, Myriam

    2016-01-01

    Plasmodium vivax is the most prevalent malaria parasite on the American continent. It generates a global burden of 80–100 million cases annually and represents a tremendous public health problem, particularly in the American and Asian continents. A malaria vaccine would be considered the most cost-effective measure against this vector-borne disease and it would contribute to a reduction in malaria cases and to eventual eradication. Although significant progress has been achieved in the search for Plasmodium falciparum antigens that could be used in a vaccine, limited progress has been made in the search for P. vivax components that might be eligible for vaccine development. This is primarily due to the lack of in vitro cultures to serve as an antigen source and to inadequate funding. While the most advanced P. falciparum vaccine candidate is currently being tested in Phase III trials in Africa, the most advanced P. vivax candidates have only advanced to Phase I trials. Herein, we describe the overall strategy and progress in P. vivax vaccine research, from antigen discovery to preclinical and clinical development and we discuss the regional potential of Latin America to develop a comprehensive platform for vaccine development. PMID:21881773

  1. Platform for Plasmodium vivax vaccine discovery and development.

    PubMed

    Valencia, Sócrates Herrera; Rodríguez, Diana Carolina; Acero, Diana Lucía; Ocampo, Vanessa; Arévalo-Herrera, Myriam

    2011-08-01

    Plasmodium vivax is the most prevalent malaria parasite on the American continent. It generates a global burden of 80-100 million cases annually and represents a tremendous public health problem, particularly in the American and Asian continents. A malaria vaccine would be considered the most cost-effective measure against this vector-borne disease and it would contribute to a reduction in malaria cases and to eventual eradication. Although significant progress has been achieved in the search for Plasmodium falciparum antigens that could be used in a vaccine, limited progress has been made in the search for P. vivax components that might be eligible for vaccine development. This is primarily due to the lack of in vitro cultures to serve as an antigen source and to inadequate funding. While the most advanced P. falciparum vaccine candidate is currently being tested in Phase III trials in Africa, the most advanced P. vivax candidates have only advanced to Phase I trials. Herein, we describe the overall strategy and progress in P. vivax vaccine research, from antigen discovery to preclinical and clinical development and we discuss the regional potential of Latin America to develop a comprehensive platform for vaccine development.

  2. UniPep - a database for human N-linked glycosites: a resource for biomarker discovery

    PubMed Central

    Zhang, Hui; Loriaux, Paul; Eng, Jimmy; Campbell, David; Keller, Andrew; Moss, Pat; Bonneau, Richard; Zhang, Ning; Zhou, Yong; Wollscheid, Bernd; Cooke, Kelly; Yi, Eugene C; Lee, Hookeun; Peskind, Elaine R; Zhang, Jing; Smith, Richard D; Aebersold, Ruedi

    2006-01-01

    There has been considerable recent interest in proteomic analyses of plasma for the purpose of discovering biomarkers. Profiling N-linked glycopeptides is a particularly promising method because the population of N-linked glycosites represents the proteomes of plasma, the cell surface, and secreted proteins at very low redundancy and provides a compelling link between the tissue and plasma proteomes. Here, we describe UniPep - a database of human N-linked glycosites - as a resource for biomarker discovery. PMID:16901351

  3. Discovery of biomarkers for oxidative stress based on cellular metabolomics.

    PubMed

    Wang, Ningli; Wei, Jianteng; Liu, Yewei; Pei, Dong; Hu, Qingping; Wang, Yu; Di, Duolong

    2016-07-01

    Oxidative stress has a close relationship with various pathologic physiology phenomena and the potential biomarkers of oxidative stress may provide evidence for clinical diagnosis or disease prevention. Metabolomics was employed to identify the potential biomarkers of oxidative stress. High-performance liquid chromatography-diode array detector, mass spectrometry and partial least squares discriminate analysis were used in this study. The 10, 15 and 13 metabolites were considered to discriminate the model group, vitamin E-treated group and l-glutathione-treated group, respectively. Some of them have been identified, namely, malic acid, vitamin C, reduced glutathione and tryptophan. Identification of other potential biomarkers should be conducted and their physiological significance also needs to be elaborated.

  4. Biomarkers identified with time-lapse imaging: discovery, validation, and practical application

    PubMed Central

    Chen, Alice A.; Tan, Lei; Suraj, Vaishali; Pera, Renee Reijo; Shen, Shehua

    2014-01-01

    “Time-lapse markers,” which are defined by time-lapse imaging and correlated with clinical outcomes, may provide embryologists with new opportunities for improving embryo selection. This article provides an overview of noninvasive biomarkers defined by time-lapse imaging studies. In addition to comprehensively reviewing the discovery of each time-lapse marker, it focuses on the criteria necessary for their successful integration into clinical practice, including [1] statistical and biological significance, [2] validation through prospective clinical studies, and [3] development of reliable technology to measure and quantify the time-lapse marker. Because manual analysis of time-lapse images is labor intensive and limits the practical use of the image data in the clinic, automated image analysis software platforms may contribute substantially to improvements in embryo selection accuracy. Ultimately, time-lapse markers that are based on a foundation of basic research, validated through prospective clinical studies, and enabled by a reliable quantification technology may improve IVF success rates, encourage broader adoption of single-embryo transfer, and reduce the risks associated with multiple gestation pregnancies. PMID:23499001

  5. Report on the International Workshop 'Cancer stem cells: the mechanisms of radioresistance and biomarker discovery'.

    PubMed

    Dubrovska, Anna

    2014-08-01

    The aim of the Workshop "Cancer stem cells: The mechanisms of radioresistance and biomarker discovery", which was held on 23-24 September 2013 at OncoRay - National Center for Radiation Research in Oncology in Dresden, Germany, was to bring together the most recent viewpoints and insights about: (i) the molecular characterization and regulation of CSC, (ii) the mechanisms of CSC radioresistance, and (iii) the discovery of new CSC targeting therapeutics and biomarkers. In this report some research aspects presented in these three topics are highlighted.

  6. Proteomic Workflows for Biomarker Identification Using Mass Spectrometry — Technical and Statistical Considerations during Initial Discovery

    PubMed Central

    Orton, Dennis J.; Doucette, Alan A.

    2013-01-01

    Identification of biomarkers capable of differentiating between pathophysiological states of an individual is a laudable goal in the field of proteomics. Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins per sample. While MS-based technologies have rapidly matured, the identification of truly informative biomarkers remains elusive, with only a handful of clinically applicable tests stemming from proteomic workflows. This underlying lack of progress is attributed in large part to erroneous experimental design, biased sample handling, as well as improper statistical analysis of the resulting data. This review will discuss in detail the importance of experimental design and provide some insight into the overall workflow required for biomarker identification experiments. Proper balance between the degree of biological vs. technical replication is required for confident biomarker identification. PMID:28250400

  7. Mass spectrometry for the discovery of biomarkers of sepsis.

    PubMed

    Ludwig, Katelyn R; Hummon, Amanda B

    2017-03-28

    Sepsis is a serious medical condition that occurs in 30% of patients in intensive care units (ICUs). Early detection of sepsis is key to prevent its progression to severe sepsis and septic shock, which can cause organ failure and death. Diagnostic criteria for sepsis are nonspecific and hinder a timely diagnosis in patients. Therefore, there is currently a large effort to detect biomarkers that can aid physicians in the diagnosis and prognosis of sepsis. Mass spectrometry is often the method of choice to detect metabolomic and proteomic changes that occur during sepsis progression. These "omics" strategies allow for untargeted profiling of thousands of metabolites and proteins from human biological samples obtained from septic patients. Differential expression of or modifications to these metabolites and proteins can provide a more reliable source of diagnostic biomarkers for sepsis. Here, we focus on the current knowledge of biomarkers of sepsis and discuss the various mass spectrometric technologies used in their detection. We consider studies of the metabolome and proteome and summarize information regarding potential biomarkers in both general and neonatal sepsis.

  8. The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

    PubMed Central

    Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

    2010-01-01

    The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

  9. Discovery and validation of blood biomarkers for suicidality

    PubMed Central

    Le-Niculescu, H; Levey, D F; Ayalew, M; Palmer, L; Gavrin, L M; Jain, N; Winiger, E; Bhosrekar, S; Shankar, G; Radel, M; Bellanger, E; Duckworth, H; Olesek, K; Vergo, J; Schweitzer, R; Yard, M; Ballew, A; Shekhar, A; Sandusky, G E; Schork, N J; Kurian, S M; Salomon, D R; Niculescu, A B

    2013-01-01

    Suicides are a leading cause of death in psychiatric patients, and in society at large. Developing more quantitative and objective ways (biomarkers) for predicting and tracking suicidal states would have immediate practical applications and positive societal implications. We undertook such an endeavor. First, building on our previous blood biomarker work in mood disorders and psychosis, we decided to identify blood gene expression biomarkers for suicidality, looking at differential expression of genes in the blood of subjects with a major mood disorder (bipolar disorder), a high-risk population prone to suicidality. We compared no suicidal ideation (SI) states and high SI states using a powerful intrasubject design, as well as an intersubject case–case design, to generate a list of differentially expressed genes. Second, we used a comprehensive Convergent Functional Genomics (CFG) approach to identify and prioritize from the list of differentially expressed gene biomarkers of relevance to suicidality. CFG integrates multiple independent lines of evidence—genetic and functional genomic data—as a Bayesian strategy for identifying and prioritizing findings, reducing the false-positives and false-negatives inherent in each individual approach. Third, we examined whether expression levels of the blood biomarkers identified by us in the live bipolar subject cohort are actually altered in the blood in an age-matched cohort of suicide completers collected from the coroner's office, and report that 13 out of the 41 top CFG scoring biomarkers (32%) show step-wise significant change from no SI to high SI states, and then to the suicide completers group. Six out of them (15%) remained significant after strict Bonferroni correction for multiple comparisons. Fourth, we show that the blood levels of SAT1 (spermidine/spermine N1–acetyltransferase 1), the top biomarker identified by us, at the time of testing for this study, differentiated future as well as past

  10. Multiplexed, Quantitative Workflow for Sensitive Biomarker Discovery in Plasma Yields Novel Candidates for Early Myocardial Injury*

    PubMed Central

    Keshishian, Hasmik; Burgess, Michael W.; Gillette, Michael A.; Mertins, Philipp; Clauser, Karl R.; Mani, D. R.; Kuhn, Eric W.; Farrell, Laurie A.; Gerszten, Robert E.; Carr, Steven A.

    2015-01-01

    -plex. These results obtained with our novel platform provide clear demonstration of the value of using isobaric mass tag reagents in plasma-based biomarker discovery experiments. PMID:25724909

  11. Cancer biomarker discovery for cholangiocarcinoma: the high-throughput approaches

    PubMed Central

    Silsirivanit, Atit; Sawanyawisuth, Kanlayanee; Riggins, Gregory J.; Wongkham, Chaisiri

    2015-01-01

    Cholangiocarcinoma (CCA) is difficult to diagnose at an early stage and most tumors are detected at late stage where surgery or other therapy is ineffective. Many advanced techniques are applied to diagnose CCA; however, most are expensive and have varying degrees of accuracy. A less invasive and simpler procedure such as serum markers would be of substantial clinical benefit for diagnosis, monitoring, and predicting outcome for CCA patients. Recent advances in “Omics” technologies offer remarkable opportunities for establishment of biomarker-related to diseases. In this review, the potential biomarkers obtained from proteomics and glycomic studies are evaluated. Several protein markers were discovered from patient specimen, using two dimensional-differential gel electrophoresis couple with liquid chromatography tandem mass spectrometry (2D-DIGE/LC-MS-MS), matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS), surface enhanced laser desorption/ionization (SELDI)-TOF-MS and capillary electrophoresis (CE)-MS, etc. Newly reported CCA-associated glycobiomarkers were identified using lectin-assisted, monoclonal antibody-assisted or specific-target strategies. The combination between carbohydrate binding-lectin and core protein-binding mAb significantly increased the values for detection of the glyco-biomarkers for CCA. Searching for specific and sensitive molecular markers to be used for population screening is worth being evaluated. This could lead to earlier diagnosis and improve outcome. Further investigation of those biomarker functions is also of value in order to better understand the tumor biology and use them as targets for future therapeutic agents. PMID:24616382

  12. Discovery of Metabolite Biomarkers for Acute Ischemic Stroke Progression.

    PubMed

    Liu, Peifang; Li, Ruiting; Antonov, Anton A; Wang, Lihua; Li, Wei; Hua, Yunfei; Guo, Huimin; Wang, Lijuan; Liu, Peijia; Chen, Lixia; Tian, Yuan; Xu, Fengguo; Zhang, Zunjian; Zhu, Yulan; Huang, Yin

    2017-02-03

    Stroke remains a major public health problem worldwide; it causes severe disability and is associated with high mortality rates. However, early diagnosis of stroke is difficult, and no reliable biomarkers are currently established. In this study, mass-spectrometry-based metabolomics was utilized to characterize the metabolic features of the serum of patients with acute ischemic stroke (AIS) to identify novel sensitive biomarkers for diagnosis and progression. First, global metabolic profiling was performed on a training set of 80 human serum samples (40 cases and 40 controls). The metabolic profiling identified significant alterations in a series of 26 metabolites with related metabolic pathways involving amino acid, fatty acid, phospholipid, and choline metabolism. Subsequently, multiple algorithms were run on a test set consisting of 49 serum samples (26 cases and 23 controls) to develop different classifiers for verifying and evaluating potential biomarkers. Finally, a panel of five differential metabolites, including serine, isoleucine, betaine, PC(5:0/5:0), and LysoPE(18:2), exhibited potential to differentiate AIS samples from healthy control samples, with area under the receiver operating characteristic curve values of 0.988 and 0.971 in the training and test sets, respectively. These findings provided insights for the development of new diagnostic tests and therapeutic approaches for AIS.

  13. Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data.

    PubMed

    McDermott, Jason E; Wang, Jing; Mitchell, Hugh; Webb-Robertson, Bobbie-Jo; Hafen, Ryan; Ramey, John; Rodland, Karin D

    2013-01-01

    INTRODUCTION: The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. AREAS COVERED: In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. EXPERT OPINION: Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.

  14. Biomarker detection for disease diagnosis using cost-effective microfluidic platforms.

    PubMed

    Sanjay, Sharma T; Fu, Guanglei; Dou, Maowei; Xu, Feng; Liu, Rutao; Qi, Hao; Li, XiuJun

    2015-11-07

    Early and timely detection of disease biomarkers can prevent the spread of infectious diseases, and drastically decrease the death rate of people suffering from different diseases such as cancer and infectious diseases. Because conventional diagnostic methods have limited application in low-resource settings due to the use of bulky and expensive instrumentation, simple and low-cost point-of-care diagnostic devices for timely and early biomarker diagnosis is the need of the hour, especially in rural areas and developing nations. The microfluidics technology possesses remarkable features for simple, low-cost, and rapid disease diagnosis. There have been significant advances in the development of microfluidic platforms for biomarker detection of diseases. This article reviews recent advances in biomarker detection using cost-effective microfluidic devices for disease diagnosis, with the emphasis on infectious disease and cancer diagnosis in low-resource settings. This review first introduces different microfluidic platforms (e.g. polymer and paper-based microfluidics) used for disease diagnosis, with a brief description of their common fabrication techniques. Then, it highlights various detection strategies for disease biomarker detection using microfluidic platforms, including colorimetric, fluorescence, chemiluminescence, electrochemiluminescence (ECL), and electrochemical detection. Finally, it discusses the current limitations of microfluidic devices for disease biomarker detection and future prospects.

  15. Data integration and systems biology approaches for biomarker discovery: challenges and opportunities for multiple sclerosis.

    PubMed

    Villoslada, Pablo; Baranzini, Sergio

    2012-07-15

    New "omic" technologies and their application to systems biology approaches offer new opportunities for biomarker discovery in complex disorders, including multiple sclerosis (MS). Recent studies using massive genotyping, DNA arrays, antibody arrays, proteomics, glycomics, and metabolomics from different tissues (blood, cerebrospinal fluid, brain) have identified many molecules associated with MS, defining both susceptibility and functional targets (e.g., biomarkers). Such discoveries involve many different levels in the complex organizational hierarchy of humans (DNA, RNA, protein, etc.), and integrating these datasets into a coherent model with regard to MS pathogenesis would be a significant step forward. Given the dynamic and heterogeneous nature of MS, validating biomarkers is mandatory. To develop accurate markers of disease prognosis or therapeutic response that are clinically useful, combining molecular, clinical, and imaging data is necessary. Such an integrative approach would pave the way towards better patient care and more effective clinical trials that test new therapies, thus bringing the paradigm of personalized medicine in MS one step closer.

  16. Biomarker discovery by proteomics-based approaches for early detection and personalized medicine in colorectal cancer.

    PubMed

    Corbo, Claudia; Cevenini, Armando; Salvatore, Francesco

    2016-12-26

    About one million people per year develop colorectal cancer (CRC) and approximately half of them die. The extent of the disease (i.e. local invasion at the time of diagnosis) is a key prognostic factor. The 5-year survival rate is almost 90% in the case of delimited CRC and 10% in the case of metastasized CRC. Hence, one of the great challenges in the battle against CRC is to improve early diagnosis strategies. Large-scale proteomic approaches are widely used in cancer research to search for novel biomarkers. Such biomarkers can help in improving the accuracy of the diagnosis and in the optimization of personalized therapy. Herein, we provide an overview of studies published in the last 5 years on CRC that led to the identification of protein biomarkers suitable for clinical application by using proteomic approaches. We discussed these findings according to biomarker application, including also the role of protein phosphorylation and cancer stem cells in biomarker discovery. Our review provides a cross section of scientific approaches and can furnish suggestions for future experimental strategies to be used as reference by scientists, clinicians and researchers interested in proteomics for biomarker discovery.

  17. Cancer Stem Cell Biomarker Discovery Using Antibody Array Technology.

    PubMed

    Burgess, Rob; Huang, Ruo-Pan

    2016-01-01

    Cancer is a complex disease involving hundreds of pathways and numerous levels of disease progression. In addition, there is a growing body of evidence that the origins and growth rates of specific types of cancer may involve "cancer stem cells," which are defined as "cells within a tumor that possess the capacity to self-renew and to cause the development of heterogeneous lineages of cancer cells that comprise the tumor.(1)" Many types of cancer are now thought to harbor cancer stem cells. These cells themselves are thought to be unique in comparison to other cells types present within the tumor and to exhibit characteristics that allow for the promotion of tumorigenesis and in some cases metastasis. In addition, it is speculated that each type of cancer stem cell exhibits a unique set of molecular and biochemical markers. These markers, alone or in combination, may act as a signature for defining not only the type of cancer but also the progressive state. These biomarkers may also double as signaling entities which act autonomously or upon neighboring cancer stem cells or other cells within the local microenvironment to promote tumorigenesis. This review describes the heterogeneic properties of cancer stem cells and outlines the identification and application of biomarkers and signaling molecules defining these cells as they relate to different forms of cancer. Other examples of biomarkers and signaling molecules expressed by neighboring cells in the local tumor microenvironment are also discussed. In addition, biochemical signatures for cancer stem cell autocrine/paracrine signaling, local site recruitment, tumorigenic potential, and conversion to a stem-like phenotype are described.

  18. A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

    PubMed Central

    Zou, Wei; She, Jianwen; Tolstikov, Vladimir V.

    2013-01-01

    Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow. PMID:24958150

  19. A comprehensive workflow of mass spectrometry-based untargeted metabolomics in cancer metabolic biomarker discovery using human plasma and urine.

    PubMed

    Zou, Wei; She, Jianwen; Tolstikov, Vladimir V

    2013-09-11

    Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC-LC), reversed-phase liquid chromatography (RP-LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow.

  20. Enhancement of MS Signal Processing For Improved Cancer Biomarker Discovery

    NASA Astrophysics Data System (ADS)

    Si, Qian

    Technological advances in proteomics have shown great potential in detecting cancer at the earliest stages. One way is to use the time of flight mass spectroscopy to identify biomarkers, or early disease indicators related to the cancer. Pattern analysis of time of flight mass spectra data from blood and tissue samples gives great hope for the identification of potential biomarkers among the complex mixture of biological and chemical samples for the early cancer detection. One of the keys issues is the pre-processing of raw mass spectra data. A lot of challenges need to be addressed: unknown noise character associated with the large volume of data, high variability in the mass spectroscopy measurements, and poorly understood signal background and so on. This dissertation focuses on developing statistical algorithms and creating data mining tools for computationally improved signal processing for mass spectrometry data. I have introduced an advanced accurate estimate of the noise model and a half-supervised method of mass spectrum data processing which requires little knowledge about the data.

  1. Aptamer-based detection of disease biomarkers in mouse models for chagas drug discovery.

    PubMed

    de Araujo, Fernanda Fortes; Nagarkatti, Rana; Gupta, Charu; Marino, Ana Paula; Debrabant, Alain

    2015-01-01

    Drug discovery initiatives, aimed at Chagas treatment, have been hampered by the lack of standardized drug screening protocols and the absence of simple pre-clinical assays to evaluate treatment efficacy in animal models. In this study, we used a simple Enzyme Linked Aptamer (ELA) assay to detect T. cruzi biomarker in blood and validate murine drug discovery models of Chagas disease. In two mice models, Apt-29 ELA assay demonstrated that biomarker levels were significantly higher in the infected group compared to the control group, and upon Benznidazole treatment, their levels reduced. However, biomarker levels in the infected treated group did not reduce to those seen in the non-infected treated group, with 100% of the mice above the assay cutoff, suggesting that parasitemia was reduced but cure was not achieved. The ELA assay was capable of detecting circulating biomarkers in mice infected with various strains of T. cruzi parasites. Our results showed that the ELA assay could detect residual parasitemia in treated mice by providing an overall picture of the infection in the host. They suggest that the ELA assay can be used in drug discovery applications to assess treatment efficacy in-vivo.

  2. Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy.

    PubMed

    Hathout, Yetrib; Brody, Edward; Clemens, Paula R; Cripe, Linda; DeLisle, Robert Kirk; Furlong, Pat; Gordish-Dressman, Heather; Hache, Lauren; Henricson, Erik; Hoffman, Eric P; Kobayashi, Yvonne Monique; Lorts, Angela; Mah, Jean K; McDonald, Craig; Mehler, Bob; Nelson, Sally; Nikrad, Malti; Singer, Britta; Steele, Fintan; Sterling, David; Sweeney, H Lee; Williams, Steve; Gold, Larry

    2015-06-09

    Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy-Cincinnati Children's Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.

  3. Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy

    PubMed Central

    Hathout, Yetrib; Brody, Edward; Clemens, Paula R.; Cripe, Linda; DeLisle, Robert Kirk; Furlong, Pat; Gordish-Dressman, Heather; Hache, Lauren; Henricson, Erik; Hoffman, Eric P.; Kobayashi, Yvonne Monique; Lorts, Angela; Mah, Jean K.; McDonald, Craig; Mehler, Bob; Nelson, Sally; Nikrad, Malti; Singer, Britta; Steele, Fintan; Sterling, David; Sweeney, H. Lee; Williams, Steve; Gold, Larry

    2015-01-01

    Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy–Cincinnati Children’s Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases. PMID:26039989

  4. Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery.

    PubMed

    Yun, Yong-Huan; Deng, Bai-Chuan; Cao, Dong-Sheng; Wang, Wei-Ting; Liang, Yi-Zeng

    2016-03-10

    Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable.

  5. New trends in molecular and cellular biomarker discovery for colorectal cancer

    PubMed Central

    Aghagolzadeh, Parisa; Radpour, Ramin

    2016-01-01

    Colorectal cancer (CRC) is the third leading cause of cancer death worldwide, which is consequence of multistep tumorigenesis of several genetic and epigenetic events. Since CRC is mostly asymptomatic until it progresses to advanced stages, the early detection using effective screening approaches, selection of appropriate therapeutic strategies and efficient follow-up programs are essential to reduce CRC mortalities. Biomarker discovery for CRC based on the personalized genotype and clinical information could facilitate the classification of patients with certain types and stages of cancer to tailor preventive and therapeutic approaches. These cancer-related biomarkers should be highly sensitive and specific in a wide range of specimen(s) (including tumor tissues, patients’ fluids or stool). Reliable biomarkers which enable the early detection of CRC, can improve early diagnosis, prognosis, treatment response prediction, and recurrence risk. Advances in our understanding of the natural history of CRC have led to the development of different CRC associated molecular and cellular biomarkers. This review highlights the new trends and approaches in CRC biomarker discovery, which could be potentially used for early diagnosis, development of new therapeutic approaches and follow-up of patients. PMID:27433083

  6. Emerging concepts in biomarker discovery; the US-Japan Workshop on Immunological Molecular Markers in Oncology.

    PubMed

    Tahara, Hideaki; Sato, Marimo; Thurin, Magdalena; Wang, Ena; Butterfield, Lisa H; Disis, Mary L; Fox, Bernard A; Lee, Peter P; Khleif, Samir N; Wigginton, Jon M; Ambs, Stefan; Akutsu, Yasunori; Chaussabel, Damien; Doki, Yuichiro; Eremin, Oleg; Fridman, Wolf Hervé; Hirohashi, Yoshihiko; Imai, Kohzoh; Jacobson, James; Jinushi, Masahisa; Kanamoto, Akira; Kashani-Sabet, Mohammed; Kato, Kazunori; Kawakami, Yutaka; Kirkwood, John M; Kleen, Thomas O; Lehmann, Paul V; Liotta, Lance; Lotze, Michael T; Maio, Michele; Malyguine, Anatoli; Masucci, Giuseppe; Matsubara, Hisahiro; Mayrand-Chung, Shawmarie; Nakamura, Kiminori; Nishikawa, Hiroyoshi; Palucka, A Karolina; Petricoin, Emanuel F; Pos, Zoltan; Ribas, Antoni; Rivoltini, Licia; Sato, Noriyuki; Shiku, Hiroshi; Slingluff, Craig L; Streicher, Howard; Stroncek, David F; Takeuchi, Hiroya; Toyota, Minoru; Wada, Hisashi; Wu, Xifeng; Wulfkuhle, Julia; Yaguchi, Tomonori; Zeskind, Benjamin; Zhao, Yingdong; Zocca, Mai-Britt; Marincola, Francesco M

    2009-06-17

    Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD) of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs) likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers were recognized that

  7. Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

    PubMed Central

    Tahara, Hideaki; Sato, Marimo; Thurin, Magdalena; Wang, Ena; Butterfield, Lisa H; Disis, Mary L; Fox, Bernard A; Lee, Peter P; Khleif, Samir N; Wigginton, Jon M; Ambs, Stefan; Akutsu, Yasunori; Chaussabel, Damien; Doki, Yuichiro; Eremin, Oleg; Fridman, Wolf Hervé; Hirohashi, Yoshihiko; Imai, Kohzoh; Jacobson, James; Jinushi, Masahisa; Kanamoto, Akira; Kashani-Sabet, Mohammed; Kato, Kazunori; Kawakami, Yutaka; Kirkwood, John M; Kleen, Thomas O; Lehmann, Paul V; Liotta, Lance; Lotze, Michael T; Maio, Michele; Malyguine, Anatoli; Masucci, Giuseppe; Matsubara, Hisahiro; Mayrand-Chung, Shawmarie; Nakamura, Kiminori; Nishikawa, Hiroyoshi; Palucka, A Karolina; Petricoin, Emanuel F; Pos, Zoltan; Ribas, Antoni; Rivoltini, Licia; Sato, Noriyuki; Shiku, Hiroshi; Slingluff, Craig L; Streicher, Howard; Stroncek, David F; Takeuchi, Hiroya; Toyota, Minoru; Wada, Hisashi; Wu, Xifeng; Wulfkuhle, Julia; Yaguchi, Tomonori; Zeskind, Benjamin; Zhao, Yingdong; Zocca, Mai-Britt; Marincola, Francesco M

    2009-01-01

    Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD) of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs) likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers were recognized that

  8. Application of 'omics technologies to biomarker discovery in inflammatory lung diseases.

    PubMed

    Wheelock, Craig E; Goss, Victoria M; Balgoma, David; Nicholas, Ben; Brandsma, Joost; Skipp, Paul J; Snowden, Stuart; Burg, Dominic; D'Amico, Arnaldo; Horvath, Ildiko; Chaiboonchoe, Amphun; Ahmed, Hassan; Ballereau, Stéphane; Rossios, Christos; Chung, Kian Fan; Montuschi, Paolo; Fowler, Stephen J; Adcock, Ian M; Postle, Anthony D; Dahlén, Sven-Erik; Rowe, Anthony; Sterk, Peter J; Auffray, Charles; Djukanovic, Ratko

    2013-09-01

    Inflammatory lung diseases are highly complex in respect of pathogenesis and relationships between inflammation, clinical disease and response to treatment. Sophisticated large-scale analytical methods to quantify gene expression (transcriptomics), proteins (proteomics), lipids (lipidomics) and metabolites (metabolomics) in the lungs, blood and urine are now available to identify biomarkers that define disease in terms of combined clinical, physiological and patho-biological abnormalities. The aspiration is that these approaches will improve diagnosis, i.e. define pathological phenotypes, and facilitate the monitoring of disease and therapy, and also, unravel underlying molecular pathways. Biomarker studies can either select predefined biomarker(s) measured by specific methods or apply an "unbiased" approach involving detection platforms that are indiscriminate in focus. This article reviews the technologies presently available to study biomarkers of lung disease within the 'omics field. The contributions of the individual 'omics analytical platforms to the field of respiratory diseases are summarised, with the goal of providing background on their respective abilities to contribute to systems medicine-based studies of lung disease.

  9. Systems biology and the discovery of diagnostic biomarkers.

    PubMed

    Wang, Kai; Lee, Inyoul; Carlson, George; Hood, Leroy; Galas, David

    2010-01-01

    Systems biology is an approach to the science that views biology as an information science, studies biological systems as a whole and their interactions with the environment. This approach, for the reasons described here, has particular power in the search for informative diagnostic biomarkers of diseases because it focuses on the fundamental causes and keys on the identification and understanding of disease- perturbed molecular networks. In this review, we describe some recent developments that have used systems biology to address complex diseases - prion disease and drug induced liver injury- and use these as examples to illustrate the importance of understanding network structure and dynamics. The knowledge of network dynamics through in vitro experimental perturbation and modeling allows us to determine the state of the networks, to identify molecular correlates, and to derive new disease treatment approaches to reverse the pathology or prevent its progress into a more severe state through the manipulation of network states. This general approach, including diagnostics and therapeutics, is becoming known as systems medicine.

  10. Urine metabolomics for kidney cancer detection and biomarker discovery.

    PubMed

    Ganti, Sheila; Weiss, Robert H

    2011-01-01

    Renal cell carcinoma (RCC) is one of the few human cancers whose incidence is increasing. The disease regularly progresses asymptomatically and is frequently metastatic upon presentation, thereby necessitating the development of an early method of detection. A metabolomic approach for biomarker detection using urine as a biofluid is appropriate since the tumor is located in close proximity to the urinary space. By comparing the composition of urine from individuals with RCC to control individuals, differences in metabolite composition of this biofluid can be identified, and these data can be utilized to create a clinically applicable and, possibly, bedside assay. Recent studies have shown that sample handling and processing greatly influences the variability seen in the urinary metabolome of both cancer and control patients. Once a standard method of collection is developed, identifying metabolic derangements associated with RCC will also lead to the investigation of novel targets for therapeutic intervention. The objective of this review is to discuss existing methods for sample collection, processing, data analysis, and recent findings in this emerging field.

  11. HGF-MET in cancer progression and biomarker discovery.

    PubMed

    Matsumoto, Kunio; Umitsu, Masataka; De Silva, Dinuka M; Roy, Arpita; Bottaro, Donald P

    2017-01-08

    Signaling driven by hepatocyte growth factor (HGF) and MET receptor facilitates conspicuous biological responses such as epithelial cell migration, 3-D morphogenesis, and survival. The dynamic migration and promotion of cell survival induced by MET activation are bases respectively for invasion-metastasis and resistance against targeted drugs in cancers. Recent studies indicated that MET in tumor-derived exosomes facilitates metastatic niche formation and metastasis in malignant melanoma. In lung cancer, gene amplification-induced MET activation and ligand-dependent MET activation in autocrine/paracrine manner are causes for resistance to EGF receptor tyrosine kinase inhibitors and ALK inhibitors. HGF secreted in the tumor microenvironment contributes to the innate and acquired resistance to RAF inhibitors. Changes in serum/plasma HGF, soluble MET, and phosphor-MET have been confirmed to be associated with disease progression, metastasis, therapy response, and survival. Higher serum/plasma HGF levels are associated with therapy resistance and/or metastasis, while lower HGF levels are associated with progression-free survival and overall survival after treatment with targeted drugs in lung cancer, gastric cancer, colon cancer, and malignant melanoma. Urinary soluble MET levels in patients with bladder cancer are higher than those in patients without bladder cancer and associated with disease progression. Some of the multi-kinase inhibitors that target MET have received regulatory approval, whereas none of the selective HGF-MET inhibitors have shown efficacy in phase III clinical trials. Validation of the HGF-MET pathway as a critical driver in cancer development/progression and utilization of appropriate biomarkers are key to development and approval of HGF-MET inhibitors for clinical use. This article is protected by copyright. All rights reserved.

  12. Extracellular vesicles in ovarian cancer: applications to tumor biology, immunotherapy and biomarker discovery.

    PubMed

    Nawaz, Muhammad; Fatima, Farah; Nazarenko, Irina; Ekström, Karin; Murtaza, Iram; Anees, Mariam; Sultan, Aneesa; Neder, Luciano; Camussi, Giovanni; Valadi, Hadi; Squire, Jeremy A; Kislinger, Thomas

    2016-01-01

    In recent years there has been tremendous interest in both the basic biology and applications of extracellular vesicles (EVs) in translational cancer research. This includes a better understanding of their biogenesis and mechanisms of selective cargo packaging, their precise roles in horizontal communication, and their application as non-invasive biomarkers. The rapid advances in next-generation omics technologies are the driving forces for these discoveries. In this review, the authors focus on recent results of EV research in ovarian cancer. A deeper understanding of ovarian cancer-derived EVs, the types of cargo molecules and their biological roles in cancer growth, metastases and drug resistance, could have significant impact on the discovery of novel biomarkers and innovative therapeutics. Insights into the role of EVs in immune regulation could lead to novel approaches built on EV-based immunotherapy.

  13. Informatics-guided procurement of patient samples for biomarker discovery projects in cancer research.

    PubMed

    Suh, K Stephen; Remache, Yvonne K; Patel, Jalpa S; Chen, Steve H; Haystrand, Russell; Ford, Peggy; Shaikh, Anadil M; Wang, Jian; Goy, Andre H

    2009-02-01

    Modern cancer research for biomarker discovery program requires solving several tasks that are directly involved with patient sample procurement. One requirement is to construct a highly efficient workflow on the clinical side for the procurement to generate a consistent supply of high quality samples for research. This undertaking needs a network of interdepartmental collaborations and participations at various levels, including physical human interactions, information technology implementations and a bioinformatics tool that is highly effective and user-friendly to busy clinicians and researchers associated with the sample procurement. Collegial participation that is sequential but continual from one department to another demands dedicated bioinformatics software coordinating between the institutional clinic and the tissue repository facility. Participants in the process include admissions, consenting process, phlebotomy, surgery center and pathology. During this multiple step procedures, clinical data are collected for detailed analytical endpoints to supplement logistics of defining and validating the discovery of biomarkers.

  14. Intact-Protein Analysis System for Discovery of Serum-Based Disease Biomarkers

    PubMed Central

    Wang, Hong; Hanash, Samir

    2015-01-01

    Profiling of serum and plasma proteins has substantial relevance to the discovery of circulating disease biomarkers. However, the extreme complexity and vast dynamic range of protein abundance in serum and plasma present a formidable challenge for protein analysis. Thus, integration of multiple technologies is required to achieve high-resolution and high-sensitivity proteomic analysis of serum or plasma. In this chapter, we describe an orthogonal multidimensional intact-protein analysis system (IPAS) (Wang et al., Mol Cell Proteomics 4:618–625, 2005) coupled with protein tagging (Faca et al., J Proteome Res 5:2009–2018, 2006) to profile the serum and plasma proteomes quantitatively, which we have applied in our biomarker discovery studies (Katayama et al., Genome Med 1:47, 2009; Faca et al., PLoS Med 5:e123, 2008; Zhang et al. Genome Biol 9:R93, 2008). PMID:21468941

  15. Intact-protein analysis system for discovery of serum-based disease biomarkers.

    PubMed

    Wang, Hong; Hanash, Samir

    2011-01-01

    Profiling of serum and plasma proteins has substantial relevance to the discovery of circulating disease biomarkers. However, the extreme complexity and vast dynamic range of protein abundance in serum and plasma present a formidable challenge for protein analysis. Thus, integration of multiple technologies is required to achieve high-resolution and high-sensitivity proteomic analysis of serum or plasma. In this chapter, we describe an orthogonal multidimensional intact-protein analysis system (IPAS) (Wang et al., Mol Cell Proteomics 4:618-625, 2005) coupled with protein tagging (Faca et al., J Proteome Res 5:2009-2018, 2006) to profile the serum and plasma proteomes quantitatively, which we have applied in our biomarker discovery studies (Katayama et al., Genome Med 1:47, 2009; Faca et al., PLoS Med 5:e123, 2008; Zhang et al. Genome Biol 9:R93, 2008).

  16. Urinary proteomics as a novel tool for biomarker discovery in kidney diseases.

    PubMed

    Wu, Jing; Chen, Yi-ding; Gu, Wei

    2010-04-01

    Urine has become one of the most attractive biofluids in clinical proteomics, for its procurement is easy and noninvasive and it contains sufficient proteins and peptides. Urinary proteomics has thus rapidly developed and has been extensively applied to biomarker discovery in clinical diseases, especially kidney diseases. In this review, we discuss two important aspects of urinary proteomics in detail, namely, sample preparation and proteomic technologies. In addition, data mining in urinary proteomics is also briefly introduced. At last, we present several successful examples on the application of urinary proteomics for biomarker discovery in kidney diseases, including diabetic nephropathy, IgA nephropathy, lupus nephritis, renal Fanconi syndrome, acute kidney injury, and renal allograft rejection.

  17. Plasma Biomarker Discovery Using 3D Protein Profiling Coupled with Label-Free Quantitation

    PubMed Central

    Beer, Lynn A.; Tang, Hsin-Yao; Barnhart, Kurt T.; Speicher, David W.

    2011-01-01

    In-depth quantitative profiling of human plasma samples for biomarker discovery remains quite challenging. One promising alternative to chemical derivatization with stable isotope labels for quantitative comparisons is direct, label-free, quantitative comparison of raw LC–MS data. But, in order to achieve high-sensitivity detection of low-abundance proteins, plasma proteins must be extensively pre-fractionated, and results from LC–MS runs of all fractions must be integrated efficiently in order to avoid misidentification of variations in fractionation from sample to sample as “apparent” biomarkers. This protocol describes a powerful 3D protein profiling method for comprehensive analysis of human serum or plasma proteomes, which combines abundant protein depletion and high-sensitivity GeLC–MS/MS with label-free quantitation of candidate biomarkers. PMID:21468938

  18. Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies.

    PubMed

    Lima, Ana Rita; Bastos, Maria de Lourdes; Carvalho, Márcia; Guedes de Pinho, Paula

    2016-08-01

    Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA) levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.

  19. Novel Biomarker Discovery for Diagnostic and Therapeutic Strategies in Prostate Cancer

    DTIC Science & Technology

    2013-03-01

    grant is to identify, isolate and characterize high affinity aptamers that distinguish between prostate cancers that are likely to remain organ...confined and those with potential to metastasize, The scope of this pilot is to generate DNA aptamers that selectively react with a prostate cancer cell...employing Cell-Selex and Aptamer -Facilitated Biomarker Discovery (AptaBiD) technology. Major Findings and Progress: (1) Non-metastatic LNCaP-Pro-5 cells

  20. Novel Biomarker Discovery for Diagnostic and Therapeutic Strategies in Prostate Cancer

    DTIC Science & Technology

    2014-03-01

    aptamers that distinguish between prostate cancers that are likely to remain organ-confined and those with potential to metastasize, The scope of this...pilot is to generate DNA aptamers that selectively react with a prostate cancer cell line that remains confined to the prostate (LNCaP) vs. a...subpopulation of this cell line that has acquired the ability to metastasize aggressively, employing Cell-Selex and Aptamer -Facilitated Biomarker Discovery

  1. Application of Glycoproteomics in the Discovery of Biomarkers for Lung Cancer

    PubMed Central

    Li, Qing Kay; Gabrielson, Edward; Zhang, Hui

    2017-01-01

    Lung cancer is the leading cause of cancer-related deaths in the United States. Approximately 40–60% of lung cancer patients present with locally advanced or metastatic disease at the time of diagnosis. In order to improve the survival rate of lung cancer patients, the discovery of early diagnostic and prognostic biomarkers is urgently needed. Lung cancer development and progression are a multistep process which is characterized by abnormal gene and protein expressions ultimately leading to phenotypic change. In lung cancer, the expression of cellular glycoproteins directly reflects the physiological and/or pathological status of the lung parenchyma. Glycoproteins have long been recognized to play fundamental roles in many physiological and pathological processes, particularly in cancer genesis and progression. Although numerous papers have already acknowledged the importance of the discovery of cancer biomarkers, the systemic study of glycoproteins in lung cancer using glycoproteomic approaches is still suboptimal. Herein, we review the recent technological development of glycoproteomics in highlighting their utility and limitations for the discovery of glycoprotein biomarkers in lung cancer. PMID:22641610

  2. Candidate biomarker discovery and selection for ‘Granny Smith' superficial scald risk management and diagnosis, poster board

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Discovery of candidate biomarkers for superficial scald, a peel disorder that develops during storage of susceptible apple cultivars, is part of a larger project aimed at developing biomarker-based risk-management and diagnostic tools for multiple apple postharvest disorders (http://www.tfrec.wsu.ed...

  3. The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

    PubMed Central

    Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa

    2012-01-01

    In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042

  4. Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria.

    PubMed

    Rousu, Juho; Agranoff, Daniel D; Sodeinde, Olugbemiro; Shawe-Taylor, John; Fernandez-Reyes, Delmiro

    2013-04-01

    Biomarker discovery aims to find small subsets of relevant variables in 'omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the 'omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant 'omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5-3% of all 'omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive 'omics measurement capabilities.

  5. Dietary exposure biomarker-lead discovery based on metabolomics analysis of urine samples.

    PubMed

    Beckmann, Manfred; Lloyd, Amanda J; Haldar, Sumanto; Favé, Gaëlle; Seal, Chris J; Brandt, Kirsten; Mathers, John C; Draper, John

    2013-08-01

    Although robust associations between dietary intake and population health are evident from conventional observational epidemiology, the outcomes of large-scale intervention studies testing the causality of those links have often proved inconclusive or have failed to demonstrate causality. This apparent conflict may be due to the well-recognised difficulty in measuring habitual food intake which may lead to confounding in observational epidemiology. Urine biomarkers indicative of exposure to specific foods offer information supplementary to the reliance on dietary intake self-assessment tools, such as FFQ, which are subject to individual bias. Biomarker discovery strategies using non-targeted metabolomics have been used recently to analyse urine from either short-term food intervention studies or from cohort studies in which participants consumed a freely-chosen diet. In the latter, the analysis of diet diary or FFQ information allowed classification of individuals in terms of the frequency of consumption of specific diet constituents. We review these approaches for biomarker discovery and illustrate both with particular reference to two studies carried out by the authors using approaches combining metabolite fingerprinting by MS with supervised multivariate data analysis. In both approaches, urine signals responsible for distinguishing between specific foods were identified and could be related to the chemical composition of the original foods. When using dietary data, both food distinctiveness and consumption frequency influenced whether differential dietary exposure could be discriminated adequately. We conclude that metabolomics methods for fingerprinting or profiling of overnight void urine, in particular, provide a robust strategy for dietary exposure biomarker-lead discovery.

  6. Mass spectrometry based translational proteomics for biomarker discovery and application in colorectal cancer.

    PubMed

    Ma, Hong; Chen, Guilin; Guo, Mingquan

    2016-04-01

    Colorectal cancer (CRC) is a leading cause of cancer-related death in the world. Clinically, early detection of the disease is the most effective approach to tackle this tough challenge. Discovery and development of reliable and effective diagnostic tools for the assessment of prognosis and prediction of response to drug therapy are urgently needed for personalized therapies and better treatment outcomes. Among many ongoing efforts in search for potential CRC biomarkers, MS-based translational proteomics provides a unique opportunity for the discovery and application of protein biomarkers toward better CRC early detection and treatment. This review updates most recent studies that use preclinical models and clinical materials for the identification of CRC-related protein markers. Some new advances in the development of CRC protein markers such as CRC stem cell related protein markers, SRM/MRM-MS and MS cytometry approaches are also discussed in order to address future directions and challenges from bench translational research to bedside clinical application of CRC biomarkers.

  7. RNA-seq SSRs and small RNA-seq SSRs: new approaches in cancer biomarker discovery.

    PubMed

    Alisoltani, Arghavan; Fallahi, Hossein; Shiran, Behrouz; Alisoltani, Anousheh; Ebrahimie, Esmaeil

    2015-04-10

    The recent exponential increase in the number of next generation sequencing studies provides a new source of data for the discovery of functional genomics based markers. The RNA-seq and small RNA-seq provide a new source for the discovery of differentially expressed SSRs (simple sequence repeats) as biomarkers in various diseases. In the present study, for the first time, we applied RNA-seq SSR to find new biomarkers for pancreatic cancer (PC) diagnosis. Analysis of RNA-seq data revealed a significant alternation in the frequency of SSR motifs during cancer progression. In particular, RNA-seq SSR showed an increase in the frequencies of GCC/GGC and GCG/CGC motifs in PC samples compared to healthy pancreas. These findings were further confirmed using meta-analysis of EST-SSR data in 11 different cancers. Interestingly, the genes containing GCC/GGC and GCG/CGC motifs in their sequences were involved in many cancer-related biological processes, particularly regulation processes. The small RNA-seq data were also mined for the conserved patterns in SSR frequencies (sRNA-seq SSR) during cancer progression. Based on the results, we suggest the potential use of GCC/GGC and GCG/CGC motifs as biomarkers in PC. Based on the findings of this study, it seems that RNA-seq SSR and sRNA-seq SSR could open a new paradigm in the diagnostic and even therapeutic strategies for PC along the other types of cancers.

  8. Urinary exosomes: a reservoir for biomarker discovery and potential mediators of intrarenal signalling.

    PubMed

    Dear, James W; Street, Jonathan M; Bailey, Matthew A

    2013-05-01

    Over the last decade, there has been increasing research interest in urinary exosomes and their relationship with kidney physiology and disease. Protocols for isolating urinary exosomes have been refined and the exosomal proteome has been extensively catalogued and reported to contain proteins from the kidney's glomerulus and all sections of the nephron. In animal and human biomarker discovery studies, this proteome changes to reflect the underlying pathophysiology of certain kidney diseases. In addition to proteins, exosomes from urine have been demonstrated to contain RNA species, another new reservoir for biomarker discovery. Exosomes have the capacity to shuttle their cargo between kidney cells and change the recipient cell's proteome and function, and may represent a mechanism for cell-to-cell signalling along the nephron. Significant challenges remain; methods for urinary exosome collection need optimisation if "real-life" clinical utility is to be achieved, consensus is needed regarding normalisation of changes in exosomal protein and RNA, larger scale exosome biomarker validation studies remain to be performed, and whether exosomes signal between cells in vivo remains an intriguing, but untested, hypothesis.

  9. Morph-X-Select: Morphology-based tissue aptamer selection for ovarian cancer biomarker discovery

    PubMed Central

    Wang, Hongyu; Li, Xin; Volk, David E.; Lokesh, Ganesh L.-R.; Elizondo-Riojas, Miguel-Angel; Li, Li; Nick, Alpa M.; Sood, Anil K.; Rosenblatt, Kevin P.; Gorenstein, David G.

    2016-01-01

    High affinity aptamer-based biomarker discovery has the advantage of simultaneously discovering an aptamer affinity reagent and its target biomarker protein. Here, we demonstrate a morphology-based tissue aptamer selection method that enables us to use tissue sections from individual patients and identify high-affinity aptamers and their associated target proteins in a systematic and accurate way. We created a combinatorial DNA aptamer library that has been modified with thiophosphate substitutions of the phosphate ester backbone at selected 5′dA positions for enhanced nuclease resistance and targeting. Based on morphological assessment, we used image-directed laser microdissection (LMD) to dissect regions of interest bound with the thioaptamer (TA) library and further identified target proteins for the selected TAs. We have successfully identified and characterized the lead candidate TA, V5, as a vimentin-specific sequence that has shown specific binding to tumor vasculature of human ovarian tissue and human microvascular endothelial cells. This new Morph-X-Select method allows us to select high-affinity aptamers and their associated target proteins in a specific and accurate way, and could be used for personalized biomarker discovery to improve medical decision-making and to facilitate the development of targeted therapies to achieve more favorable outcomes. PMID:27839510

  10. Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry.

    PubMed

    Powell, David W; Merchant, Michael L; Link, Andrew J

    2006-02-01

    An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.

  11. MetaCoMET: a web platform for discovery and visualization of the core microbiome

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A key component of the analysis of microbiome datasets is the identification of OTUs shared between multiple experimental conditions, commonly referred to as the core microbiome. Results: We present a web platform named MetaCoMET that enables the discovery and visualization of the core microbiome an...

  12. PAA: an R/bioconductor package for biomarker discovery with protein microarrays

    PubMed Central

    Turewicz, Michael; Ahrens, Maike; May, Caroline; Marcus, Katrin; Eisenacher, Martin

    2016-01-01

    Summary: The R/Bioconductor package Protein Array Analyzer (PAA) facilitates a flexible analysis of protein microarrays for biomarker discovery (esp., ProtoArrays). It provides a complete data analysis workflow including preprocessing and quality control, uni- and multivariate feature selection as well as several different plots and results tables to outline and evaluate the analysis results. As a main feature, PAA’s multivariate feature selection methods are based on recursive feature elimination (e.g. SVM-recursive feature elimination, SVM-RFE) with stability ensuring strategies such as ensemble feature selection. This enables PAA to detect stable and reliable biomarker candidate panels. Availability and implementation: PAA is freely available (BSD 3-clause license) from http://www.bioconductor.org/packages/PAA/. Contact: michael.turewicz@rub.de or martin.eisenacher@rub.de PMID:26803161

  13. Solvent interaction analysis as a proteomic approach to structure-based biomarker discovery and clinical diagnostics.

    PubMed

    Zaslavsky, Boris Y; Uversky, Vladimir N; Chait, Arnon

    2016-01-01

    Proteins have several measurable features in biological fluids that may change under pathological conditions. The current disease biomarker discovery is mostly based on protein concentration in the sample as the measurable feature. Changes in protein structures, such as post-translational modifications and in protein-partner interactions are known to accompany pathological processes. Changes in glycosylation profiles are well-established for many plasma proteins in various types of cancer and other diseases. The solvent interaction analysis method is based on protein partitioning in aqueous two-phase systems and is highly sensitive to changes in protein structure and protein-protein- and protein-partner interactions while independent of the protein concentration in the biological sample. It provides quantitative index: partition coefficient representing changes in protein structure and interactions with partners. The fundamentals of the method are presented with multiple examples of applications of the method to discover and monitor structural protein biomarkers as disease-specific diagnostic indicators.

  14. Emergent Transcriptomic Technologies and Their Role in the Discovery of Biomarkers of Liver Transplant Tolerance

    PubMed Central

    Mastoridis, Sotiris; Martínez-Llordella, Marc; Sanchez-Fueyo, Alberto

    2015-01-01

    Liver transplantation offers a unique window into transplant immunology due, in part, to the considerable proportion of recipients who develop immunological tolerance to their allograft. Biomarkers are able to identify and predict such a state of tolerance, and thereby able to establish suitable candidates for the minimization of hazardous immunosuppressive therapies, are not only of great potential clinical benefit but might also shed light on the immunological mechanisms underlying tolerance and rejection. Here, we review the emergent transcriptomic technologies serving as drivers of biomarker discovery, we appraise efforts to identify a molecular signature of liver allograft tolerance, and we consider the implications of this work on the mechanistic understanding of immunological tolerance. PMID:26157438

  15. UPLC-MS(E) application in disease biomarker discovery: the discoveries in proteomics to metabolomics.

    PubMed

    Zhao, Ying-Yong; Lin, Rui-Chao

    2014-05-25

    In the last decade, proteomics and metabolomics have contributed substantially to our understanding of different diseases. Proteomics and metabolomics aims to comprehensively identify proteins and metabolites to gain insight into the cellular signaling pathways underlying disease and to discover novel biomarkers for screening, early detection and diagnosis, as well as for determining prognoses and predicting responses to specific treatments. For comprehensive analysis of cellular proteins and metabolites, analytical methods of wider dynamic range higher resolution and good sensitivity are required. Ultra performance liquid chromatography-mass spectrometry(Elevated Energy) (UPLC-MS(E)) is currently one of the most versatile techniques. UPLC-MS(E) is an established technology in proteomics studies and is now expanding into metabolite research. MS(E) was used for simultaneous acquisition of precursor ion information and fragment ion data at low and high collision energy in one analytical run, providing similar information to conventional MS(2). In this review, UPLC-MS(E) application in proteomics and metabolomics was highlighted to assess protein and metabolite changes in different diseases, including cancer, neuropsychiatric pharmacology studies from clinical trials and animal models. In addition, the future prospects for complete proteomics and metabolomics are discussed.

  16. Robustness of chemometrics-based feature selection methods in early cancer detection and biomarker discovery.

    PubMed

    Lee, Hae Woo; Lawton, Carl; Na, Young Jeong; Yoon, Seongkyu

    2013-03-13

    In omics studies aimed at the early detection and diagnosis of cancer, bioinformatics tools play a significant role when analyzing high dimensional, complex datasets, as well as when identifying a small set of biomarkers. However, in many cases, there are ambiguities in the robustness and the consistency of the discovered biomarker sets, since the feature selection methods often lead to irreproducible results. To address this, both the stability and the classification power of several chemometrics-based feature selection algorithms were evaluated using the Monte Carlo sampling technique, aiming at finding the most suitable feature selection methods for early cancer detection and biomarker discovery. To this end, two data sets were analyzed, which comprised of MALDI-TOF-MS and LC/TOF-MS spectra measured on serum samples in order to diagnose ovarian cancer. Using these datasets, the stability and the classification power of multiple feature subsets found by different feature selection methods were quantified by varying either the number of selected features, or the number of samples in the training set, with special emphasis placed on the property of stability. The results show that high consistency does not necessarily guarantee high predictive power. In addition, differences in the stability, as well as agreement in feature lists between several feature selection methods, depend on several factors, such as the number of available samples, feature sizes, quality of the information in the dataset, etc. Among the tested methods, only the variable importance in projection (VIP)-based method shows complementary properties, providing both highly consistent and accurate subsets of features. In addition, successive projection analysis (SPA) was excellent with regards to maintaining high stability over a wide range of experimental conditions. The stability of several feature selection methods is highly variable, stressing the importance of making the proper choice among

  17. Biomarker discovery for ovine paratuberculosis (Johne's disease) by proteomic serum profiling.

    PubMed

    Zhong, L; Taylor, D; Begg, D J; Whittington, R J

    2011-07-01

    Paratuberculosis (Johne's disease) is a chronic granulomatous enteritis affecting ruminants and other species. It is caused by Mycobacterium avium subsp. paratuberculosis (MAP). In this study, surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI TOF-MS) was used as a platform to identify candidate biomarkers from sheep serum. Multivariate biomarker models which aimed to differentiate sheep with paratuberculosis and vaccinated-exposed sheep from unexposed animals were proposed based on classification and regression tree (CART) and linear discriminant analysis (LDA) algorithms from two array types. The accuracy of classification of sheep into unexposed or exposed groups ranged from 75 to 100% among models. SELDI was used to monitor protein profile changes over time during an experimental infection trial by examining sera collected at 4-, 8- and 13-months post infection. Although three different SELDI instruments were used, nine consistent proteomic features were observed associated with exposure to MAP. Two of the putative serum biomarkers were purified from serum using chromatographic methods and were identified as transthyretin and alpha haemoglobin by tandem mass spectrometry. They belong to highly abundant, acute phase reactants in the serum proteome and have also been discovered as serum biomarkers in human inflammatory conditions and cancer. Their relationship to the pathogenesis of Johne's disease remains to be elucidated.

  18. Open for collaboration: an academic platform for drug discovery and development at SciLifeLab.

    PubMed

    Arvidsson, Per I; Sandberg, Kristian; Forsberg-Nilsson, Karin

    2016-10-01

    The Science for Life Laboratory Drug Discovery and Development (SciLifeLab DDD) platform reaches out to Swedish academia with an industry-standard infrastructure for academic drug discovery, supported by earmarked funds from the Swedish government. In this review, we describe the build-up and operation of the platform, and reflect on our first two years of operation, with the ambition to share learnings and best practice with academic drug discovery centers globally. We also discuss how the Swedish Teacher Exemption Law, an internationally unique aspect of the innovation system, has shaped the operation. Furthermore, we address how this investment in infrastructure and expertise can be utilized to facilitate international collaboration between academia and industry in the best interest of those ultimately benefiting the most from translational pharmaceutical research - the patients.

  19. Mass Spectrometry-based Proteomics and Peptidomics for Systems Biology and Biomarker Discovery

    PubMed Central

    Cunningham, Robert; Ma, Di; Li, Lingjun

    2013-01-01

    The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology. Proteomics technologies have evolved to produce large datasets of proteins or peptides involved in various biological and disease progression processes producing testable hypothesis for complex biological questions. This review provides an introduction and insight to relevant topics in proteomics and peptidomics including biological material selection, sample preparation, separation techniques, peptide fragmentation, post-translation modifications, quantification, bioinformatics, and biomarker discovery and validation. In addition, current literature and remaining challenges and emerging technologies for proteomics and peptidomics are presented. PMID:24504115

  20. Probing the O-glycoproteome of gastric cancer cell lines for biomarker discovery.

    PubMed

    Campos, Diana; Freitas, Daniela; Gomes, Joana; Magalhães, Ana; Steentoft, Catharina; Gomes, Catarina; Vester-Christensen, Malene B; Ferreira, José Alexandre; Afonso, Luis P; Santos, Lúcio L; Pinto de Sousa, João; Mandel, Ulla; Clausen, Henrik; Vakhrushev, Sergey Y; Reis, Celso A

    2015-06-01

    Circulating O-glycoproteins shed from cancer cells represent important serum biomarkers for diagnostic and prognostic purposes. We have recently shown that selective detection of cancer-associated aberrant glycoforms of circulating O-glycoprotein biomarkers can increase specificity of cancer biomarker assays. However, the current knowledge of secreted and circulating O-glycoproteins is limited. Here, we used the COSMC KO "SimpleCell" (SC) strategy to characterize the O-glycoproteome of two gastric cancer SimpleCell lines (AGS, MKN45) as well as a gastric cell line (KATO III) which naturally expresses at least partially truncated O-glycans. Overall, we identified 499 O-glycoproteins and 1236 O-glycosites in gastric cancer SimpleCells, and a total 47 O-glycoproteins and 73 O-glycosites in the KATO III cell line. We next modified the glycoproteomic strategy to apply it to pools of sera from gastric cancer and healthy individuals to identify circulating O-glycoproteins with the STn glycoform. We identified 37 O-glycoproteins in the pool of cancer sera, and only nine of these were also found in sera from healthy individuals. Two identified candidate O-glycoprotein biomarkers (CD44 and GalNAc-T5) circulating with the STn glycoform were further validated as being expressed in gastric cancer tissue. A proximity ligation assay was used to show that CD44 was expressed with the STn glycoform in gastric cancer tissues. The study provides a discovery strategy for aberrantly glycosylated O-glycoproteins and a set of O-glycoprotein candidates with biomarker potential in gastric cancer.

  1. Indications of success: Strategies for utilizing neuroimaging biomarkers in CNS drug discovery and development: CINP/JSNP working group report.

    PubMed

    Suhara, Tetsuya; Chaki, Shigeyuki; Kimura, Haruhide; Furusawa, Makoto; Matsumoto, Mitsuyuki; Ogura, Hiroo; Negishi, Takaaki; Saijo, Takeaki; Higuchi, Makoto; Omura, Tomohiro; Watanabe, Rira; Miyoshi, Sosuke; Nakatani, Noriaki; Yamamoto, Noboru; Liou, Shyh-Yuh; Takado, Yuhei; Maeda, Jun; Okamoto, Yasumasa; Okubo, Yoshiaki; Yamada, Makiko; Ito, Hiroshi; Walton, Noah M; Yamawaki, Shigeto

    2016-12-28

    Despite large unmet medical needs in the field for several decades, central nervous system (CNS) drug discovery and development has been largely unsuccessful. Biomarkers, particularly those utilizing neuroimaging, have played important roles in aiding CNS drug development, including dosing determination of investigational new drugs (INDs). The utility of biomarkers as tools to overcome issues of CNS drug development is the subject for this review.In this review aimed at employing biomarkers as tools to overcome issues surrounding CNS drug development, we first analyzed problems in utilizing biomarkers in processes of drug discovery and development for CNS disorders. Based on this analysis, we propose a new paradigm containing five distinct tiers to further clarify the use of biomarkers and establish new strategies for decision-making in the context of clinical drug development. Specifically, we discuss more rational ways to determine optimal dose for INDs with novel mechanisms and targets, and propose additional categorization criteria to further the use of biomarkers in patient stratification and efficacy prediction. Finally, we propose validation and development of new neuroimaging biomarkers through Public-Private-Partnerships to realize rational and successful drug discovery and development for CNS disorders.

  2. A surface topography assisted droplet manipulation platform for biomarker detection and pathogen identification.

    PubMed

    Zhang, Yi; Park, Seungkyung; Liu, Kelvin; Tsuan, Jennifer; Yang, Samuel; Wang, Tza-Huei

    2011-02-07

    This paper reports a droplet microfluidic, sample-to-answer platform for the detection of disease biomarkers and infectious pathogens using crude biosamples. The platform exploited the dual functionality of silica superparamagnetic particles (SSP) for solid phase extraction of DNA and magnetic actuation. This enabled the integration of sample preparation and genetic analysis within discrete droplets, including the steps of cell lysis, DNA binding, washing, elution, amplification and detection. The microfluidic device was self contained, with all reagents stored in droplets, thereby eliminating the need for fluidic coupling to external reagent reservoirs. The device incorporated unique surface topographic features to assist droplet manipulation. Pairs of micro-elevations were created to form slits that facilitated efficient splitting of SSP from droplets. In addition, a compact sample handling stage, which integrated the magnet manipulator, the droplet microfluidic device and a Peltier thermal cycler, was built for convenient droplet manipulation and real-time detection. The feasibility of the platform was demonstrated by analysing ovarian cancer biomarker Rsf-1 and detecting Escherichia coli with real time polymerase chain reaction and real time helicase dependent amplification.

  3. Development of Transcriptomics-based Biomarkers for Selected Endocrine Disrupting Chemicals in Zebrafish (Danio rerio)

    EPA Science Inventory

    Genome-wide transcriptional profiling by microarrays provides a powerful platform for gene expression-based biomarker discovery. After their wide acceptance in human disease diagnosis, prognosis, and drug discovery, these gene signatures are increasingly being adopted for environ...

  4. Development of transcriptomics-based biomarkers for selected endocrine disrupting chemicals in zebrafish (Danio rerio)

    EPA Science Inventory

    Genome-wide transcriptional profiling by microarrays provides a powerful platform for gene expression-based biomarker discovery. After their wide acceptance in human disease diagnosis, prognosis, and drug discovery, these gene signatures are increasingly being adopted for environ...

  5. SITC/iSBTc Cancer Immunotherapy Biomarkers Resource Document: Online resources and useful tools - a compass in the land of biomarker discovery

    PubMed Central

    2011-01-01

    Recent positive clinical results in cancer immunotherapy point to the potential of immune-based strategies to provide effective treatment of a variety of cancers. In some patients, the responses to cancer immunotherapy are durable, dramatically extending survival. Extensive research efforts are being made to identify and validate biomarkers that can help identify subsets of cancer patients that will benefit most from these novel immunotherapies. In addition to the clear advantage of such predictive biomarkers, immune biomarkers are playing an important role in the development, clinical evaluation and monitoring of cancer immunotherapies. This Cancer Immunotherapy Resource Document, prepared by the Society for Immunotherapy of Cancer (SITC, formerly the International Society for Biological Therapy of Cancer, iSBTc), provides key references and online resources relevant to the discovery, evaluation and clinical application of immune biomarkers. These key resources were identified by experts in the field who are actively pursuing research in biomarker identification and validation. This organized collection of the most useful references, online resources and tools serves as a compass to guide discovery of biomarkers essential to advancing novel cancer immunotherapies. PMID:21929757

  6. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.

    PubMed

    Yang, Wanjuan; Soares, Jorge; Greninger, Patricia; Edelman, Elena J; Lightfoot, Howard; Forbes, Simon; Bindal, Nidhi; Beare, Dave; Smith, James A; Thompson, I Richard; Ramaswamy, Sridhar; Futreal, P Andrew; Haber, Daniel A; Stratton, Michael R; Benes, Cyril; McDermott, Ultan; Garnett, Mathew J

    2013-01-01

    Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.

  7. [Gastroenterological Cancer Diagnosis by Metabolomics-Discovery of Pancreatic Cancer Biomarker].

    PubMed

    Yoshida, Masaru; Nishiumi, Shin; Azuma, Takeshi

    2015-04-01

    The field of omics involves comprehensive investigations based on genomics, transcriptomics, proteomics, and metabolomics, and omics studies have developed rapidly. Metabolomics, metabolome analysis, involves technology to analyze the concentrations of low-molecular-weight metabolites comprehensively, and has recently rapidly developed along with improvements in analytical technology. Therefore, metabolome analysis is just beginning to be applied to not only food science and environmental research fields but also medical research. In the medical research field, especially, metabolome analysis plays an important role in novel disease biomarker discovery. The metabolome represents the endpoint of the omics cascade and, therefore, is considered to be closer to the phenotype. In addition, there is also a possibility that the metabolome is affected by exogenous factors such as environmental and food factors, as well as endogenous factors such as DNA/mRNA/protein. Therefore, metabolome analysis can be a useful approach for discovering effective biomarkers. Here, we explain the characteristics of metabolome analysis, and also outline metabolome analysis using a liquid chromatograph mass spectrometer (LC-MS), gas chromatograph mass spectrometer (GC-MS), capillary electrophoresis mass spectrometer (CE-MS), and matrix-assisted laser desorption ionization mass spectrometer (MALDI-MS). Then, we describe the findings of studies that used metabolome analysis in an attempt to discover biomarker candidates for pancreatic cancer, and discuss metabolome analysis-based disease diagnosis.

  8. Schizophrenia genomics and proteomics: are we any closer to biomarker discovery?

    PubMed Central

    2009-01-01

    The field of proteomics has made leaps and bounds in the last 10 years particularly in the fields of oncology and cardiovascular medicine. In comparison, neuroproteomics is still playing catch up mainly due to the relative complexity of neurological disorders. Schizophrenia is one such disorder, believed to be the results of multiple factors both genetic and environmental. Affecting over 2 million people in the US alone, it has become a major clinical and public health concern worldwide. This paper gives an update of schizophrenia biomarker research as reviewed by Lakhan in 2006 and gives us a rundown of the progress made during the last two years. Several studies demonstrate the potential of cerebrospinal fluid as a source of neuro-specific biomarkers. Genetic association studies are making headway in identifying candidate genes for schizophrenia. In addition, metabonomics, bioinformatics, and neuroimaging techniques are aiming to complete the picture by filling in knowledge gaps. International cooperation in the form of genomics and protein databases and brain banks is facilitating research efforts. While none of the recent developments described here in qualifies as biomarker discovery, many are likely to be stepping stones towards that goal. PMID:19128481

  9. Defining the purity of exosomes required for diagnostic profiling of small RNA suitable for biomarker discovery

    PubMed Central

    Bellingham, Shayne A.; Scicluna, Benjamin J.; Shambrook, Mitch C.; Sharples, Robyn A.; Cheng, Lesley

    2017-01-01

    ABSTRACT Small non-coding RNAs (ncRNA), including microRNAs (miRNA), enclosed in exosomes are being utilised for biomarker discovery in disease. Two common exosome isolation methods involve differential ultracentrifugation or differential ultracentrifugation coupled with Optiprep gradient fractionation. Generally, the incorporation of an Optiprep gradient provides better separation and increased purity of exosomes. The question of whether increased purity of exosomes is required for small ncRNA profiling, particularly in diagnostic and biomarker purposes, has not been addressed and highly debated. Utilizing an established neuronal cell system, we used next-generation sequencing to comprehensively profile ncRNA in cells and exosomes isolated by these 2 isolation methods. By comparing ncRNA content in exosomes from these two methods, we found that exosomes from both isolation methods were enriched with miRNAs and contained a diverse range of rRNA, small nuclear RNA, small nucleolar RNA and piwi-interacting RNA as compared with their cellular counterparts. Additionally, tRNA fragments (30–55 nucleotides in length) were identified in exosomes and may act as potential modulators for repressing protein translation. Overall, the outcome of this study confirms that ultracentrifugation-based method as a feasible approach to identify ncRNA biomarkers in exosomes. PMID:28005467

  10. Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers.

    PubMed

    Labaj, Wojciech; Papiez, Anna; Polanski, Andrzej; Polanska, Joanna

    2017-03-01

    Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.

  11. Enabling Metabolomics Based Biomarker Discovery Studies Using Molecular Phenotyping of Exosome-Like Vesicles

    PubMed Central

    Altadill, Tatiana; Campoy, Irene; Lanau, Lucia; Gill, Kirandeep; Rigau, Marina; Gil-Moreno, Antonio; Reventos, Jaume; Byers, Stephen; Colas, Eva; Cheema, Amrita K.

    2016-01-01

    Identification of sensitive and specific biomarkers with clinical and translational utility will require smart experimental strategies that would augment expanding the breadth and depth of molecular measurements within the constraints of currently available technologies. Exosomes represent an information rich matrix to discern novel disease mechanisms that are thought to contribute to pathologies such as dementia and cancer. Although proteomics and transcriptomic studies have been reported using Exosomes-Like Vesicles (ELVs) from different sources, exosomal metabolome characterization and its modulation in health and disease remains to be elucidated. Here we describe methodologies for UPLC-ESI-MS based small molecule profiling of ELVs from human plasma and cell culture media. In this study, we present evidence that indeed ELVs carry a rich metabolome that could not only augment the discovery of low abundance biomarkers but may also help explain the molecular basis of disease progression. This approach could be easily translated to other studies seeking to develop predictive biomarkers that can subsequently be used with simplified targeted approaches. PMID:26974972

  12. Top-Down Proteomics with Mass Spectrometry Imaging: A Pilot Study towards Discovery of Biomarkers for Neurodevelopmental Disorders

    PubMed Central

    Ye, Hui; Mandal, Rakesh; Catherman, Adam; Thomas, Paul M.; Kelleher, Neil L.; Ikonomidou, Chrysanthy; Li, Lingjun

    2014-01-01

    In the developing mammalian brain, inhibition of NMDA receptor can induce widespread neuroapoptosis, inhibit neurogenesis and cause impairment of learning and memory. Although some mechanistic insights into adverse neurological actions of these NMDA receptor antagonists exist, our understanding of the full spectrum of developmental events affected by early exposure to these chemical agents in the brain is still limited. Here we attempt to gain insights into the impact of pharmacologically induced excitatory/inhibitory imbalance in infancy on the brain proteome using mass spectrometric imaging (MSI). Our goal was to study changes in protein expression in postnatal day 10 (P10) rat brains following neonatal exposure to the NMDA receptor antagonist dizocilpine (MK801). Analysis of rat brains exposed to vehicle or MK801 and comparison of their MALDI MS images revealed differential relative abundances of several proteins. We then identified these markers such as ubiquitin, purkinje cell protein 4 (PEP-19), cytochrome c oxidase subunits and calmodulin, by a combination of reversed-phase (RP) HPLC fractionation and top-down tandem MS platform. More in-depth large scale study along with validation experiments will be carried out in the future. Overall, our findings indicate that a brief neonatal exposure to a compound that alters excitatory/inhibitory balance in the brain has a long term effect on protein expression patterns during subsequent development, highlighting the utility of MALDI-MSI as a discovery tool for potential biomarkers. PMID:24710523

  13. Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

    PubMed

    Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

    2015-08-01

    Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention.

  14. The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity

    SciTech Connect

    Amacher, David E.

    2010-05-15

    Biomarkers are biometric measurements that provide critical quantitative information about the biological condition of the animal or individual being tested. In drug safety studies, established toxicity biomarkers are used along with other conventional study data to determine dose-limiting organ toxicity, and to define species sensitivity for new chemical entities intended for possible use as human medicines. A continuing goal of drug safety scientists in the pharmaceutical industry is to discover and develop better trans-species biomarkers that can be used to determine target organ toxicities for preclinical species in short-term studies at dose levels that are some multiple of the intended human dose and again later in full development for monitoring clinical trials at lower therapeutic doses. Of particular value are early, predictive, noninvasive biomarkers that have in vitro, in vivo, and clinical transferability. Such translational biomarkers bridge animal testing used in preclinical science and human studies that are part of subsequent clinical testing. Although suitable for in vivo preclinical regulatory studies, conventional hepatic safety biomarkers are basically confirmatory markers because they signal organ toxicity after some pathological damage has occurred, and are therefore not well-suited for short-term, predictive screening assays early in the discovery-to-development progression of new chemical entities (NCEs) available in limited quantities. Efforts between regulatory agencies and the pharmaceutical industry are underway for the coordinated discovery, qualification, verification and validation of early predictive toxicity biomarkers. Early predictive safety biomarkers are those that are detectable and quantifiable prior to the onset of irreversible tissue injury and which are associated with a mechanism of action relevant to a specific type of potential hepatic injury. Potential drug toxicity biomarkers are typically endogenous macromolecules in

  15. A Comparison of Methods for Data-Driven Cancer Outlier Discovery, and An Application Scheme to Semisupervised Predictive Biomarker Discovery

    PubMed Central

    Karrila, Seppo; Lee, Julian Hock Ean; Tucker-Kellogg, Greg

    2011-01-01

    A core component in translational cancer research is biomarker discovery using gene expression profiling for clinical tumors. This is often based on cell line experiments; one population is sampled for inference in another. We disclose a semisupervised workflow focusing on binary (switch-like, bimodal) informative genes that are likely cancer relevant, to mitigate this non-statistical problem. Outlier detection is a key enabling technology of the workflow, and aids in identifying the focus genes. We compare outlier detection techniques MOST, LSOSS, COPA, ORT, OS, and t-test, using a publicly available NSCLC dataset. Removing genes with Gaussian distribution is computationally efficient and matches MOST particularly well, while also COPA and OS pick prognostically relevant genes in their top ranks. Also our stability assessment is in favour of both MOST and COPA; the latter does not pair well with prefiltering for non-Gaussianity, but can handle data sets lacking non-cancer cases. We provide R code for replicating our approach or extending it. PMID:21584264

  16. A comparison of methods for data-driven cancer outlier discovery, and an application scheme to semisupervised predictive biomarker discovery.

    PubMed

    Karrila, Seppo; Lee, Julian Hock Ean; Tucker-Kellogg, Greg

    2011-04-18

    A core component in translational cancer research is biomarker discovery using gene expression profiling for clinical tumors. This is often based on cell line experiments; one population is sampled for inference in another. We disclose a semisupervised workflow focusing on binary (switch-like, bimodal) informative genes that are likely cancer relevant, to mitigate this non-statistical problem. Outlier detection is a key enabling technology of the workflow, and aids in identifying the focus genes.We compare outlier detection techniques MOST, LSOSS, COPA, ORT, OS, and t-test, using a publicly available NSCLC dataset. Removing genes with Gaussian distribution is computationally efficient and matches MOST particularly well, while also COPA and OS pick prognostically relevant genes in their top ranks. Also our stability assessment is in favour of both MOST and COPA; the latter does not pair well with prefiltering for non-Gaussianity, but can handle data sets lacking non-cancer cases.We provide R code for replicating our approach or extending it.

  17. Sodium channel inhibitor drug discovery using automated high throughput electrophysiology platforms.

    PubMed

    Castle, Neil; Printzenhoff, David; Zellmer, Shannon; Antonio, Brett; Wickenden, Alan; Silvia, Christopher

    2009-01-01

    Voltage dependent sodium channels are widely recognized as valuable targets for the development of therapeutic interventions for neuroexcitatory disorders such as epilepsy and pain as well as cardiac arrhythmias. An ongoing challenge for sodium channel drug discovery is the ability to readily evaluate state dependent interactions, which are known to underlie inhibition by many clinically used local anesthetic, antiepileptic and antiarrhythmic sodium channel blockers. While patch-clamp electrophysiology is still considered the most effective way of measuring ion channel function and pharmacology, it does not have the throughput to be useful in early stages of drug discovery in which there is often a need to evaluate many thousands to hundreds of thousands of compounds. Fortunately over the past five years, there has been significant progress in developing much higher throughput electrophysiology platforms like the PatchXpress and IonWorks, which are now widely used in drug discovery. This review highlights the strengths and weaknesses of these two high throughput devices for use in sodium channel inhibitor drug discovery programs. Overall, the PatchXpress and IonWorks electrophysiology platforms have individual strengths that make them complementary to each other. Both platforms are capable of measuring state dependent modulation of sodium channels. IonWorks has the throughput to allow for effective screening of libraries of tens of thousands of compounds whereas the PatchXpress has more flexibility to provide quantitative voltage clamp, which is useful in structure activity evaluations for the hit-to-lead and lead optimization stages of sodium channel drug discovery.

  18. Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery

    PubMed Central

    Okamura, Yasunobu; Motoike, Ikuko N.; Katoh, Yasutake; Kurosawa, Yasuhiro; Saijyo, Reina; Koshiba, Seizo; Yasuda, Jun; Motohashi, Hozumi; Sugawara, Junichi; Tanabe, Osamu; Kinoshita, Kengo; Yamamoto, Masayuki

    2016-01-01

    Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens’ pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases. PMID:27579980

  19. An integrated approach to blood-based cancer diagnosis and biomarker discovery.

    PubMed

    Min, Martin Renqiang; Chowdhury, Salim; Qi, Yanjun; Stewart, Alex; Ostroff, Rachel

    2014-01-01

    Disrupted or abnormal biological processes responsible for cancers often quantitatively manifest as disrupted additive and multiplicative interactions of gene/protein expressions correlating with cancer progression. However, the examination of all possible combinatorial interactions between gene features in most case-control studies with limited training data is computationally infeasible. In this paper, we propose a practically feasible data integration approach, QUIRE (QUadratic Interactions among infoRmative fEatures), to identify discriminative complex interactions among informative gene features for cancer diagnosis and biomarker discovery directly based on patient blood samples. QUIRE works in two stages, where it first identifies functionally relevant gene groups for the disease with the help of gene functional annotations and available physical protein interactions, then it explores the combinatorial relationships among the genes from the selected informative groups. Based on our private experimentally generated data from patient blood samples using a novel SOMAmer (Slow Off-rate Modified Aptamer) technology, we apply QUIRE to cancer diagnosis and biomarker discovery for Renal Cell Carcinoma (RCC) and Ovarian Cancer (OVC). To further demonstrate the general applicability of our approach, we also apply QUIRE to a publicly available Colorectal Cancer (CRC) dataset that can be used to prioritize our SOMAmer design. Our experimental results show that QUIRE identifies gene-gene interactions that can better identify the different cancer stages of samples, as compared to other state-of-the-art feature selection methods. A literature survey shows that many of the interactions identified by QUIRE play important roles in the development of cancer.

  20. Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification

    EPA Science Inventory

    Book Chapter 18, titled Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification, will be published in the book titled High Performance Liquid Chromatography in Pesticide Residue Analysis (Part of the C...

  1. Biomarkers Discovery for Colorectal Cancer: A Review on Tumor Endothelial Markers as Perspective Candidates

    PubMed Central

    2016-01-01

    Colorectal cancer (CRC) is the third most common cancer in the world. The early detection of CRC, during the promotion/progression stages, is an enormous challenge for a successful outcome and remains a fundamental problem in clinical approach. Despite the continuous advancement in diagnostic and therapeutic methods, there is a need for discovery of sensitive and specific, noninvasive biomarkers. Tumor endothelial markers (TEMs) are associated with tumor-specific angiogenesis and are potentially useful to discriminate between tumor and normal endothelium. The most promising TEMs for oncogenic signaling in CRC appeared to be the TEM1, TEM5, TEM7, and TEM8. Overexpression of TEMs especially TEM1, TEM7, and TEM8 in colorectal tumor tissue compared to healthy tissue suggests their role in tumor blood vessels formation. Thus TEMs appear to be perspective candidates for early detection, monitoring, and treatment of CRC patients. This review provides an update on recent data on tumor endothelial markers and their possible use as biomarkers for screening, diagnosis, and therapy of colorectal cancer patients. PMID:27965519

  2. Translating discovery in zebrafish pancreatic development to human pancreatic cancer: biomarkers, targets, pathogenesis, and therapeutics.

    PubMed

    Yee, Nelson S; Kazi, Abid A; Yee, Rosemary K

    2013-06-01

    Abstract Experimental studies in the zebrafish have greatly facilitated understanding of genetic regulation of the early developmental events in the pancreas. Various approaches using forward and reverse genetics, chemical genetics, and transgenesis in zebrafish have demonstrated generally conserved regulatory roles of mammalian genes and discovered novel genetic pathways in exocrine pancreatic development. Accumulating evidence has supported the use of zebrafish as a model of human malignant diseases, including pancreatic cancer. Studies have shown that the genetic regulators of exocrine pancreatic development in zebrafish can be translated into potential clinical biomarkers and therapeutic targets in human pancreatic adenocarcinoma. Transgenic zebrafish expressing oncogenic K-ras and zebrafish tumor xenograft model have emerged as valuable tools for dissecting the pathogenetic mechanisms of pancreatic cancer and for drug discovery and toxicology. Future analysis of the pancreas in zebrafish will continue to advance understanding of the genetic regulation and biological mechanisms during organogenesis. Results of those studies are expected to provide new insights into how aberrant developmental pathways contribute to formation and growth of pancreatic neoplasia, and hopefully generate valid biomarkers and targets as well as effective and safe therapeutics in pancreatic cancer.

  3. SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications.

    PubMed

    Anjo, Sandra Isabel; Santa, Cátia; Manadas, Bruno

    2017-02-01

    In the era of quantitative proteomics, where mass spectrometry plays a pivotal role, in particular associated with the use of data-independent acquisition, it is time to perform an overview of this growing field with special focus on one of the most promising approaches: SWATH-MS, and to present future perspectives for its application as a translational tool. Therefore, a summary of this technique is presented focusing on two key relevant concepts associated with its application in biomarker discovery: the protein library and the individual digital maps concepts. It is also the purpose of this review to document the likely impact of SWATH-MS in both fundamental and translational research including biomarker identification and creation of diagnostic tools. To that end, the two concepts referred above were integrated with ongoing technical developments. Finally, some of the current restrictions for the implementation of SWATH-MS on a large scale are identified, and potential solutions presented, namely protocol standardization combined with the use of the proper standards.

  4. Metabolic Profiling of an Echinostoma caproni Infection in the Mouse for Biomarker Discovery

    PubMed Central

    Saric, Jasmina; Li, Jia V.; Wang, Yulan; Keiser, Jennifer; Bundy, Jake G.; Holmes, Elaine; Utzinger, Jürg

    2008-01-01

    Background Metabolic profiling holds promise with regard to deepening our understanding of infection biology and disease states. The objectives of our study were to assess the global metabolic responses to an Echinostoma caproni infection in the mouse, and to compare the biomarkers extracted from different biofluids (plasma, stool, and urine) in terms of characterizing acute and chronic stages of this intestinal fluke infection. Methodology/Principal Findings Twelve female NMRI mice were infected with 30 E. caproni metacercariae each. Plasma, stool, and urine samples were collected at 7 time points up to day 33 post-infection. Samples were also obtained from non-infected control mice at the same time points and measured using 1H nuclear magnetic resonance (NMR) spectroscopy. Spectral data were subjected to multivariate statistical analyses. In plasma and urine, an altered metabolic profile was already evident 1 day post-infection, characterized by reduced levels of plasma choline, acetate, formate, and lactate, coupled with increased levels of plasma glucose, and relatively lower concentrations of urinary creatine. The main changes in the urine metabolic profile started at day 8 post-infection, characterized by increased relative concentrations of trimethylamine and phenylacetylglycine and lower levels of 2-ketoisocaproate and showed differentiation over the course of the infection. Conclusion/Significance The current investigation is part of a broader NMR-based metabonomics profiling strategy and confirms the utility of this approach for biomarker discovery. In the case of E. caproni, a diagnosis based on all three biofluids would deliver the most comprehensive fingerprint of an infection. For practical purposes, however, future diagnosis might aim at a single biofluid, in which case urine would be chosen for further investigation, based on quantity of biomarkers, ease of sampling, and the degree of differentiation from the non-infected control group. PMID

  5. Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease.

    PubMed

    Barallobre-Barreiro, Javier; Chung, Yuen-Li; Mayr, Manuel

    2013-08-01

    In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. In this review, we highlight some of the "omics" methods used to assess protein and metabolite changes in cardiovascular disease. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, "omics" platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease.

  6. Antibody biomarker discovery through in vitro directed evolution of consensus recognition epitopes.

    PubMed

    Ballew, John T; Murray, Joseph A; Collin, Pekka; Mäki, Markku; Kagnoff, Martin F; Kaukinen, Katri; Daugherty, Patrick S

    2013-11-26

    To enable discovery of serum antibodies indicative of disease and simultaneously develop reagents suitable for diagnosis, in vitro directed evolution was applied to identify consensus peptides recognized by patients' serum antibodies. Bacterial cell-displayed peptide libraries were quantitatively screened for binders to serum antibodies from patients with celiac disease (CD), using cell-sorting instrumentation to identify two distinct consensus epitope families specific to CD patients (PEQ and (E)/DxFV(Y)/FQ). Evolution of the (E)/DxFV(Y)/FQ consensus epitope identified a celiac-specific epitope, distinct from the two CD hallmark antigens tissue transglutaminase-2 and deamidated gliadin, exhibiting 71% sensitivity and 99% specificity (n = 231). Expansion of the first-generation PEQ consensus epitope via in vitro evolution yielded octapeptides QPEQAFPE and PFPEQxFP that identified ω- and γ-gliadins, and their deamidated forms, as immunodominant B-cell epitopes in wheat and related cereal proteins. The evolved octapeptides, but not first-generation peptides, discriminated one-way blinded CD and non-CD sera (n = 78) with exceptional accuracy, yielding 100% sensitivity and 98% specificity. Because this method, termed antibody diagnostics via evolution of peptides, does not require prior knowledge of pathobiology, it may be broadly useful for de novo discovery of antibody biomarkers and reagents for their detection.

  7. SANIST: a rapid mass spectrometric SACI/ESI data acquisition and elaboration platform for verifying potential candidate biomarkers

    PubMed Central

    Briga, Daniela; Conti, Matteo; Bruno, Antonino; Farioli, Daniela; Canali, Sara; Sogno, Ilaria; D'Ambrosio, Gioacchino; Consonni, Paolo; Noonan, Douglas M.

    2015-01-01

    Rationale Surface‐Activated Chemical Ionization/Electrospray Ionization mass spectrometry (SACI/ESI‐MS) is a technique with high sensitivity and low noise that allows accurate biomarker discovery studies. We developed a dedicated SACI/ESI software, named SANIST, for both biomarker fingerprint data acquisition and as a diagnostic tool, using prostate cancer (PCa) as the disease of interest. Methods Liquid chromatography (LC)/SACI/ESI‐MS technology was employed to detect a potential biomarker panel for PCa disease prediction. Serum from patients with histologically confirmed or negative prostate biopsies for PCa was employed. The biomarker data (m/z or Thompson value, retention time and extraction mass chromatogram peak area) were stored in an ascii database. SANIST software allowed identification of potential biomarkers. A Bayesian scoring algorithm developed in house allowed sample separation based on comparison with samples in the database. Results Biomarker candidates from the carnitine family were detected at significantly lower levels in patients showing histologically confirmed PCa. Using these biomarkers, the SANIST scoring algorithm allowed separation of patients with PCa from biopsy negative subjects with high accuracy and sensitivity. Conclusions SANIST was able to rapidly identify and perform a preliminary evaluation of the potential diagnostic efficiency of potential biomarkers for PCa. © 2015 The Authors. Rapid Communications in Mass Spectrometry published by John Wiley & Sons Ltd. PMID:26331920

  8. The path forward to biomarker discovery in psoriatic disease: a report from the GRAPPA 2010 annual meeting.

    PubMed

    Gladman, Dafna D; Ritchlin, Christopher T; Fitzgerald, Oliver

    2012-02-01

    At the 2010 annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA), wide-ranging discussions were held regarding biomarker research in psoriatic disease. Consensus was reached on 2 areas of priority: (1) the study of soluble biomarkers of radiographic progression in psoriatic arthritis (PsA); and (2) the analysis of comorbidity biomarkers, specifically cardiovascular and articular, in a psoriasis inception cohort. For each of these areas, rigorous definition of the clinical phenotype of PsA will be essential. To date, 2 instruments have been identified to define the phenotype: the ClASsification of Psoriatic ARthritis criteria and various screening questionnaires. In this overview, we discuss the challenges of the clinical phenotype of PsA and review GRAPPA plans for developing a research program for biomarker discovery.

  9. Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort

    PubMed Central

    Adeola, Henry A.; Smith, Muneerah; Kaestner, Lisa; Blackburn, Jonathan M.; Zerbini, Luiz F.

    2016-01-01

    There is a growing need for high throughput diagnostic tools for early diagnosis and treatment monitoring of prostate cancer (PCa) in Africa. The role of cancer-testis antigens (CTAs) in PCa in men of African descent is poorly researched. Hence, we aimed to elucidate the role of 123 Tumour Associated Antigens (TAAs) using antigen microarray platform in blood samples (N = 67) from a South African PCa, Benign prostatic hyperplasia (BPH) and disease control (DC) cohort. Linear (fold-over-cutoff) and differential expression quantitation of autoantibody signal intensities were performed. Molecular signatures of candidate PCa antigen biomarkers were identified and analyzed for ethnic group variation. Potential cancer diagnostic and immunotherapeutic inferences were drawn. We identified a total of 41 potential diagnostic/therapeutic antigen biomarkers for PCa. By linear quantitation, four antigens, GAGE1, ROPN1, SPANXA1 and PRKCZ were found to have higher autoantibody titres in PCa serum as compared with BPH where MAGEB1 and PRKCZ were highly expressed. Also, p53 S15A and p53 S46A were found highly expressed in the disease control group. Statistical analysis by differential expression revealed twenty-four antigens as upregulated in PCa samples, while 11 were downregulated in comparison to BPH and DC (FDR = 0.01). FGFR2, COL6A1and CALM1 were verifiable biomarkers of PCa analysis using urinary shotgun proteomics. Functional pathway annotation of identified biomarkers revealed similar enrichment both at genomic and proteomic level and ethnic variations were observed. Cancer antigen arrays are emerging useful in potential diagnostic and immunotherapeutic antigen biomarker discovery. PMID:26885621

  10. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers.

    PubMed

    Riahi, Reza; Shaegh, Seyed Ali Mousavi; Ghaderi, Masoumeh; Zhang, Yu Shrike; Shin, Su Ryon; Aleman, Julio; Massa, Solange; Kim, Duckjin; Dokmeci, Mehmet Remzi; Khademhosseini, Ali

    2016-04-21

    There is an increasing interest in developing microfluidic bioreactors and organs-on-a-chip platforms combined with sensing capabilities for continual monitoring of cell-secreted biomarkers. Conventional approaches such as ELISA and mass spectroscopy cannot satisfy the needs of continual monitoring as they are labor-intensive and not easily integrable with low-volume bioreactors. This paper reports on the development of an automated microfluidic bead-based electrochemical immunosensor for in-line measurement of cell-secreted biomarkers. For the operation of the multi-use immunosensor, disposable magnetic microbeads were used to immobilize biomarker-recognition molecules. Microvalves were further integrated in the microfluidic immunosensor chip to achieve programmable operations of the immunoassay including bead loading and unloading, binding, washing, and electrochemical sensing. The platform allowed convenient integration of the immunosensor with liver-on-chips to carry out continual quantification of biomarkers secreted from hepatocytes. Transferrin and albumin productions were monitored during a 5-day hepatotoxicity assessment in which human primary hepatocytes cultured in the bioreactor were treated with acetaminophen. Taken together, our unique microfluidic immunosensor provides a new platform for in-line detection of biomarkers in low volumes and long-term in vitro assessments of cellular functions in microfluidic bioreactors and organs-on-chips.

  11. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers

    NASA Astrophysics Data System (ADS)

    Riahi, Reza; Shaegh, Seyed Ali Mousavi; Ghaderi, Masoumeh; Zhang, Yu Shrike; Shin, Su Ryon; Aleman, Julio; Massa, Solange; Kim, Duckjin; Dokmeci, Mehmet Remzi; Khademhosseini, Ali

    2016-04-01

    There is an increasing interest in developing microfluidic bioreactors and organs-on-a-chip platforms combined with sensing capabilities for continual monitoring of cell-secreted biomarkers. Conventional approaches such as ELISA and mass spectroscopy cannot satisfy the needs of continual monitoring as they are labor-intensive and not easily integrable with low-volume bioreactors. This paper reports on the development of an automated microfluidic bead-based electrochemical immunosensor for in-line measurement of cell-secreted biomarkers. For the operation of the multi-use immunosensor, disposable magnetic microbeads were used to immobilize biomarker-recognition molecules. Microvalves were further integrated in the microfluidic immunosensor chip to achieve programmable operations of the immunoassay including bead loading and unloading, binding, washing, and electrochemical sensing. The platform allowed convenient integration of the immunosensor with liver-on-chips to carry out continual quantification of biomarkers secreted from hepatocytes. Transferrin and albumin productions were monitored during a 5-day hepatotoxicity assessment in which human primary hepatocytes cultured in the bioreactor were treated with acetaminophen. Taken together, our unique microfluidic immunosensor provides a new platform for in-line detection of biomarkers in low volumes and long-term in vitro assessments of cellular functions in microfluidic bioreactors and organs-on-chips.

  12. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers

    PubMed Central

    Riahi, Reza; Shaegh, Seyed Ali Mousavi; Ghaderi, Masoumeh; Zhang, Yu Shrike; Shin, Su Ryon; Aleman, Julio; Massa, Solange; Kim, Duckjin; Dokmeci, Mehmet Remzi; Khademhosseini, Ali

    2016-01-01

    There is an increasing interest in developing microfluidic bioreactors and organs-on-a-chip platforms combined with sensing capabilities for continual monitoring of cell-secreted biomarkers. Conventional approaches such as ELISA and mass spectroscopy cannot satisfy the needs of continual monitoring as they are labor-intensive and not easily integrable with low-volume bioreactors. This paper reports on the development of an automated microfluidic bead-based electrochemical immunosensor for in-line measurement of cell-secreted biomarkers. For the operation of the multi-use immunosensor, disposable magnetic microbeads were used to immobilize biomarker-recognition molecules. Microvalves were further integrated in the microfluidic immunosensor chip to achieve programmable operations of the immunoassay including bead loading and unloading, binding, washing, and electrochemical sensing. The platform allowed convenient integration of the immunosensor with liver-on-chips to carry out continual quantification of biomarkers secreted from hepatocytes. Transferrin and albumin productions were monitored during a 5-day hepatotoxicity assessment in which human primary hepatocytes cultured in the bioreactor were treated with acetaminophen. Taken together, our unique microfluidic immunosensor provides a new platform for in-line detection of biomarkers in low volumes and long-term in vitro assessments of cellular functions in microfluidic bioreactors and organs-on-chips. PMID:27098564

  13. Phage antibody display libraries: a powerful antibody discovery platform for immunotherapy.

    PubMed

    Zhao, Aizhi; Tohidkia, Mohammad R; Siegel, Donald L; Coukos, George; Omidi, Yadollah

    2016-01-01

    Phage display technology (PDT), a combinatorial screening approach, provides a molecular diversity tool for creating libraries of peptides/proteins and discovery of new recombinant therapeutics. Expression of proteins such as monoclonal antibodies (mAbs) on the surface of filamentous phage can permit the selection of high affinity and specificity therapeutic mAbs against virtually any target antigen. Using a number of diverse selection platforms (e.g. solid phase, solution phase, whole cell and in vivo biopannings), phage antibody libraries (PALs) from the start point provides great potential for the isolation of functional mAb fragments with diagnostic and/or therapeutic purposes. Given the pivotal role of PDT in the discovery of novel therapeutic/diagnostic mAbs, in the current review, we provide an overview on PALs and discuss their impact in the advancement of engineered mAbs.

  14. An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products

    PubMed Central

    Johnston, Chad W.; Skinnider, Michael A.; Wyatt, Morgan A.; Li, Xiang; Ranieri, Michael R. M.; Yang, Lian; Zechel, David L.; Ma, Bin; Magarvey, Nathan A.

    2015-01-01

    Bacterial natural products are a diverse and valuable group of small molecules, and genome sequencing indicates that the vast majority remain undiscovered. The prediction of natural product structures from biosynthetic assembly lines can facilitate their discovery, but highly automated, accurate, and integrated systems are required to mine the broad spectrum of sequenced bacterial genomes. Here we present a genome-guided natural products discovery tool to automatically predict, combinatorialize and identify polyketides and nonribosomal peptides from biosynthetic assembly lines using LC–MS/MS data of crude extracts in a high-throughput manner. We detail the directed identification and isolation of six genetically predicted polyketides and nonribosomal peptides using our Genome-to-Natural Products platform. This highly automated, user-friendly programme provides a means of realizing the potential of genetically encoded natural products. PMID:26412281

  15. Microfluidic strategies applied to biomarker discovery and validation for multivariate diagnostics.

    PubMed

    Tarasow, Theodore M; Penny, Laura; Patwardhan, Anil; Hamren, Sarah; McKenna, Michael P; Urdea, Mickey S

    2011-10-01

    Complex diseases are caused by combinatorial genetic, environmental and lifestyle factors. The emergence of multibiomarker tests to define these diseases and to identify the early, presymptomatic stages offers several advantages to the conventional use of single marker tests. The development of multibiomarker protein-based tests remains constrained by technological and operational limitations in assaying hundreds to thousands of proteins in thousands of samples. In order to develop a multibiomarker test that stratifies risk for Type 2 diabetes, we took a candidate-driven immunoassay approach utilizing a microfluidics platform to analyze 89 candidate proteins in thousands of samples, which allowed us to move from discovery to a commercial test in 2 years. Future multibiomarker test development will be enhanced by advancements in the number of proteins that can be analyzed, analytical sensitivity and throughput, and sample volume requirements, all of which depend on the further advancement of microfluidics, detection technologies and affinity-based reagents.

  16. Discovery of Biomarkers for Tasmanian Devil Cancer (DFTD) by Metabolic Profiling of Serum.

    PubMed

    Karu, Naama; Wilson, Richard; Hamede, Rodrigo; Jones, Menna; Woods, Gregory M; Hilder, Emily F; Shellie, Robert A

    2016-10-07

    Devil facial tumor disease (DFTD) is a transmissible cancer threatening Tasmanian devils (Sarcophilus harrisii) with extinction. There is no preclinical test available for DFTD, and thus our aim was to find biomarkers for DFTD by metabolic fingerprinting. Seventy serum samples from wild Tasmanian devils (35 controls, 35 with tumors) were analyzed by liquid chromatography-high-resolution mass spectrometry. Features were selected by multivariate models (PLS/DA, random forests) comparing age-matched training set (n = 20 × 2) and further complying with fold-change threshold (≥1.4) and Mann-Whitney U-tests with correction for multiple hypotheses (false discovery rate (FDR) q < 0.05). An array of overlapping peptide segments of the N-terminal end of fibrinogen were the strongest positive DFTD markers. These peptides recorded fold-change up to 90, FDR-corrected p value below 0.01, and area under ROC curve of at least 0.80 and also correlated with tumor size (Spearman R > 0.45, p < 0.01). Additional potential markers included amino acid and lipid metabolites, while cortisol and urea were the most significant health predictors (AUC ≥ 0.90). PLS/DA resulted in AUC = 0.997 for the training set and overall sensitivity of 91% and specificity of 97%. A support vector machine model utilizing only the major peptide marker and seven other metabolites led to overall 94% sensitivity and specificity. The novel findings in this first DFTD metabolomics study shed light on metabolic changes in Tasmanian devils affected by DFTD and provide a valuable step toward the development of prognostic biomarkers.

  17. Biomarkers for Heart Failure in Asia.

    PubMed

    Richards, Arthur Mark

    2015-10-01

    Contributions from the Asian biomedical community to knowledge of biomarkers in heart failure have grown rapidly since 2000. Japan has made world-leading contributions in the discovery and application of cardiac natriuretic peptides as biomarkers in heart failure, but there has been rapid growth in reports from China. Contributions also come from Taiwan, South Korea, Singapore, and Hong Kong. Centers in Asia have established clinical cohorts providing powerful platforms for the discovery and validation of biomarkers in heart failure. This century, Asian enquiry into biomarkers in heart failure will include peptides, cytokines, metabolites, nucleic acids, and other analytes.

  18. MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data

    PubMed Central

    Wang, Xiaojun; Su, Xiaoquan

    2015-01-01

    As more than 90% of species in a microbial community could not be isolated and cultivated, the metagenomic methods have become one of the most important methods to analyze microbial community as a whole. With the fast accumulation of metagenomic samples and the advance of next-generation sequencing techniques, it is now possible to qualitatively and quantitatively assess all taxa (features) in a microbial community. A set of taxa with presence/absence or their different abundances could potentially be used as taxonomical biomarkers for identification of the corresponding microbial community’s phenotype. Though there exist some bioinformatics methods for metagenomic biomarker discovery, current methods are not robust, accurate and fast enough at selection of non-redundant biomarkers for prediction of microbial community’s phenotype. In this study, we have proposed a novel method, MetaBoot, that combines the techniques of mRMR (minimal redundancy maximal relevance) and bootstrapping, for discover of non-redundant biomarkers for microbial communities through mining of metagenomic data. MetaBoot has been tested and compared with other methods on well-designed simulated datasets considering normal and gamma distribution as well as publicly available metagenomic datasets. Results have shown that MetaBoot was robust across datasets of varied complexity and taxonomical distribution patterns and could also select discriminative biomarkers with quite high accuracy and biological consistency. Thus, MetaBoot is suitable for robustly and accurately discover taxonomical biomarkers for different microbial communities. PMID:26213658

  19. Proteomic analysis of eccrine sweat: implications for the discovery of schizophrenia biomarker proteins.

    PubMed

    Raiszadeh, Michelle M; Ross, Mark M; Russo, Paul S; Schaepper, Mary Ann; Zhou, Weidong; Deng, Jianghong; Ng, Daniel; Dickson, April; Dickson, Cindy; Strom, Monica; Osorio, Carolina; Soeprono, Thomas; Wulfkuhle, Julia D; Petricoin, Emanuel F; Liotta, Lance A; Kirsch, Wolff M

    2012-04-06

    Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) proteomics analyses were performed on eccrine sweat of healthy controls, and the results were compared with those from individuals diagnosed with schizophrenia (SZ). This is the first large scale study of the sweat proteome. First, we performed LC-MS/MS on pooled SZ samples and pooled control samples for global proteomics analysis. Results revealed a high abundance of diverse proteins and peptides in eccrine sweat. Most of the proteins identified from sweat samples were found to be different than the most abundant proteins from serum, which indicates that eccrine sweat is not simply a plasma transudate and may thereby be a source of unique disease-associated biomolecules. A second independent set of patient and control sweat samples were analyzed by LC-MS/MS and spectral counting to determine qualitative protein differential abundances between the control and disease groups. Differential abundances of selected proteins, initially determined by spectral counting, were verified by MRM-MS analyses. Seventeen proteins showed a differential abundance of approximately 2-fold or greater between the SZ pooled sample and the control pooled sample. This study demonstrates the utility of LC-MS/MS and MRM-MS as a viable strategy for the discovery and verification of potential sweat protein disease biomarkers.

  20. Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

    PubMed

    Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E; Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E; Joel, Suresh E; Shetty, Teena; Marinelli, Luca; Mullick, Rakesh; Ravishankar, Hariharan; Madhavan, Radhika

    2016-08-01

    Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

  1. Weighted gene co-expression based biomarker discovery for psoriasis detection.

    PubMed

    Sundarrajan, Sudharsana; Arumugam, Mohanapriya

    2016-11-15

    Psoriasis is a chronic inflammatory disease of the skin with an unknown aetiology. The disease manifests itself as red and silvery scaly plaques distributed over the scalp, lower back and extensor aspects of the limbs. After receiving scant consideration for quite a few years, psoriasis has now become a prominent focus for new drug development. A group of closely connected and differentially co-expressed genes may act in a network and may serve as molecular signatures for an underlying phenotype. A weighted gene coexpression network analysis (WGCNA), a system biology approach has been utilized for identification of new molecular targets for psoriasis. Gene coexpression relationships were investigated in 58 psoriatic lesional samples resulting in five gene modules, clustered based on the gene coexpression patterns. The coexpression pattern was validated using three psoriatic datasets. 10 highly connected and informative genes from each module was selected and termed as psoriasis specific hub signatures. A random forest based binary classifier built using the expression profiles of signature genes robustly distinguished psoriatic samples from the normal samples in the validation set with an accuracy of 0.95 to 1. These signature genes may serve as potential candidates for biomarker discovery leading to new therapeutic targets. WGCNA, the network based approach has provided an alternative path to mine out key controllers and drivers of psoriasis. The study principle from the current work can be extended to other pathological conditions.

  2. Proteomic Approaches to Biomarker Discovery in Cutaneous T-Cell Lymphoma

    PubMed Central

    Papagheorghe, Laura Maria Lucia; Lisievici, Cristina

    2016-01-01

    Cutaneous T-cell lymphoma (CTCL) is the most frequently encountered type of skin lymphoma in humans. CTCL encompasses multiple variants, but the most common types are mycosis fungoides (MF) and Sezary syndrome (SS). While most cases of MF run a mild course over a period of many years, other subtypes of CTCL are very aggressive. The rapidly expanding fields of proteomics and genomics have not only helped increase knowledge concerning the carcinogenesis and tumor biology of CTCL but also led to the discovery of novel markers for targeted therapy. Although multiple biomarkers linked to CTCL have been known for a relatively long time (e.g., CD25, CD45, CD45RA, and CD45R0), compared to other cancers (lymphoma, melanoma, colon carcinoma, head and neck cancer, renal cancer, and cutaneous B-cell lymphoma), information about the antigenicity of CTCL remains relatively limited and no dependable protein marker for CTCL has been discovered. Considering the aggressive nature of some types of CTCL, it is necessary to identify circulating molecules that can help in the early diagnosis, differentiation from inflammatory skin diseases (psoriasis, nummular eczema), and aid in predicting the prognosis and evolution of this pathology. This review aims to bring together some of the information concerning protein markers linked to CTCL, in an effort to further the understanding of the convolute processes involved in this complex pathology. PMID:27821903

  3. Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery.

    PubMed

    Patel, Seema; Ahmed, Shadab

    2015-03-25

    Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.

  4. BluePen Biomarkers LLC: integrated biomarker solutions.

    PubMed

    Blair, Ian A; Mesaros, Clementina; Lilley, Patrick; Nunez, Matthew

    2016-06-01

    BluePen Biomarkers provides a unique comprehensive multi-omics biomarker discovery and validation platform. We can quantify, integrate and analyze genomics, proteomics, metabolomics and lipidomics biomarkers, alongside clinical data, demographics and other phenotypic data. A unique bio-inspired signal processing analytic approach is used that has the proven ability to identify biomarkers in a wide variety of diseases. The resulting biomarkers can be used for diagnosis, prognosis, mechanistic studies and predicting treatment response, in contexts from core research through clinical trials. BluePen Biomarkers provides an additional groundbreaking research goal: identifying surrogate biomarkers from different modalities. This not only provides new biological insights, but enables least invasive, least-cost tests that meet or exceed the predictive quality of current tests.

  5. Discovery and validation of potential urinary biomarkers for bladder cancer diagnosis using a pseudotargeted GC-MS metabolomics method.

    PubMed

    Zhou, Yang; Song, Ruixiang; Ma, Chong; Zhou, Lina; Liu, Xinyu; Yin, Peiyuan; Zhang, Zhensheng; Sun, Yinghao; Xu, Chuanliang; Lu, Xin; Xu, Guowang

    2017-02-01

    Bladder cancer (BC) is the second most prevalent malignancy in the urinary system and is associated with significant mortality; thus, there is an urgent need for novel noninvasive diagnostic biomarkers. A urinary pseudotargeted method based on gas chromatography-mass spectrometry was developed and validated for a BC metabolomics study. The method exhibited good repeatability, intraday and interday precision, linearity and metabolome coverage. A total of 76 differential metabolites were defined in the discovery sample set, 58 of which were verified using an independent validation urine set. The verified differential metabolites revealed that energy metabolism, anabolic metabolism and cell redox states were disordered in BC. Based on a binary logistic regression analysis, a four-biomarker panel was defined for the diagnosis of BC. The area under the receiving operator characteristic curve was 0.885 with 88.0% sensitivity and 85.7% specificity in the discovery set and 0.804 with 78.0% sensitivity and 70.3% specificity in the validation set. The combinatorial biomarker panel was also useful for the early diagnosis of BC. This approach can be used to discriminate non-muscle invasive and low-grade BCs from healthy controls with satisfactory sensitivity and specificity. The results show that the developed urinary metabolomics method can be employed to effectively screen noninvasive biomarkers.

  6. Interferometric biosensing platform for multiplexed digital detection of viral pathogens and biomarkers

    NASA Astrophysics Data System (ADS)

    Daaboul, George

    Label-free optical biosensors have been established as proven tools for monitoring specific biomolecular interactions. However, compact and robust embodiments of such instruments have yet to be introduced in order to provide sensitive, quantitative, and high-throughput biosensing for low-cost research and clinical applications. Here we present the interferometric reflectance-imaging sensor (IRIS). IRIS allows sensitive label free analysis using an inexpensive and durable multi-color LED illumination source on a silicon based surface. IRIS monitors biomolecular interaction through measurement of biomass addition to the sensor's surface. We demonstrate the capability of this system to dynamically monitor antigen---antibody interactions with a noise floor of 5.2 pg/mm 2 and DNA single mismatch detection under isothermal melting conditions in an array format. Ensemble detection of binding events using IRIS did not provide the sensitivity needed for detection of infectious disease and biomarkers at clinically relevant concentrations. Therefore, a new approach was adapted to the IRIS platform that allowed the detection and identification of individual nanoparticles on the sensor's surface. The new detection method was termed single-particle IRIS (SP-IRIS). We developed two detection modalities for SP-IRIS. The first modality is when the target is a nanoparticle such as a virus. We verified that SP-IRIS can accurately detect and size individual viral particles. Then we demonstrated that single nanoparticle counting and sizing methodology on SP-IRIS leads to a specific and sensitive virus sensor that can be multiplexed. Finally, we developed an assay for the detection of Ebola and Marburg. A detection limit of 3 x 103 PFU/ml was demonstrated for vesicular stomatitis virus (VSV) pseudotyped with Ebola or Marburg virus glycoprotein. We have demonstrated that virus detection can be done in human whole blood directly without the need for sample preparation. The second modality

  7. Using MALDI-IMS and MRM to stablish a pipeline for discovery and validation of tumor neovasculature biomarker candidates. — EDRN Public Portal

    Cancer.gov

    In an effort to circumvent the limitations associated with biomarker discovery workflows involving cell lines and cell cultures, histology-directed MALDI protein profiling and imaging mass spectrometry will be used for identification of vascular endothelial biomarkers suitable for early prostate cancer detection by CEUS targeted molecular imaging

  8. LC-MS-based serum metabolic profiling for genitourinary cancer classification and cancer type-specific biomarker discovery.

    PubMed

    Lin, Lin; Huang, Zhenzhen; Gao, Yao; Chen, Yongjing; Hang, Wei; Xing, Jinchun; Yan, Xiaomei

    2012-08-01

    Bladder cancer (BC) and kidney cancer (KC) are the first two commonly occurring genitourinary cancers in China. In this study, a comprehensive LC-MS-based method, which utilizes both reversed phase liquid chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of BC, KC, and noncancer controls. An independent test set consisting of different patients has been used to objectively evaluate the predictive ability of the analysis platform. Excellent sensitivity and specificity have been achieved in detection of KC and BC. The results suggest that serum metabolic profiling could be used for different types of genitourinary cancer diagnosis. Furthermore, cancer type-specific biomarkers were found through a critical selection criterion. As a result, eicosatrienol, azaprostanoic acid, docosatrienol, retinol, and 14'-apo-beta-carotenal  were found as specific biomarkers for BC; and PE(P-16:0e/0:0), glycerophosphorylcholine, ganglioside GM3 (d18:1/22:1), C17 sphinganine, and SM(d18:0/16:1(9Z)) were found as specific biomarkers for KC. Receiver operating characteristic (ROC) analysis was used for the preliminary evaluation of the biomarkers. These biomarkers have great potential to be used in the clinical diagnosis after further rigorous assessment.

  9. Plenario: A Spatio-Temporal Platform for Discovery and Exploration of Urban Science Data

    NASA Astrophysics Data System (ADS)

    Engler, W. H.; Malik, T.; Catlett, C.; Foster, I.; Goldstein, B.

    2015-12-01

    The past decade has seen the widespread release of open data concerning city services, conditions, and activities by government bodies and public institutions of all sizes. Hundreds of open data portals now host thousands of datasets of many different types. These new data sources represent enormous potential for improved understanding of urban dynamics and processes—and, ultimately, for more livable, efficient, and prosperous communities. However, those who seek to realize this potential quickly discover that discovering and applying those data relevant to any particular question can be extraordinarily difficult, due to decentralized storage, heterogeneous formats, and poor documentation. In this context, we introduce Plenario, a platform designed to automating time-consuming tasks associated with the discovery, exploration, and application of open city data—and, in so doing, reduce barriers to data use for researchers, policymakers, service providers, journalists, and members of the general public. Key innovations include a geospatial data warehouse that allows data from many sources to be registered into a common spatial and temporal frame; simple and intuitive interfaces that permit rapid discovery and exploration of data subsets pertaining to a particular area and time, regardless of type and source; easy export of such data subsets for further analysis; a user-configurable data ingest framework for automated importing and periodic updating of new datasets into the data warehouse; cloud hosting for elastic scaling and rapid creation of new Plenario instances; and an open source implementation to enable community contributions. We describe here the architecture and implementation of the Plenario platform, discuss lessons learned from its use by several communities, and outline plans for future work.

  10. Discovery and Validation of Predictive Biomarkers of Survival for Non-small Cell Lung Cancer Patients Undergoing Radical Radiotherapy: Two Proteins With Predictive Value.

    PubMed

    Walker, Michael J; Zhou, Cong; Backen, Alison; Pernemalm, Maria; Williamson, Andrew J K; Priest, Lynsey J C; Koh, Pek; Faivre-Finn, Corinne; Blackhall, Fiona H; Dive, Caroline; Whetton, Anthony D

    2015-08-01

    Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment.

  11. In-depth proteomic analysis of six types of exudative pleural effusions for nonsmall cell lung cancer biomarker discovery.

    PubMed

    Liu, Pei-Jun; Chen, Chi-De; Wang, Chih-Liang; Wu, Yi-Cheng; Hsu, Chia-Wei; Lee, Chien-Wei; Huang, Lien-Hung; Yu, Jau-Song; Chang, Yu-Sun; Wu, Chih-Ching; Yu, Chia-Jung

    2015-04-01

    Pleural effusion (PE), a tumor-proximal body fluid, may be a promising source for biomarker discovery in human cancers. Because a variety of pathological conditions can lead to PE, characterization of the relative PE proteomic profiles from different types of PEs would accelerate discovery of potential PE biomarkers specifically used to diagnose pulmonary disorders. Using quantitative proteomic approaches, we identified 772 nonredundant proteins from six types of exudative PEs, including three malignant PEs (MPE, from lung, breast, and gastric cancers), one lung cancer paramalignant PE, and two benign diseases (tuberculosis and pneumonia). Spectral counting was utilized to semiquantify PE protein levels. Principal component analysis, hierarchical clustering, and Gene Ontology of cellular process analyses revealed differential levels and functional profiling of proteins in each type of PE. We identified 30 candidate proteins with twofold higher levels (q<0.05) in lung cancer MPEs than in the two benign PEs. Three potential markers, MET, DPP4, and PTPRF, were further verified by ELISA using 345 PE samples. The protein levels of these potential biomarkers were significantly higher in lung cancer MPE than in benign diseases or lung cancer paramalignant PE. The area under the receiver-operator characteristic curve for three combined biomarkers in discriminating lung cancer MPE from benign diseases was 0.903. We also observed that the PE protein levels were more clearly discriminated in effusions in which the cytological examination was positive and that they would be useful in rescuing the false negative of cytological examination in diagnosis of nonsmall cell lung cancer-MPE. Western blotting analysis further demonstrated that MET overexpression in lung cancer cells would contribute to the elevation of soluble MET in MPE. Our results collectively demonstrate the utility of label-free quantitative proteomic approaches in establishing differential PE proteomes and

  12. Concepts and Principles of Photodynamic Therapy as an Alternative Antifungal Discovery Platform

    PubMed Central

    Dai, Tianhong; Fuchs, Beth B.; Coleman, Jeffrey J.; Prates, Renato A.; Astrakas, Christos; St. Denis, Tyler G.; Ribeiro, Martha S.; Mylonakis, Eleftherios; Hamblin, Michael R.; Tegos, George P.

    2012-01-01

    Opportunistic fungal pathogens may cause superficial or serious invasive infections, especially in immunocompromised and debilitated patients. Invasive mycoses represent an exponentially growing threat for human health due to a combination of slow diagnosis and the existence of relatively few classes of available and effective antifungal drugs. Therefore systemic fungal infections result in high attributable mortality. There is an urgent need to pursue and deploy novel and effective alternative antifungal countermeasures. Photodynamic therapy (PDT) was established as a successful modality for malignancies and age-related macular degeneration but photodynamic inactivation has only recently been intensively investigated as an alternative antimicrobial discovery and development platform. The concept of photodynamic inactivation requires microbial exposure to either exogenous or endogenous photosensitizer molecules, followed by visible light energy, typically wavelengths in the red/near infrared region that cause the excitation of the photosensitizers resulting in the production of singlet oxygen and other reactive oxygen species that react with intracellular components, and consequently produce cell inactivation and death. Antifungal PDT is an area of increasing interest, as research is advancing (i) to identify the photochemical and photophysical mechanisms involved in photoinactivation; (ii) to develop potent and clinically compatible photosensitizers; (iii) to understand how photoinactivation is affected by key microbial phenotypic elements multidrug resistance and efflux, virulence and pathogenesis determinants, and formation of biofilms; (iv) to explore novel photosensitizer delivery platforms; and (v) to identify photoinactivation applications beyond the clinical setting such as environmental disinfectants. PMID:22514547

  13. NETL's Energy Data Exchange (EDX) - a coordination, collaboration, and data resource discovery platform for energy science

    NASA Astrophysics Data System (ADS)

    Rose, K.; Rowan, C.; Rager, D.; Dehlin, M.; Baker, D. V.; McIntyre, D.

    2015-12-01

    Multi-organizational research teams working jointly on projects often encounter problems with discovery, access to relevant existing resources, and data sharing due to large file sizes, inappropriate file formats, or other inefficient options that make collaboration difficult. The Energy Data eXchange (EDX) from Department of Energy's (DOE) National Energy Technology Laboratory (NETL) is an evolving online research environment designed to overcome these challenges in support of DOE's fossil energy goals while offering improved access to data driven products of fossil energy R&D such as datasets, tools, and web applications. In 2011, development of NETL's Energy Data eXchange (EDX) was initiated and offers i) a means for better preserving of NETL's research and development products for future access and re-use, ii) efficient, discoverable access to authoritative, relevant, external resources, and iii) an improved approach and tools to support secure, private collaboration and coordination between multi-organizational teams to meet DOE mission and goals. EDX presently supports fossil energy and SubTER Crosscut research activities, with an ever-growing user base. EDX is built on a heavily customized instance of the open source platform, Comprehensive Knowledge Archive Network (CKAN). EDX connects users to externally relevant data and tools through connecting to external data repositories built on different platforms and other CKAN platforms (e.g. Data.gov). EDX does not download and repost data or tools that already have an online presence. This leads to redundancy and even error. If a relevant resource already has an online instance, is hosted by another online entity, EDX will point users to that external host either using web services, inventorying URLs and other methods. EDX offers users the ability to leverage private-secure capabilities custom built into the system. The team is presently working on version 3 of EDX which will incorporate big data analytical

  14. High-yield peptide-extraction method for the discovery of subnanomolar biomarkers from small serum samples.

    PubMed

    Kawashima, Yusuke; Fukutomi, Toshiyuki; Tomonaga, Takeshi; Takahashi, Hiroki; Nomura, Fumio; Maeda, Tadakazu; Kodera, Yoshio

    2010-04-05

    Serum proteins/peptides reflect physiological or pathological states in humans and are an attractive target for the discovery of disease biomarkers. However, the existence of high-abundance proteins and the large dynamic range of serum proteins/peptides make any quantitative analysis of low-abundance proteins/peptides challenging. Furthermore, analyses of peptides, including the cleaved fragments of proteins, are difficult because of carrier protein binding. Here, we developed a differential solubilization (DS) method to extract low-molecular-weight proteins/peptides in serum with good reproducibility and yield as compared to typical peptide-extraction methods such as organic solvent precipitation and ultrafiltration. Using the DS method combined with reverse-phase HPLC fractionation followed by MALDI-TOF-MS, we performed high-quality comparative analyses of more than 1500 peptides from 1 microL of serum samples, including low-abundance peptides in the subnanomolar range and containing many peptides bound to carrier proteins such as albumin. We applied this method and successfully discovered four new biomarker candidates of colon cancer, none of which have previously been observed in serum and one of which is a fragment of the protein zyxin that possibly originated from tumor cells. Our results indicate that serum peptide analyses based on the DS method should greatly contribute to the discovery of novel low-abundance biomarkers.

  15. A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

    PubMed Central

    Stern, Andrew M.; Schurdak, Mark E.; Bahar, Ivet; Berg, Jeremy M.; Taylor, D. Lansing

    2016-01-01

    Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)–driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point. PMID:26962875

  16. Application of proteomics in the discovery of candidate protein biomarkers in a Diabetes Autoantibody Standardization Program (DASP) sample subset

    PubMed Central

    Metz, Thomas O.; Qian, Wei-Jun; Jacobs, Jon M.; Gritsenko, Marina A.; Moore, Ronald J.; Polpitiya, Ashoka D.; Monroe, Matthew E.; Camp, David G.; Mueller, Patricia W.; Smith, Richard D.

    2009-01-01

    Novel biomarkers of type 1 diabetes must be identified and validated in initial, exploratory studies before they can be assessed in proficiency evaluations. Currently, untargeted “-omics” approaches are under-utilized in profiling studies of clinical samples. This report describes the evaluation of capillary liquid chromatography (LC) coupled with mass spectrometry (MS) in a pilot proteomic analysis of human plasma and serum from a subset of control and type 1 diabetic individuals enrolled in the Diabetes Autoantibody Standardization Program with the goal of identifying candidate biomarkers of type 1 diabetes. Initial high-resolution capillary LC-MS/MS experiments were performed to augment an existing plasma peptide database, while subsequent LC-FTICR studies identified quantitative differences in the abundance of plasma proteins. Analysis of LC-FTICR proteomic data identified five candidate protein biomarkers of type 1 diabetes. Alpha-2-glycoprotein 1 (zinc), corticosteroid-binding globulin, and lumican were 2-fold up-regulated in type 1 diabetic samples relative to control samples, whereas clusterin and serotransferrin were 2-fold up-regulated in control samples relative to type 1 diabetic samples. Observed perturbations in the levels of all five proteins are consistent with the metabolic aberrations found in type 1 diabetes. While the discovery of these candidate protein biomarkers of type 1 diabetes is encouraging, follow up studies are required for validation in a larger population of individuals and for determination of laboratory-defined sensitivity and specificity values using blinded samples. PMID:18092746

  17. Theoretical modeling of masking DNA application in aptamer-facilitated biomarker discovery.

    PubMed

    Cherney, Leonid T; Obrecht, Natalia M; Krylov, Sergey N

    2013-04-16

    In aptamer-facilitated biomarker discovery (AptaBiD), aptamers are selected from a library of random DNA (or RNA) sequences for their ability to specifically bind cell-surface biomarkers. The library is incubated with intact cells, and cell-bound DNA molecules are separated from those unbound and amplified by the polymerase chain reaction (PCR). The partitioning/amplification cycle is repeated multiple times while alternating target cells and control cells. Efficient aptamer selection in AptaBiD relies on the inclusion of masking DNA within the cell and library mixture. Masking DNA lacks primer regions for PCR amplification and is typically taken in excess to the library. The role of masking DNA within the selection mixture is to outcompete any nonspecific binding sequences within the initial library, thus allowing specific DNA sequences (i.e., aptamers) to be selected more efficiently. Efficient AptaBiD requires an optimum ratio of masking DNA to library DNA, at which aptamers still bind specific binding sites but nonaptamers within the library do not bind nonspecific binding sites. Here, we have developed a mathematical model that describes the binding processes taking place within the equilibrium mixture of masking DNA, library DNA, and target cells. An obtained mathematical solution allows one to estimate the concentration of masking DNA that is required to outcompete the library DNA at a desirable ratio of bound masking DNA to bound library DNA. The required concentration depends on concentrations of the library and cells as well as on unknown cell characteristics. These characteristics include the concentration of total binding sites on the cell surface, N, and equilibrium dissociation constants, K(nsL) and K(nsM), for nonspecific binding of the library DNA and masking DNA, respectively. We developed a theory that allows the determination of N, K(nsL), and K(nsM) based on measurements of EC50 values for cells mixed separately with the library and masking DNA

  18. The future of liquid chromatography-mass spectrometry (LC-MS) in metabolic profiling and metabolomic studies for biomarker discovery

    PubMed Central

    Metz, Thomas O.; Zhang, Qibin; Page, Jason S.; Shen, Yufeng; Callister, Stephen J.; Jacobs, Jon M.; Smith, Richard D.

    2008-01-01

    SUMMARY The future utility of liquid chromatography-mass spectrometry (LC-MS) in metabolic profiling and metabolomic studies for biomarker discover will be discussed, beginning with a brief description of the evolution of metabolomics and the utilization of the three most popular analytical platforms in such studies: NMR, GC-MS, and LC-MS. Emphasis is placed on recent developments in high-efficiency LC separations, sensitive electrospray ionization approaches, and the benefits to incorporating both in LC-MS-based approaches. The advantages and disadvantages of various quantitative approaches are reviewed, followed by the current LC-MS-based tools available for candidate biomarker characterization and identification. Finally, a brief prediction on the future path of LC-MS-based methods in metabolic profiling and metabolomic studies is given. PMID:19177179

  19. Alzheimer's disease biomarker discovery using in silico literature mining and clinical validation

    PubMed Central

    2012-01-01

    Background Alzheimer’s Disease (AD) is the most widespread form of dementia in the elderly but despite progress made in recent years towards a mechanistic understanding, there is still an urgent need for disease modification therapy and for early diagnostic tests. Substantial international efforts are being made to discover and validate biomarkers for AD using candidate analytes and various data-driven 'omics' approaches. Cerebrospinal fluid is in many ways the tissue of choice for biomarkers of brain disease but is limited by patient and clinician acceptability, and increasing attention is being paid to the search for blood-based biomarkers. The aim of this study was to use a novel in silico approach to discover a set of candidate biomarkers for AD. Methods We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of assertional metadata derived from relevant legacy information. We then assessed the validity of this approach using direct assays of the identified biomarkers in plasma by immunodetection methods. Results Using this in silico approach, we identified 25 biomarker candidates, at least three of which have subsequently been reported to be altered in blood or CSF from AD patients. Two further candidate biomarkers, indicated from the in silico approach, were choline acetyltransferase and urokinase-type plasminogen activator receptor. Using immunodetection, we showed that, in a large sample set, these markers are either altered in disease or correlate with MRI markers of atrophy. Conclusions These data support as a proof of concept the use of data mining and in silico analyses to derive valid biomarker candidates for AD and, by extension, for other disorders. PMID:23113945

  20. A Small-Molecule Screening Platform for the Discovery of Inhibitors of Undecaprenyl Diphosphate Synthase.

    PubMed

    Czarny, Tomasz L; Brown, Eric D

    2016-07-08

    The bacterial cell wall has long been a celebrated target for antibacterial drug discovery due to its critical nature in bacteria and absence in mammalian systems. At the heart of the cell wall biosynthetic pathway lies undecaprenyl phosphate (Und-P), the lipid-linked carrier upon which the bacterial cell wall is built. This study exploits recent insights into the link between late-stage wall teichoic acid inhibition and Und-P production, in Gram-positive organisms, to develop a cell-based small-molecule screening platform that enriches for inhibitors of undecaprenyl diphosphate synthase (UppS). Screening a chemical collection of 142,000 small molecules resulted in the identification of 6 new inhibitors of UppS. To date, inhibitors of UppS have generally shown off-target effects on membrane potential due to their physical-chemical characteristics. We demonstrate that MAC-0547630, one of the six inhibitors identified, exhibits selective, nanomolar inhibition against UppS without off-target effects on membrane potential. Such characteristics make it a unique chemical probe for exploring the inhibition of UppS in bacterial cell systems.

  1. An in vivo platform for rapid high-throughput antitubercular drug discovery.

    PubMed

    Takaki, Kevin; Cosma, Christine L; Troll, Mark A; Ramakrishnan, Lalita

    2012-07-26

    Treatment of tuberculosis, like other infectious diseases, is increasingly hindered by the emergence of drug resistance. Drug discovery efforts would be facilitated by facile screening tools that incorporate the complexities of human disease. Mycobacterium marinum-infected zebrafish larvae recapitulate key aspects of tuberculosis pathogenesis and drug treatment. Here, we develop a model for rapid in vivo drug screening using fluorescence-based methods for serial quantitative assessment of drug efficacy and toxicity. We provide proof-of-concept that both traditional bacterial-targeting antitubercular drugs and newly identified host-targeting drugs would be discovered through the use of this model. We demonstrate the model's utility for the identification of synergistic combinations of antibacterial drugs and demonstrate synergy between bacterial- and host-targeting compounds. Thus, the platform can be used to identify new antibacterial agents and entirely new classes of drugs that thwart infection by targeting host pathways. The methods developed here should be widely applicable to small-molecule screens for other infectious and noninfectious diseases.

  2. Development of urinary pseudotargeted LC-MS-based metabolomics method and its application in hepatocellular carcinoma biomarker discovery.

    PubMed

    Shao, Yaping; Zhu, Bin; Zheng, Ruiyin; Zhao, Xinjie; Yin, Peiyuan; Lu, Xin; Jiao, Binghua; Xu, Guowang; Yao, Zhenzhen

    2015-02-06

    Hepatocellular carcinoma (HCC) is one of the pestilent malignancies leading to cancer-related death. Discovering effective biomarkers for HCC diagnosis is an urgent demand. To identify potential metabolite biomarkers, we developed a urinary pseudotargeted method based on liquid chromatography-hybrid triple quadrupole linear ion trap mass spectrometry (LC-QTRAP MS). Compared with nontargeted method, the pseudotargeted method can achieve better data quality, which benefits differential metabolites discovery. The established method was applied to cirrhosis (CIR) and HCC investigation. It was found that urinary nucleosides, bile acids, citric acid, and several amino acids were significantly changed in liver disease groups compared with the controls, featuring the dysregulation of purine metabolism, energy metabolism, and amino metabolism in liver diseases. Furthermore, some metabolites such as cyclic adenosine monophosphate, glutamine, and short- and medium-chain acylcarnitines were the differential metabolites of HCC and CIR. On the basis of binary logistic regression, butyrylcarnitine (carnitine C4:0) and hydantoin-5-propionic acid were defined as combinational markers to distinguish HCC from CIR. The area under curve was 0.786 and 0.773 for discovery stage and validation stage samples, respectively. These data show that the established pseudotargeted method is a complementary one of targeted and nontargeted methods for metabolomics study.

  3. Tumor-derived exosomes and microvesicles in head and neck cancer: implications for tumor biology and biomarker discovery.

    PubMed

    Principe, Simona; Hui, Angela Bik-Yu; Bruce, Jeff; Sinha, Ankit; Liu, Fei-Fei; Kislinger, Thomas

    2013-05-01

    Exosomes and microvesicles (MVs) are nanometer-sized, membranous vesicles secreted from many cell types into their surrounding extracellular space and into body fluids. These two classes of extracellular vesicles are regarded as a novel mechanism through which cancer cells, including virally infected cancer cells, regulate their micro-environment via the horizontal transfer of bioactive molecules: proteins, lipids, and nucleic acids (DNA, mRNA, micro-RNAs; oncogenic cargo hence often referred to as oncosomes). In head and neck cancer (HNC), exosomes and MVs have been described in Epstein Barr Virus (EBV)-associated nasopharyngeal cancer (NPC), as well as being positively correlated with oral squamous cell carcinoma (OSCC) progression. It has therefore been suggested that HNC-derived vesicles could represent a useful source for biomarker discovery, enriched in tumor antigens and cargo; hence fundamentally important for cancer progression. This current review offers an overall perspective on the roles of exosomes and MVs in HNC biology, focusing on EBV-associated NPC and OSCC. We also highlight the importance of saliva as a proximal and easily accessible bio-fluid for HNC detection, and propose that salivary vesicles might serve as an alternative model in the discovery of novel HNC biomarkers.

  4. Mass spectrometric discovery and selective reaction monitoring (SRM) of putative protein biomarker candidates in first trimester Trisomy 21 maternal serum.

    PubMed

    Lopez, Mary F; Kuppusamy, Ramesh; Sarracino, David A; Prakash, Amol; Athanas, Michael; Krastins, Bryan; Rezai, Taha; Sutton, Jennifer N; Peterman, Scott; Nicolaides, Kypros

    2011-01-07

    The accurate diagnosis of Trisomy 21 requires invasive procedures that carry a risk of miscarriage. The current state-of-the-art maternal serum screening tests measure levels of PAPP-A, free bhCG, AFP, and uE3 in various combinations with a maximum sensitivity of 60-75% and a false positive rate of 5%. There is currently an unmet need for noninvasive screening tests with high selectivity that can detect pregnancies at risk, preferably within the first trimester. The aim of this study was to apply proteomics and mass spectrometry techniques for the discovery of new putative biomarkers for Trisomy 21 in first trimester maternal serum coupled with the immediate development of quantitative selective reaction monitoring (SRM) assays. The results of the novel workflow were 2-fold: (1) we identified a list of differentially expressed proteins in Trisomy 21 vs Normal samples, including PAPP-A, and (2) we developed a multiplexed, high-throughput SRM assay for verification of 12 new putative markers identified in the discovery experiments. To narrow down the initial large list of differentially expressed candidates resulting from the discovery experiments, we incorporated receiver operating characteristic (ROC) curve algorithms early in the data analysis process. We believe this approach provides a substantial advantage in sifting through the large and complex data typically obtained from discovery experiments. The workflow efficiently mined information derived from high-resolution LC-MS/MS discovery data for the seamless construction of rapid, targeted assays that were performed on unfractionated serum digests. The SRM assay lower limit of detection (LLOD) for the target peptides in a background of digested serum matrix was approximately 250-500 attomoles on column and the limit of accurate quantitation (LOQ) was approximately 1-5 femtomoles on column. The assay error as determined by coefficient of variation at LOQ and above ranged from 0 to 16%. The workflow developed in

  5. Targeted Biomarker Discovery by High Throughput Glycosylation Profiling of Human Plasma Alpha1-Antitrypsin and Immunoglobulin A

    PubMed Central

    Ruhaak, L. Renee; Koeleman, Carolien A. M.; Uh, Hae-Won; Stam, Jord C.; van Heemst, Diana; Maier, Andrea B.; Houwing-Duistermaat, Jeanine J.; Hensbergen, Paul J.; Slagboom, P. Eline; Deelder, André M.; Wuhrer, Manfred

    2013-01-01

    Protein N-glycosylation patterns are known to show vast genetic as well as physiological and pathological variation and represent a large pool of potential biomarkers. Large-scale studies are needed for the identification and validation of biomarkers, and the analytical techniques required have recently been developed. Such methods have up to now mainly been applied to complex mixtures of glycoproteins in biofluids (e.g. plasma). Here, we analyzed N-glycosylation profiles of alpha1-antitrypsin (AAT) and immunoglobulin A (IgA) enriched fractions by 96-well microtitration plate based high-throughput immuno-affinity capturing and N-glycan analysis using multiplexed capillary gel electrophoresis with laser-induced fluorescence detection (CGE-LIF). Human plasma samples were from the Leiden Longevity Study comprising 2415 participants of different chronological and biological ages. Glycosylation patterns of AAT enriched fractions were found to be associated with chronological (calendar) age and they differed between females and males. Moreover, several glycans in the AAT enriched fraction were associated with physiological parameters marking cardiovascular and metabolic diseases. Pronounced differences were found between males and females in the glycosylation profiles of IgA enriched fractions. Our results demonstrate that large-scale immuno-affinity capturing of proteins from human plasma using a bead-based method combined with high-throughput N-glycan analysis is a powerful tool for the discovery of glycosylation-based biomarker candidates. PMID:24039863

  6. Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery

    PubMed Central

    Farrar, Jason E.; Considine, Michael; Wei, Yingying; Meshinchi, Soheil; Arceci, Robert J.

    2014-01-01

    Pathway deregulation has been identified as a key driver of carcinogenesis, with proteins in signaling pathways serving as primary targets for drug development. Deregulation can be driven by a number of molecular events, including gene mutation, epigenetic changes in gene promoters, overexpression, and gene amplifications or deletions. We demonstrate a novel approach that identifies pathways of interest by integrating outlier analysis within and across molecular data types with gene set analysis. We use the results to seed the top-scoring pair algorithm to identify robust biomarkers associated with pathway deregulation. We demonstrate this methodology on pediatric acute myeloid leukemia (AML) data. We develop a biomarker in primary AML tumors, demonstrate robustness with an independent primary tumor data set, and show that the identified biomarkers also function well in relapsed pediatric AML tumors. PMID:26356020

  7. Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery.

    PubMed

    Ochs, Michael F; Farrar, Jason E; Considine, Michael; Wei, Yingying; Meshinchi, Soheil; Arceci, Robert J

    2014-01-01

    Pathway deregulation has been identified as a key driver of carcinogenesis, with proteins in signaling pathways serving as primary targets for drug development. Deregulation can be driven by a number of molecular events, including gene mutation, epigenetic changes in gene promoters, overexpression, and gene amplifications or deletions. We demonstrate a novel approach that identifies pathways of interest by integrating outlier analysis within and across molecular data types with gene set analysis. We use the results to seed the top-scoring pair algorithm to identify robust biomarkers associated with pathway deregulation. We demonstrate this methodology on pediatric acute myeloid leukemia (AML) data. We develop a biomarker in primary AML tumors, demonstrate robustness with an independent primary tumor data set, and show that the identified biomarkers also function well in relapsed pediatric AML tumors.

  8. Discovery and bio-optimization of human antibody therapeutics using the XenoMouse® transgenic mouse platform.

    PubMed

    Foltz, Ian N; Gunasekaran, Kannan; King, Chadwick T

    2016-03-01

    Since the late 1990s, the use of transgenic animal platforms has transformed the discovery of fully human therapeutic monoclonal antibodies. The first approved therapy derived from a transgenic platform--the epidermal growth factor receptor antagonist panitumumab to treat advanced colorectal cancer--was developed using XenoMouse(®) technology. Since its approval in 2006, the science of discovering and developing therapeutic monoclonal antibodies derived from the XenoMouse(®) platform has advanced considerably. The emerging array of antibody therapeutics developed using transgenic technologies is expected to include antibodies and antibody fragments with novel mechanisms of action and extreme potencies. In addition to these impressive functional properties, these antibodies will be designed to have superior biophysical properties that enable highly efficient large-scale manufacturing methods. Achieving these new heights in antibody drug discovery will ultimately bring better medicines to patients. Here, we review best practices for the discovery and bio-optimization of monoclonal antibodies that fit functional design goals and meet high manufacturing standards.

  9. Creating a Discovery Platform for Confined-Space Chemistry and Materials: Metal-Organic Frameworks.

    SciTech Connect

    Allendorf, Mark D.; Greathouse, Jeffery A.; Simmons, Blake

    2008-09-01

    Metal organic frameworks (MOF) are a recently discovered class of nanoporous, defect-free crystalline materials that enable rational design and exploration of porous materials at the molecular level. MOFs have tunable monolithic pore sizes and cavity environments due to their crystalline nature, yielding properties exceeding those of most other porous materials. These include: the lowest known density (91% free space); highest surface area; tunable photoluminescence; selective molecular adsorption; and methane sorption rivaling gas cylinders. These properties are achieved by coupling inorganic metal complexes such as ZnO4 with tunable organic ligands that serve as struts, allowing facile manipulation of pore size and surface area through reactant selection. MOFs thus provide a discovery platform for generating both new understanding of chemistry in confined spaces and novel sensors and devices based on their unique properties. At the outset of this project in FY06, virtually nothing was known about how to couple MOFs to substrates and the science of MOF properties and how to tune them was in its infancy. An integrated approach was needed to establish the required knowledge base for nanoscale design and develop methodologies integrate MOFs with other materials. This report summarizes the key accomplishments of this project, which include creation of a new class of radiation detection materials based on MOFs, luminescent MOFs for chemical detection, use of MOFs as templates to create nanoparticles of hydrogen storage materials, MOF coatings for stress-based chemical detection using microcantilevers, and "flexible" force fields that account for structural changes in MOFs that occur upon molecular adsorption/desorption. Eight journal articles, twenty presentations at scientific conferences, and two patent applications resulted from the work. The project created a basis for continuing development of MOFs for many Sandia applications and succeeded in securing $2.75 M in

  10. Hi-Fi SELEX: A High-Fidelity Digital-PCR Based Therapeutic Aptamer Discovery Platform.

    PubMed

    Ouellet, Eric; Foley, Jonathan H; Conway, Edward M; Haynes, Charles

    2015-08-01

    Current technologies for aptamer discovery typically leverage the systematic evolution of ligands by exponential enrichment (SELEX) concept by recursively panning semi-combinatorial ssDNA or RNA libraries against a molecular target. The expectation is that this iterative selection process will be sufficiently stringent to identify a candidate pool of specific high-affinity aptamers. However, failure of this process to yield promising aptamers is common, due in part to (i) limitations in library designs, (ii) retention of non-specific aptamers during screening rounds, (iii) excessive accumulation of amplification artifacts, and (iv) the use of screening criteria (binding affinity) that does not reflect therapeutic activity. We report a new selection platform, High-Fidelity (Hi-Fi) SELEX, that introduces fixed-region blocking elements to safeguard the functional diversity of the library. The chemistry of the target-display surface and the composition of the equilibration solvent are engineered to strongly inhibit non-specific retention of aptamers. Partition efficiencies approaching 10(6) are thereby realized. Retained members are amplified in Hi-Fi SELEX by digital PCR in a manner that ensures both elimination of amplification artifacts and stoichiometric conversion of amplicons into the single-stranded library required for the next selection round. Improvements to aptamer selections are first demonstrated using human α-thrombin as the target. Three clinical targets (human factors IXa, X, and D) are then subjected to Hi-Fi SELEX. For each, rapid enrichment of ssDNA aptamers offering an order-nM mean equilibrium dissociation constant (Kd) is achieved within three selection rounds, as quantified by a new label-free qPCR assay reported here. Therapeutic candidates against factor D are identified.

  11. Chronological Profiling of Plasma Native Peptides after Hepatectomy in Pigs: Toward the Discovery of Human Biomarkers for Liver Regeneration

    PubMed Central

    Iguchi, Kohta; Hatano, Etsuro; Nirasawa, Takashi; Iwasaki, Noriyuki; Sato, Motohiko; Yamamoto, Gen; Yamanaka, Kenya; Okamoto, Tatsuya; Kasai, Yosuke; Nakamura, Naohiko; Fuji, Hiroaki; Sakai, Tomohito; Kakuda, Nobuto; Seo, Satoru; Taura, Kojiro; Tashiro, Kei; Uemoto, Shinji

    2017-01-01

    Liver regeneration after partial hepatectomy (PHx) is a time-dependent process, which is tightly regulated by multiple signaling cascades. Failure of this complex process leads to posthepatectomy liver failure (PHLF), which is associated with a high rate of mortality. Thus, it is extremely important to establish a useful biomarker of liver regeneration to help prevent PHLF. Here, we hypothesized that alterations in the plasma peptide profile may predict liver regeneration following PHx and hence we set up a diagnostic platform for monitoring posthepatectomy outcome. We chronologically analyzed plasma peptidomic profiles of 5 partially hepatectomized microminipigs using the ClinProtTM system, which consists of magnetic beads and MALDI-TOF/TOF MS. We identified endogenous circulating peptides specific to each phase of the postoperative course after PHx in pigs. Notably, peptide fragments of histones were detected immediately after PHx; the presence of these fragments may trigger liver regeneration in the very acute phase after PHx. An N-terminal fragment of hemoglobin subunit α (3627 m/z) was detected as an acute-phase-specific peptide. In the recovery phase, the short N-terminal fragments of albumin (3028, 3042 m/z) were decreased, whereas the long N-terminal fragment of the protein (8926 m/z) was increased. To further validate and extract phase-specific biomarkers using plasma peptidome after PHx, plasma specimens of 4 patients who underwent PHx were analyzed using the same method as we applied to pigs. It revealed that there was also phase-specificity in peptide profiles, one of which was represented by a fragment of complement C4b (2378 m/z). The strategy described herein is highly efficient for the identification and characterization of peptide biomarkers of liver regeneration in a swine PHx model. This strategy is feasible for application to human biomarker studies and will yield clues for understanding liver regeneration in human clinical trials. PMID

  12. Quantitative proteomic analysis of microdissected oral epithelium for cancer biomarker discovery.

    PubMed

    Xiao, Hua; Langerman, Alexander; Zhang, Yan; Khalid, Omar; Hu, Shen; Cao, Cheng-Xi; Lingen, Mark W; Wong, David T W

    2015-11-01

    Specific biomarkers are urgently needed for the detection and progression of oral cancer. The objective of this study was to discover cancer biomarkers from oral epithelium through utilizing high throughput quantitative proteomics approaches. Morphologically malignant, epithelial dysplasia, and adjacent normal epithelial tissues were laser capture microdissected (LCM) from 19 patients and used for proteomics analysis. Total proteins from each group were extracted, digested and then labelled with corresponding isobaric tags for relative and absolute quantitation (iTRAQ). Labelled peptides from each sample were combined and analyzed by liquid chromatography-mass spectrometry (LC-MS/MS) for protein identification and quantification. In total, 500 proteins were identified and 425 of them were quantified. When compared with adjacent normal oral epithelium, 17 and 15 proteins were consistently up-regulated or down-regulated in malignant and epithelial dysplasia, respectively. Half of these candidate biomarkers were discovered for oral cancer for the first time. Cornulin was initially confirmed in tissue protein extracts and was further validated in tissue microarray. Its presence in the saliva of oral cancer patients was also explored. Myoglobin and S100A8 were pre-validated by tissue microarray. These data demonstrated that the proteomic biomarkers discovered through this strategy are potential targets for oral cancer detection and salivary diagnostics.

  13. Targeted proteomics for biomarker discovery and validation of hepatocellular carcinoma in hepatitis C infected patients

    PubMed Central

    Mustafa, Gul M; Larry, Denner; Petersen, John R; Elferink, Cornelis J

    2015-01-01

    Hepatocellular carcinoma (HCC)-related mortality is high because early detection modalities are hampered by inaccuracy, expense and inherent procedural risks. Thus there is an urgent need for minimally invasive, highly specific and sensitive biomarkers that enable early disease detection when therapeutic intervention remains practical. Successful therapeutic intervention is predicated on the ability to detect the cancer early. Similar unmet medical needs abound in most fields of medicine and require novel methodological approaches. Proteomic profiling of body fluids presents a sensitive diagnostic tool for early cancer detection. Here we describe such a strategy of comparative proteomics to identify potential serum-based biomarkers to distinguish high-risk chronic hepatitis C virus infected patients from HCC patients. In order to compensate for the extraordinary dynamic range in serum proteins, enrichment methods that compress the dynamic range without surrendering proteome complexity can help minimize the problems associated with many depletion methods. The enriched serum can be resolved using 2D-difference in-gel electrophoresis and the spots showing statistically significant changes selected for identification by liquid chromatography-tandem mass spectrometry. Subsequent quantitative verification and validation of these candidate biomarkers represent an obligatory and rate-limiting process that is greatly enabled by selected reaction monitoring (SRM). SRM is a tandem mass spectrometry method suitable for identification and quantitation of target peptides within complex mixtures independent on peptide-specific antibodies. Ultimately, multiplexed SRM and dynamic multiple reaction monitoring can be utilized for the simultaneous analysis of a biomarker panel derived from support vector machine learning approaches, which allows monitoring a specific disease state such as early HCC. Overall, this approach yields high probability biomarkers for clinical validation in

  14. Biomarkers research in Europe: focus on personalized medicine.

    PubMed

    Metodiev, Metodi V

    2011-09-01

    The sixth annual European Biomarkers Summit took place in London, UK, on 18-19 May 2011. It was part of a larger event, organized by Select Biosciences, with meetings on molecular diagnostics, single cell analysis and theranostics for personalized medicine. The Biomarkers Summit featured 17 invited talks from academics and industry researchers, a number of poster presentations and exhibitions from several companies marketing biomarker-related technologies and consumables. The focus was broad, covering various aspects of biomarker discovery, qualification, and applications, and a variety of diseases including cancer, neurodegenerative conditions and infectious diseases. Gene-based, as well as protein-based, platforms for biomarkers identification and analysis were discussed.

  15. Biomarkers of ALI/ARDS: pathogenesis, discovery, and relevance to clinical trials.

    PubMed

    Janz, David R; Ware, Lorraine B

    2013-08-01

    Despite the high incidence and poor prognosis of acute lung injury (ALI) and the acute respiratory distress syndrome (ARDS), it remains challenging to identify patients who are at highest risk of developing these syndromes, differentiate these syndromes from other causes of acute respiratory failure, and accurately prognosticate once the diagnosis is made. The identification and validation of biological markers of ALI has the potential to ameliorate these challenges by facilitating studies of therapies aimed at prevention, identifying patients more accurately that have ALI so they can benefit from evidence-based therapies and enrollment in clinical trials, and determining which patients are unlikely to have a positive outcome to guide therapeutic choices and trials of experimental rescue therapies. This article reviews the current state of biomarker research in ALI/ARDS. New methodologies for identification of novel biomarkers of ALI, including metabolomics, proteomics, gene expression, and genetic studies are also discussed.

  16. Diverse Roles of Macrophages in Atherosclerosis: From Inflammatory Biology to Biomarker Discovery

    PubMed Central

    Gui, Ting; Shimokado, Aiko; Sun, Yujing; Akasaka, Takashi; Muragaki, Yasuteru

    2012-01-01

    Cardiovascular disease, a leading cause of mortality in developed countries, is mainly caused by atherosclerosis, a chronic inflammatory disease. Macrophages, which differentiate from monocytes that are recruited from the blood, account for the majority of leukocytes in atherosclerotic plaques. Apoptosis and the suppressed clearance of apoptotic macrophages (efferocytosis) are associated with vulnerable plaques that are prone to rupture, leading to thrombosis. Based on the central functions of macrophages in atherogenesis, cytokines, chemokines, enzymes, or microRNAs related to or produced by macrophages have become important clinical prognostic or diagnostic biomarkers. This paper discusses the impact of monocyte-derived macrophages in early atherogenesis and advanced disease. The role and possible future development of macrophage inflammatory biomarkers are also described. PMID:22577254

  17. CE-MS-based proteomics in biomarker discovery and clinical application.

    PubMed

    Pontillo, Claudia; Filip, Szymon; Borràs, Daniel M; Mullen, William; Vlahou, Antonia; Mischak, Harald

    2015-04-01

    CE-MS is applied in clinical proteomics for both the identification of biomarkers of disease and assessment of biomarkers in clinical diagnosis. The analysis is reproducible, fast, and requires only small sample volumes. However, successful CE-MS analysis depends on several critical steps that can be consolidated as follows: (i) proper sample preparation and fractionation, (ii) application of suitable capillary coating and appropriate CE-MS interfaces, to ensure the reproducibility and stability of the analysis, and (iii) an optimized clinical and statistical study design to increase the chances for obtaining clinically relevant results. In this review, we cover all these aspects, and present several examples of the application of CE-MS in clinical proteomics.

  18. Current and future trends in biomarker discovery and development of companion diagnostics for arthritis.

    PubMed

    Gibson, David S; Bustard, Michael J; McGeough, Cathy M; Murray, Helena A; Crockard, Martin A; McDowell, Andrew; Blayney, Jayne K; Gardiner, Philip V; Bjourson, Anthony J

    2015-02-01

    Musculoskeletal diseases such as rheumatoid arthritis are complex multifactorial disorders that are chronic in nature and debilitating for patients. A number of drug families are available to clinicians to manage these disorders but few tests exist to target these to the most responsive patients. As a consequence, drug failure and switching to drugs with alternate modes of action is common. In parallel, a limited number of laboratory tests are available which measure biological indicators or 'biomarkers' of disease activity, autoimmune status, or joint damage. There is a growing awareness that assimilating the fields of drug selection and diagnostic tests into 'companion diagnostics' could greatly advance disease management and improve outcomes for patients. This review aims to highlight: the current applications of biomarkers in rheumatology with particular focus on companion diagnostics; developments in the fields of proteomics, genomics, microbiomics, imaging and bioinformatics and how integration of these technologies into clinical practice could support therapeutic decisions.

  19. Discovery of Hyperpolarized Molecular Imaging Biomarkers in a Novel Prostate Tissue Slice Culture Model

    DTIC Science & Technology

    2013-06-01

    compatible bioreactor optimized in year 1 to identify hyperpolarized metabolic biomarkers of prostate cancer presence and aggressiveness. To...accomplish this goal my group finished the engineering of a 5 mm bioreactor and acquired hyperpolarized [1-13C]pyruvate data indicating that similar signal...to noise and quality data can be achieved with 4 to 5 prostate tissue slices in the 5 mm bioreactor as was acquired from 30-40 tissue slices in the

  20. Discovery of Hyperpolarized Molecular Imaging Biomarkers in a Novel Prostate Tissue Slice Culture Model

    DTIC Science & Technology

    2013-06-01

    bioreactor optimized in year 1 to identify hyperpolarized metabolic biomarkers of prostate cancer presence and aggressiveness. To accomplish this goal my...group finished the engineering of a 5 mm bioreactor and acquired hyperpolarized [1-13C]pyruvate data indicating that similar signal to noise and...quality data can be achieved with 4 to 5 prostate tissue slices in the 5 mm bioreactor as was acquired from 30-40 tissue slices in the prior 10 mm

  1. Discovery of Hyperpolarized Molecular Imaging Biomarkers in a Novel Prostate Tissue Slice Culture Model

    DTIC Science & Technology

    2013-06-01

    compatible bioreactor and that hyperpolarized 13C spectroscopy could be employed to study real-time metabolism of normal and malignant tissues. The...function of prostate tissue slice cultures (TCSs) in an nuclear magnetic resonance (NMR)-compatible, 3-dimensional tissue culture bioreactor , (2) to use...the TSC/NMR bioreactor model to identify hyperpolarized metabolic biomarkers of prostate cancer presence and aggressiveness, and (3) to use the TSC

  2. Proteomics-based discovery of biomarkers for paediatric acute lymphoblastic leukaemia: challenges and opportunities

    PubMed Central

    López Villar, Elena; Wu, Duojiao; Cho, William C; Madero, Luis; Wang, Xiangdong

    2014-01-01

    There are important breakthroughs in the treatment of paediatric acute lymphoblastic leukaemia (ALL) since 1950, by which the prognosis of the child majority suffered from ALL has been improved. However, there are urgent needs to have disease-specific biomarkers to monitor the therapeutic efficacy and predict the patient prognosis. The present study overviewed proteomics-based research on paediatric ALL to discuss important advances to combat cancer cells and search novel and real protein biomarkers of resistance or sensitivity to drugs which target the signalling networks. We highlighted the importance and significance of a proper phospho-quantitative design and strategy for paediatric ALL between relapse and remission, when human body fluids from cerebrospinal, peripheral blood, or bone-marrow were applied. The present article also assessed the schedule for the analysis of body fluids from patients at different states, importance of proteomics-based tools to discover ALL-specific and sensitive biomarkers, to stimulate paediatric ALL research via proteomics to ‘build’ the reference map of the signalling networks from leukemic cells at relapse, and to monitor significant clinical therapies for ALL-relapse. PMID:24912534

  3. Lectin chromatography/mass spectrometry discovery workflow identifies putative biomarkers of aggressive breast cancers.

    PubMed

    Drake, Penelope M; Schilling, Birgit; Niles, Richard K; Prakobphol, Akraporn; Li, Bensheng; Jung, Kwanyoung; Cho, Wonryeon; Braten, Miles; Inerowicz, Halina D; Williams, Katherine; Albertolle, Matthew; Held, Jason M; Iacovides, Demetris; Sorensen, Dylan J; Griffith, Obi L; Johansen, Eric; Zawadzka, Anna M; Cusack, Michael P; Allen, Simon; Gormley, Matthew; Hall, Steven C; Witkowska, H Ewa; Gray, Joe W; Regnier, Fred; Gibson, Bradford W; Fisher, Susan J

    2012-04-06

    We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (n=5/subtype). The samples were fractionated using the lectins Aleuria aurantia (AAL) and Sambucus nigra agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically N-deglycosylated and analyzed by LC-MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype.

  4. Proteomics-based discovery of biomarkers for paediatric acute lymphoblastic leukaemia: challenges and opportunities.

    PubMed

    López Villar, Elena; Wu, Duojiao; Cho, William C; Madero, Luis; Wang, Xiangdong

    2014-07-01

    There are important breakthroughs in the treatment of paediatric acute lymphoblastic leukaemia (ALL) since 1950, by which the prognosis of the child majority suffered from ALL has been improved. However, there are urgent needs to have disease-specific biomarkers to monitor the therapeutic efficacy and predict the patient prognosis. The present study overviewed proteomics-based research on paediatric ALL to discuss important advances to combat cancer cells and search novel and real protein biomarkers of resistance or sensitivity to drugs which target the signalling networks. We highlighted the importance and significance of a proper phospho-quantitative design and strategy for paediatric ALL between relapse and remission, when human body fluids from cerebrospinal, peripheral blood, or bone-marrow were applied. The present article also assessed the schedule for the analysis of body fluids from patients at different states, importance of proteomics-based tools to discover ALL-specific and sensitive biomarkers, to stimulate paediatric ALL research via proteomics to 'build' the reference map of the signalling networks from leukemic cells at relapse, and to monitor significant clinical therapies for ALL-relapse.

  5. Total cellular glycomics allows characterizing cells and streamlining the discovery process for cellular biomarkers.

    PubMed

    Fujitani, Naoki; Furukawa, Jun-ichi; Araki, Kayo; Fujioka, Tsuyoshi; Takegawa, Yasuhiro; Piao, Jinhua; Nishioka, Taiki; Tamura, Tomohiro; Nikaido, Toshio; Ito, Makoto; Nakamura, Yukio; Shinohara, Yasuro

    2013-02-05

    Although many of the frequently used pluripotency biomarkers are glycoconjugates, a glycoconjugate-based exploration of novel cellular biomarkers has proven difficult due to technical difficulties. This study reports a unique approach for the systematic overview of all major classes of oligosaccharides in the cellular glycome. The proposed method enabled mass spectrometry-based structurally intensive analyses, both qualitatively and quantitatively, of cellular N- and O-linked glycans derived from glycoproteins, glycosaminoglycans, and glycosphingolipids, as well as free oligosaccharides of human embryonic stem cells (hESCs), induced pluripotent stem cells (hiPSCs), and various human cells derived from normal and carcinoma cells. Cellular total glycomes were found to be highly cell specific, demonstrating their utility as unique cellular descriptors. Structures of glycans of all classes specifically observed in hESCs and hiPSCs tended to be immature in general, suggesting the presence of stem cell-specific glycosylation spectra. The current analysis revealed the high similarity of the total cellular glycome between hESCs and hiPSCs, although it was suggested that hESCs are more homogeneous than hiPSCs from a glycomic standpoint. Notably, this study enabled a priori identification of known pluripotency biomarkers such as SSEA-3, -4, and -5 and Tra-1-60/81, as well as a panel of glycans specifically expressed by hESCs and hiPSCs.

  6. Custom database development and biomarker discovery methods for MALDI-TOF mass spectrometry-based identification of high-consequence bacterial pathogens.

    PubMed

    Tracz, Dobryan M; Tyler, Andrea D; Cunningham, Ian; Antonation, Kym S; Corbett, Cindi R

    2017-03-01

    A high-quality custom database of MALDI-TOF mass spectral profiles was developed with the goal of improving clinical diagnostic identification of high-consequence bacterial pathogens. A biomarker discovery method is presented for identifying and evaluating MALDI-TOF MS spectra to potentially differentiate biothreat bacteria from less-pathogenic near-neighbour species.

  7. Cancer serum biomarkers based on aberrant post-translational modifications of glycoproteins: Clinical value and discovery strategies.

    PubMed

    Silva, M Luísa S

    2015-12-01

    Due to the increase in life expectancy in the last decades, as well as changes in lifestyle, cancer has become one of the most common diseases both in developed and developing countries. Early detection remains the most promising approach to improve long-term survival of cancer patients and this may be achieved by efficient screening of biomarkers in biological fluids. Great efforts have been made to identify specific alterations during oncogenesis. Changes at the cellular glycosylation profiles are among such alterations. The "glycosylation machinery" of cells is affected by malignant transformation due to the altered expression of glycogens, leading to changes in glycan biosynthesis and diversity. Alterations in the post-translational modifications of proteins that occur in cancer result in the expression of antigenically distinct glycoproteins. Therefore, these aberrant and cancer-specific glycoproteins and the autoantibodies that are produced in response to their presence constitute targets for cancer biomarkers' search. Different strategies have been implemented for the discovery of cancer glycobiomarkers and are herein reviewed, along with their potentialities and limitations. Practical issues related with serum analysis are also addressed, as well as the challenges that this area faces in the near future.

  8. Improving low-level plasma protein mass spectrometry-based detection for candidate biomarker discovery and validation

    SciTech Connect

    Page, Jason S.; Kelly, Ryan T.; Camp, David G.; Smith, Richard D.

    2008-09-01

    Methods. To improve the detection of low abundance protein candidate biomarker discovery and validation, particularly in complex biological fluids such as blood plasma, increased sensitivity is desired using mass spectrometry (MS)-based instrumentation. A key current limitation on the sensitivity of electrospray ionization (ESI) MS is due to the fact that many sample molecules in solution are never ionized, and the vast majority of the ions that are created are lost during transmission from atmospheric pressure to the low pressure region of the mass analyzer. Two key technologies, multi-nanoelectrospray emitters and the electrodynamic ion funnel have recently been developed and refined at Pacific Northwest National Laboratory (PNNL) to greatly improve the ionization and transmission efficiency of ESI MS based analyses. Multi-emitter based ESI enables the flow from a single source (typically a liquid chromatography [LC] column) to be divided among an array of emitters (Figure 1). The flow rate delivered to each emitter is thus reduced, allowing the well-documented benefits of nanoelectrospray 1 for both sensitivity and quantitation to be realized for higher flow rate separations. To complement the increased ionization efficiency afforded by multi-ESI, tandem electrodynamic ion funnels have also been developed at PNNL, and shown to greatly improve ion transmission efficiency in the ion source interface.2, 3 These technologies have been integrated into a triple quadrupole mass spectrometer for multiple reaction monitoring (MRM) of probable biomarker candidates in blood plasma and show promise for the identification of new species even at low level concentrations.

  9. Endo-β-N-acetylglucosaminidase H de-N-glycosylation in a domestic microwave oven: application to biomarker discovery.

    PubMed

    Frisch, Elena; Schwedler, Christian; Kaup, Matthias; Iona Braicu, Elena; Gröne, Jörn; Lauscher, Johannes C; Sehouli, Jalid; Zimmermann, Matthias; Tauber, Rudolf; Berger, Markus; Blanchard, Véronique

    2013-02-01

    Sample preparation is the rate-limiting step in glycan analysis workflows. Among all of the steps, enzymatic digestions, which are usually performed overnight, are the most time-consuming. In the current study, we report an economical and fast preparation of N-glycans from serum, including microwave-assisted enzymatic digestion in the absence of denaturing chemicals and solvents during the release. To this end, we used a household microwave oven to accelerate both pronase and endo-β-N-acetylglucosaminidase H (Endo H) digestions. Purification was then performed using self-made SP20SS and carbon tips. We were able to prepare samples in 55 min instead of 21 h. Finally, the method was applied in the context of oncological biomarker discovery exemplarily to ovarian and colon cancer. We observed a significant downregulation of sialylated hybrid structures in ovarian cancer samples using capillary electrophoresis-laser-induced fluorescence (CE-LIF). Furthermore, sepsis, a systemic inflammatory response syndrome, was also included in the study to understand whether the changes observed in ovarian cancer patients were due to the cancer itself or to the inflammation that usually accompanies its development. Because sialylated hybrid structures were upregulated in sepsis samples, the downregulation of these structures in ovarian cancer is specific to the cancer itself and, therefore, could be used as a biomarker.

  10. Biomarkers in pediatrics: children as biomarker orphans.

    PubMed

    Savage, William J; Everett, Allen D

    2010-12-01

    Biomarkers have enormous potential to improve patient care by establishing tests of diagnosis, prognosis, and treatment effects. Successfully translating a biomarker from discovery to clinical application demands high-quality discovery research and high-quality clinical studies for biomarker validation; however, there are additional challenges that face biomarker research in pediatrics. There are also additional characteristics of pediatric medicine that make biomarker research especially needed. This review focuses on the fundamentals of biomarkers, the additional considerations needed for applying biomarker research to children, and recommendations for advancing pediatric biomarker research.

  11. Integrated use of biomarkers in the mussel Mytilus galloprovincialis for assessing off-shore gas platforms in the Adriatic Sea: results of a two-year biomonitoring program.

    PubMed

    Gomiero, Alessio; Da Ros, Luisa; Nasci, Cristina; Meneghetti, Francesca; Spagnolo, Alessandra; Fabi, Gianna

    2011-11-01

    Despite a large number of gas platforms existing in the Adriatic Sea, which is a semi-enclosed basin characterized by a slow turnover rate and increasing industrial as well as other anthropogenic activities, the effects of these structures on the aquatic ecosystem require further investigation. Since 1998, multidisciplinary studies have been performed by CNR-ISMAR to comply with legislation and to support the development of protocols for the monitoring of offshore activities in the Adriatic Sea. The present study was developed to implement a biomonitoring plan to assess the ecotoxicological effects of the extraction activities of an off-shore gas platform. Biomarkers were evaluated in mussels collected from the platform in relation to physiological stress, DNA damage, cellular damage, oxidative stress and exposure effects. Organic contaminants and trace element bioaccumulation were also assessed in the soft body of the mussels to correlate bioaccumulation of pollutants with biomarker responses. The results indicate an absence of platform-related environmental stress.

  12. Plasma metabolomic changes following PI3K inhibition as pharmacodynamic biomarkers: preclinical discovery to Phase I trial evaluation

    PubMed Central

    Ang, Joo Ern; Pandher, Rupinder; Ang, Joo Chew; Asad, Yasmin J; Henley, Alan; Valenti, Melanie; Box, Gary; de haven Brandon, Alexis; Baird, Richard R.; Friedman, Lori; Derynck, Mika; Vanhaesebroeck, Bart; Eccles, Suzanne A; Kaye, Stan B; Workman, Paul; de Bono, Johann S; Raynaud, Florence I

    2017-01-01

    Phosphoinositide-3-kinase (PI3K) plays a key role in cellular metabolism and cancer. Using a mass spectrometry-based metabolomics platform, we discovered that plasma concentrations of 26 metabolites, including amino acids, acylcarnitines and phosphatidylcholines, were decreased in mice bearing PTEN-deficient tumors compared with non-tumor bearing controls and in addition were increased following dosing with Class I PI3K inhibitor pictilisib (GDC-0941). These candidate metabolomics biomarkers were evaluated in a Phase I dose-escalation clinical trial of pictilisib. Time- and dose-dependent effects were observed in patients for 22 plasma metabolites. The changes exceeded baseline variability, resolved after drug washout and were recapitulated on continuous dosing. Our study provides a link between modulation of the PI3K pathway and changes in the plasma metabolome and demonstrates that plasma metabolomics is a feasible and promising strategy for biomarker evaluation. Also, our findings provide additional support for an association between insulin resistance, branched-chain amino acids and related metabolites following PI3K inhibition. PMID:27048952

  13. The role of quantitative mass spectrometry in the discovery of pancreatic cancer biomarkers for translational science

    PubMed Central

    2014-01-01

    In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current quantitative mass spectrometric technologies and analyses that have been developed and applied in the last decade in the context of pancreatic cancer. Examples of candidate biomarkers that have been identified from these pancreas studies include among others, asporin, CD9, CXC chemokine ligand 7, fibronectin 1, galectin-1, gelsolin, intercellular adhesion molecule 1, insulin-like growth factor binding protein 2, metalloproteinase inhibitor 1, stromal cell derived factor 4, and transforming growth factor beta-induced protein. Many of these proteins are involved in various steps in pancreatic tumor progression including cell proliferation, adhesion, migration, invasion, metastasis, immune response and angiogenesis. These new protein candidates may provide essential information for the development of protein diagnostics and targeted therapies. We further argue that new strategies must be advanced and established for the integration of proteomic, transcriptomic and genomic data, in order to enhance biomarker translation. Large scale studies with meta data processing will pave the way for novel and unexpected correlations within pancreatic cancer, that will benefit the patient, with targeted treatment. PMID:24708694

  14. Proteomic analysis of temporally stimulated ovarian cancer cells for biomarker discovery.

    PubMed

    Marzinke, Mark A; Choi, Caitlin H; Chen, Li; Shih, Ie-Ming; Chan, Daniel W; Zhang, Hui

    2013-02-01

    While ovarian cancer remains the most lethal gynecological malignancy in the United States, there are no biomarkers available that are able to predict therapeutic responses to ovarian malignancies. One major hurdle in the identification of useful biomarkers has been the ability to obtain enough ovarian cancer cells from primary tissues diagnosed in the early stages of serous carcinomas, the most deadly subtype of ovarian tumor. In order to detect ovarian cancer in a state of hyperproliferation, we analyzed the implications of molecular signaling cascades in the ovarian cancer cell line OVCAR3 in a temporal manner, using a mass-spectrometry-based proteomics approach. OVCAR3 cells were treated with EGF(1), and the time course of cell progression was monitored based on Akt phosphorylation and growth dynamics. EGF-stimulated Akt phosphorylation was detected at 12 h post-treatment, but an effect on proliferation was not observed until 48 h post-exposure. Growth-stimulated cellular lysates were analyzed for protein profiles between treatment groups and across time points using iTRAQ labeling and mass spectrometry. The protein response to EGF treatment was identified via iTRAQ analysis in EGF-stimulated lysates relative to vehicle-treated specimens across the treatment time course. Validation studies were performed on one of the differentially regulated proteins, lysosomal-associated membrane protein 1 (LAMP-1), in human tissue lysates and ovarian tumor tissue sections. Further, tissue microarray analysis was performed to demarcate LAMP-1 expression across different stages of epithelial ovarian cancers. These data support the use of this approach for the efficient identification of tissue-based markers in tumor development related to specific signaling pathways. LAMP-1 is a promising biomarker for studies of the progression of EGF-stimulated ovarian cancers and might be useful in predicting treatment responses involving tyrosine kinase inhibitors or EGF receptor

  15. The role of quantitative mass spectrometry in the discovery of pancreatic cancer biomarkers for translational science.

    PubMed

    Ansari, Daniel; Aronsson, Linus; Sasor, Agata; Welinder, Charlotte; Rezeli, Melinda; Marko-Varga, György; Andersson, Roland

    2014-04-05

    In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current quantitative mass spectrometric technologies and analyses that have been developed and applied in the last decade in the context of pancreatic cancer. Examples of candidate biomarkers that have been identified from these pancreas studies include among others, asporin, CD9, CXC chemokine ligand 7, fibronectin 1, galectin-1, gelsolin, intercellular adhesion molecule 1, insulin-like growth factor binding protein 2, metalloproteinase inhibitor 1, stromal cell derived factor 4, and transforming growth factor beta-induced protein. Many of these proteins are involved in various steps in pancreatic tumor progression including cell proliferation, adhesion, migration, invasion, metastasis, immune response and angiogenesis. These new protein candidates may provide essential information for the development of protein diagnostics and targeted therapies. We further argue that new strategies must be advanced and established for the integration of proteomic, transcriptomic and genomic data, in order to enhance biomarker translation. Large scale studies with meta data processing will pave the way for novel and unexpected correlations within pancreatic cancer, that will benefit the patient, with targeted treatment.

  16. High-Resolution Taxonomic Profiling of the Subgingival Microbiome for Biomarker Discovery and Periodontitis Diagnosis

    PubMed Central

    Szafranski, Szymon P.; Wos-Oxley, Melissa L.; Vilchez-Vargas, Ramiro; Jáuregui, Ruy; Plumeier, Iris; Klawonn, Frank; Tomasch, Jürgen; Meisinger, Christa; Kühnisch, Jan; Sztajer, Helena; Pieper, Dietmar H.

    2014-01-01

    The oral microbiome plays a key role for caries, periodontitis, and systemic diseases. A method for rapid, high-resolution, robust taxonomic profiling of subgingival bacterial communities for early detection of periodontitis biomarkers would therefore be a useful tool for individualized medicine. Here, we used Illumina sequencing of the V1-V2 and V5-V6 hypervariable regions of the 16S rRNA gene. A sample stratification pipeline was developed in a pilot study of 19 individuals, 9 of whom had been diagnosed with chronic periodontitis. Five hundred twenty-three operational taxonomic units (OTUs) were obtained from the V1-V2 region and 432 from the V5-V6 region. Key periodontal pathogens like Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia could be identified at the species level with both primer sets. Principal coordinate analysis identified two outliers that were consistently independent of the hypervariable region and method of DNA extraction used. The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 80 OTU-level biomarkers of periodontitis and 17 of health. Health- and periodontitis-related clusters of OTUs were identified using a connectivity analysis, and the results confirmed previous studies with several thousands of samples. A machine learning algorithm was developed which was trained on all but one sample and then predicted the diagnosis of the left-out sample (jackknife method). Using a combination of the 10 best biomarkers, 15 of 17 samples were correctly diagnosed. Training the algorithm on time-resolved community profiles might provide a highly sensitive tool to detect the onset of periodontitis. PMID:25452281

  17. Discovery of Metabolic Biomarkers for Duchenne Muscular Dystrophy within a Natural History Study

    PubMed Central

    Nishida, Maki; Harris, Michael; Rao, Shruti; Cheema, Amrita K.; Gill, Kirandeep; Seol, Haeri; Morgenroth, Lauren P.; Henricson, Erik; McDonald, Craig; Mah, Jean K.; Clemens, Paula R.; Hoffman, Eric P.; Hathout, Yetrib; Madhavan, Subha

    2016-01-01

    Serum metabolite profiling in Duchenne muscular dystrophy (DMD) may enable discovery of valuable molecular markers for disease progression and treatment response. Serum samples from 51 DMD patients from a natural history study and 22 age-matched healthy volunteers were profiled using liquid chromatography coupled to mass spectrometry (LC-MS) for discovery of novel circulating serum metabolites associated with DMD. Fourteen metabolites were found significantly altered (1% false discovery rate) in their levels between DMD patients and healthy controls while adjusting for age and study site and allowing for an interaction between disease status and age. Increased metabolites included arginine, creatine and unknown compounds at m/z of 357 and 312 while decreased metabolites included creatinine, androgen derivatives and other unknown yet to be identified compounds. Furthermore, the creatine to creatinine ratio is significantly associated with disease progression in DMD patients. This ratio sharply increased with age in DMD patients while it decreased with age in healthy controls. Overall, this study yielded promising metabolic signatures that could prove useful to monitor DMD disease progression and response to therapies in the future. PMID:27082433

  18. Discovery of Metabolic Biomarkers for Duchenne Muscular Dystrophy within a Natural History Study.

    PubMed

    Boca, Simina M; Nishida, Maki; Harris, Michael; Rao, Shruti; Cheema, Amrita K; Gill, Kirandeep; Seol, Haeri; Morgenroth, Lauren P; Henricson, Erik; McDonald, Craig; Mah, Jean K; Clemens, Paula R; Hoffman, Eric P; Hathout, Yetrib; Madhavan, Subha

    2016-01-01

    Serum metabolite profiling in Duchenne muscular dystrophy (DMD) may enable discovery of valuable molecular markers for disease progression and treatment response. Serum samples from 51 DMD patients from a natural history study and 22 age-matched healthy volunteers were profiled using liquid chromatography coupled to mass spectrometry (LC-MS) for discovery of novel circulating serum metabolites associated with DMD. Fourteen metabolites were found significantly altered (1% false discovery rate) in their levels between DMD patients and healthy controls while adjusting for age and study site and allowing for an interaction between disease status and age. Increased metabolites included arginine, creatine and unknown compounds at m/z of 357 and 312 while decreased metabolites included creatinine, androgen derivatives and other unknown yet to be identified compounds. Furthermore, the creatine to creatinine ratio is significantly associated with disease progression in DMD patients. This ratio sharply increased with age in DMD patients while it decreased with age in healthy controls. Overall, this study yielded promising metabolic signatures that could prove useful to monitor DMD disease progression and response to therapies in the future.

  19. Evaluation of qPCR curve analysis methods for reliable biomarker discovery: bias, resolution, precision, and implications.

    PubMed

    Ruijter, Jan M; Pfaffl, Michael W; Zhao, Sheng; Spiess, Andrej N; Boggy, Gregory; Blom, Jochen; Rutledge, Robert G; Sisti, Davide; Lievens, Antoon; De Preter, Katleen; Derveaux, Stefaan; Hellemans, Jan; Vandesompele, Jo

    2013-01-01

    RNA transcripts such as mRNA or microRNA are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription (RT) in combination with quantitative PCR (qPCR) has become the method of choice to quantify small amounts of such RNA molecules. In parallel with the democratization of RT-qPCR and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the amplification curve with respect to the cycle-axis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efficiency of the PCR reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the PCR efficiency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze amplification curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly available curve analysis methods were thoroughly compared using a previously published large clinical data set (Vermeulen et al., 2009) [11]. The original developers of these methods applied their algorithms and are co-author on this study. We assessed the curve analysis methods' impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patient-classification accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the

  20. An optimized procedure for exosome isolation and analysis using serum samples: Application to cancer biomarker discovery.

    PubMed

    Li, Mu; Rai, Alex J; DeCastro, G Joel; Zeringer, Emily; Barta, Timothy; Magdaleno, Susan; Setterquist, Robert; Vlassov, Alexander V

    2015-10-01

    Exosomes are RNA and protein-containing nanovesicles secreted by all cell types and found in abundance in body fluids, including blood, urine and cerebrospinal fluid. These vesicles seem to be a perfect source of biomarkers, as their cargo largely reflects the content of parental cells, and exosomes originating from all organs can be obtained from circulation through minimally invasive or non-invasive means. Here we describe an optimized procedure for exosome isolation and analysis using clinical samples, starting from quick and robust extraction of exosomes with Total exosome isolation reagent, then isolation of RNA followed by qRT-PCR. Effectiveness of this workflow is exemplified by analysis of the miRNA content of exosomes derived from serum samples - obtained from the patients with metastatic prostate cancer, treated prostate cancer patients who have undergone prostatectomy, and control patients without prostate cancer. Three promising exosomal microRNA biomarkers were identified, discriminating these groups: hsa-miR375, hsa-miR21, hsa-miR574.

  1. The proteome of Hypobaric Induced Hypoxic Lung: Insights from Temporal Proteomic Profiling for Biomarker Discovery

    PubMed Central

    Ahmad, Yasmin; Sharma, Narendra K.; Ahmad, Mohammad Faiz; Sharma, Manish; Garg, Iti; Srivastava, Mousami; Bhargava, Kalpana

    2015-01-01

    Exposure to high altitude induces physiological responses due to hypoxia. Lungs being at the first level to face the alterations in oxygen levels are critical to counter and balance these changes. Studies have been done analysing pulmonary proteome alterations in response to exposure to hypobaric hypoxia. However, such studies have reported the alterations at specific time points and do not reflect the gradual proteomic changes. These studies also identify the various biochemical pathways and responses induced after immediate exposure and the resolution of these effects in challenge to hypobaric hypoxia. In the present study, using 2-DE/MS approach, we attempt to resolve these shortcomings by analysing the proteome alterations in lungs in response to different durations of exposure to hypobaric hypoxia. Our study thus highlights the gradual and dynamic changes in pulmonary proteome following hypobaric hypoxia. For the first time, we also report the possible consideration of SULT1A1, as a biomarker for the diagnosis of high altitude pulmonary edema (HAPE). Higher SULT1A1 levels were observed in rats as well as in humans exposed to high altitude, when compared to sea-level controls. This study can thus form the basis for identifying biomarkers for diagnostic and prognostic purposes in responses to hypobaric hypoxia. PMID:26022216

  2. Biology of chronic graft-vs-host disease: Immune mechanisms and progress in biomarker discovery

    PubMed Central

    Presland, Richard B

    2016-01-01

    Chronic graft-vs-host disease (cGVHD) is the leading cause of long-term morbidity and mortality following allogeneic hematopoietic stem cell transplantation. It presents as a chronic inflammatory and sclerotic autoimmune-like condition that most frequently affects the skin, oral mucosa, liver, eyes and gastrointestinal tract. Both clinical and animal studies have shown that multiple T cell subsets including Th1, Th2, Th17, T follicular helper cells and regulatory T-cells play some role in cGVHD development and progression; B cells also play an important role in the disease including the production of antibodies to HY and nuclear antigens that can cause serious tissue damage. An array of cytokines and chemokines produced by different types of immune cells also mediate tissue inflammation and damage of cGVHD target tissues such as the skin and oral cavity. Many of these same immune regulators have been studied as candidate cGVHD biomarkers. Recent studies suggest that some of these biomarkers may be useful for determining disease prognosis and planning long-term clinical follow-up of cGVHD patients. PMID:28058210

  3. A novel class dependent feature selection method for cancer biomarker discovery.

    PubMed

    Zhou, Wengang; Dickerson, Julie A

    2014-04-01

    Identifying key biomarkers for different cancer types can improve diagnosis accuracy and treatment. Gene expression data can help differentiate between cancer subtypes. However the limitation of having a small number of samples versus a larger number of genes represented in a dataset leads to the overfitting of classification models. Feature selection methods can help select the most distinguishing feature sets for classifying different cancers. A new class dependent feature selection approach integrates the F-statistic, Maximum Relevance Binary Particle Swarm Optimization (MRBPSO) and Class Dependent Multi-category Classification (CDMC) system. This feature selection method combines filter and wrapper based methods. A set of highly differentially expressed genes (features) are pre-selected using the F statistic for each dataset as a filter for selecting the most meaningful features. MRBPSO and CDMC function as a wrapper to select desirable feature subsets for each class and classify the samples using those chosen class-dependent feature subsets. The performance of the proposed methods is evaluated on eight real cancer datasets. The results indicate that the class-dependent approaches can effectively identify biomarkers related to each cancer type and improve classification accuracy compared to class independent feature selection methods.

  4. Alzheimer's disease biomarker discovery in symptomatic and asymptomatic patients: experimental approaches and future clinical applications.

    PubMed

    Ho, Lap; Fivecoat, Hayley; Wang, Jun; Pasinetti, Giulio Maria

    2010-01-01

    Alzheimer's disease (AD) is the most common form of dementia in the elderly. Current treatments for AD are not as effective as needed, nor is there any definitive antemortem diagnostic. Understanding the biological processes that occur during AD onset and/or progression will improve disease diagnosis and treatment. Recent applications of microarray technologies for analysis of messenger (m) RNA expression profiles have elucidated distinct changes in the brain as a function of AD dementia initiation and progression. However, mRNA analysis underestimates post-transcriptional modifications and therefore provides only a partial view of the molecular changes in the AD brain. Combining mRNA studies with protein expression analysis may provide a more global picture of the biological processes associated with AD dementia. Information gathered could lead to the development of select biological indices (biomarkers) for guiding AD diagnosis and therapy. We will provide a brief background on AD, followed by a review on the applications of microarray, proteomics, as well as microRNA expression profile analysis to develop novel diagnostic strategies that may be useful for the diagnosis AD and for monitoring disease progression. The availability of biomarkers that promote early disease diagnosis, particularly among asymptomatic patients, will lead to the application of personalized medicine in AD.

  5. Enhanced Biosensor Platforms for Detecting the Atherosclerotic Biomarker VCAM1 Based on Bioconjugation with Uniformly Oriented VCAM1-Targeting Nanobodies

    PubMed Central

    Ta, Duy Tien; Guedens, Wanda; Vranken, Tom; Vanschoenbeek, Katrijn; Steen Redeker, Erik; Michiels, Luc; Adriaensens, Peter

    2016-01-01

    Surface bioconjugation of biomolecules has gained enormous attention for developing advanced biomaterials including biosensors. While conventional immobilization (by physisorption or covalent couplings using the functional groups of the endogenous amino acids) usually results in surfaces with low activity, reproducibility and reusability, the application of methods that allow for a covalent and uniformly oriented coupling can circumvent these limitations. In this study, the nanobody targeting Vascular Cell Adhesion Molecule-1 (NbVCAM1), an atherosclerotic biomarker, is engineered with a C-terminal alkyne function via Expressed Protein Ligation (EPL). Conjugation of this nanobody to azidified silicon wafers and Biacore™ C1 sensor chips is achieved via Copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) “click” chemistry to detect VCAM1 binding via ellipsometry and surface plasmon resonance (SPR), respectively. The resulting surfaces, covered with uniformly oriented nanobodies, clearly show an increased antigen binding affinity, sensitivity, detection limit, quantitation limit and reusability as compared to surfaces prepared by random conjugation. These findings demonstrate the added value of a combined EPL and CuAAC approach as it results in strong control over the surface orientation of the nanobodies and an improved detecting power of their targets—a must for the development of advanced miniaturized, multi-biomarker biosensor platforms. PMID:27399790

  6. Development of a Chip/Chip/SRM platform using digital chip isoelectric focusing and LC-Chip mass spectrometry for enrichment and quantitation of low abundance protein biomarkers in human plasma

    PubMed Central

    Rafalko, Agnes; Dai, Shujia; Hancock, William S.; Karger, Barry L.; Hincapie, Marina

    2013-01-01

    Protein biomarkers are critical for diagnosis, prognosis, and treatment of disease. The transition from protein biomarker discovery to verification can be a rate limiting step in clinical development of new diagnostics. Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM MS) is becoming an important tool for biomarker verification studies in highly complex biological samples. Analyte enrichment or sample fractionation is often necessary to reduce sample complexity and improve sensitivity of SRM for quantitation of clinically relevant biomarker candidates present at the low ng/mL range in blood. In this paper, we describe an alternative method for sample preparation for LC-SRM MS, which does not rely on availability of antibodies. This new platform is based on selective enrichment of proteotypic peptides from complex biological peptide mixtures via isoelectric focusing (IEF) on a digital ProteomeChip (dPC™) for SRM quantitation using a triple quadrupole (QQQ) instrument with an LC-Chip (Chip/Chip/SRM). To demonstrate the value of this approach, the optimization of the Chip/Chip/SRM platform was performed using prostate specific antigen (PSA) added to female plasma as a model system. The combination of immunodepletion of albumin and IgG with peptide fractionation on the dPC, followed by SRM analysis, resulted in a limit of quantitation of PSA added to female plasma at the level of ~1–2.5 ng/mL with a CV of ~13%. The optimized platform was applied to measure levels of PSA in plasma of a small cohort of male patients with prostate cancer (PCa) and healthy matched controls with concentrations ranging from 1.5 to 25 ng/mL. A good correlation (r2 = 0.9459) was observed between standard clinical ELISA tests and the SRM-based-assay. Our data demonstrate that the combination of IEF on the dPC and SRM (Chip/Chip/SRM) can be successfully applied for verification of low abundance protein biomarkers in complex samples. PMID:22098410

  7. “Omics”-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

    PubMed Central

    Matthews, Holly; Hanison, James; Nirmalan, Niroshini

    2016-01-01

    The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the “data-heavy” outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences—genomics, transcriptomics, proteomics and metabolomics—have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process. PMID:28248238

  8. Bovine Tuberculosis in Cattle: Vaccines, DIVA Tests, and Host Biomarker Discovery.

    PubMed

    Vordermeier, H Martin; Jones, Gareth J; Buddle, Bryce M; Hewinson, R Glyn; Villarreal-Ramos, Bernardo

    2016-01-01

    Bovine tuberculosis remains a major economic and animal welfare concern worldwide. Cattle vaccination is being considered as part of control strategies. This approach, used alongside conventional control policies, also requires the development of vaccine-compatible diagnostic assays to distinguish vaccinated from infected animals (DIVA). We discuss progress made on optimizing the only potentially available vaccine, bacille Calmette Guérin (BCG), and on strategies to improve BCG efficacy. We also describe recent advances in DIVA development based on the detection of host cellular immune responses by blood-testing or skin-testing approaches. Finally, to accelerate vaccine development, definition of host biomarkers that provide meaningful stage-gating criteria to select vaccine candidates for further testing is highly desirable. Some progress has also been made in this area of research, and we summarize studies that defined either markers predicting vaccine success or markers that correlate with disease stage or severity.

  9. Mass Spectrometry and Tandem Mass Spectrometry for Protein Biomarker Discovery and Bacterial Speciation

    NASA Astrophysics Data System (ADS)

    Fox, Alvin; Fox, Karen

    After culture, MALDI-MS protein profiling, for species characterization, is widely used. DNA-based identification of bacterial species (with or without prior culture) often involves PCR and/or sequencing. 16S rRNA sequence cataloging is the gold standard but discrimination is often only at the genus level. This chapter discusses protein marker discovery and chemotaxonomy for threat agents using MS and MS/MS. Characterization of small acid soluble proteins (SASPs) of Bacillus anthracis and related species are used for illustrative purposes. The ultimate goal of our studies is universal applicability with species-level certainty in these identifications including biodetection without culture.

  10. Detection of Hepatocellular Carcinoma in Hepatitis C Patients: Biomarker Discovery by LC-MS

    PubMed Central

    Bowers, Jeremiah; Hughes, Emma; Skill, Nicholas; Maluccio, Mary; Raftery, Daniel

    2014-01-01

    Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed the best performance using p-values alone, the PLS-DA model was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between highrisk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLSDA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application. PMID:24666728

  11. Discovery and Validation of Biomarkers to Guide Clinical Management of Pneumonia in African Children

    PubMed Central

    Huang, Honglei; Ideh, Readon C.; Gitau, Evelyn; Thézénas, Marie L.; Jallow, Muminatou; Ebruke, Bernard; Chimah, Osaretin; Oluwalana, Claire; Karanja, Henri; Mackenzie, Grant; Adegbola, Richard A.; Kwiatkowski, Dominic; Kessler, Benedikt M.; Berkley, James A.; Howie, Stephen R. C.; Casals-Pascual, Climent

    2014-01-01

    Background. Pneumonia is the leading cause of death in children globally. Clinical algorithms remain suboptimal for distinguishing severe pneumonia from other causes of respiratory distress such as malaria or distinguishing bacterial pneumonia and pneumonia from others causes, such as viruses. Molecular tools could improve diagnosis and management. Methods. We conducted a mass spectrometry–based proteomic study to identify and validate markers of severity in 390 Gambian children with pneumonia (n = 204) and age-, sex-, and neighborhood-matched controls (n = 186). Independent validation was conducted in 293 Kenyan children with respiratory distress (238 with pneumonia, 41 with Plasmodium falciparum malaria, and 14 with both). Predictive value was estimated by the area under the receiver operating characteristic curve (AUC). Results. Lipocalin 2 (Lpc-2) was the best protein biomarker of severe pneumonia (AUC, 0.71 [95% confidence interval, .64–.79]) and highly predictive of bacteremia (78% [64%–92%]), pneumococcal bacteremia (84% [71%–98%]), and “probable bacterial etiology” (91% [84%–98%]). These results were validated in Kenyan children with severe malaria and respiratory distress who also met the World Health Organization definition of pneumonia. The combination of Lpc-2 and haptoglobin distinguished bacterial versus malaria origin of respiratory distress with high sensitivity and specificity in Gambian children (AUC, 99% [95% confidence interval, 99%–100%]) and Kenyan children (82% [74%–91%]). Conclusions. Lpc-2 and haptoglobin can help discriminate the etiology of clinically defined pneumonia and could be used to improve clinical management. These biomarkers should be further evaluated in prospective clinical studies. PMID:24696240

  12. Metabolomic Discovery of Novel Urinary Galabiosylceramide Analogs as Fabry Disease Biomarkers

    NASA Astrophysics Data System (ADS)

    Boutin, Michel; Auray-Blais, Christiane

    2015-03-01

    Fabry disease is an X-linked, complex, multisystemic lysosomal storage disorder presenting marked phenotypic and genotypic variability among affected male and female patients. Glycosphingolipids, mainly globotriaosylceramide (Gb3) isoforms/analogs, globotriaosylsphingosine (lyso-Gb3) and analogs, as well as galabiosylceramide (Ga2) isoforms/analogs accumulate in the vascular endothelium, nerves, cardiomyocytes, renal glomerular and tubular epithelial cells, and biological fluids. The search for biomarkers reflecting disease severity and progression is still on-going. A metabolomic study using quadrupole time-of-flight mass spectrometry has revealed 22 galabiosylceramide isoforms/analogs in urine of untreated Fabry patients classified in seven groups according to their chemical structure: (1) Saturated fatty acid; (2) one extra double bond; (3) two extra double bonds; (4) hydroxylated saturated fatty acid; (5) hydroxylated fatty acid and one extra double bond; (6) hydrated sphingosine and hydroxylated fatty acid; (7) methylated amide linkage. Relative quantification of both Ga2 and Gb3 isoforms/analogs was performed. All these biomarkers are significantly more abundant in urine samples from untreated Fabry males compared with healthy male controls. A significant amount of Ga2 isoforms/analogs, accounting for 18% of all glycosphingolipids analyzed (Ga2 + Gb3 and respective isoforms/analogs), were present in urine of Fabry patients. Gb3 isoforms containing saturated fatty acids are the most abundant (60.9%) compared with 26.3% for Ga2. A comparison between Ga2 isoforms/analogs and their Gb3 counterparts also showed that the proportion of analogs with hydroxylated fatty acids is significantly greater for Ga2 (35.8%) compared with Gb3 (1.9%). These results suggest different biological pathways involved in the synthesis and/or degradation of Gb3 and Ga2 metabolites.

  13. Blood diagnostic biomarkers for major depressive disorder using multiplex DNA methylation profiles: discovery and validation.

    PubMed

    Numata, Shusuke; Ishii, Kazuo; Tajima, Atsushi; Iga, Jun-ichi; Kinoshita, Makoto; Watanabe, Shinya; Umehara, Hidehiro; Fuchikami, Manabu; Okada, Satoshi; Boku, Shuken; Hishimoto, Akitoyo; Shimodera, Shinji; Imoto, Issei; Morinobu, Shigeru; Ohmori, Tetsuro

    2015-01-01

    Aberrant DNA methylation in the blood of patients with major depressive disorder (MDD) has been reported in several previous studies. However, no comprehensive studies using medication-free subjects with MDD have been conducted. Furthermore, the majority of these previous studies has been limited to the analysis of the CpG sites in CpG islands (CGIs) in the gene promoter regions. The main aim of the present study is to identify DNA methylation markers that distinguish patients with MDD from non-psychiatric controls. Genome-wide DNA methylation profiling of peripheral leukocytes was conducted in two set of samples, a discovery set (20 medication-free patients with MDD and 19 controls) and a replication set (12 medication-free patients with MDD and 12 controls), using Infinium HumanMethylation450 BeadChips. Significant diagnostic differences in DNA methylation were observed at 363 CpG sites in the discovery set. All of these loci demonstrated lower DNA methylation in patients with MDD than in the controls, and most of them (85.7%) were located in the CGIs in the gene promoter regions. We were able to distinguish patients with MDD from the control subjects with high accuracy in the discriminant analysis using the top DNA methylation markers. We also validated these selected DNA methylation markers in the replication set. Our results indicate that multiplex DNA methylation markers may be useful for distinguishing patients with MDD from non-psychiatric controls.

  14. Genome-wide and gene-specific epigenomic platforms for hepatocellular carcinoma biomarker development trials.

    PubMed

    Michailidi, Christina; Soudry, Ethan; Brait, Mariana; Maldonado, Leonel; Jaffe, Andrew; Ili-Gangas, Carmen; Brebi-Mieville, Priscilla; Perez, Jimena; Kim, Myoung Sook; Zhong, Xiaoli; Yang, Quiang; Valle, Blanca; Meltzer, Stephen J; Torbenson, Michael; Esteller, Manel; Sidransky, David; Guerrero-Preston, Rafael

    2014-01-01

    The majority of the epigenomic reports in hepatocellular carcinoma have focused on identifying novel differentially methylated drivers or passengers of the oncogenic process. Few reports have considered the technologies in place for clinical translation of newly identified biomarkers. The aim of this study was to identify epigenomic technologies that need only a small number of samples to discriminate HCC from non-HCC tissue, a basic requirement for biomarker development trials. To assess that potential, we used quantitative Methylation Specific PCR, oligonucleotide tiling arrays, and Methylation BeadChip assays. Concurrent global DNA hypomethylation, gene-specific hypermethylation, and chromatin alterations were observed as a hallmark of HCC. A global loss of promoter methylation was observed in HCC with the Illumina BeadChip assays and the Nimblegen oligonucleotide arrays. HCC samples had lower median methylation peak scores and a reduced number of significant promoter-wide methylated probes. Promoter hypermethylation of RASSF1A, SSBP2, and B4GALT1 quantified by qMSP had a sensitivity ranging from 38% to 52%, a specificity of 100%, and an AUC from 0.58 to 0.75. A panel combining these genes with HCC risk factors had a sensitivity of 87%, a specificity of 100%, and an AUC of 0.91.

  15. Genome-Wide and Gene-Specific Epigenomic Platforms for Hepatocellular Carcinoma Biomarker Development Trials

    PubMed Central

    Michailidi, Christina; Jaffe, Andrew; Ili-Gangas, Carmen; Brebi-Mieville, Priscilla; Perez, Jimena; Kim, Myoung Sook; Zhong, Xiaoli; Yang, Quiang; Valle, Blanca; Meltzer, Stephen J.; Torbenson, Michael; Esteller, Manel; Sidransky, David; Guerrero-Preston, Rafael

    2014-01-01

    The majority of the epigenomic reports in hepatocellular carcinoma have focused on identifying novel differentially methylated drivers or passengers of the oncogenic process. Few reports have considered the technologies in place for clinical translation of newly identified biomarkers. The aim of this study was to identify epigenomic technologies that need only a small number of samples to discriminate HCC from non-HCC tissue, a basic requirement for biomarker development trials. To assess that potential, we used quantitative Methylation Specific PCR, oligonucleotide tiling arrays, and Methylation BeadChip assays. Concurrent global DNA hypomethylation, gene-specific hypermethylation, and chromatin alterations were observed as a hallmark of HCC. A global loss of promoter methylation was observed in HCC with the Illumina BeadChip assays and the Nimblegen oligonucleotide arrays. HCC samples had lower median methylation peak scores and a reduced number of significant promoter-wide methylated probes. Promoter hypermethylation of RASSF1A, SSBP2, and B4GALT1 quantified by qMSP had a sensitivity ranging from 38% to 52%, a specificity of 100%, and an AUC from 0.58 to 0.75. A panel combining these genes with HCC risk factors had a sensitivity of 87%, a specificity of 100%, and an AUC of 0.91. PMID:24829571

  16. Knowledge Discovery for Smart Grid Operation, Control, and Situation Awareness -- A Big Data Visualization Platform

    SciTech Connect

    Gu, Yi; Jiang, Huaiguang; Zhang, Yingchen; Zhang, Jun Jason; Gao, Tianlu; Muljadi, Eduard

    2016-11-21

    In this paper, a big data visualization platform is designed to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. The spawn of smart sensors at both grid side and customer side can provide large volume of heterogeneous data that collect information in all time spectrums. Extracting useful knowledge from this big-data poll is still challenging. In this paper, the Apache Spark, an open source cluster computing framework, is used to process the big-data to effectively discover the hidden knowledge. A high-speed communication architecture utilizing the Open System Interconnection (OSI) model is designed to transmit the data to a visualization platform. This visualization platform uses Google Earth, a global geographic information system (GIS) to link the geological information with the SG knowledge and visualize the information in user defined fashion. The University of Denver's campus grid is used as a SG test bench and several demonstrations are presented for the proposed platform.

  17. Discovery

    ERIC Educational Resources Information Center

    de Mestre, Neville

    2010-01-01

    All common fractions can be written in decimal form. In this Discovery article, the author suggests that teachers ask their students to calculate the decimals by actually doing the divisions themselves, and later on they can use a calculator to check their answers. This article presents a lesson based on the research of Bolt (1982).

  18. A multiplexable, microfluidic platform for the rapid quantitation of a biomarker panel for early ovarian cancer detection at the point-of-care.

    PubMed

    Shadfan, Basil H; Simmons, Archana R; Simmons, Glennon W; Ho, Andy; Wong, Jorge; Lu, Karen H; Bast, Robert C; McDevitt, John T

    2015-01-01

    Point-of-care (POC) diagnostic platforms have the potential to enable low-cost, large-scale screening. As no single biomarker is shed by all ovarian cancers, multiplexed biomarker panels promise improved sensitivity and specificity to address the unmet need for early detection of ovarian cancer. We have configured the programmable bio-nano-chip (p-BNC)-a multiplexable, microfluidic, modular platform-to quantify a novel multi-marker panel comprising CA125, HE4, MMP-7, and CA72-4. The p-BNC is a bead-based immunoanalyzer system with a credit-card-sized footprint that integrates automated sample metering, bubble and debris removal, reagent storage and waste disposal, permitting POC analysis. Multiplexed p-BNC immunoassays demonstrated high specificity, low cross-reactivity, low limits of detection suitable for early detection, and a short analysis time of 43 minutes. Day-to-day variability, a critical factor for longitudinally monitoring biomarkers, ranged between 5.4% and 10.5%, well below the biologic variation for all four markers. Biomarker concentrations for 31 late-stage sera correlated well (R(2) = 0.71 to 0.93 for various biomarkers) with values obtained on the Luminex platform. In a 31 patient cohort encompassing early- and late-stage ovarian cancers along with benign and healthy controls, the multiplexed p-BNC panel was able to distinguish cases from controls with 68.7% sensitivity at 80% specificity. Utility for longitudinal biomarker monitoring was demonstrated with prediagnostic plasma from 2 cases and 4 controls. Taken together, the p-BNC shows strong promise as a diagnostic tool for large-scale screening that takes advantage of faster results and lower costs while leveraging possible improvement in sensitivity and specificity from biomarker panels.

  19. A multiplexable, microfluidic platform for the rapid quantitation of a biomarker panel for early ovarian cancer detection at the point-of-care

    PubMed Central

    Shadfan, Basil H.; Simmons, Archana R.; Simmons, Glennon W.; Ho, Andy; Wong, Jorge; Lu, Karen H.; Bast, Robert C.; McDevitt, John T.

    2015-01-01

    Point-of-care (POC) diagnostic platforms have the potential to enable low-cost, large-scale screening. As no single biomarker is shed by all ovarian cancers, multiplexed biomarker panels promise improved sensitivity and specificity to address the unmet need for early detection of ovarian cancer. We have configured the programmable bio-nano-chip (p-BNC) - a multiplexable, microfluidic, modular platform - to quantify a novel multimarker panel comprising CA125, HE4, MMP-7 and CA72-4. The p-BNC is a bead-based immunoanalyzer system with a credit-card-sized footprint that integrates automated sample metering, bubble and debris removal, reagent storage and waste disposal, permitting POC analysis. Multiplexed p-BNC immunoassays demonstrated high specificity, low cross-reactivity, low limits of detection suitable for early detection, and a short analysis time of 43 minutes. Day-to-day variability, a critical factor for longitudinally monitoring biomarkers, ranged between 5.4–10.5%, well below the biological variation for all four markers. Biomarker concentrations for 31 late-stage sera correlated well (R2 = 0.71 to 0.93 for various biomarkers) with values obtained on the Luminex® platform. In a 31 patient cohort encompassing early- and late-stage ovarian cancers along with benign and healthy controls, the multiplexed p-BNC panel was able to distinguish cases from controls with 68.7% sensitivity at 80% specificity. Utility for longitudinal biomarker monitoring was demonstrated with pre-diagnostic sera from 2 cases and 4 controls. Taken together, the p-BNC shows strong promise as a diagnostic tool for large-scale screening that takes advantage of faster results and lower costs while leveraging possible improvement in sensitivity and specificity from biomarker panels. PMID:25388014

  20. Evaluation of Multi-Protein Immunoaffinity Subtraction for Plasma Proteomics and Candidate Biomarker Discovery Using Mass Spectrometry

    SciTech Connect

    Liu, Tao; Qian, Weijun; Mottaz, Heather M.; Gritsenko, Marina A.; Norbeck, Angela D.; Moore, Ronald J.; Purvine, Samuel O.; Camp, David G.; Smith, Richard D.

    2006-11-01

    The detection of low-abundance protein disease biomarkers from human blood poses significant challenges due to the high dynamic range of protein concentrations that span more than 10 orders of magnitude, as well as the extreme complexity of the serum/plasma proteome. Therefore, experimental strategies that include the removal of high-abundance proteins have been increasingly utilized in proteomic studies of serum, plasma, and other body fluids to enhance detection of low-abundance proteins and achieve broader proteome coverage. However, both the specificity and reproducibility of the high-abundance protein depletion process represent common concerns. Here, we report a detailed evaluation of the performance of two commercially available immunoaffinity subtraction systems commonly used in human serum/plasma proteome characterization by high resolution LC-MS/MS. One system uses mammalian IgG antibodies to remove six of the most abundant plasma proteins, and the other uses chicken immunoglobulin yolk (IgY) antibodies to remove twelve of the most abundant plasma proteins. Plasma samples were repeatedly processed using these two systems, and the resulting flow-through fractions and bound fractions were individually analyzed for comparison. Removal of target proteins by both immunoaffinity subtraction systems proved reproducible and efficient. Nontarget proteins, including spiked protein standards, were also observed to bind to the columns, but in a fairly reproducible manner. The results suggest that these multi-protein immunoaffinity subtraction systems are both highly effective and reproducible for removing high-abundance proteins and therefore, can be readily integrated into quantitative strategies to enhance detection of low-abundance proteins in biomarker discovery studies.

  1. Differential expression profiling of serum proteins and metabolites for biomarker discovery

    NASA Astrophysics Data System (ADS)

    Roy, Sushmita Mimi; Anderle, Markus; Lin, Hua; Becker, Christopher H.

    2004-11-01

    A liquid chromatography-mass spectrometry (LC-MS) proteomics and metabolomics platform is presented for quantitative differential expression analysis. Proteome profiles obtained from 1.5 [mu]L of human serum show ~5000 de-isotoped and quantifiable molecular ions. Approximately 1500 metabolites are observed from 100 [mu]L of serum. Quantification is based on reproducible sample preparation and linear signal intensity as a function of concentration. The platform is validated using human serum, but is generally applicable to all biological fluids and tissues. The median coefficient of variation (CV) for ~5000 proteomic and ~1500 metabolomic molecular ions is approximately 25%. For the case of C-reactive protein, results agree with quantification by immunoassay. The independent contributions of two sources of variance, namely sample preparation and LC-MS analysis, are respectively quantified as 20.4 and 15.1% for the proteome, and 19.5 and 13.5% for the metabolome, for median CV values. Furthermore, biological diversity for ~20 healthy individuals is estimated by measuring the variance of ~6500 proteomic and metabolomic molecular ions in sera for each sample; the median CV is 22.3% for the proteome and 16.7% for the metabolome. Finally, quantitative differential expression profiling is applied to a clinical study comparing healthy individuals and rheumatoid arthritis (RA) patients.

  2. High-throughput proteomics integrated with gene microarray for discovery of colorectal cancer potential biomarkers

    PubMed Central

    Zhong, Chenhan; Li, Dan; Zhai, Xiaohui; Hu, Wangxiong; Guo, Cheng; Yuan, Ying; Zheng, Shu

    2016-01-01

    Proteins, as executives of genes' instructions, are responsible for cellular phenotypes. Integrating proteomics with gene microarray, we conducted this study to identify potential protein biomarkers of colorectal cancer (CRC). Isobaric tags with related and absolute quantitation (iTRAQ) labeling mass spectrometry (MS) was applied to screen and identify differentially expressed proteins between paired CRC and adjacent normal mucosa. Meanwhile, Affymetrix U133plus2.0 microarrays were used to perform gene microarray analysis. Verification experiments included immunohistochemistry (IHC), western blot and enzyme-linked immunosorbent assay (ELISA) of selected proteins. Overall, 5469 differentially expressed proteins were detected with iTRAQ-MS from 24 matched CRC and adjacent normal tissues. And gene microarray identified 39859 differential genes from 52 patients. Of these, 3083 differential proteins had corresponding differentially expressed genes, with 245 proteins and their genes showed >1.5-fold change in expression level. Gene ontology enrichment analysis revealed that up-regulated proteins were more involved in cell adhesion and motion than down-regulated proteins. In addition, up-regulated proteins were more likely to be located in nucleus and vesicles. Further verification experiments with IHC confirmed differential expression levels of 5 proteins (S100 calcium-binding protein A9, annexin A3, nicotinamide phosphoribosyltransferase, carboxylesterase 2 and calcium activated chloride channel A1) between CRC and normal tissues. Besides, western blot showed a stepwise increase of annexin A3 abundance in normal colorectal mucosa, adenoma and CRC tissues. ELISA results revealed significantly higher serum levels of S100 calcium-binding protein A9 and annexin A3 in CRC patients than healthy controls, validating diagnostic value of these proteins. Cell experiments showed that inhibition of annexin A3 could suppress CRC cell proliferation and aggressiveness. S100 calcium

  3. Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with Forecaster, a novel platform for drug discovery.

    PubMed

    Therrien, Eric; Englebienne, Pablo; Arrowsmith, Andrew G; Mendoza-Sanchez, Rodrigo; Corbeil, Christopher R; Weill, Nathanael; Campagna-Slater, Valérie; Moitessier, Nicolas

    2012-01-23

    As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.

  4. A novel compact mass detection platform for the open access (OA) environment in drug discovery and early development.

    PubMed

    Gao, Junling; Ceglia, Scott S; Jones, Michael D; Simeone, Jennifer; Antwerp, John Van; Zhang, Li-Kang; Ross, Charles W; Helmy, Roy

    2016-04-15

    A new 'compact mass detector' co-developed with an instrument manufacturer (Waters Corporation) as an interface for liquid chromatography (LC), specifically Ultra-high performance LC(®) (UPLC(®) or UHPLC) analysis was evaluated as a potential new Open Access (OA) LC-MS platform in the Drug Discovery and Early Development space. This new compact mass detector based platform was envisioned to provide increased reliability and speed while exhibiting significant cost, noise, and footprint reductions. The new detector was evaluated in batch mode (typically 1-3 samples per run) to monitor reactions and check purity, as well as in High Throughput Screening (HTS) mode to run 24, 48, and 96 well plates. The latter workflows focused on screening catalysis conditions, process optimization, and library work. The objective of this investigation was to assess the performance, reliability, and flexibility of the compact mass detector in the OA setting for a variety of applications. The compact mass detector results were compared to those obtained by current OA LC-MS systems, and the capabilities and benefits of the compact mass detector in the open access setting for chemists in the drug discovery and development space are demonstrated.

  5. cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification

    PubMed Central

    2015-01-01

    Background Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity. Methods We develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures. Results The best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction. Conclusions Preliminary results show the feasibility of our framework

  6. 2D-DIGE as a proteomic biomarker discovery tool in environmental studies with Procambarus clarkii.

    PubMed

    Fernández-Cisnal, Ricardo; García-Sevillano, Miguel A; Gómez-Ariza, José L; Pueyo, Carmen; López-Barea, Juan; Abril, Nieves

    2017-04-15

    A 2D-DIGE/MS approach was used to assess protein abundance differences in the red swamp crayfish Procambarus clarkii from polluted aquatic ecosystems of Doñana National Park and surrounding areas with different pollution loads. Procambarus clarkii accumulated metals in the digestive glands and gills reflecting sediment concentrations. We first stated that, probably related to elements accumulation, pollution increased oxidative damage in P. clarkii tissues, as shown by the thiol oxidation status of proteins and MDA levels. In these animals, the altered redox status might be responsible for the deregulated abundance of proteins involved in cellular responses to oxidative stress including protein folding, mitochondrial imbalance and inflammatory processes. Interestingly, polluted P. clarkii crayfish also displayed a metabolic shift to enhanced aerobic glycolysis, most likely aimed at generating ATP and reduction equivalents in an oxidative stress situation that alters mitochondrial integrity. The deregulated proteins define the physiological processes affected by pollutants in DNP and its surrounding areas and may help us to unravel the molecular mechanisms underlying the toxicity of environmental pollutants. In addition, these proteins might be used as exposure biomarkers in environmental risk assessment. The results obtained might be extrapolated to many other locations all over the world and have the added value of providing information about the molecular responses of this environmentally and economically interesting animal.

  7. Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue.

    PubMed

    Liu, Ning Qing; Braakman, René B H; Stingl, Christoph; Luider, Theo M; Martens, John W M; Foekens, John A; Umar, Arzu

    2012-06-01

    Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate statistical methods will lead to false positive hits. All these issues have hampered the identification of reliable protein markers. A workflow, which integrates reproducible and robust sample preparation and data handling methods, is highly desirable in clinical proteomics investigations. Here we describe a label-free tissue proteomics pipeline, which encompasses laser capture microdissection (LCM) followed by nanoscale liquid chromatography and high resolution MS. This pipeline routinely identifies on average ∼10,000 peptides corresponding to ∼1,800 proteins from sub-microgram amounts of protein extracted from ∼4,000 LCM breast cancer epithelial cells. Highly reproducible abundance data were generated from different technical and biological replicates. As a proof-of-principle, comparative proteome analysis was performed on estrogen receptor α positive or negative (ER+/-) samples, and commonly known differentially expressed proteins related to ER expression in breast cancer were identified. Therefore, we show that our tissue proteomics pipeline is robust and applicable for the identification of breast cancer specific protein markers.

  8. NMR metabolic fingerprints of murine melanocyte and melanoma cell lines: application to biomarker discovery.

    PubMed

    Santana-Filho, Arquimedes Paixão de; Jacomasso, Thiago; Riter, Daniel Suss; Barison, Andersson; Iacomini, Marcello; Winnischofer, Sheila Maria Brochado; Sassaki, Guilherme Lanzi

    2017-02-15

    Melanoma is the most aggressive type of skin cancer and efforts to improve the diagnosis of this neoplasia are largely based on the use of cell lines. Metabolomics is currently undergoing great advancements towards its use to screening for disease biomarkers. Although NMR metabolomics includes both 1D and 2D methodologies, there is a lack of data in the literature regarding heteronuclear 2D NMR assignments of the metabolome from eukaryotic cell lines. The present study applied NMR-based metabolomics strategies to characterize aqueous and lipid extracts from murine melanocytes and melanoma cell lines with distinct tumorigenic potential, successfully obtaining fingerprints of the metabolites from the extracts of the cell lines by means of 2D NMR HSQC correlation maps. Relative amounts of the identified metabolites were compared between the 4 cell lines. Multivariate analysis of (1)H NMR data was able not only to differentiate the melanocyte cell line from the tumorigenic ones but also distinguish among the 3 tumorigenic cell lines. We also investigated the effects of mitogenic agents, and found that they can markedly influence the metabolome of the melanocyte cell line, resembling the pattern of most proliferative cell lines.

  9. Biomarker discovery and gene expression responses in Lycopersicon esculentum root exposed to lead.

    PubMed

    Hou, Jing; Bai, Lili; Xie, Yujia; Liu, Xinhui; Cui, Baoshan

    2015-12-15

    Gene expression analysis has shown particular promise for the identification of molecular biomarkers that can be used for further evaluation of potential toxicity of chemicals present in agricultural soil. In the study, we focused on the development of molecular markers to detect Pb toxicity in agricultural soil. Using the results obtained from microarray analysis, twelve Pb-responsive genes were selected and tested in different Pb concentrations to examine their concentration-response characteristics using real-time quantitative polymerase chain reaction (RT-qPCR). All the Pb treatments set in our study could generally induce the differential expression of the 12 genes, while the lowest observable adverse effect concentration (LOAEC) of Pb for seed germination, root elongation, biomass and structural modification derived from 1,297, 177, 177, and 1,297 mg Pb/kg soil, respectively, suggesting that the transcriptional approach was more sensitive than the traditional end points of death, growth, and morphology for the evaluation of Pb toxicity. The relative expression of glycoalkaloid metabolism 1 (P=-0.790), ethylene-responsive transcription factor ERF017 (P=-0.686) and CASP-like protein 4C2 (P=-0.652) demonstrates a dose-dependent response with Pb content in roots, implying that the three genes can be used as sensitive bioindicators of Pb stress in Lycopersicon esculentum.

  10. NMR metabolic fingerprints of murine melanocyte and melanoma cell lines: application to biomarker discovery

    PubMed Central

    Santana-Filho, Arquimedes Paixão de; Jacomasso, Thiago; Riter, Daniel Suss; Barison, Andersson; Iacomini, Marcello; Winnischofer, Sheila Maria Brochado; Sassaki, Guilherme Lanzi

    2017-01-01

    Melanoma is the most aggressive type of skin cancer and efforts to improve the diagnosis of this neoplasia are largely based on the use of cell lines. Metabolomics is currently undergoing great advancements towards its use to screening for disease biomarkers. Although NMR metabolomics includes both 1D and 2D methodologies, there is a lack of data in the literature regarding heteronuclear 2D NMR assignments of the metabolome from eukaryotic cell lines. The present study applied NMR-based metabolomics strategies to characterize aqueous and lipid extracts from murine melanocytes and melanoma cell lines with distinct tumorigenic potential, successfully obtaining fingerprints of the metabolites from the extracts of the cell lines by means of 2D NMR HSQC correlation maps. Relative amounts of the identified metabolites were compared between the 4 cell lines. Multivariate analysis of 1H NMR data was able not only to differentiate the melanocyte cell line from the tumorigenic ones but also distinguish among the 3 tumorigenic cell lines. We also investigated the effects of mitogenic agents, and found that they can markedly influence the metabolome of the melanocyte cell line, resembling the pattern of most proliferative cell lines. PMID:28198377

  11. Differential Secreted Proteome Approach in Murine Model for Candidate Biomarker Discovery in Colon Cancer

    PubMed Central

    Rangiah, Kannan; Tippornwong, Montri; Sangar, Vineet; Austin, David; Tétreault, Marie-Pier; Rustgi, Anil K.; Blair, Ian A.; Yu, Kenneth H.

    2009-01-01

    The complexity and heterogeneity of the plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. We used cell culture as a model system and identified differentially expressed, secreted proteins which may constitute serological biomarkers. A stable isotope labeling by amino acids in cell culture (SILAC) approach was used to label the entire secreted proteomes of the CT26 murine colon cancer cell line and normal young adult mouse colon (YAMC) cell line, thereby creating a stable isotope labeled proteome (SILAP) standard. This SILAP standard was added to unlabeled murine CT26 colon cancer cell or normal murine YAMC colon epithelial cell secreted proteome samples. A multidimensional approach combining isoelectric focusing (IEF), strong cation exchange (SCX) followed by reversed phase liquid chromatography was used for extensive protein and peptide separation. A total of 614 and 929 proteins were identified from the YAMC and CT26 cell lines, with 418 proteins common to both cell lines. Twenty highly abundant differentially expressed proteins from these groups were selected for liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) analysis in sera. Differential secretion into the serum was observed for several proteins when Apcmin mice were compared with control mice. These findings were then confirmed by Western blot analysis. PMID:19769411

  12. Applying bioinformatics to proteomics: is machine learning the answer to biomarker discovery for PD and MSA?

    PubMed

    Mattison, Hayley A; Stewart, Tessandra; Zhang, Jing

    2012-11-01

    Bioinformatics tools are increasingly being applied to proteomic data to facilitate the identification of biomarkers and classification of patients. In the June, 2012 issue, Ishigami et al. used principal component analysis (PCA) to extract features and support vector machine (SVM) to differentiate and classify cerebrospinal fluid (CSF) samples from two small cohorts of patients diagnosed with either Parkinson's disease (PD) or multiple system atrophy (MSA) based on differences in the patterns of peaks generated with matrix-assisted desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). PCA accurately segregated patients with PD and MSA from controls when the cohorts were combined, but did not perform well when segregating PD from MSA. On the other hand, SVM, a machine learning classification model, correctly classified the samples from patients with early PD or MSA, and the peak at m/z 6250 was identified as a strong contributor to the ability of SVM to distinguish the proteomic profiles of either cohort when trained on one cohort. This study, while preliminary, provides promising results for the application of bioinformatics tools to proteomic data, an approach that may eventually facilitate the ability of clinicians to differentiate and diagnose closely related parkinsonian disorders.

  13. A lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform for identification of multiple liver cancer biomarkers in human plasma.

    PubMed

    Ahn, Yeong Hee; Shin, Park Min; Oh, Na Ree; Park, Gun Wook; Kim, Hoguen; Yoo, Jong Shin

    2012-09-18

    Aberrantly glycosylated proteins related to liver cancer progression were captured with specific lectin and identified from human plasma by multiple reaction monitoring (MRM) mass spectrometry as multiple biomarkers for hepatocellular carcinoma (HCC). The lectin fractionation for fucosylated protein glycoforms in human plasma was conducted with a fucose-specific aleuria aurantia lectin (AAL). Following tryptic digestion of the lectin-captured fraction, plasma samples from 30 control cases (including 10 healthy, 10 hepatitis B virus [HBV], and 10 cirrhosis cases) and 10 HCC cases were quantitatively analyzed by MRM to identify which glycoproteins are viable HCC biomarkers. A1AG1, AACT, A1AT, and CERU were found to be potent biomarkers to differentiate HCC plasma from control plasmas. The AUROC generated independently from these four biomarker candidates ranged from 0.73 to 0.92. However, the lectin-coupled MRM assay with multiple combinations of biomarker candidates is superior statistically to those generated from the individual candidates with AUROC more than 0.95, which can be an alternative to the immunoassay inevitably requiring tedious development of multiple antibodies against biomarker candidates to be verified. Eventually the lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform was found to be efficient to identify multiple biomarkers from human plasma according to cancer progression.

  14. Understanding the structural mechanisms of antibiotic resistance sets the platform for new discovery.

    PubMed

    Reeve, Stephanie M; Lombardo, Michael N; Anderson, Amy C

    2015-01-01

    Understanding the structural basis of antibacterial resistance may enable rational design principles that avoid and subvert that resistance, thus leading to the discovery of more effective antibiotics. In this review, we explore the use of crystal structures to guide new discovery of antibiotics that are effective against resistant organisms. Structures of efflux pumps bound to substrates and inhibitors have aided the design of compounds with lower affinity for the pump or inhibitors that more effectively block the pump. Structures of β-lactamase enzymes have revealed the mechanisms of action toward key carbapenems and structures of gyrase have aided the design of compounds that are less susceptible to point mutations.

  15. High-throughput cellular microarray platforms: applications in drug discovery, toxicology and stem cell research

    PubMed Central

    Fernandes, Tiago G.; Diogo, Maria Margarida; Clark, Douglas S.; Dordick, Jonathan S.; Cabral, Joaquim M.S.

    2017-01-01

    Cellular microarrays are powerful experimental tools for high-throughput screening of large numbers of test samples. Miniaturization increases assay throughput while reducing reagent consumption and the number of cells required, making these systems attractive for a wide range of assays in drug discovery, toxicology, stem cell research and potentially therapy. Here, we provide an overview of the emerging technologies that can be used to generate cellular microarrays, and we highlight recent significant advances in the field. This emerging and multidisciplinary approach offers new opportunities for the design and control of stem cells in tissue engineering and cellular therapies and promises to expedite drug discovery in the biotechnology and pharmaceutical industries. PMID:19398140

  16. Understanding the structural mechanisms of antibiotic resistance sets the platform for new discovery

    PubMed Central

    Reeve, Stephanie M; Lombardo, Michael N; Anderson, Amy C

    2015-01-01

    Understanding the structural basis of antibacterial resistance may enable rational design principles that avoid and subvert that resistance, thus leading to the discovery of more effective antibiotics. In this review, we explore the use of crystal structures to guide new discovery of antibiotics that are effective against resistant organisms. Structures of efflux pumps bound to substrates and inhibitors have aided the design of compounds with lower affinity for the pump or inhibitors that more effectively block the pump. Structures of β-lactamase enzymes have revealed the mechanisms of action toward key carbapenems and structures of gyrase have aided the design of compounds that are less susceptible to point mutations. PMID:26516790

  17. Microfluidic droplet-based PCR instrumentation for high-throughput gene expression profiling and biomarker discovery

    PubMed Central

    Hayes, Christopher J.; Dalton, Tara M.

    2015-01-01

    PCR is a common and often indispensable technique used in medical and biological research labs for a variety of applications. Real-time quantitative PCR (RT-qPCR) has become a definitive technique for quantitating differences in gene expression levels between samples. Yet, in spite of this importance, reliable methods to quantitate nucleic acid amounts in a higher throughput remain elusive. In the following paper, a unique design to quantify gene expression levels at the nanoscale in a continuous flow system is presented. Fully automated, high-throughput, low volume amplification of deoxynucleotides (DNA) in a droplet based microfluidic system is described. Unlike some conventional qPCR instrumentation that use integrated fluidic circuits or plate arrays, the instrument performs qPCR in a continuous, micro-droplet flowing process with droplet generation, distinctive reagent mixing, thermal cycling and optical detection platforms all combined on one complete instrument. Detailed experimental profiling of reactions of less than 300 nl total volume is achieved using the platform demonstrating the dynamic range to be 4 order logs and consistent instrument sensitivity. Furthermore, reduced pipetting steps by as much as 90% and a unique degree of hands-free automation makes the analytical possibilities for this instrumentation far reaching. In conclusion, a discussion of the first demonstrations of this approach to perform novel, continuous high-throughput biological screens is presented. The results generated from the instrument, when compared with commercial instrumentation, demonstrate the instrument reliability and robustness to carry out further studies of clinical significance with added throughput and economic benefits. PMID:27077035

  18. ARQiv-HTS, a versatile whole-organism screening platform enabling in vivo drug discovery at high-throughput rates.

    PubMed

    White, David T; Eroglu, Arife Unal; Wang, Guohua; Zhang, Liyun; Sengupta, Sumitra; Ding, Ding; Rajpurohit, Surendra K; Walker, Steven L; Ji, Hongkai; Qian, Jiang; Mumm, Jeff S

    2016-12-01

    The zebrafish has emerged as an important model for whole-organism small-molecule screening. However, most zebrafish-based chemical screens have achieved only mid-throughput rates. Here we describe a versatile whole-organism drug discovery platform that can achieve true high-throughput screening (HTS) capacities. This system combines our automated reporter quantification in vivo (ARQiv) system with customized robotics, and is termed 'ARQiv-HTS'. We detail the process of establishing and implementing ARQiv-HTS: (i) assay design and optimization, (ii) calculation of sample size and hit criteria, (iii) large-scale egg production, (iv) automated compound titration, (v) dispensing of embryos into microtiter plates, and (vi) reporter quantification. We also outline what we see as best practice strategies for leveraging the power of ARQiv-HTS for zebrafish-based drug discovery, and address technical challenges of applying zebrafish to large-scale chemical screens. Finally, we provide a detailed protocol for a recently completed inaugural ARQiv-HTS effort, which involved the identification of compounds that elevate insulin reporter activity. Compounds that increased the number of insulin-producing pancreatic beta cells represent potential new therapeutics for diabetic patients. For this effort, individual screening sessions took 1 week to conclude, and sessions were performed iteratively approximately every other day to increase throughput. At the conclusion of the screen, more than a half million drug-treated larvae had been evaluated. Beyond this initial example, however, the ARQiv-HTS platform is adaptable to almost any reporter-based assay designed to evaluate the effects of chemical compounds in living small-animal models. ARQiv-HTS thus enables large-scale whole-organism drug discovery for a variety of model species and from numerous disease-oriented perspectives.

  19. Exposure of mayfly Ephemera orientalis (Ephemeroptera) eggs to heavy metals and discovery of biomarkers.

    PubMed

    Mo, Hyoung-ho; Lee, Sung-Eun; Son, Jino; Hwang, Jeong Mi; Bae, Yeon Jae; Cho, Kijong

    2013-11-01

    The objective of this study was to assess acute toxicity of heavy metals in eggs of mayfly Ephemera orientalis McLachlan, and to elucidate relationships between heavy metal toxicity and protein expression patterns determined using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Acute toxicity analysis was conducted using five heavy metals (cadmium, chromium, copper, lead, and mercury), and the toxicity endpoint was established from the egg hatching rate during a 14-day exposure period. Median hatching toxicity (HC₅₀) values were determined for each heavy metal, and the most toxic heavy metal was found to be mercury (0.11 mg/L), followed by copper (0.32 mg/L) and lead (4.39 mg/L). E. orientalis eggs were highly tolerant to cadmium and chromium (>120 mg/L). Proteinchip array analysis using a strong anion exchange proteinchip (Q10) in conjunction with SELDI-TOF-MS was used to assess the protein expression patterns after exposure to heavy metals at the EHC10 (prohibiting hatching concentration to 10% eggs), except for cadmium and chromium, which were used at concentrations of 1, 10, and 100mg/L. Three novel biomarker candidate proteins, i.e., 4269, 4283, and 4623 m/z, were identified for the detection of heavy metal toxicity in aquatic ecosystems at the level of HC₁₀ in E. orientalis eggs. SELDI-TOF MS analysis for detecting differential expression of proteins was found to be more effective than Q10 proteinchip separation in the mayfly eggs.

  20. Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

    PubMed Central

    2014-01-01

    Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic–based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research. PMID:24679154

  1. Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development.

    PubMed

    Frantzi, Maria; Bhat, Akshay; Latosinska, Agnieszka

    2014-03-29

    Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.

  2. Evaluation of Multi-Protein Immunoaffinity Subtraction for Plasma Proteomics and Candidate Biomarker Discovery Using Mass Spectrometry

    PubMed Central

    Liu, Tao; Qian, Wei-Jun; Mottaz, Heather M.; Gritsenko, Marina A.; Norbeck, Angela D.; Moore, Ronald J.; Purvine, Samuel O.; Camp, David G.; Smith, Richard D.

    2007-01-01

    SUMMARY Strategies for removal of high-abundance proteins have been increasingly utilized in proteomic studies of serum/plasma and other body fluids to enhance the detection of low-abundance proteins and achieve broader proteome coverage; however, both the reproducibility and specificity of the high-abundance protein depletion process still represent common concerns. Here, we report a detailed evaluation of immunoaffinity subtraction performed applying the ProteomeLab IgY-12 system which is commonly used in human serum/plasma proteome characterization in combination with high resolution LC-MS/MS. Plasma samples were repeatedly processed implementing this system, and the resulting flow-through fractions and bound fractions were individually analyzed for comparison. The removal of target proteins by the immunoaffinity subtraction system and the overall process was highly reproducible. Non-target proteins, including one spiked protein standard (rabbit glyceraldehyde-3-phosphate dehydrogenase), were also observed to bind to the column at different levels, but in a reproducible manner. The results suggest that multi-protein immunoaffinity subtraction systems can be readily integrated into quantitative strategies to enhance detection of low-abundance proteins in biomarker discovery studies. PMID:16854842

  3. Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

    PubMed Central

    2011-01-01

    Background Analysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data. Results A novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis. Conclusions Fusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach. PMID:21696593

  4. Sensitive Detection of Protein and miRNA Cancer Biomarkers using Silicon-Based Photonic Crystals and A Resonance Coupling Laser Scanning Platform

    PubMed Central

    George, Sherine; Chaudhery, Vikram; Lu, Meng; Takagi, Miki; Amro, Nabil; Pokhriyal, Anusha; Tan, Yafang; Ferreira, Placid; Cunningham, Brian T.

    2013-01-01

    Enhancement of the fluorescent output of surface-based fluorescence assays by performing them upon nanostructured photonic crystal (PC) surfaces has been demonstrated to increase signal intensities by >8000×. Using the multiplicative effects of optical resonant coupling to the PC in increasing the electric field intensity experienced by fluorescent labels (“enhanced excitation”) and the spatially biased funneling of fluorophore emissions through coupling to PC resonances (“enhanced extraction”), PC enhanced fluorescence (PCEF) can be adapted to reduce the limits of detection of disease biomarker assays, and to reduce the size and cost of high sensitivity detection instrumentation. In this work, we demonstrate the first silicon-based PCEF detection platform for multiplexed biomarker assay. The sensor in this platform is a silicon-based PC structure, comprised of a SiO2 grating that is overcoated with a thin film of high refractive index TiO2 and is produced in a semiconductor foundry for low cost, uniform, and reproducible manufacturing. The compact detection instrument that completes this platform was designed to efficiently couples fluorescence excitation from a semiconductor laser to the resonant optical modes of the PC, resulting in elevated electric field strength that is highly concentrated within the region <100 nm from the PC surface. This instrument utilizes a cylindrically focused line to scan a microarray in <1 minute. To demonstrate the capabilities of this sensor-detector platform, microspot fluorescent sandwich immunoassays using secondary antibodies labeled with Cy5 for two cancer biomarkers (TNF-α and IL-3) were performed. Biomarkers were detected at concentrations as low as 0.1 pM. In a fluorescent microarray for detection of a breast cancer miRNA biomarker miR-21, the miRNA was detectable at a concentration of 0.6 pM. PMID:23963502

  5. Sensitive detection of protein and miRNA cancer biomarkers using silicon-based photonic crystals and a resonance coupling laser scanning platform.

    PubMed

    George, Sherine; Chaudhery, Vikram; Lu, Meng; Takagi, Miki; Amro, Nabil; Pokhriyal, Anusha; Tan, Yafang; Ferreira, Placid; Cunningham, Brian T

    2013-10-21

    Enhancement of the fluorescent output of surface-based fluorescence assays by performing them upon nanostructured photonic crystal (PC) surfaces has been demonstrated to increase signal intensities by >8000×. Using the multiplicative effects of optical resonant coupling to the PC in increasing the electric field intensity experienced by fluorescent labels ("enhanced excitation") and the spatially biased funneling of fluorophore emissions through coupling to PC resonances ("enhanced extraction"), PC enhanced fluorescence (PCEF) can be adapted to reduce the limits of detection of disease biomarker assays, and to reduce the size and cost of high sensitivity detection instrumentation. In this work, we demonstrate the first silicon-based PCEF detection platform for multiplexed biomarker assay. The sensor in this platform is a silicon-based PC structure, comprised of a SiO2 grating that is overcoated with a thin film of high refractive index TiO2 and is produced in a semiconductor foundry for low cost, uniform, and reproducible manufacturing. The compact detection instrument that completes this platform was designed to efficiently couple fluorescence excitation from a semiconductor laser to the resonant optical modes of the PC, resulting in elevated electric field strength that is highly concentrated within the region <100 nm from the PC surface. This instrument utilizes a cylindrically focused line to scan a microarray in <1 min. To demonstrate the capabilities of this sensor-detector platform, microspot fluorescent sandwich immunoassays using secondary antibodies labeled with Cy5 for two cancer biomarkers (TNF-α and IL-3) were performed. Biomarkers were detected at concentrations as low as 0.1 pM. In a fluorescent microarray for detection of a breast cancer miRNA biomarker miR-21, the miRNA was detectable at a concentration of 0.6 pM.

  6. Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

    PubMed Central

    Pérot, Philippe; Cheynet, Valérie; Decaussin-Petrucci, Myriam; Oriol, Guy; Mugnier, Nathalie; Rodriguez-Lafrasse, Claire; Ruffion, Alain; Mallet, François

    2013-01-01

    The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, particularly in low dosage values1​​. ‘How to use PSA' remains a current issue, either for diagnosis as a gray zone corresponding to a concentration in serum of 2.5-10 ng/ml which does not allow a clear differentiation to be made between cancer and noncancer2 or for patient follow-up as analysis of post-operative PSA kinetic parameters can pose considerable challenges for their practical application3,4. Alternatively, noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease, e.g. PCA3 in prostate cancer5,6 and to reveal uncharacterized aspects of tumor biology. Moreover, data from the ENCODE project published in 2012 showed that different RNA types cover about 62% of the genome. It also appears that the amount of transcriptional regulatory motifs is at least 4.5x higher than the one corresponding to protein-coding exons. Thus, long terminal repeats (LTRs) of human endogenous retroviruses (HERVs) constitute a wide range of putative/candidate transcriptional regulatory sequences, as it is their primary function in infectious retroviruses. HERVs, which are spread throughout the human genome, originate from ancestral and independent infections within the germ line, followed by copy-paste propagation processes and leading to multicopy families occupying 8% of the human genome (note that exons span 2% of our genome). Some HERV loci still express proteins that have been associated with several pathologies including cancer7-10. We have designed a high-density microarray, in Affymetrix format, aiming to optimally characterize individual HERV loci expression, in order to better understand whether they can be active, if they drive ncRNA transcription or modulate coding gene expression. This tool has been applied in the prostate cancer field (Figure 1

  7. Isolation of urinary exosomes for RNA biomarker discovery using a simple, fast, and highly scalable method.

    PubMed

    Alvarez, M Lucrecia

    2014-01-01

    Urinary exosomes are nanovesicles (40-100 nm) of endocytic origin that are secreted into the urine when a multivesicular body fuses with the membrane of cells from all nephron segments. Interest in urinary exosomes intensified after the discovery that they contain not only protein and mRNA but also microRNA (miRNA) markers of renal dysfunction and structural injury. Currently, the most widely used protocol for the isolation of urinary exosomes is based on ultracentrifugation, a method that is time consuming, requires expensive equipment, and has low scalability, which limits its applicability in the clinical practice. In this chapter, a simple, fast, and highly scalable step-by-step method for isolation of urinary exosomes is described. This method starts with a 10-min centrifugation of 10 ml urine, then the supernatant is saved (SN1), and the pellet is treated with dithiothreitol and heat to release and recover those exosomes entrapped by polymeric Tamm-Horsfall protein. The treated pellet is then resuspended and centrifuged, and the supernatant obtained (SN2) is combined with the first supernatant, SN1. Next, 3.3 ml of ExoQuick-TC, a commercial exosome precipitation reagent, is added to the total supernatant (SN1 + SN2), mixed well, and saved for at least 12 h at 4 °C. Finally, a pellet of exosomes is obtained after a 30-min centrifugation of the supernatant/ExoQuick-TC mix. We previously compared this method with five others used to isolate urinary exosomes and found that this is the simplest, fastest, and most effective alternative to ultracentrifugation-based protocols if the goal of the study is RNA profiling. A method for isolation and quantification of miRNAs and mRNAs from urinary exosomes is also described here. In addition, we provide a step-by-step description of exosomal miRNA profiling using universal reverse transcription and SYBR qPCR.

  8. Discovery Proteomics And Nonparametric Modeling Pipeline In The Development Of A Candidate Biomarker Panel For Dengue Hemorrhagic Fever

    PubMed Central

    Brasier, Allan R; Garcia, Josefina; Wiktorowicz, John E.; Spratt, Heidi M.; Comach, Guillermo; Ju, Hyunsu; Recinos, Adrian; Soman, Kizhake; Forshey, Brett M.; Halsey, Eric S.; Blair, Patrick J.; Rocha, Claudio; Bazan, Isabel; Victor, Sundar S; Wu, Zheng; Stafford, Susan; Watts, Douglas; Morrison, Amy C.; Scott, Thomas W.; Kochel, Tadeusz J.

    2013-01-01

    Secondary Dengue viral infection can produce capillary leakage associated with increased mortality known as Dengue Hemorrhagic Fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective Dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute Dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, 9 cytokines and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject’s gender, clinical parameters, 2 cytokines and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on 8 discriminant features with an AUC of 0.999. Model analysis indicated that the feature-outcome relationship were non-linear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches. PMID:22376251

  9. Cellphone-based detection platform for rbST biomarker analysis in milk extracts using a microsphere fluorescence immunoassay.

    PubMed

    Ludwig, Susann K J; Zhu, Hongying; Phillips, Stephen; Shiledar, Ashutosh; Feng, Steve; Tseng, Derek; van Ginkel, Leendert A; Nielen, Michel W F; Ozcan, Aydogan

    2014-11-01

    Current contaminant and residue monitoring throughout the food chain is based on sampling, transport, administration, and analysis in specialized control laboratories. This is a highly inefficient and costly process since typically more than 99% of the samples are found to be compliant. On-site simplified prescreening may provide a scenario in which only samples that are suspect are transported and further processed. Such a prescreening can be performed using a small attachment on a cellphone. To this end, a cellphone-based imaging platform for a microsphere fluorescence immunoassay that detects the presence of anti-recombinant bovine somatotropin (rbST) antibodies in milk extracts was developed. RbST administration to cows increases their milk production, but is illegal in the EU and a public health concern in the USA. The cellphone monitors the presence of anti-rbST antibodies (rbST biomarker), which are endogenously produced upon administration of rbST and excreted in milk. The rbST biomarker present in milk extracts was captured by rbST covalently coupled to paramagnetic microspheres and labeled by quantum dot (QD)-coupled detection antibodies. The emitted fluorescence light from these captured QDs was then imaged using the cellphone camera. Additionally, a dark-field image was taken in which all microspheres present were visible. The fluorescence and dark-field microimages were analyzed using a custom-developed Android application running on the same cellphone. With this setup, the microsphere fluorescence immunoassay and cellphone-based detection were successfully applied to milk sample extracts from rbST-treated and untreated cows. An 80% true-positive rate and 95% true-negative rate were achieved using this setup. Next, the cellphone-based detection platform was benchmarked against a newly developed planar imaging array alternative and found to be equally performing versus the much more sophisticated alternative. Using cellphone-based on-site analysis in

  10. Harvest: a web-based biomedical data discovery and reporting application development platform.

    PubMed

    Italia, Michael J; Pennington, Jeffrey W; Ruth, Byron; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; Miller, Jeffrey; White, Peter S

    2013-01-01

    Biomedical researchers share a common challenge of making complex data understandable and accessible. This need is increasingly acute as investigators seek opportunities for discovery amidst an exponential growth in the volume and complexity of laboratory and clinical data. To address this need, we developed Harvest, an open source framework that provides a set of modular components to aid the rapid development and deployment of custom data discovery software applications. Harvest incorporates visual representations of multidimensional data types in an intuitive, web-based interface that promotes a real-time, iterative approach to exploring complex clinical and experimental data. The Harvest architecture capitalizes on standards-based, open source technologies to address multiple functional needs critical to a research and development environment, including domain-specific data modeling, abstraction of complex data models, and a customizable web client.

  11. Interesting Spatio-Temporal Region Discovery Computations Over Gpu and Mapreduce Platforms

    NASA Astrophysics Data System (ADS)

    McDermott, M.; Prasad, S. K.; Shekhar, S.; Zhou, X.

    2015-07-01

    Discovery of interesting paths and regions in spatio-temporal data sets is important to many fields such as the earth and atmospheric sciences, GIS, public safety and public health both as a goal and as a preliminary step in a larger series of computations. This discovery is usually an exhaustive procedure that quickly becomes extremely time consuming to perform using traditional paradigms and hardware and given the rapidly growing sizes of today's data sets is quickly outpacing the speed at which computational capacity is growing. In our previous work (Prasad et al., 2013a) we achieved a 50 times speedup over sequential using a single GPU. We were able to achieve near linear speedup over this result on interesting path discovery by using Apache Hadoop to distribute the workload across multiple GPU nodes. Leveraging the parallel architecture of GPUs we were able to drastically reduce the computation time of a 3-dimensional spatio-temporal interest region search on a single tile of normalized difference vegetative index for Saudi Arabia. We were further able to see an almost linear speedup in compute performance by distributing this workload across several GPUs with a simple MapReduce model. This increases the speed of processing 10 fold over the comparable sequential while simultaneously increasing the amount of data being processed by 384 fold. This allowed us to process the entirety of the selected data set instead of a constrained window.

  12. Integrated project views: decision support platform for drug discovery project teams.

    PubMed

    Baede, Eric J; den Bekker, Ernest; Boiten, Jan-Willem; Cronin, Deborah; van Gammeren, Rob; de Vlieg, Jacob

    2012-06-25

    Drug discovery teams continuously have to decide which compounds to progress and which experiments to perform next, but the data required to make informed decisions is often scattered, inaccessible, or inconsistent. In particular, data tend to be stored and represented in a compound-centric or assay-centric manner rather than project-centric as often needed for effective use in drug discovery teams. The Integrated Project Views (IPV) system has been created to fill this gap; it integrates and consolidates data from various sources in a project-oriented manner. Its automatic gathering and updating of project data not only ensures that the information is comprehensive and available on a timely basis, but also improves the data consistency. Due to the lack of suitable off-the-shelf solutions, we were prompted to develop custom functionality and algorithms geared specifically to our drug discovery decision making process. In 10 years of usage, the resulting IPV application has become very well-accepted and appreciated, which is perhaps best evidenced by the observation that standalone Excel spreadsheets are largely eliminated from project team meetings.

  13. Drug discovery applications for KNIME: an open source data mining platform.

    PubMed

    Mazanetz, Michael P; Marmon, Robert J; Reisser, Catherine B T; Morao, Inaki

    2012-01-01

    Technological advances in high-throughput screening methods, combinatorial chemistry and the design of virtual libraries have evolved in the pursuit of challenging drug targets. Over the last two decades a vast amount of data has been generated within these fields and as a consequence data mining methods have been developed to extract key pieces of information from these large data pools. Much of this data is now available in the public domain. This has been helpful in the arena of drug discovery for both academic groups and for small to medium sized enterprises which previously would not have had access to such data resources. Commercial data mining software is sometimes prohibitively expensive and the alternate open source data mining software is gaining momentum in both academia and in industrial applications as the costs of research and development continue to rise. KNIME, the Konstanz Information Miner, has emerged as a leader in open source data mining tools. KNIME provides an integrated solution for the data mining requirements across the drug discovery pipeline through a visual assembly of data workflows drawing from an extensive repository of tools. This review will examine KNIME as an open source data mining tool and its applications in drug discovery.

  14. Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

    PubMed

    Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

    2014-01-01

    Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

  15. Establishment of a novel whole animal HTS technology platform for melioidosis drug discovery.

    PubMed

    Lakshmanan, Umayal; Yap, Amelia; Fulwood, Justina; Yichun, Li; Hoon, Sim Siew; Lim, Jolander; Ting, Audrey; Sem, Xiao Hui; Kreisberg, Jason F; Tan, Patrick; Tan, Gladys; Flotow, Horst

    2014-01-01

    Melioidosis is a serious emerging endemic infectious disease caused by Burkholderia pseudomallei, a gram-negative pathogen. Septicemic melioidosis has a mortality rate of 50% even with treatment. Like other gram-negative bacteria, B. pseudomallei is resistant to a number of antibiotics and multi-drug resistant B. pseudomallei is beginning to be encountered in hospitals. There is a clear medical need to develop new treatment options to manage this disease. We used Burkholderia thailandensis (a BSL-2 class organism) to infect Caenorhabditis elegans and set up a surrogate whole animal infection model of melioidosis that we could run in a 384 microtitre plate and establish a whole animal HTS assay. We have optimized and validated this assay in a fluorescence-based format that can be run on our automated screening platforms. This assay has now been used to screen over 300,000 compounds from our small molecule library and we are in the process of characterizing the hits obtained and select compounds for further studies. We have thus established a biologically relevant assay technology platform to screen for antibacterial compounds and used this platform to identify new compounds that may find application in treating melioidosis infections.

  16. A study of a self diagnostic platform for the detection of A2 biomarker for Leishmania donovani

    NASA Astrophysics Data System (ADS)

    Roche, Philip J. R.; Cheung, Maurice C.; Najih, Mohamed; McCall, Laura-Isobel; Fakih, Ibrahim; Chodavarapu, Vamsy P.; Ward, Brian; Ndao, Momar; Kirk, Andrew G.

    2012-03-01

    Visceral leishmaniasis (L.donovani) is a protozoan infection that attacks mononuclear phagocytes and causes the liver and spleen damage that can cause death. The investigation presented is a proof of concept development applying a plasmonic diagnostic platform with simple microfluidic sample delivery and optical readout. An immune-assay method is applied to the quantification of A2 protein, a highly immunogenic biomarker for the pathogen. Quantification of A2 was performed in the ng/ml range, analysis by ELISA suggested that a limit of 0.1ng/ml of A2 is approximate to 1 pathogen per ml and the sensing system shows the potential to deliver a similar level of quantification. Significant reduction in assay complexity as further enzyme linked enhancement is not required when applying a plasmonic methodology to an immunoassay. The basic instrumentation required for a portable device and potential dual optical readout where both plasmonic and photoluminescent response are assessed and investigated including consideration of the application of the device to testing where non-literate communication of results is considered and issues of performance are addressed.

  17. High-Throughput Screening Platform for the Discovery of New Immunomodulator Molecules from Natural Product Extract Libraries.

    PubMed

    Pérez Del Palacio, José; Díaz, Caridad; de la Cruz, Mercedes; Annang, Frederick; Martín, Jesús; Pérez-Victoria, Ignacio; González-Menéndez, Víctor; de Pedro, Nuria; Tormo, José R; Algieri, Francesca; Rodriguez-Nogales, Alba; Rodríguez-Cabezas, M Elena; Reyes, Fernando; Genilloud, Olga; Vicente, Francisca; Gálvez, Julio

    2016-07-01

    It is widely accepted that central nervous system inflammation and systemic inflammation play a significant role in the progression of chronic neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, neurotropic viral infections, stroke, paraneoplastic disorders, traumatic brain injury, and multiple sclerosis. Therefore, it seems reasonable to propose that the use of anti-inflammatory drugs might diminish the cumulative effects of inflammation. Indeed, some epidemiological studies suggest that sustained use of anti-inflammatory drugs may prevent or slow down the progression of neurodegenerative diseases. However, the anti-inflammatory drugs and biologics used clinically have the disadvantage of causing side effects and a high cost of treatment. Alternatively, natural products offer great potential for the identification and development of bioactive lead compounds into drugs for treating inflammatory diseases with an improved safety profile. In this work, we present a validated high-throughput screening approach in 96-well plate format for the discovery of new molecules with anti-inflammatory/immunomodulatory activity. The in vitro models are based on the quantitation of nitrite levels in RAW264.7 murine macrophages and interleukin-8 in Caco-2 cells. We have used this platform in a pilot project to screen a subset of 5976 noncytotoxic crude microbial extracts from the MEDINA microbial natural product collection. To our knowledge, this is the first report on an high-throughput screening of microbial natural product extracts for the discovery of immunomodulators.

  18. Discovery by a proteomic approach of possible early biomarkers of drug-induced nephrotoxicity in medication-overuse headache

    PubMed Central

    2013-01-01

    Background Medication-overuse headache (MOH) is a chronic headache condition that results from the overuse of analgesics drugs, triptans, or other antimigraine compounds. The epidemiology of drug-induced disorders suggests that medication overuse could lead to nephrotoxicity, particularly in chronic patients. The aim of this work was to confirm and extend the results obtained from a previous study, in which we analyzed the urinary proteome of 3 MOH patients groups: non-steroidal anti-inflammatory drugs (NSAIDs), triptans and mixtures abusers, in comparison with non-abusers individuals (controls). Methods In the present work we employed specialized proteomic techniques, namely two-dimensional gel electrophoresis (2-DE) coupled with mass spectrometry (MS), and the innovative Surface-Enhanced Laser Desorption/Ionization Time-of-Flight mass spectrometry (SELDI-TOF-MS), to discover characteristic proteomic profiles associated with MOH condition. Results By 2-DE and MS analysis we identified 21 over-excreted proteins in MOH patients, particularly in NSAIDs abusers, and the majority of these proteins were involved in a variety of renal impairments, as resulted from a literature search. Urine protein profiles generated by SELDI-TOF-MS analysis showed different spectra among groups. Moreover, significantly higher number of total protein spots and protein peaks were detected in NSAIDs and mixtures abusers. Conclusions These findings confirm the presence of alterations in proteins excretion in MOH patients. Analysis of urinary proteins by powerful proteomic technologies could lead to the discovery of early candidate biomarkers, that might allow to identify MOH patients prone to develop potential drug overuse-induced nephrotoxicity. PMID:23565828

  19. An efficient T-cell epitope discovery strategy using in silico prediction and the iTopia assay platform

    PubMed Central

    Fridman, Arthur; Finnefrock, Adam C.; Peruzzi, Daniela; Pak, Irene; La Monica, Nicola; Bagchi, Ansuman; Casimiro, Danilo R.; Ciliberto, Gennaro; Aurisicchio, Luigi

    2012-01-01

    Functional T-cell epitope discovery is a key process for the development of novel immunotherapies, particularly for cancer immunology. In silico epitope prediction is a common strategy to try to achieve this objective. However, this approach suffers from a significant rate of false-negative results and epitope ranking lists that often are not validated by practical experience. A high-throughput platform for the identification and prioritization of potential T-cell epitopes is the iTopiaTM Epitope Discovery SystemTM, which allows measuring binding and stability of selected peptides to MHC Class I molecules. So far, the value of iTopia combined with in silico epitope prediction has not been investigated systematically. In this study, we have developed a novel in silico selection strategy based on three criteria: (1) predicted binding to one out of five common MHC Class I alleles; (2) uniqueness to the antigen of interest; and (3) increased likelihood of natural processing. We predicted in silico and characterized by iTopia 225 candidate T-cell epitopes and fixed-anchor analogs from three human tumor-associated antigens: CEA, HER2 and TERT. HLA-A2-restricted fragments were further screened for their ability to induce cell-mediated responses in HLA-A2 transgenic mice. The iTopia binding assay was only marginally informative while the stability assay proved to be a valuable experimental screening method complementary to in silico prediction. Thirteen novel T-cell epitopes and analogs were characterized and additional potential epitopes identified, providing the basis for novel anticancer immunotherapies. In conclusion, we show that combination of in silico prediction and an iTopia-based assay may be an accurate and efficient method for MHC Class I epitope discovery among tumor-associated antigens. PMID:23243589

  20. Development of a carbonate platform with potential for large discoveries - an example from Vietnam

    SciTech Connect

    Mayall, M.; Bent, A.; Dale, B. )

    1996-01-01

    In offshore central and southern Vietnam a number of carbonate accumulations can be recognized. Platform carbonates form basin-wide units of carbonate characterized by strong, continuous parallel seismic reflectors. Facies are dominated by bioclastic wackestones with poor-moderate reservoir quality. On the more isolated highs, large buildups developed. These are typically 5-10 km across and 300 m thick. They unconformably overlie the platform carbonate facies which are extensively karstified. In places these are pinnacles, typically 2-5 km across, 300 m+ thick with chaotic or mounded internal seismic facies. The large carbonate buildups are characterized by steep sided slopes with talus cones, reef-margin rims usually developed around only part of the buildup, and a prominent back-stepping geometry. Buildup interior facies form the main potential reservoirs They are dominated by fine to coarse grained coralgal packstones. Fine grained carbonates are associated with deeper water events and multiple karst surfaces can also be identified. Reservoir quality is excellent, largely controlled by extensive dissolution and dolomitization believed to be related to the exposure events. Gas has been found in a number of reservoirs. Heterogeneities can be recognized which could potentially effect production. These include the extensive finer grained facies, cementation or open fissures associated with the karst surfaces, a more cemented reef rim, shallowing upwards facies cycles and faults.

  1. Development of a carbonate platform with potential for large discoveries - an example from Vietnam

    SciTech Connect

    Mayall, M.; Bent, A.; Dale, B.

    1996-12-31

    In offshore central and southern Vietnam a number of carbonate accumulations can be recognized. Platform carbonates form basin-wide units of carbonate characterized by strong, continuous parallel seismic reflectors. Facies are dominated by bioclastic wackestones with poor-moderate reservoir quality. On the more isolated highs, large buildups developed. These are typically 5-10 km across and 300 m thick. They unconformably overlie the platform carbonate facies which are extensively karstified. In places these are pinnacles, typically 2-5 km across, 300 m+ thick with chaotic or mounded internal seismic facies. The large carbonate buildups are characterized by steep sided slopes with talus cones, reef-margin rims usually developed around only part of the buildup, and a prominent back-stepping geometry. Buildup interior facies form the main potential reservoirs They are dominated by fine to coarse grained coralgal packstones. Fine grained carbonates are associated with deeper water events and multiple karst surfaces can also be identified. Reservoir quality is excellent, largely controlled by extensive dissolution and dolomitization believed to be related to the exposure events. Gas has been found in a number of reservoirs. Heterogeneities can be recognized which could potentially effect production. These include the extensive finer grained facies, cementation or open fissures associated with the karst surfaces, a more cemented reef rim, shallowing upwards facies cycles and faults.

  2. Integrating `-omics' and natural product discovery platforms to investigate metabolic exchange in microbiomes

    PubMed Central

    Yang, Jane Y; Karr, Jessica R; Watrous, Jeramie D; Dorrestein, Pieter C

    2011-01-01

    The microbiome is an abundance of microorganisms within a host (e.g. human microbiome). These microorganisms produce small molecules and metabolites that have been shown to affect and dictate the physiology of an individual. Functional knowledge of these molecules, often produced for communication or defense, will reveal the interplay between microbes and host in health and disease. The vast diversity in structure and function of microbiome-associated small molecules necessitate tools that will utilize multiple `-omics' strategies to understand the interactions within the human microbiome. This review discusses the importance of these investigations and the integration of current `-omics' technologies with tools established in natural product discovery in order to identify and characterize uncharacterized small molecules in the effort towards diagnostic modeling of the human microbiome. PMID:21087892

  3. In situ studies of a platform for metastable inorganic crystal growth and materials discovery

    PubMed Central

    Shoemaker, Daniel P.; Hu, Yung-Jin; Chung, Duck Young; Halder, Gregory J.; Chupas, Peter J.; Soderholm, L.; Mitchell, J. F.; Kanatzidis, Mercouri G.

    2014-01-01

    Rapid shifts in the energy, technological, and environmental demands of materials science call for focused and efficient expansion of the library of functional inorganic compounds. To achieve the requisite efficiency, we need a materials discovery and optimization paradigm that can rapidly reveal all possible compounds for a given reaction and composition space. Here we provide such a paradigm via in situ X-ray diffraction measurements spanning solid, liquid flux, and recrystallization processes. We identify four new ternary sulfides from reactive salt fluxes in a matter of hours, simultaneously revealing routes for ex situ synthesis and crystal growth. Changing the flux chemistry, here accomplished by increasing sulfur content, permits comparison of the allowable crystalline building blocks in each reaction space. The speed and structural information inherent to this method of in situ synthesis provide an experimental complement to computational efforts to predict new compounds and uncover routes to targeted materials by design. PMID:25024201

  4. Academic College of Emergency Experts in India's INDO-US Joint Working Group and OPUS12 Foundation Consensus Statement on Creating A Coordinated, Multi-Disciplinary, Patient-Centered, Global Point-of-Care Biomarker Discovery Network.

    PubMed

    Stawicki, Stanislaw P; Stoltzfus, Jill C; Aggarwal, Praveen; Bhoi, Sanjeev; Bhatt, Shashi; Kalra, O P; Bhalla, Ashish; Hoey, Brian A; Galwankar, Sagar C; Paladino, Lorenzo; Papadimos, Thomas J

    2014-07-01

    Biomarker science brings great promise to clinical medicine. This is especially true in the era of technology miniaturization, rapid dissemination of knowledge, and point-of-care (POC) implementation of novel diagnostics. Despite this tremendous progress, the journey from a candidate biomarker to a scientifically validated biomarker continues to be an arduous one. In addition to substantial financial resources, biomarker research requires considerable expertise and a multidisciplinary approach. Investigational designs must also be taken into account, with the randomized controlled trial remaining the "gold standard". The authors present a condensed overview of biomarker science and associated investigational methods, followed by specific examples from clinical areas where biomarker development and/or implementation resulted in tangible enhancements in patient care. This manuscript also serves as a call to arms for the establishment of a truly global, well-coordinated infrastructure dedicated to biomarker research and development, with focus on delivery of the latest discoveries directly to the patient via point-of-care technology.

  5. Application of proteomics in the discovery of candidate protein biomarkers in a Diabetes Autoantibody Standardization Program sample subset

    SciTech Connect

    Metz, Thomas O.; Qian, Weijun; Jacobs, Jon M.; Gritsenko, Marina A.; Moore, Ronald J.; Polpitiya, Ashoka D.; Monroe, Matthew E.; Camp, David G.; mueller, Patricia W.; Smith, Richard D.

    2008-02-01

    Objective. Before biomarkers predictive of type 1 diabetes can be evaluated in proficiency evaluations, they must be identified and validated in initial, exploratory studies. Hypothesis-driven comparative studies may be performed to identify candidate biomarkers but are limited to the current knowledge of metabolic, signaling, and inflammatory pathways in the context of type 1 diabetes. Alternatively, untargeted “-omics” approaches may be employed in profiling studies to identify candidate biomarkers of type 1 diabetes.

  6. Microdroplets in microfluidics: an evolving platform for discoveries in chemistry and biology.

    PubMed

    Theberge, Ashleigh B; Courtois, Fabienne; Schaerli, Yolanda; Fischlechner, Martin; Abell, Chris; Hollfelder, Florian; Huck, Wilhelm T S

    2010-08-09

    Microdroplets in microfluidics offer a great number of opportunities in chemical and biological research. They provide a compartment in which species or reactions can be isolated, they are monodisperse and therefore suitable for quantitative studies, they offer the possibility to work with extremely small volumes, single cells, or single molecules, and are suitable for high-throughput experiments. The aim of this Review is to show the importance of these features in enabling new experiments in biology and chemistry. The recent advances in device fabrication are highlighted as are the remaining technological challenges. Examples are presented to show how compartmentalization, monodispersity, single-molecule sensitivity, and high throughput have been exploited in experiments that would have been extremely difficult outside the microfluidics platform.

  7. High-throughput platform for the discovery of elicitors of silent bacterial gene clusters

    PubMed Central

    Seyedsayamdost, Mohammad R.

    2014-01-01

    Over the past decade, bacterial genome sequences have revealed an immense reservoir of biosynthetic gene clusters, sets of contiguous genes that have the potential to produce drugs or drug-like molecules. However, the majority of these gene clusters appear to be inactive for unknown reasons prompting terms such as “cryptic” or “silent” to describe them. Because natural products have been a major source of therapeutic molecules, methods that rationally activate these silent clusters would have a profound impact on drug discovery. Herein, a new strategy is outlined for awakening silent gene clusters using small molecule elicitors. In this method, a genetic reporter construct affords a facile read-out for activation of the silent cluster of interest, while high-throughput screening of small molecule libraries provides potential inducers. This approach was applied to two cryptic gene clusters in the pathogenic model Burkholderia thailandensis. The results not only demonstrate a prominent activation of these two clusters, but also reveal that the majority of elicitors are themselves antibiotics, most in common clinical use. Antibiotics, which kill B. thailandensis at high concentrations, act as inducers of secondary metabolism at low concentrations. One of these antibiotics, trimethoprim, served as a global activator of secondary metabolism by inducing at least five biosynthetic pathways. Further application of this strategy promises to uncover the regulatory networks that activate silent gene clusters while at the same time providing access to the vast array of cryptic molecules found in bacteria. PMID:24808135

  8. Bioluminescence Resonance Energy Transfer (BRET)-Based Synthetic Sensor Platform for Drug Discovery.

    PubMed

    Woo, Jongchan; Hong, Jason; Dinesh-Kumar, Savithramma P

    2017-04-03

    Bioluminescence resonance energy transfer (BRET) is a technique that analyzes protein-protein interactions (PPIs). The unique feature of BRET delineates that the resonance energy is generated by the resonance energy donor, Renilla luciferase by the oxidative decarboxylation of coelenterazine substrate. BRET is superior to FRET where issues such as autofluorescence, photobleaching, and light scattering can occur. Recently, BRET has been applied to design synthetic biosensors for monitoring autophagy in vivo and in vitro. Here, we report the methods for constructing a biosensor of human HsLC3a as a probe for autophagy biogenesis and the optimization of the intramolecular BRET assay that allows for high-throughput screening of chemical modulators of autophagy. User-friendly working interface with the BRET-based synthetic sensor of HsLC3a makes drug discovery easy and amenable for high-throughput. The BRET protocol described here could be easily applicable to generate other biosensors for monitoring PPIs by measurement of intermolecular BRET. © 2017 by John Wiley & Sons, Inc.

  9. Biomarkers in Veterinary Medicine.

    PubMed

    Myers, Michael J; Smith, Emily R; Turfle, Phillip G

    2017-02-08

    This article summarizes the relevant definitions related to biomarkers; reviews the general processes related to biomarker discovery and ultimate acceptance and use; and finally summarizes and reviews, to the extent possible, examples of the types of biomarkers used in animal species within veterinary clinical practice and human and veterinary drug development. We highlight opportunities for collaboration and coordination of research within the veterinary community and leveraging of resources from human medicine to support biomarker discovery and validation efforts for veterinary medicine.

  10. Comparison of protein, microRNA, and mRNA yields using different methods of urinary exosome isolation for the discovery of kidney disease biomarkers.

    PubMed

    Alvarez, M Lucrecia; Khosroheidari, Mahdieh; Kanchi Ravi, Rupesh; DiStefano, Johanna K

    2012-11-01

    Urinary exosomes are 40-100 nm vesicles containing protein, mRNA, and microRNA that may serve as biomarkers of renal dysfunction and structural injury. Currently, there is a need for more sensitive and specific biomarkers of renal injury and disease progression. Here we sought to identify the best exosome isolation methods for both proteomic analysis and RNA profiling as a first step for biomarker discovery. We used six different protocols; three were based on ultracentrifugation, one used a nanomembrane concentrator-based approach, and two utilized a commercial exosome precipitation reagent. The highest yield of exosomes was obtained using a modified exosome precipitation protocol, which also yielded the highest quantities of microRNA and mRNA and, therefore, is ideal for subsequent RNA profiling. This method is likewise suitable for downstream proteomic analyses if an ultracentrifuge is not available and/or a large number of samples are to be processed. Two of the ultracentrifugation methods, however, are better options for exosome isolation if an ultracentrifuge is available and few samples will be processed for proteomic analysis. Thus, our modified exosome precipitation method is a simple, fast, highly scalable, and effective alternative for the isolation of exosomes, and may facilitate the identification of exosomal biomarkers from urine.

  11. A Sorghum Mutant Resource as an Efficient Platform for Gene Discovery in Grasses[OPEN

    PubMed Central

    Burke, John; Chen, Junping; Wang, Bo; Hayes, Chad; Emendack, Yves

    2016-01-01

    Sorghum (Sorghum bicolor) is a versatile C4 crop and a model for research in family Poaceae. High-quality genome sequence is available for the elite inbred line BTx623, but functional validation of genes remains challenging due to the limited genomic and germplasm resources available for comprehensive analysis of induced mutations. In this study, we generated 6400 pedigreed M4 mutant pools from EMS-mutagenized BTx623 seeds through single-seed descent. Whole-genome sequencing of 256 phenotyped mutant lines revealed >1.8 million canonical EMS-induced mutations, affecting >95% of genes in the sorghum genome. The vast majority (97.5%) of the induced mutations were distinct from natural variations. To demonstrate the utility of the sequenced sorghum mutant resource, we performed reverse genetics to identify eight genes potentially affecting drought tolerance, three of which had allelic mutations and two of which exhibited exact cosegregation with the phenotype of interest. Our results establish that a large-scale resource of sequenced pedigreed mutants provides an efficient platform for functional validation of genes in sorghum, thereby accelerating sorghum breeding. Moreover, findings made in sorghum could be readily translated to other members of the Poaceae via integrated genomics approaches. PMID:27354556

  12. A Sorghum Mutant Resource as an Efficient Platform for Gene Discovery in Grasses.

    PubMed

    Jiao, Yinping; Burke, John; Chopra, Ratan; Burow, Gloria; Chen, Junping; Wang, Bo; Hayes, Chad; Emendack, Yves; Ware, Doreen; Xin, Zhanguo

    2016-07-01

    Sorghum (Sorghum bicolor) is a versatile C4 crop and a model for research in family Poaceae. High-quality genome sequence is available for the elite inbred line BTx623, but functional validation of genes remains challenging due to the limited genomic and germplasm resources available for comprehensive analysis of induced mutations. In this study, we generated 6400 pedigreed M4 mutant pools from EMS-mutagenized BTx623 seeds through single-seed descent. Whole-genome sequencing of 256 phenotyped mutant lines revealed >1.8 million canonical EMS-induced mutations, affecting >95% of genes in the sorghum genome. The vast majority (97.5%) of the induced mutations were distinct from natural variations. To demonstrate the utility of the sequenced sorghum mutant resource, we performed reverse genetics to identify eight genes potentially affecting drought tolerance, three of which had allelic mutations and two of which exhibited exact cosegregation with the phenotype of interest. Our results establish that a large-scale resource of sequenced pedigreed mutants provides an efficient platform for functional validation of genes in sorghum, thereby accelerating sorghum breeding. Moreover, findings made in sorghum could be readily translated to other members of the Poaceae via integrated genomics approaches.

  13. Hexosamine template. A platform for modulating gene expression and for sugar-based drug discovery.

    PubMed

    Elmouelhi, Noha; Aich, Udayanath; Paruchuri, Venkata D P; Meledeo, M Adam; Campbell, Christopher T; Wang, Jean J; Srinivas, Raja; Khanna, Hargun S; Yarema, Kevin J

    2009-04-23

    This study investigates the breadth of cellular responses engendered by short chain fatty acid (SCFA)-hexosamine hybrid molecules, a class of compounds long used in "metabolic glycoengineering" that are now emerging as drug candidates. First, a "mix and match" strategy showed that different SCFA (n-butyrate and acetate) appended to the same core sugar altered biological activity, complementing previous results [Campbell et al. J. Med. Chem. 2008, 51, 8135-8147] where a single type of SCFA elicited distinct responses. Microarray profiling then compared transcriptional responses engendered by regioisomerically modified ManNAc, GlcNAc, and GalNAc analogues in MDA-MB-231 cells. These data, which were validated by qRT-PCR or Western analysis for ID1, TP53, HPSE, NQO1, EGR1, and VEGFA, showed a two-pronged response where a core set of genes was coordinately regulated by all analogues while each analogue simultaneously uniquely regulated a larger number of genes. Finally, AutoDock modeling supported a mechanism where the analogues directly interact with elements of the NF-kappaB pathway. Together, these results establish the SCFA-hexosamine template as a versatile platform for modulating biological activity and developing new therapeutics.

  14. Earthdata 3.0: A Unified Experience and Platform for Earth Science Discovery

    NASA Astrophysics Data System (ADS)

    Plofchan, P.; McLaughlin, B. D.

    2015-12-01

    NASA's EOSDIS (Earth Observing System Data and Information System) as a multitude of websites and applications focused on serving the Earth Science community's extensive data needs. With no central user interface, theme, or mechanism for accessing that data, interrelated systems are confusing and potentially disruptive in users' searches for EOSDIS data holdings. In an effort to bring consistency across these systems, an effort was undertaken to develop Earthdata 3.0: a complete information architecture overhaul of the Earthdata website, a significant update to the Earthdata user experience and user interface, and an increased focus on searching across EOSDIS data holdings, including those housed and made available through DAAC websites. As part of this effort, and in a desire to unify the user experience across related websites, the Earthdata User Interface (EUI) was developed. The EUI is a collection of responsive design components and layouts geared toward creating websites and applications within the Earthdata ecosystem. Each component and layout has been designed specifically for Earth science-related projects which eliminates some of the complexities of building a website or application from the ground up. Its adoption will ensure both consistent markup and a unified look and feel for end users, thereby increasing usability and accessibility. Additionally, through the user of a Google Search Appliance, custom Clojure code, and in cooperation with DAACs, Earthdata 3.0 presents a variety of search results upon a user's keyword(s) entry. These results are not just textual links, but also direct links to downloadable datasets, visualizations of datasets and collections of data, and related articles and videos for further research. The end result of the development of the EUI and the enhanced multi-response type search is a consistent and usable platform for Earth scientists and users to navigate and locate data to further their research.

  15. A High-Throughput Screening Platform of Microbial Natural Products for the Discovery of Molecules with Antibiofilm Properties against Salmonella

    PubMed Central

    Paytubi, Sonia; de La Cruz, Mercedes; Tormo, Jose R.; Martín, Jesús; González, Ignacio; González-Menendez, Victor; Genilloud, Olga; Reyes, Fernando; Vicente, Francisca; Madrid, Cristina; Balsalobre, Carlos

    2017-01-01

    In this report, we describe a High-Throughput Screening (HTS) to identify compounds that inhibit biofilm formation or cause the disintegration of an already formed biofilm using the Salmonella Enteritidis 3934 strain. Initially, we developed a new methodology for growing Salmonella biofilms suitable for HTS platforms. The biomass associated with biofilm at the solid-liquid interface was quantified by staining both with resazurin and crystal violet, to detect living cells and total biofilm mass, respectively. For a pilot project, a subset of 1120 extracts from the Fundación MEDINA's collection was examined to identify molecules with antibiofilm activity. This is the first validated HTS assay of microbial natural product extracts which allows for the detection of four types of activities which are not mutually exclusive: inhibition of biofilm formation, detachment of the preformed biofilm and antimicrobial activity against planktonic cells or biofilm embedded cells. Currently, several extracts have been selected for further fractionation and purification of the active compounds. In one of the natural extracts patulin has been identified as a potent molecule with antimicrobial activity against both, planktonic cells and cells within the biofilm. These findings provide a proof of concept that the developed HTS can lead to the discovery of new natural compounds with antibiofilm activity against Salmonella and its possible use as an alternative to antimicrobial therapies and traditional disinfectants. PMID:28303128

  16. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

    PubMed Central

    Piñero, Janet; Queralt-Rosinach, Núria; Bravo, Àlex; Deu-Pons, Jordi; Bauer-Mehren, Anna; Baron, Martin; Sanz, Ferran; Furlong, Laura I.

    2015-01-01

    DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380 000 associations between >16 000 genes and 13 000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/ PMID:25877637

  17. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes.

    PubMed

    Piñero, Janet; Queralt-Rosinach, Núria; Bravo, Àlex; Deu-Pons, Jordi; Bauer-Mehren, Anna; Baron, Martin; Sanz, Ferran; Furlong, Laura I

    2015-01-01

    DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380,000 associations between >16,000 genes and 13,000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/

  18. A High-Throughput Screening Platform of Microbial Natural Products for the Discovery of Molecules with Antibiofilm Properties against Salmonella.

    PubMed

    Paytubi, Sonia; de La Cruz, Mercedes; Tormo, Jose R; Martín, Jesús; González, Ignacio; González-Menendez, Victor; Genilloud, Olga; Reyes, Fernando; Vicente, Francisca; Madrid, Cristina; Balsalobre, Carlos

    2017-01-01

    In this report, we describe a High-Throughput Screening (HTS) to identify compounds that inhibit biofilm formation or cause the disintegration of an already formed biofilm using the Salmonella Enteritidis 3934 strain. Initially, we developed a new methodology for growing Salmonella biofilms suitable for HTS platforms. The biomass associated with biofilm at the solid-liquid interface was quantified by staining both with resazurin and crystal violet, to detect living cells and total biofilm mass, respectively. For a pilot project, a subset of 1120 extracts from the Fundación MEDINA's collection was examined to identify molecules with antibiofilm activity. This is the first validated HTS assay of microbial natural product extracts which allows for the detection of four types of activities which are not mutually exclusive: inhibition of biofilm formation, detachment of the preformed biofilm and antimicrobial activity against planktonic cells or biofilm embedded cells. Currently, several extracts have been selected for further fractionation and purification of the active compounds. In one of the natural extracts patulin has been identified as a potent molecule with antimicrobial activity against both, planktonic cells and cells within the biofilm. These findings provide a proof of concept that the developed HTS can lead to the discovery of new natural compounds with antibiofilm activity against Salmonella and its possible use as an alternative to antimicrobial therapies and traditional disinfectants.

  19. The Discovery of Novel Genomic, Transcriptomic, and Proteomic Biomarkers in Cardiovascular and Peripheral Vascular Disease: The State of the Art

    PubMed Central

    de Franciscis, Stefano; Metzinger, Laurent; Serra, Raffaele

    2016-01-01

    Cardiovascular disease (CD) and peripheral vascular disease (PVD) are leading causes of mortality and morbidity in western countries and also responsible of a huge burden in terms of disability, functional decline, and healthcare costs. Biomarkers are measurable biological elements that reflect particular physiological or pathological states or predisposition towards diseases and they are currently widely studied in medicine and especially in CD. In this context, biomarkers can also be used to assess the severity or the evolution of several diseases, as well as the effectiveness of particular therapies. Genomics, transcriptomics, and proteomics have opened new windows on disease phenomena and may permit in the next future an effective development of novel diagnostic and prognostic medicine in order to better prevent or treat CD. This review will consider the current evidence of novel biomarkers with clear implications in the improvement of risk assessment, prevention strategies, and medical decision making in the field of CD. PMID:27298828

  20. Discovery of serum protein biomarkers in the mdx mouse model and cross-species comparison to Duchenne muscular dystrophy patients

    PubMed Central

    Hathout, Yetrib; Marathi, Ramya L.; Rayavarapu, Sree; Zhang, Aiping; Brown, Kristy J.; Seol, Haeri; Gordish-Dressman, Heather; Cirak, Sebahattin; Bello, Luca; Nagaraju, Kanneboyina; Partridge, Terry; Hoffman, Eric P.; Takeda, Shin'ichi; Mah, Jean K.; Henricson, Erik; McDonald, Craig

    2014-01-01

    It is expected that serum protein biomarkers in Duchenne muscular dystrophy (DMD) will reflect disease pathogenesis, progression and aid future therapy developments. Here, we describe use of quantitative in vivo stable isotope labeling in mammals to accurately compare serum proteomes of wild-type and dystrophin-deficient mdx mice. Biomarkers identified in serum from two independent dystrophin-deficient mouse models (mdx-Δ52 and mdx-23) were concordant with those identified in sera samples of DMD patients. Of the 355 mouse sera proteins, 23 were significantly elevated and 4 significantly lower in mdx relative to wild-type mice (P-value < 0.001). Elevated proteins were mostly of muscle origin: including myofibrillar proteins (titin, myosin light chain 1/3, myomesin 3 and filamin-C), glycolytic enzymes (aldolase, phosphoglycerate mutase 2, beta enolase and glycogen phosphorylase), transport proteins (fatty acid-binding protein, myoglobin and somatic cytochrome-C) and others (creatine kinase M, malate dehydrogenase cytosolic, fibrinogen and parvalbumin). Decreased proteins, mostly of extracellular origin, included adiponectin, lumican, plasminogen and leukemia inhibitory factor receptor. Analysis of sera from 1 week to 7 months old mdx mice revealed age-dependent changes in the level of these biomarkers with most biomarkers acutely elevated at 3 weeks of age. Serum analysis of DMD patients, with ages ranging from 4 to 15 years old, confirmed elevation of 20 of the murine biomarkers in DMD, with similar age-related changes. This study provides a panel of biomarkers that reflect muscle activity and pathogenesis and should prove valuable tool to complement natural history studies and to monitor treatment efficacy in future clinical trials. PMID:25027324

  1. Discovery of serum protein biomarkers in the mdx mouse model and cross-species comparison to Duchenne muscular dystrophy patients.

    PubMed

    Hathout, Yetrib; Marathi, Ramya L; Rayavarapu, Sree; Zhang, Aiping; Brown, Kristy J; Seol, Haeri; Gordish-Dressman, Heather; Cirak, Sebahattin; Bello, Luca; Nagaraju, Kanneboyina; Partridge, Terry; Hoffman, Eric P; Takeda, Shin'ichi; Mah, Jean K; Henricson, Erik; McDonald, Craig

    2014-12-15

    It is expected that serum protein biomarkers in Duchenne muscular dystrophy (DMD) will reflect disease pathogenesis, progression and aid future therapy developments. Here, we describe use of quantitative in vivo stable isotope labeling in mammals to accurately compare serum proteomes of wild-type and dystrophin-deficient mdx mice. Biomarkers identified in serum from two independent dystrophin-deficient mouse models (mdx-Δ52 and mdx-23) were concordant with those identified in sera samples of DMD patients. Of the 355 mouse sera proteins, 23 were significantly elevated and 4 significantly lower in mdx relative to wild-type mice (P-value < 0.001). Elevated proteins were mostly of muscle origin: including myofibrillar proteins (titin, myosin light chain 1/3, myomesin 3 and filamin-C), glycolytic enzymes (aldolase, phosphoglycerate mutase 2, beta enolase and glycogen phosphorylase), transport proteins (fatty acid-binding protein, myoglobin and somatic cytochrome-C) and others (creatine kinase M, malate dehydrogenase cytosolic, fibrinogen and parvalbumin). Decreased proteins, mostly of extracellular origin, included adiponectin, lumican, plasminogen and leukemia inhibitory factor receptor. Analysis of sera from 1 week to 7 months old mdx mice revealed age-dependent changes in the level of these biomarkers with most biomarkers acutely elevated at 3 weeks of age. Serum analysis of DMD patients, with ages ranging from 4 to 15 years old, confirmed elevation of 20 of the murine biomarkers in DMD, with similar age-related changes. This study provides a panel of biomarkers that reflect muscle activity and pathogenesis and should prove valuable tool to complement natural history studies and to monitor treatment efficacy in future clinical trials.

  2. Respiratory Toxicity Biomarkers

    EPA Science Inventory

    The advancement in high throughput genomic, proteomic and metabolomic techniques have accelerated pace of lung biomarker discovery. A recent growth in the discovery of new lung toxicity/disease biomarkers have led to significant advances in our understanding of pathological proce...

  3. Proteome analysis of acute kidney injury - Discovery of new predominantly renal candidates for biomarker of kidney disease.

    PubMed

    Malagrino, Pamella Araujo; Venturini, Gabriela; Yogi, Patrícia Schneider; Dariolli, Rafael; Padilha, Kallyandra; Kiers, Bianca; Gois, Tamiris Carneiro; Cardozo, Karina Helena Morais; Carvalho, Valdemir Melechco; Salgueiro, Jéssica Silva; Girardi, Adriana Castello Costa; Titan, Silvia Maria de Oliveira; Krieger, José Eduardo; Pereira, Alexandre Costa

    2017-01-16

    The main bottleneck in studies aiming to identify novel biomarkers in acute kidney injury (AKI) has been the identification of markers that are organ and process specific. Here, we have used different tissues from a controlled porcine renal ischemia/reperfusion (I/R) model to identify new, predominantly renal biomarker candidates for kidney disease. Urine and serum samples were analyzed in pre-ischemia, ischemia (60min) and 4, 11 and 16h post-reperfusion, and renal cortex samples after 24h of reperfusion. Peptides were analyzed on the Q-Exactive™. In renal cortex proteome, we observed an increase in the synthesis of proteins in the ischemic kidney compared to the contralateral, highlighted by transcription factors and epithelial adherens junction proteins. Intersecting the set of proteins up- or down-regulated in the ischemic tissue with both serum and urine proteomes, we identified 6 proteins in the serum that may provide a set of targets for kidney injury. Additionally, we identified 49, being 4 predominantly renal, proteins in urine. As prove of concept, we validated one of the identified biomarkers, dipeptidyl peptidase IV, in a set of patients with diabetic nephropathy. In conclusion, we identified 55 systemic proteins, some of them predominantly renal, candidates for biomarkers of renal disease.

  4. DNA sequences of Pima (Gossypium barbadense L.) cotton leaf for examining transcriptome diversity and SNP biomarker discovery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    As an initial step to explore the transcriptome genetic diversity and to discover single nucleotide polymorphic (SNP)-biomarkers for marker assisted breeding within Pima (Gossypium barbadense L.) cotton, leaves from 25 day plants of three diverse genotypes were used to develop cDNA libraries. Using ...

  5. Regulatory Forum Opinion Piece*: Veterinary Pathologists in Translational Pharmacology and Biomarker Integration in Drug Discovery and Development.

    PubMed

    Ramaiah, Shashi K; Walker, Dana B

    2016-02-01

    This article highlights emerging roles for veterinary pathologists outside of traditional functions and in line with the translational research (TR) approach. Veterinary pathologists offer unique and valuable expertise toward addressing particular TR and associated translational pharmacology questions, identifying gaps and risks in biomarker and pathology strategies, and advancing TR team decision making. Veterinary pathologists' attributes that are integral to the TR approach include (i) well-developed understanding of comparative physiology, pathology, and disease; (ii) extensive experience in interpretation and integration of complex data sets on whole-body responses and utilizing this for deciphering pathogenesis and translating events between laboratory species and man; (iii) proficiency in recognizing differences in disease end points among individuals, animal species and strains, and assessing correlations between these differences and other investigative (including biomarker) findings; and (iv) strong background in a wide spectrum of research technologies that can address pathomechanistic questions and biomarker needs. Some of the more evident roles in which veterinary pathologists can offer their greatest contributions to address questions and strategies of TR and biomarker integration will be emphasized.

  6. Development of a universal metabolome-standard method for long-term LC-MS metabolome profiling and its application for bladder cancer urine-metabolite-biomarker discovery.

    PubMed

    Peng, Jun; Chen, Yi-Ting; Chen, Chien-Lun; Li, Liang

    2014-07-01

    Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and

  7. Glycoengineered cell models for the characterization of cancer O-glycoproteome: an innovative strategy for biomarker discovery.

    PubMed

    Campos, Diana; Freitas, Daniela; Gomes, Joana; Reis, Celso A

    2015-08-01

    Glycosylation is one of the most abundant forms of protein posttranslational modification. O-glycosylation is a major type of protein glycosylation, comprising different types and structures expressed in several physiologic and pathologic conditions. The understanding of protein attachment site and glycan structure is of the utmost importance for the clarification of the role glycosylation plays in normal cells and in pathological conditions. Neoplastic transformation frequently shows the expression of immature truncated O-glycans. These aberrantly expressed O-glycans have been shown to induce oncogenic properties and can be detected in premalignant lesions, meaning that they are an important source of biomarkers. This article addresses the recent application of genetically engineered cancer cell models to produce simplified homogenous O-glycans allowing the characterization of cancer cells O-glycoproteomes, using advanced mass spectrometry methods and the identification of potential cancer-specific O-glycosylation sites. This article will also discuss possible applications of these biomarkers in the cancer field.

  8. An Integrated Analysis of Heterogeneous Drug Responses in Acute Myeloid Leukemia That Enables the Discovery of Predictive Biomarkers.

    PubMed

    Chen, Weihsu C; Yuan, Julie S; Xing, Yan; Mitchell, Amanda; Mbong, Nathan; Popescu, Andreea C; McLeod, Jessica; Gerhard, Gitte; Kennedy, James A; Bogdanoski, Goce; Lauriault, Stevan; Perdu, Sofie; Merkulova, Yulia; Minden, Mark D; Hogge, Donna E; Guidos, Cynthia; Dick, John E; Wang, Jean C Y

    2016-03-01

    Many promising new cancer drugs proceed through preclinical testing and early-phase trials only to fail in late-stage clinical testing. Thus, improved models that better predict survival outcomes and enable the development of biomarkers are needed to identify patients most likely to respond to and benefit from therapy. Here, we describe a comprehensive approach in which we incorporated biobanking, xenografting, and multiplexed phospho-flow (PF) cytometric profiling to study drug response and identify predictive biomarkers in acute myeloid leukemia (AML) patients. To test the efficacy of our approach, we evaluated the investigational JAK2 inhibitor fedratinib (FED) in 64 patient samples. FED robustly reduced leukemia in mouse xenograft models in 59% of cases and was also effective in limiting the protumorigenic activity of leukemia stem cells as shown by serial transplantation assays. In parallel, PF profiling identified FED-mediated reduction in phospho-STAT5 (pSTAT5) levels as a predictive biomarker of in vivo drug response with high specificity (92%) and strong positive predictive value (93%). Unexpectedly, another JAK inhibitor, ruxolitinib (RUX), was ineffective in 8 of 10 FED-responsive samples. Notably, this outcome could be predicted by the status of pSTAT5 signaling, which was unaffected by RUX treatment. Consistent with this observed discrepancy, PF analysis revealed that FED exerted its effects through multiple JAK2-independent mechanisms. Collectively, this work establishes an integrated approach for testing novel anticancer agents that captures the inherent variability of response caused by disease heterogeneity and in parallel, facilitates the identification of predictive biomarkers that can help stratify patients into appropriate clinical trials.

  9. Analysis of serum metabolites for the discovery of amino acid biomarkers and the effect of galangin on cerebral ischemia.

    PubMed

    Gao, Jian; Yang, Hongjun; Chen, Jianxin; Fang, Jiansong; Chen, Chang; Liang, Rixin; Yang, Gengliang; Wu, Hongwei; Wu, Chuanhong; Li, Shaojing

    2013-09-01

    Ischemic stroke, a devastating disease with a complex pathophysiology, is a leading cause of death and disability worldwide. In our previous study, we reported that galangin provided direct protection against ischemic injury and acted as a potential neuroprotective agent. However, its associated neuroprotective mechanism has not yet been clarified. In this paper, we explored the potential AA biomarkers in the acute phase of cerebral ischemia and the effect of galangin on those potential biomarkers. In our study, 12 AAs were quantified in rat serum and found to be impaired by middle cerebral artery occlusion (MCAO)-induced focal cerebral ischemia. Using partial least squares discriminate analysis (PLS-DA), we identified the following amino acids as potential biomarkers of cerebral ischemia: glutamic acid (Glu), homocysteine (Hcy), methionine (Met), tryptophan (Trp), aspartic acid (Asp), alanine (Ala) and tyrosine (Tyr). Moreover, four amino acids (Hcy, Met, Glu and Trp) showed significant change in galangin-treated (100 and 50 mg kg(-1)) groups compared to vehicle groups. Furthermore, we identified three pathway-related enzymes tyrosine aminotransferase (TAT), glutamine synthetase (GLUL) and monocarboxylate transporter (SLC16A10) by multiplex interactions with Glu and Hcy, which have been previously reported to be closely related to cerebral ischemia. Through an analysis of the metabolite-protein network analysis, we identified 16 proteins that were associated with two amino acids by multiple interactions with three enzymes; five of them may become potential biomarkers of galangin for acute ischemic stroke as the result of molecule docking. Our results may help develop novel strategies to explore the mechanism of cerebral ischemia, discover potential targets for drug candidates and elucidate the related regulatory signal network.

  10. Nonylphenol Toxicity Evaluation and Discovery of Biomarkers in Rat Urine by a Metabolomics Strategy through HPLC-QTOF-MS

    PubMed Central

    Zhang, Yan-Xin; Yang, Xin; Zou, Pan; Du, Peng-Fei; Wang, Jing; Jin, Fen; Jin, Mao-Jun; She, Yong-Xin

    2016-01-01

    Nonylphenol (NP) was quantified using liquid chromatography tandem mass spectrometry (LC-MS/MS) in the urine and plasma of rats treated with 0, 50, and 250 mg/kg/day of NP for four consecutive days. A urinary metabolomic strategy was originally implemented by high performance liquid chromatography time of flight mass spectrometry (HPLC-QTOF-MS) to explore the toxicological effects of NP and determine the overall alterations in the metabolite profiles so as to find potential biomarkers. It is essential to point out that from the observation, the metabolic data were clearly clustered and separated for the three groups. To further identify differentiated metabolites, multivariate analysis, including principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA), high-resolution MS/MS analysis, as well as searches of Metlin and Massbank databases, were conducted on a series of metabolites between the control and dose groups. Finally, five metabolites, including glycine, glycerophosphocholine, 5-hydroxytryptamine, malonaldehyde (showing an upward trend), and tryptophan (showing a downward trend), were identified as the potential urinary biomarkers of NP-induced toxicity. In order to validate the reliability of these potential biomarkers, an independent validation was performed by using the multiple reaction monitoring (MRM)-based targeted approach. The oxidative stress reflected by urinary 8-oxo-deoxyguanosine (8-oxodG) levels was elevated in individuals highly exposed to NP, supporting the hypothesis that mitochondrial dysfunction was a result of xenoestrogen accumulation. This study reveals a promising approach to find biomarkers to assist researchers in monitoring NP. PMID:27187439

  11. Genome to Phenome: Integromic Approaches to Define Networks of Host-Pathogen Interactions and Vaccine Biomarker Discovery

    DTIC Science & Technology

    2008-12-01

    reveal phases of progression of illness to a) provide stage- specific diagnosis and b) identify potential molecular targets for stage-appropriate...reactions within hours of exposure and may lead to death in a few days. In contrast, Brucella infection (B. melitensis 16M) produces late- onset of...throughput computing approaches to study diseases is now allowing us to apply an integrative systems biology work frame for drug and biomarker

  12. A Multidisciplinary Biospecimen Bank of Renal Cell Carcinomas Compatible with Discovery Platforms at Mayo Clinic, Scottsdale, Arizona.

    PubMed

    Ho, Thai H; Nateras, Rafael Nunez; Yan, Huihuang; Park, Jin G; Jensen, Sally; Borges, Chad; Lee, Jeong Heon; Champion, Mia D; Tibes, Raoul; Bryce, Alan H; Carballido, Estrella M; Todd, Mark A; Joseph, Richard W; Wong, William W; Parker, Alexander S; Stanton, Melissa L; Castle, Erik P

    2015-01-01

    To address the need to study frozen clinical specimens using next-generation RNA, DNA, chromatin immunoprecipitation (ChIP) sequencing and protein analyses, we developed a biobank work flow to prospectively collect biospecimens from patients with renal cell carcinoma (RCC). We describe our standard operating procedures and work flow to annotate pathologic results and clinical outcomes. We report quality control outcomes and nucleic acid yields of our RCC submissions (N=16) to The Cancer Genome Atlas (TCGA) project, as well as newer discovery platforms, by describing mass spectrometry analysis of albumin oxidation in plasma and 6 ChIP sequencing libraries generated from nephrectomy specimens after histone H3 lysine 36 trimethylation (H3K36me3) immunoprecipitation. From June 1, 2010, through January 1, 2013, we enrolled 328 patients with RCC. Our mean (SD) TCGA RNA integrity numbers (RINs) were 8.1 (0.8) for papillary RCC, with a 12.5% overall rate of sample disqualification for RIN <7. Banked plasma had significantly less albumin oxidation (by mass spectrometry analysis) than plasma kept at 25 °C (P<.001). For ChIP sequencing, the FastQC score for average read quality was at least 30 for 91% to 95% of paired-end reads. In parallel, we analyzed frozen tissue by RNA sequencing; after genome alignment, only 0.2% to 0.4% of total reads failed the default quality check steps of Bowtie2, which was comparable to the disqualification ratio (0.1%) of the 786-O RCC cell line that was prepared under optimal RNA isolation conditions. The overall correlation coefficients for gene expression between Mayo Clinic vs TCGA tissues ranged from 0.75 to 0.82. These data support the generation of high-quality nucleic acids for genomic analyses from banked RCC. Importantly, the protocol does not interfere with routine clinical care. Collections over defined time points during disease treatment further enhance collaborative efforts to integrate genomic information with outcomes.

  13. A Multidisciplinary Biospecimen Bank of Renal Cell Carcinomas Compatible with Discovery Platforms at Mayo Clinic, Scottsdale, Arizona

    PubMed Central

    Ho, Thai H.; Nateras, Rafael Nunez; Yan, Huihuang; Park, Jin G.; Jensen, Sally; Borges, Chad; Lee, Jeong Heon; Champion, Mia D.; Tibes, Raoul; Bryce, Alan H.; Carballido, Estrella M.; Todd, Mark A.; Joseph, Richard W.; Wong, William W.; Parker, Alexander S.; Stanton, Melissa L.; Castle, Erik P.

    2015-01-01

    To address the need to study frozen clinical specimens using next-generation RNA, DNA, chromatin immunoprecipitation (ChIP) sequencing and protein analyses, we developed a biobank work flow to prospectively collect biospecimens from patients with renal cell carcinoma (RCC). We describe our standard operating procedures and work flow to annotate pathologic results and clinical outcomes. We report quality control outcomes and nucleic acid yields of our RCC submissions (N=16) to The Cancer Genome Atlas (TCGA) project, as well as newer discovery platforms, by describing mass spectrometry analysis of albumin oxidation in plasma and 6 ChIP sequencing libraries generated from nephrectomy specimens after histone H3 lysine 36 trimethylation (H3K36me3) immunoprecipitation. From June 1, 2010, through January 1, 2013, we enrolled 328 patients with RCC. Our mean (SD) TCGA RNA integrity numbers (RINs) were 8.1 (0.8) for papillary RCC, with a 12.5% overall rate of sample disqualification for RIN <7. Banked plasma had significantly less albumin oxidation (by mass spectrometry analysis) than plasma kept at 25°C (P<.001). For ChIP sequencing, the FastQC score for average read quality was at least 30 for 91% to 95% of paired-end reads. In parallel, we analyzed frozen tissue by RNA sequencing; after genome alignment, only 0.2% to 0.4% of total reads failed the default quality check steps of Bowtie2, which was comparable to the disqualification ratio (0.1%) of the 786-O RCC cell line that was prepared under optimal RNA isolation conditions. The overall correlation coefficients for gene expression between Mayo Clinic vs TCGA tissues ranged from 0.75 to 0.82. These data support the generation of high-quality nucleic acids for genomic analyses from banked RCC. Importantly, the protocol does not interfere with routine clinical care. Collections over defined time points during disease treatment further enhance collaborative efforts to integrate genomic information with outcomes. PMID

  14. A novel approach to the discovery of survival biomarkers in glioblastoma using a joint analysis of DNA methylation and gene expression.

    PubMed

    Smith, Ashley A; Huang, Yen-Tsung; Eliot, Melissa; Houseman, E Andres; Marsit, Carmen J; Wiencke, John K; Kelsey, Karl T

    2014-06-01

    Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention.

  15. A novel approach to the discovery of survival biomarkers in glioblastoma using a joint analysis of DNA methylation and gene expression

    PubMed Central

    Smith, Ashley A; Huang, Yen-Tsung; Eliot, Melissa; Houseman, E Andres; Marsit, Carmen J; Wiencke, John K; Kelsey, Karl T

    2014-01-01

    Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention. PMID:24670968

  16. Morphological and functional MDCT: problem-solving tool and surrogate biomarker for hepatic disease clinical care and drug discovery in the era of personalized medicine.

    PubMed

    Wang, Liang

    2010-08-17

    This article explains the significant role of morphological and functional multidetector computer tomography (MDCT) in combination with imaging postprocessing algorithms served as a problem-solving tool and noninvasive surrogate biomarker to effectively improve hepatic diseases characterization, detection, tumor staging and prognosis, therapy response assessment, and novel drug discovery programs, partial liver resection and transplantation, and MDCT-guided interventions in the era of personalized medicine. State-of-the-art MDCT depicts and quantifies hepatic disease over conventional CT for not only depicting lesion location, size, and extent but also detecting changes in tumor biologic behavior caused by therapy or tumor progression before morphologic changes. Color-encoded parameter display provides important functional information on blood flow, permeability, leakage space, and blood volume. Together with other relevant biomarkers and genomics, the imaging modality is being developed and validated as a biomarker to early response to novel, targeted anti-VEGF(R)/PDGFR or antivascular/angiogenesis agents as its parameters correlate with immunohistochemical surrogates of tumor angiogenesis and molecular features of malignancies. MDCT holds incremental value to World Health Organization response criteria and Response Evaluation Criteria in Solid Tumors in liver disease management. MDCT volumetric measurement of future remnant liver is the most important factor influencing the outcome of patients who underwent partial liver resection and transplantation. MDCT-guided interventional methods deliver personalized therapies locally in the human body. MDCT will hold more scientific impact when it is fused with other imaging probes to yield comprehensive information regarding changes in liver disease at different levels (anatomic, metabolic, molecular, histologic, and other levels).

  17. Potentially Novel Candidate Biomarkers for Head and Neck Squamous Cell Carcinoma Identified Using an Integrated Cell Line-based Discovery Strategy*

    PubMed Central

    Sepiashvili, Lusia; Hui, Angela; Ignatchenko, Vladimir; Shi, Willa; Su, Susie; Xu, Wei; Huang, Shao Hui; O'Sullivan, Brian; Waldron, John; Irish, Jonathan C.; Perez-Ordonez, Bayardo; Liu, Fei-Fei; Kislinger, Thomas

    2012-01-01

    Head and neck squamous cell carcinomas (HNSCC) can arise from the oral cavity, oropharynx, larynx or hypopharynx, and is the sixth leading cancer by incidence worldwide. The 5-year survival rate of HNSCC patients remains static at 40–60%. Hence, biomarkers which can improve detection of HNSCC or early recurrences should improve clinical outcome. Mass spectrometry-based proteomics methods have emerged as promising approaches for biomarker discovery. As one approach, mass-spectrometric identification of proteins shed or secreted from cancer cells can contribute to the identification of potential biomarkers for HNSCC and our understanding of tumor behavior. In the current study, mass spectrometry-based proteomic profiling was performed on the conditioned media (i.e. secretome) of head and neck cancer (HNC) cell lines (FaDu, UTSCC8 and UTSCC42a) in addition to gene expression microarrays to identify over-expressed transcripts in the HNSCC cells in comparison to a normal control cell line. This integrated data set was systematically mined using publicly available resources (Human Protein Atlas and published proteomic/transcriptomic data) to prioritize putative candidates for validation. Subsequently, quantitative real-time PCR (qRT-PCR), Western blotting, immunohistochemistry (IHC), and ELISAs were performed to verify selected markers. Our integrated analyses identified 90 putative protein biomarkers that were secreted or shed to the extracellular space and over-expressed in HNSCC cell lines, relative to controls. Subsequently, the over-expression of five markers was verified in vitro at the transcriptional and translational levels using qRT-PCR and Western blotting, respectively. IHC-based validation conducted in two independent cohorts comprising of 40 and 39 HNSCC biopsies revealed that high tumor expression of PLAU, IGFBP7, MMP14 and THBS1 were associated with inferior disease-free survival, and increased risk of disease progression or relapse. Furthermore, as

  18. Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Bunn, Amoret L.; Bailey, Vanessa L.

    2011-01-01

    Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ~87.0%, ~71.4%, ~87.5%, and 100% for the four groups; Control (non-amended system), low-dose (amended at 10 µg U L-1), medium dose (amended at 100 µg U L-1), and high dose (500 µg U L-1). The SVM model achieved an overall cross-validated classification accuracy of ~87% in contrast to ~59% for the best linear classifier.

  19. Differential integrative omic analysis for mechanism insights and biomarker discovery of abnormal Savda syndrome and its unique Munziq prescription.

    PubMed

    Guo, Xia; Bakri, Iskandar; Abudula, Abulizi; Arken, Kalbinur; Mijit, Mahmut; Mamtimin, Batur; Upur, Halmurat

    2016-06-14

    Research has shown that many cancers have acommon pathophysiological origin and often present with similar symptoms. In terms of Traditional Uighur Medicine (TUM) Hilit (body fluid) theory, abnormal Savda syndrome (ASS) formed by abnormal Hilit is the common phenotype of complex diseases and in particular tumours. Abnormal Savda Munziq (ASMq), one representative of TUM, has been effective in the treatment of cancer since ancient times. Despite the physiopathology of ASS, the relationship between causative factors and the molecular mechanism of ASMq are not fully understood. The current study expanded upon earlier work by integrating traditional diagnostic approaches with others utilizing systems biology technology for the analysis of proteomic (iTRAQ) and metabolomic ((1)H-NMR) profiles of Uighur Medicine target organ lesion (liver) tumours. The candidate proteins were analyzed by enrichment analysis of the biological process and biomarker filters. Subsequently, 3Omics web-based tools were used to determine the relationships between proteins and appropriate metabolites. ELISA assay and IHC methods were used to verify the proteomic result; the protein von Willebrand factor (vWF) may be the "therapeutic window" of ASMq and biomarkers of ASS. This study is likely to be of great significance for the standardization and modernization of TUM.

  20. Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for Cancer Classification.

    PubMed

    Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Pirhadi, Shiva; Garshasbi, Masoud

    2015-01-01

    The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer's types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets.

  1. Discovery and validation of plasma biomarkers for major depressive disorder classification based on liquid chromatography-mass spectrometry.

    PubMed

    Liu, Xinyu; Zheng, Peng; Zhao, Xinjie; Zhang, Yuqing; Hu, Chunxiu; Li, Jia; Zhao, Jieyu; Zhou, Jingjing; Xie, Peng; Xu, Guowang

    2015-05-01

    Major depressive disorder (MDD) is a debilitating mental disease with a pronounced impact on the quality of life of many people; however, it is still difficult to diagnose MDD accurately. In this study, a nontargeted metabolomics approach based on ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was used to find the differential metabolites in plasma samples from patients with MDD and healthy controls. Furthermore, a validation analysis focusing on the differential metabolites was performed in another batch of samples using a targeted approach based on the dynamic multiple reactions monitoring method. Levels of acyl carnitines, ether lipids, and tryptophan pronouncedly decreased, whereas LPCs, LPEs, and PEs markedly increased in MDD subjects as compared with the healthy controls. Disturbed pathways, mainly located in acyl carnitine metabolism, lipid metabolism, and tryptophan metabolism, were clearly brought to light in MDD subjects. The binary logistic regression result showed that carnitine C10:1, PE-O 36:5, LPE 18:1 sn-2, and tryptophan can be used as a combinational biomarker to distinguish not only moderate but also severe MDD from healthy control with good sensitivity and specificity. Our findings, on one hand, provide critical insight into the pathological mechanism of MDD and, on the other hand, supply a combinational biomarker to aid the diagnosis of MDD in clinical usage.

  2. Differential integrative omic analysis for mechanism insights and biomarker discovery of abnormal Savda syndrome and its unique Munziq prescription

    PubMed Central

    Guo, Xia; Bakri, Iskandar; Abudula, Abulizi; Arken, Kalbinur; Mijit, Mahmut; Mamtimin, Batur; Upur, Halmurat

    2016-01-01

    Research has shown that many cancers have acommon pathophysiological origin and often present with similar symptoms. In terms of Traditional Uighur Medicine (TUM) Hilit (body fluid) theory, abnormal Savda syndrome (ASS) formed by abnormal Hilit is the common phenotype of complex diseases and in particular tumours. Abnormal Savda Munziq (ASMq), one representative of TUM, has been effective in the treatment of cancer since ancient times. Despite the physiopathology of ASS, the relationship between causative factors and the molecular mechanism of ASMq are not fully understood. The current study expanded upon earlier work by integrating traditional diagnostic approaches with others utilizing systems biology technology for the analysis of proteomic (iTRAQ) and metabolomic (1H-NMR) profiles of Uighur Medicine target organ lesion (liver) tumours. The candidate proteins were analyzed by enrichment analysis of the biological process and biomarker filters. Subsequently, 3Omics web-based tools were used to determine the relationships between proteins and appropriate metabolites. ELISA assay and IHC methods were used to verify the proteomic result; the protein von Willebrand factor (vWF) may be the “therapeutic window” of ASMq and biomarkers of ASS. This study is likely to be of great significance for the standardization and modernization of TUM. PMID:27296761

  3. 3D cell culture systems--towards primary drug discovery platforms: an interview with Heinz Ruffner (Novartis) and Jan Lichtenberg (InSphero).

    PubMed

    Ruffner, Heinz; Lichtenberg, Jan

    2012-07-01

    Advanced cell culture systems for regenerative medicine, drug efficacy and toxicity testing, enabling technologies to create and analyze 3D cell culture systems were the topics of the 3D cell culture meeting taking place in March 14-16, 2012 at the Technopark in Zurich, Switzerland. At this meeting Biotechnology Journal had the pleasure to talk to Dr. Heinz Ruffner, Novartis AG, and Dr. Jan Lichtenberg, co-founder and CEO of InSphero AG, about challenges and perspectives in using 3D cell culture systems as primary drug discovery platforms.

  4. Zebrafish swimming behavior as a biomarker for ototoxicity-induced hair cell damage: a high-throughput drug development platform targeting hearing loss.

    PubMed

    Niihori, Maki; Platto, Terry; Igarashi, Suzu; Hurbon, Audriana; Dunn, Allison M; Tran, Phi; Tran, Hung; Mudery, Jordan A; Slepian, Marvin J; Jacob, Abraham

    2015-11-01

    Hearing loss is one of the most common human sensory disabilities, adversely affecting communication, socialization, mood, physical functioning, and quality of life. In addition to age and noise-induced damage, ototoxicity is a common cause of sensorineural hearing loss with chemotherapeutic agents, for example, cisplatin, being a major contributor. Zebrafish (Danio rerio) are an excellent model to study hearing loss as they have neurosensory hair cells on their body surface that are structurally similar to those within the human inner ear. Anatomic assays of toxin-mediated hair cell damage in zebrafish have been established; however, using fish swimming behavior--rheotaxis--as a biomarker for this anatomic damage was only recently described. We hypothesized that, in parallel, multilane measurements of rheotaxis could be used to create a high-throughput platform for drug development assessing both ototoxic and potentially otoprotective compounds in real time. Such a device was created, and results demonstrated a clear dose response between cisplatin exposure, progressive hair cell damage, and reduced rheotaxis in zebrafish. Furthermore, pre-exposure to the otoprotective medication dexamethasone, before cisplatin exposure, partially rescued rheotaxis swimming behavior and hair cell integrity. These results provide the first evidence that rescued swimming behavior can serve as a biomarker for rescued hair cell function. Developing a drug against hearing loss represents an unmet clinical need with global implications. Because hearing loss from diverse etiologies may result from common end-effects at the hair cell level, lessons learned from the present study may be broadly used.

  5. Prediction of Anti-inflammatory Plants and Discovery of Their Biomarkers by Machine Learning Algorithms and Metabolomic Studies.

    PubMed

    Chagas-Paula, Daniela Aparecida; Oliveira, Tiago Branquinho; Zhang, Tong; Edrada-Ebel, RuAngelie; Da Costa, Fernando Batista

    2015-04-01

    Nonsteroidal anti-inflammatory drugs are the most used anti-inflammatory medicines in the world. Side effects still occur, however, and some inflammatory pathologies lack efficient treatment. Cyclooxygenase and lipoxygenase pathways are of utmost importance in inflammatory processes; therefore, novel inhibitors are currently needed for both of them. Dual inhibitors of cyclooxygenase-1 and 5-lipoxygenase are anti-inflammatory drugs with high efficacy and low side effects. In this work, 57 leaf extracts (EtOH-H2O 7 : 3, v/v) from Asteraceae species with in vitro dual inhibition of cyclooxygenase-1 and 5-lipoxygenase were analyzed by high-performance liquid chromatography-high-resolution-ORBITRAP-mass spectrometry analysis and subjected to in silico studies using machine learning algorithms. The data from all samples were processed by employing differential expression analysis software coupled to the Dictionary of Natural Products for dereplication studies. The 6052 chromatographic peaks (ESI positive and negative modes) of the extracts were selected by a genetic algorithm according to their respective anti-inflammatory properties; after this procedure, 1241 of them remained. A study using a decision tree classifier was carried out, and 11 compounds were determined to be biomarkers due to their anti-inflammatory potential. Finally, a model to predict new biologically active extracts from Asteraceae species using liquid chromatography-mass spectrometry information with no prior knowledge of their biological data was built using a multilayer perceptron (artificial neural networks) with the back-propagation algorithm using the biomarker data. As a result, a new and robust artificial neural network model for predicting the anti-inflammatory activity of natural compounds was obtained, resulting in a high percentage of correct predictions (81 %), high precision (100 %) for dual inhibition, and low error values (mean absolute error = 0.3), as also shown in the

  6. Discovery and Validation of Prognostic Biomarker Models to Guide Triage among Adult Dengue Patients at Early Infection

    PubMed Central

    Tolfvenstam, Thomas; Thein, Tun-Linn; Naim, Ahmad Nazri Mohamed; Ling, Ling; Chow, Angelia; Chen, Mark I-Cheng; Ooi, Eng Eong; Leo, Yee Sin; Hibberd, Martin L.

    2016-01-01

    Background Dengue results in a significant public health burden in endemic regions. The World Health Organization (WHO) recommended the use of warning signs (WS) to stratify patients at risk of severe dengue disease in 2009. However, WS is limited in stratifying adult dengue patients at early infection (Day 1–3 post fever), who require close monitoring in hospitals to prevent severe dengue. The aim of this study is to identify and validate prognostic models, built with differentially expressed biomarkers, that enable the early identification of those with early dengue infection that require close clinical monitoring. Methods RNA microarray and protein assays were performed to identify differentially expressed biomarkers of severity among 92 adult dengue patients recruited at early infection from years 2005–2008. This comprised 47 cases who developed WS after first presentation and required hospitalization (WS+Hosp), as well as 45 controls who did not develop WS after first presentation and did not require hospitalization (Non-WS+Non-Hosp). Independent validation was conducted with 80 adult dengue patients recruited from years 2009–2012. Prognostic models were developed based on forward stepwise and backward elimination estimation, using multiple logistic regressions. Prognostic power was estimated by the area under the receiver operating characteristic curve (AUC). Results The WS+Hosp group had significantly higher viral load (P<0.001), lower platelet (P<0.001) and lymphocytes counts (P = 0.004) at early infection compared to the Non-WS+Non-Hosp group. From the RNA microarray and protein assays, the top single RNA and protein prognostic models at early infection were CCL8 RNA (AUC:0.73) and IP-10 protein (AUC:0.74), respectively. The model with CCL8, VPS13C RNA, uPAR protein, and with CCL8, VPS13C RNA and platelets were the best biomarker models for stratifying adult dengue patients at early infection, with sensitivity and specificity up to 83% and 84

  7. Discovery of a Metastatic Immune Escape Mechanism Initiated by the Loss of Expression of the Tumour Biomarker Interleukin-33

    PubMed Central

    Saranchova, Iryna; Han, Jeffrey; Huang, Hui; Fenninger, Franz; Choi, Kyung Bok; Munro, Lonna; Pfeifer, Cheryl; Welch, Ian; Wyatt, Alexander W.; Fazli, Ladan; Gleave, Martin E.; Jefferies, Wilfred A.

    2016-01-01

    A new paradigm for understanding immune-surveillance and immune escape in cancer is described here. Metastatic carcinomas express reduced levels of IL-33 and diminished levels of antigen processing machinery (APM), compared to syngeneic primary tumours. Complementation of IL-33 expression in metastatic tumours upregulates APM expression and functionality of major histocompatibility complex (MHC)-molecules, resulting in reduced tumour growth rates and a lower frequency of circulating tumour cells. Parallel studies in humans demonstrate that low tumour expression of IL-33 is an immune biomarker associated with recurrent prostate and kidney renal clear cell carcinomas. Thus, IL-33 has a significant role in cancer immune-surveillance against primary tumours, which is lost during the metastatic transition that actuates immune escape in cancer. PMID:27619158

  8. Discovery of a Metastatic Immune Escape Mechanism Initiated by the Loss of Expression of the Tumour Biomarker Interleukin-33.

    PubMed

    Saranchova, Iryna; Han, Jeffrey; Huang, Hui; Fenninger, Franz; Choi, Kyung Bok; Munro, Lonna; Pfeifer, Cheryl; Welch, Ian; Wyatt, Alexander W; Fazli, Ladan; Gleave, Martin E; Jefferies, Wilfred A

    2016-09-13

    A new paradigm for understanding immune-surveillance and immune escape in cancer is described here. Metastatic carcinomas express reduced levels of IL-33 and diminished levels of antigen processing machinery (APM), compared to syngeneic primary tumours. Complementation of IL-33 expression in metastatic tumours upregulates APM expression and functionality of major histocompatibility complex (MHC)-molecules, resulting in reduced tumour growth rates and a lower frequency of circulating tumour cells. Parallel studies in humans demonstrate that low tumour expression of IL-33 is an immune biomarker associated with recurrent prostate and kidney renal clear cell carcinomas. Thus, IL-33 has a significant role in cancer immune-surveillance against primary tumours, which is lost during the metastatic transition that actuates immune escape in cancer.

  9. Metabolomic study of lipids in serum for biomarker discovery in Alzheimer's disease using direct infusion mass spectrometry.

    PubMed

    González-Domínguez, R; García-Barrera, T; Gómez-Ariza, J L

    2014-09-01

    In this study, we demonstrated the potential of direct infusion mass spectrometry for the lipidomic characterization of Alzheimer's disease. Serum samples were extracted for lipids recovery, and directly analyzed using an electrospray source. Metabolomic fingerprints were subjected to multivariate analysis in order to discriminate between groups of patients and healthy controls, and then some key-compounds were identified as possible markers of Alzheimer's disease. Major differences were found in lipids, although some low molecular weight metabolites also showed significant changes. Thus, important metabolic pathways involved in neurodegeneration could be studied on the basis of these perturbations, such as membrane breakdown (phospholipids and diacylglycerols), oxidative stress (prostaglandins, imidazole and histidine), alterations in neurotransmission systems (oleamide and putrescine) and hyperammonaemia (guanidine and arginine). Moreover, it is noteworthy that some of these potential biomarkers have not been previously described for Alzheimer's disease.

  10. Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples.

    PubMed

    Jonsson, Pär; Wuolikainen, Anna; Thysell, Elin; Chorell, Elin; Stattin, Pär; Wikström, Pernilla; Antti, Henrik

    Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation.

  11. SELDI-TOF MS-based discovery of a biomarker in Cucumis sativus seeds exposed to CuO nanoparticles.

    PubMed

    Moon, Young-Sun; Park, Eun-Sil; Kim, Tae-Oh; Lee, Hoi-Seon; Lee, Sung-Eun

    2014-11-01

    Metal oxide nanoparticles (NPs) can inhibit plant seed germination and root elongation via the release of metal ions. In the present study, two acute phytotoxicity tests, seed germination and root elongation tests, were conducted on cucumber seeds (Cucumis sativus) treated with bulk copper oxide (CuO) and CuO NPs. Two concentrations of bulk CuO and CuO NPs, 200 and 600ppm, were used to test the inhibition rate of root germination; both concentrations of bulk CuO weakly inhibited seed germination, whereas CuO NPs significantly inhibited germination, showing a low germination rate of 23.3% at 600ppm. Root elongation tests demonstrated that CuO NPs were much stronger inhibitors than bulk CuO. SELDI-TOF MS analysis showed that 34 proteins were differentially expressed in cucumber seeds after exposure to CuO NPs, with the expression patterns of at least 9 proteins highly differing from those in seeds treated with bulk CuO and in control plants. Therefore, these 9 proteins were used to identify CuO NP-specific biomarkers in cucumber plants exposed to CuO NPs. A 5977-m/z protein was the most distinguishable biomarker for determining phytotoxicity by CuO NPs. Principal component analysis (PCA) of the SELDI-TOF MS results showed variability in the modes of inhibitory action on cucumber seeds and roots. To our knowledge, this is the first study to demonstrate that the phytotoxic effect of metal oxide NPs on plants is not caused by the same mode of action as other toxins.

  12. The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

    PubMed Central

    Milioli, Heloisa Helena; Vimieiro, Renato; Riveros, Carlos; Tishchenko, Inna; Berretta, Regina; Moscato, Pablo

    2015-01-01

    Background The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction. Methods and Findings The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method. Conclusions The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes

  13. Off surface matrix based on-chip electrochemical biosensor platform for protein biomarker detection in undiluted serum.

    PubMed

    Arya, Sunil K; Kongsuphol, Patthara; Park, Mi Kyoung

    2017-06-15

    The manuscript describes a concept of using off surface matrix modified with capturing biomolecule for on-chip electrochemical biosensing. 3D matrix made by laser engraving of polymethyl methacrylate (PMMA) sheet as off surface matrix was integrated in very close vicinity of the electrode surface. Laser engraving and holes in PMMA along with spacing from surface provide fluidic channel and incubation chamber. Covalent binding of capturing biomolecule (anti-TNF-α antibody) on off-surface matrix was achieved via azide group activity of 4-fluoro-3-nitro-azidobenzene (FNAB), which act as cross-linker and further covalently binds to anti-TNF-α antibody via thermal reaction. Anti-TNF-α/FNAB/PMMA matrix was then integrated over comb structured gold electrode array based sensor chip. Separate surface modification followed by integration of sensor helped to prevent the sensor chip surface from fouling during functionalization. Nonspecific binding was prevented using starting block T20 (PBS). Results for estimating protein biomarker (TNF-α) in undiluted serum using Anti-TNF-α/FNAB/PMMA/Au reveal that system can detect TNF-α in 100pg/ml to 100ng/ml range with high sensitivity of 119nA/(ng/ml), with negligible interference from serum proteins and other cytokines. Thus, use of off surface matrix may provide the opportunity to electrochemically sense biomarkers sensitively to ng/ml range with negligible nonspecific binding and false signal in undiluted serum.

  14. A superior strategy for single-cell mutational screening via multiplex-targeted QPCR using the BioMark HD microfluidic platform.

    PubMed

    Li, Guangliang; Teng, Lisong

    2014-03-01

    A major challenge in cancer therapy lies in its complexity and heterogeneity, with increasing recognition of many tumor subtypes that have different biological characteristics and responses to therapies. To effectively address this challenge, personalized medicine has been the 'vogue' currently. Dissecting the detailed clonal architecture of cancer by cancer genomics, which holds the promise of personalized medicine, has significant clinical implications. Substantial advances have been made in DNA-based, high-throughput genomic technologies. However, current methods are still in its infancy, significantly limited by error rates, low cell throughput, high cost and labor intensive. The study under evaluation develops a superior strategy for a comprehensive interrogation of the complex genomics of cancer cells by using multiplex-targeted DNA amplification from flow-sorted single cells followed by high-throughput quantitative PCR using the BioMark HD microfluidic platform. The platform demonstrated a successful rate of approximately 75%, a highly efficient single-cell sorting rate of 96-98%, a high-throughput analysis of 200-300 leukemic cells, and was able to simultaneously detected chimeric fusion genes, copy number alterations and single-nucleotide variants in a single cell sample.

  15. Ratiometric fluorescence transduction by hybridization after isothermal amplification for determination of zeptomole quantities of oligonucleotide biomarkers with a paper-based platform and camera-based detection.

    PubMed

    Noor, M Omair; Hrovat, David; Moazami-Goudarzi, Maryam; Espie, George S; Krull, Ulrich J

    2015-07-23

    Paper is a promising platform for the development of decentralized diagnostic assays owing to the low cost and ease of use of paper-based analytical devices (PADs). It can be challenging to detect on PADs very low concentrations of nucleic acid biomarkers of lengths as used in clinical assays. Herein we report the use of thermophilic helicase-dependent amplification (tHDA) in combination with a paper-based platform for fluorescence detection of probe-target hybridization. Paper substrates were patterned using wax printing. The cellulosic fibers were chemically derivatized with imidazole groups for the assembly of the transduction interface that consisted of immobilized quantum dot (QD)-probe oligonucleotide conjugates. Green-emitting QDs (gQDs) served as donors with Cy3 as the acceptor dye in a fluorescence resonance energy transfer (FRET)-based transduction method. After probe-target hybridization, a further hybridization event with a reporter sequence brought the Cy3 acceptor dye in close proximity to the surface of immobilized gQDs, triggering a FRET sensitized emission that served as an analytical signal. Ratiometric detection was evaluated using both an epifluorescence microscope and a low-cost iPad camera as detectors. Addition of the tHDA method for target amplification to produce sequences of ∼100 base length allowed for the detection of zmol quantities of nucleic acid targets using the two detection platforms. The ratiometric QD-FRET transduction method not only offered improved assay precision, but also lowered the limit of detection of the assay when compared with the non-ratiometric QD-FRET transduction method. The selectivity of the hybridization assays was demonstrated by the detection of single nucleotide polymorphism.

  16. Biomarkers of safety and immune protection for genetically modified live attenuated leishmania vaccines against visceral leishmaniasis - discovery and implications.

    PubMed

    Gannavaram, Sreenivas; Dey, Ranadhir; Avishek, Kumar; Selvapandiyan, Angamuthu; Salotra, Poonam; Nakhasi, Hira L

    2014-01-01

    Despite intense efforts there is no safe and efficacious vaccine against visceral leishmaniasis, which is fatal and endemic in many tropical countries. A major shortcoming in the vaccine development against blood-borne parasitic agents such as Leishmania is the inadequate predictive power of the early immune responses mounted in the host against the experimental vaccines. Often immune correlates derived from in-bred animal models do not yield immune markers of protection that can be readily extrapolated to humans. The limited efficacy of vaccines based on DNA, subunit, heat killed parasites has led to the realization that acquisition of durable immunity against the protozoan parasites requires a controlled infection with a live attenuated organism. Recent success of irradiated malaria parasites as a vaccine candidate further strengthens this approach to vaccination. We developed several gene deletion mutants in Leishmania donovani as potential live attenuated vaccines and reported extensively on the immunogenicity of LdCentrin1 deleted mutant in mice, hamsters, and dogs. Additional limited studies using genetically modified live attenuated Leishmania parasites as vaccine candidates have been reported. However, for the live attenuated parasite vaccines, the primary barrier against widespread use remains the absence of clear biomarkers associated with protection and safety. Recent studies in evaluation of vaccines, e.g., influenza and yellow fever vaccines, using systems biology tools demonstrated the power of such strategies in understanding the immunological mechanisms that underpin a protective phenotype. Applying similar tools in isolated human tissues such as PBMCs from healthy individuals infected with live attenuated parasites such as LdCen(-/-) in vitro followed by human microarray hybridization experiments will enable us to understand how early vaccine-induced gene expression profiles and the associated immune responses are coordinately regulated in normal

  17. Integration of High-Volume Molecular and Imaging Data for Composite Biomarker Discovery in the Study of Melanoma

    PubMed Central

    Chatziioannou, Aristotelis

    2014-01-01

    In this work the effects of simple imputations are studied, regarding the integration of multimodal data originating from different patients. Two separate datasets of cutaneous melanoma are used, an image analysis (dermoscopy) dataset together with a transcriptomic one, specifically DNA microarrays. Each modality is related to a different set of patients, and four imputation methods are employed to the formation of a unified, integrative dataset. The application of backward selection together with ensemble classifiers (random forests), followed by principal components analysis and linear discriminant analysis, illustrates the implication of the imputations on feature selection and dimensionality reduction methods. The results suggest that the expansion of the feature space through the data integration, achieved by the exploitation of imputation schemes in general, aids the classification task, imparting stability as regards the derivation of putative classifiers. In particular, although the biased imputation methods increase significantly the predictive performance and the class discrimination of the datasets, they still contribute to the study of prominent features and their relations. The fusion of separate datasets, which provide a multimodal description of the same pathology, represents an innovative, promising avenue, enhancing robust composite biomarker derivation and promoting the interpretation of the biomedical problem studied. PMID:24527435

  18. Application of quantitative proteomic analysis using tandem mass tags for discovery and identification of novel biomarkers in periodontal disease.

    PubMed

    Tsuchida, Sachio; Satoh, Mamoru; Kawashima, Yusuke; Sogawa, Kazuyuki; Kado, Sayaka; Sawai, Setsu; Nishimura, Motoi; Ogita, Mayumi; Takeuchi, Yasuo; Kobyashi, Hiroaki; Aoki, Akira; Kodera, Yoshio; Matsushita, Kazuyuki; Izumi, Yuichi; Nomura, Fumio

    2013-08-01

    Periodontal disease is a bacterial infection that destroys the gingiva and surrounding tissues of the oral cavity. Gingival crevicular fluid (GCF) is extracted from the gingival sulcus and pocket. Analysis of biochemical markers in GCF, which predict the progression of periodontal disease, may facilitate disease diagnosis. However, no useful GCF biochemical markers with high sensitivity for detecting periodontal disease have been identified. Thus, the search for biochemical markers of periodontal disease is of continued interest in experimental and clinical periodontal disease research. Using tandem mass tag (TMT) labeling, we analyzed GCF samples from healthy subjects and patients with periodontal disease, and identified a total of 619 GCF proteins based on proteomic analysis. Of these, we focused on two proteins, matrix metalloproteinase (MMP)-9 and neutrophil gelatinase-associated lipocalin (LCN2), which are involved in the progression of periodontal disease. Western blot analysis revealed that the levels of MMP-9 and LCN2 were significantly higher in patients with periodontal disease than in healthy subjects. In addition, ELISA also detected significantly higher levels of LCN2 in patients with periodontal disease than in healthy subjects. Thus, LC-MS/MS analyses of GCF using TMT labeling led to the identification of LCN2, which may be a promising GCF biomarker for the detection of periodontal disease.

  19. In-depth cDNA library sequencing provides quantitative gene expression profiling in cancer biomarker discovery.

    PubMed

    Yang, Wanling; Ying, Dingge; Lau, Yu-Lung

    2009-06-01

    Quantitative gene expression analysis plays an important role in identifying differentially expressed genes in various pathological states, gene expression regulation and co-regulation, shedding light on gene functions. Although microarray is widely used as a powerful tool in this regard, it is suboptimal quantitatively and unable to detect unknown gene variants. Here we demonstrated effective detection of differential expression and co-regulation of certain genes by expressed sequence tag analysis using a selected subset of cDNA libraries. We discussed the issues of sequencing depth and library preparation, and propose that increased sequencing depth and improved preparation procedures may allow detection of many expression features for less abundant gene variants. With the reduction of sequencing cost and the emerging of new generation sequencing technology, in-depth sequencing of cDNA pools or libraries may represent a better and powerful tool in gene expression profiling and cancer biomarker detection. We also propose using sequence-specific subtraction to remove hundreds of the most abundant housekeeping genes to increase sequencing depth without affecting relative expression ratio of other genes, as transcripts from as few as 300 most abundantly expressed genes constitute about 20% of the total transcriptome. In-depth sequencing also represents a unique advantage of detecting unknown forms of transcripts, such as alternative splicing variants, fusion genes, and regulatory RNAs, as well as detecting mutations and polymorphisms that may play important roles in disease pathogenesis.

  20. Proteomics approaches in the quest of kidney disease biomarkers.

    PubMed

    Frantzi, M; Bitsika, V; Charonis, A; Vlahou, A

    2011-01-01

    Proteomics refers to a group of analytical techniques for high throughput protein analysis, providing evidence for protein expression levels, subcellular localization, post-translational modifications and molecular interactions. As such, proteomics has contributed largely to our knowledge regarding molecular mechanisms underlying health and disease and pinpointed potential disease biomarkers. The scope of this review is to briefly introduce the principles of major proteomics techniques employed in biological research, including novel quantitative and molecular imaging mass spectrometry-based platforms. A few examples from the application of these techniques in biomarker discovery for kidney diseases are also provided.

  1. An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework

    PubMed Central

    Chen, Yi-An; Tripathi, Lokesh P.; Mizuguchi, Kenji

    2016-01-01

    Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org PMID:26989145

  2. An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

    PubMed

    Chen, Yi-An; Tripathi, Lokesh P; Mizuguchi, Kenji

    2016-01-01

    Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org.

  3. Metabolomics-Based Discovery of Small Molecule Biomarkers in Serum Associated with Dengue Virus Infections and Disease Outcomes

    PubMed Central

    Voge, Natalia V.; Perera, Rushika; Mahapatra, Sebabrata; Gresh, Lionel; Balmaseda, Angel; Loroño-Pino, María A.; Hopf-Jannasch, Amber S.; Belisle, John T.; Harris, Eva; Blair, Carol D.; Beaty, Barry J.

    2016-01-01

    Background Epidemic dengue fever (DF) and dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS) are overwhelming public health capacity for diagnosis and clinical care of dengue patients throughout the tropical and subtropical world. The ability to predict severe dengue disease outcomes (DHF/DSS) using acute phase clinical specimens would be of enormous value to physicians and health care workers for appropriate triaging of patients for clinical management. Advances in the field of metabolomics and analytic software provide new opportunities to identify host small molecule biomarkers (SMBs) in acute phase clinical specimens that differentiate dengue disease outcomes. Methodology/Principal Findings Exploratory metabolomic studies were conducted to characterize the serum metabolome of patients who experienced different dengue disease outcomes. Serum samples from dengue patients from Nicaragua and Mexico were retrospectively obtained, and hydrophilic interaction liquid chromatography (HILIC)-mass spectrometry (MS) identified small molecule metabolites that were associated with and statistically differentiated DHF/DSS, DF, and non-dengue (ND) diagnosis groups. In the Nicaraguan samples, 191 metabolites differentiated DF from ND outcomes and 83 differentiated DHF/DSS and DF outcomes. In the Mexican samples, 306 metabolites differentiated DF from ND and 37 differentiated DHF/DSS and DF outcomes. The structural identities of 13 metabolites were confirmed using tandem mass spectrometry (MS/MS). Metabolomic analysis of serum samples from patients diagnosed as DF who progressed to DHF/DSS identified 65 metabolites that predicted dengue disease outcomes. Differential perturbation of the serum metabolome was demonstrated following infection with different DENV serotypes and following primary and secondary DENV infections. Conclusions/Significance These results provide proof-of-concept that a metabolomics approach can be used to identify metabolites or SMBs in serum

  4. Discovery and analysis of time delay sources in the USGS personal computer data collection platform (PCDCP) system

    USGS Publications Warehouse

    White, Timothy C.; Sauter, Edward A.; Stewart, Duff C.

    2014-01-01

    Intermagnet is an international oversight group which exists to establish a global network for geomagnetic observatories. This group establishes data standards and standard operating procedures for members and prospective members. Intermagnet has proposed a new One-Second Data Standard, for that emerging geomagnetic product. The standard specifies that all data collected must have a time stamp accuracy of ±10 milliseconds of the top-of-the-second Coordinated Universal Time. Therefore, the U.S. Geological Survey Geomagnetism Program has designed and executed several tests on its current data collection system, the Personal Computer Data Collection Platform. Tests are designed to measure the time shifts introduced by individual components within the data collection system, as well as to measure the time shift introduced by the entire Personal Computer Data Collection Platform. Additional testing designed for Intermagnet will be used to validate further such measurements. Current results of the measurements showed a 5.0–19.9 millisecond lag for the vertical channel (Z) of the Personal Computer Data Collection Platform and a 13.0–25.8 millisecond lag for horizontal channels (H and D) of the collection system. These measurements represent a dynamically changing delay introduced within the U.S. Geological Survey Personal Computer Data Collection Platform.

  5. Molecular Elucidation of Disease Biomarkers at the Interface of Chemistry and Biology.

    PubMed

    Zhang, Liqin; Wan, Shuo; Jiang, Ying; Wang, Yanyue; Fu, Ting; Liu, Qiaoling; Cao, Zhijuan; Qiu, Liping; Tan, Weihong

    2017-02-22

    Disease-related biomarkers are objectively measurable molecular signatures of physiological status that can serve as disease indicators or drug targets in clinical diagnosis and therapy, thus acting as a tool in support of personalized medicine. For example, the prostate-specific antigen (PSA) biomarker is now widely used to screen patients for prostate cancer. However, few such biomarkers are currently available, and the process of biomarker identification and validation is prolonged and complicated by inefficient methods of discovery and few reliable analytical platforms. Therefore, in this Perspective, we look at the advanced chemistry of aptamer molecules and their significant role as molecular probes in biomarker studies. As a special class of functional nucleic acids evolved from an iterative technology termed Systematic Evolution of Ligands by Exponential Enrichment (SELEX), these single-stranded oligonucleotides can recognize their respective targets with selectivity and affinity comparable to those of protein antibodies. Because of their fast turnaround time and exceptional chemical properties, aptamer probes can serve as novel molecular tools for biomarker investigations, particularly in assisting identification of new disease-related biomarkers. More importantly, aptamers are able to recognize biomarkers from complex biological environments such as blood serum and cell surfaces, which can provide direct evidence for further clinical applications. This Perspective highlights several major advancements of aptamer-based biomarker discovery strategies and their potential contribution to the practice of precision medicine.

  6. Biomarker discovery to improve prediction of breast cancer survival: using gene expression profiling, meta-analysis, and tissue validation

    PubMed Central

    Meng, Liwei; Xu, Yingchun; Xu, Chaoyang; Zhang, Wei

    2016-01-01

    approach, we have identified a three-gene signature with independent prognostic impact. Furthermore, CLDN11 may offer a biomarker to predict prognosis as well as a new target for prognostic and therapeutic intervention for human breast IDC. PMID:27785066

  7. ICan: an optimized ion-current-based quantification procedure with enhanced quantitative accuracy and sensitivity in biomarker discovery.

    PubMed

    Tu, Chengjian; Sheng, Quanhu; Li, Jun; Shen, Xiaomeng; Zhang, Ming; Shyr, Yu; Qu, Jun

    2014-12-05

    The rapidly expanding availability of high-resolution mass spectrometry has substantially enhanced the ion-current-based relative quantification techniques. Despite the increasing interest in ion-current-based methods, quantitative sensitivity, accuracy, and false discovery rate remain the major concerns; consequently, comprehensive evaluation and development in these regards are urgently needed. Here we describe an integrated, new procedure for data normalization and protein ratio estimation, termed ICan, for improved ion-current-based analysis of data generated by high-resolution mass spectrometry (MS). ICan achieved significantly better accuracy and precision, and lower false-positive rate for discovering altered proteins, over current popular pipelines. A spiked-in experiment was used to evaluate the performance of ICan to detect small changes. In this study E. coli extracts were spiked with moderate-abundance proteins from human plasma (MAP, enriched by IgY14-SuperMix procedure) at two different levels to set a small change of 1.5-fold. Forty-five (92%, with an average ratio of 1.71 ± 0.13) of 49 identified MAP protein (i.e., the true positives) and none of the reference proteins (1.0-fold) were determined as significantly altered proteins, with cutoff thresholds of ≥ 1.3-fold change and p ≤ 0.05. This is the first study to evaluate and prove competitive performance of the ion-current-based approach for assigning significance to proteins with small changes. By comparison, other methods showed remarkably inferior performance. ICan can be broadly applicable to reliable and sensitive proteomic survey of multiple biological samples with the use of high-resolution MS. Moreover, many key features evaluated and optimized here such as normalization, protein ratio determination, and statistical analyses are also valuable for data analysis by isotope-labeling methods.

  8. Quick identification of xanthine oxidase inhibitor and antioxidant from Erycibe obtusifolia by a drug discovery platform composed of multiple mass spectrometric platforms and thin-layer chromatography bioautography.

    PubMed

    Chen, Zhiyong; Tao, Hongxun; Liao, Liping; Zhang, Zijia; Wang, Zhengtao

    2014-08-01

    As a final step of the purine metabolism process, xanthine oxidase catalyzes the oxidation of hypoxanthine and xanthine into uric acid. Our research has demonstrated that Erycibe obtusifolia has xanthine oxidase inhibitory properties. The purpose of this paper is to describe a new strategy based on a combination of multiple mass spectrometric platforms and thin-layer chromatography bioautography for effectively screening the xanthine oxidase inhibitory and antioxidant properties of E. obtusifolia. This strategy was accomplished through the following steps. (i) Separate the extract of E. obtusifolia into fractions by an autopurification system controlled by liquid chromatography with mass spectrometry. (ii) Determine the active fractions of E. obtusifolia by thin-layer chromatography bioautography. (iii) Identify the structure of the main active compounds with the information provided by direct analysis in real time mass spectrometry. (iv) Calculate the IC50 value of each compound against xanthine oxidase using high-performance liquid chromatography. Using the caulis of E. obtusifolia as the experimental material, seven target peaks were screened out as xanthine oxidase inhibitors or antioxidants. Our screening strategy allows for rapid analysis of small molecules with almost no sample preparation and can be completed within a week, making it a useful assay to identify unstable compounds and provide the empirical foundation for E. obtusifolia as a natural remedy for gout and oxidative-stress-related diseases.

  9. Discovery of safety biomarkers for realgar in rat urine using UFLC-IT-TOF/MS and 1H NMR based metabolomics.

    PubMed

    Huang, Yin; Tian, Yuan; Li, Geng; Li, Yuanyuan; Yin, Xinjuan; Peng, Can; Xu, Fengguo; Zhang, Zunjian

    2013-05-01

    As an arsenical, realgar (As4S4) is known as a poison and paradoxically as a therapeutic agent. However, a complete understanding of the precise biochemical alterations accompanying the toxicity and therapy effects of realgar is lacking. Using a combined ultrafast liquid chromatography (UFLC) coupled with ion trap time-of-flight mass spectrometry (IT-TOF/MS) and (1)H NMR spectroscopy based metabolomics approach, we were able to delineate significantly altered metabolites in the urine samples of realgar-treated rats. The platform stability of the liquid chromatography LC/MS and NMR techniques was systematically investigated, and the data processing method was carefully optimized. Our results indicate significant perturbations in amino acid metabolism, citric acid cycle, choline metabolism, and porphyrin metabolism. Thirty-six metabolites were proposed as potential safety biomarkers related to disturbances caused by realgar, and glycine and serine are expected to serve as the central contacts in the metabolic pathways related to realgar-induced disturbance. The LC/MS and NMR based metabolomics approach established provided a systematic and holistic view of the biochemical effects of realgar on rats, and might be employed to investigate other drugs or xenobiotics in the future.

  10. False-Positive Rate Determination of Protein Target Discovery using a Covalent Modification- and Mass Spectrometry-Based Proteomics Platform

    NASA Astrophysics Data System (ADS)

    Strickland, Erin C.; Geer, M. Ariel; Hong, Jiyong; Fitzgerald, Michael C.

    2014-01-01

    Detection and quantitation of protein-ligand binding interactions is important in many areas of biological research. Stability of proteins from rates of oxidation (SPROX) is an energetics-based technique for identifying the proteins targets of ligands in complex biological mixtures. Knowing the false-positive rate of protein target discovery in proteome-wide SPROX experiments is important for the correct interpretation of results. Reported here are the results of a control SPROX experiment in which chemical denaturation data is obtained on the proteins in two samples that originated from the same yeast lysate, as would be done in a typical SPROX experiment except that one sample would be spiked with the test ligand. False-positive rates of 1.2-2.2 % and <0.8 % are calculated for SPROX experiments using Q-TOF and Orbitrap mass spectrometer systems, respectively. Our results indicate that the false-positive rate is largely determined by random errors associated with the mass spectral analysis of the isobaric mass tag (e.g., iTRAQ®) reporter ions used for peptide quantitation. Our results also suggest that technical replicates can be used to effectively eliminate such false positives that result from this random error, as is demonstrated in a SPROX experiment to identify yeast protein targets of the drug, manassantin A. The impact of ion purity in the tandem mass spectral analyses and of background oxidation on the false-positive rate of protein target discovery using SPROX is also discussed.

  11. Semantic Registration and Discovery System of Subsystems and Services within an Interoperable Coordination Platform in Smart Cities

    PubMed Central

    Rubio, Gregorio; Martínez, José Fernán; Gómez, David; Li, Xin

    2016-01-01

    Smart subsystems like traffic, Smart Homes, the Smart Grid, outdoor lighting, etc. are built in many urban areas, each with a set of services that are offered to citizens. These subsystems are managed by self-contained embedded systems. However, coordination and cooperation between them are scarce. An integration of these systems which truly represents a “system of systems” could introduce more benefits, such as allowing the development of new applications and collective optimization. The integration should allow maximum reusability of available services provided by entities (e.g., sensors or Wireless Sensor Networks). Thus, it is of major importance to facilitate the discovery and registration of available services and subsystems in an integrated way. Therefore, an ontology-based and automatic system for subsystem and service registration and discovery is presented. Using this proposed system, heterogeneous subsystems and services could be registered and discovered in a dynamic manner with additional semantic annotations. In this way, users are able to build customized applications across different subsystems by using available services. The proposed system has been fully implemented and a case study is presented to show the usefulness of the proposed method. PMID:27347965

  12. Semantic Registration and Discovery System of Subsystems and Services within an Interoperable Coordination Platform in Smart Cities.

    PubMed

    Rubio, Gregorio; Martínez, José Fernán; Gómez, David; Li, Xin

    2016-06-24

    Smart subsystems like traffic, Smart Homes, the Smart Grid, outdoor lighting, etc. are built in many urban areas, each with a set of services that are offered to citizens. These subsystems are managed by self-contained embedded systems. However, coordination and cooperation between them are scarce. An integration of these systems which truly represents a "system of systems" could introduce more benefits, such as allowing the development of new applications and collective optimization. The integration should allow maximum reusability of available services provided by entities (e.g., sensors or Wireless Sensor Networks). Thus, it is of major importance to facilitate the discovery and registration of available services and subsystems in an integrated way. Therefore, an ontology-based and automatic system for subsystem and service registration and discovery is presented. Using this proposed system, heterogeneous subsystems and services could be registered and discovered in a dynamic manner with additional semantic annotations. In this way, users are able to build customized applications across different subsystems by using available services. The proposed system has been fully implemented and a case study is presented to show the usefulness of the proposed method.

  13. False-positive rate determination of protein target discovery using a covalent modification- and mass spectrometry-based proteomics platform.

    PubMed

    Strickland, Erin C; Geer, M Ariel; Hong, Jiyong; Fitzgerald, Michael C

    2014-01-01

    Detection and quantitation of protein-ligand binding interactions is important in many areas of biological research. Stability of proteins from rates of oxidation (SPROX) is an energetics-based technique for identifying the proteins targets of ligands in complex biological mixtures. Knowing the false-positive rate of protein target discovery in proteome-wide SPROX experiments is important for the correct interpretation of results. Reported here are the results of a control SPROX experiment in which chemical denaturation data is obtained on the proteins in two samples that originated from the same yeast lysate, as would be done in a typical SPROX experiment except that one sample would be spiked with the test ligand. False-positive rates of 1.2-2.2% and <0.8% are calculated for SPROX experiments using Q-TOF and Orbitrap mass spectrometer systems, respectively. Our results indicate that the false-positive rate is largely determined by random errors associated with the mass spectral analysis of the isobaric mass tag (e.g., iTRAQ®) reporter ions used for peptide quantitation. Our results also suggest that technical replicates can be used to effectively eliminate such false positives that result from this random error, as is demonstrated in a SPROX experiment to identify yeast protein targets of the drug, manassantin A. The impact of ion purity in the tandem mass spectral analyses and of background oxidation on the false-positive rate of protein target discovery using SPROX is also discussed.

  14. False Positive Rate Determination of Protein Target Discovery using a Covalent Modification- and Mass Spectrometry-Based Proteomics Platform

    PubMed Central

    Strickland, Erin C.; Geer, M. Ariel; Hong, Jiyong; Fitzgerald, Michael C.

    2013-01-01

    Detection and quantitation of protein-ligand binding interactions is important in many areas of biological research. The Stability of Proteins from Rates of Oxidation (SPROX) technique is an energetics-based technique for identifying the proteins targets of ligands in complex biological mixtures. Knowing the false positive rate of protein target discovery in proteome-wide SPROX experiments is important for the correct interpretation of results. Reported here are the results of a control SPROX experiment in which chemical denaturation data is obtained on the proteins in two samples that originated from the same yeast lysate, as would be done in a typical SPROX experiment except that one sample would be spiked with the test ligand. False positive rates of 1.2–2.2% and <0.8% are calculated for SPROX experiments using Q-TOF and orbitrap mass spectrometer systems, respectively. Our results indicate that the false positive rate is largely determined by random errors associated with the mass spectral analysis of the isobaric mass tag (e.g., iTRAQ®) reporter ions used for peptide quantitation. Our results also suggest that technical replicates can be used to effectively eliminate such false positives that result from this random error, as is demonstrated in a SPROX experiment to identify yeast protein targets of the drug, manassantin A. The impact of ion purity in the tandem mass spectral analyses and of background oxidation on the false positive rate of protein target discovery using SPROX is also discussed. PMID:24114261

  15. High-throughput screening platform for natural product-based drug discovery against 3 neglected tropical diseases: human African trypanosomiasis, leishmaniasis, and Chagas disease.

    PubMed

    Annang, F; Pérez-Moreno, G; García-Hernández, R; Cordon-Obras, C; Martín, J; Tormo, J R; Rodríguez, L; de Pedro, N; Gómez-Pérez, V; Valente, M; Reyes, F; Genilloud, O; Vicente, F; Castanys, S; Ruiz-Pérez, L M; Navarro, M; Gamarro, F; González-Pacanowska, D

    2015-01-01

    African trypanosomiasis, leishmaniasis, and Chagas disease are 3 neglected tropical diseases for which current therapeutic interventions are inadequate or toxic. There is an urgent need to find new lead compounds against these diseases. Most drug discovery strategies rely on high-throughput screening (HTS) of synthetic chemical libraries using phenotypic and target-based approaches. Combinatorial chemistry libraries contain hundreds of thousands of compounds; however, they lack the structural diversity required to find entirely novel chemotypes. Natural products, in contrast, are a highly underexplored pool of unique chemical diversity that can serve as excellent templates for the synthesis of novel, biologically active molecules. We report here a validated HTS platform for the screening of microbial extracts against the 3 diseases. We have used this platform in a pilot project to screen a subset (5976) of microbial extracts from the MEDINA Natural Products library. Tandem liquid chromatography-mass spectrometry showed that 48 extracts contain potentially new compounds that are currently undergoing de-replication for future isolation and characterization. Known active components included actinomycin D, bafilomycin B1, chromomycin A3, echinomycin, hygrolidin, and nonactins, among others. The report here is, to our knowledge, the first HTS of microbial natural product extracts against the above-mentioned kinetoplastid parasites.

  16. Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry.

    PubMed

    Chen, Shili; Kong, Hongwei; Lu, Xin; Li, Yong; Yin, Peiyuan; Zeng, Zhongda; Xu, Guowang

    2013-09-03

    Untargeted analysis performed using full-scan mass spectrometry (MS) coupled with liquid chromatography (LC) is commonly used in metabolomics. Although they are commonly employed, full-scan MS methods such as quadrupole-time-of-flight (Q-TOF) MS have been restricted by various factors including their limited linear range and complicated data processing. LC coupled with triple quadrupole (QQQ) MS operated in the multiple reaction monitoring (MRM) mode is the gold standard for metabolite quantification; however, only known metabolites are generally quantified, limiting its applications in metabolomic analysis. In this study, a pseudotargeted approach was proposed to perform serum metabolomic analysis using an ultra high-performance liquid chromatography (UHPLC)/QQQ MS system operated in the MRM mode, for which the MRM ion pairs were acquired from the serum samples through untargeted tandem MS using UHPLC/Q-TOF MS. The UHPLC/QQQ MRM MS-based pseudotargeted method displayed better repeatability and wider linear range than the traditional UHPLC/Q-TOF MS-based untargeted metabolomics method, and no complicated peak alignment was required. The developed method was applied to discover serum biomarkers for patients with hepatocellular carcinoma (HCC). Patients with HCC had decreased lysophosphatidylcholine, increased long-chain and decreased medium-chain acylcarnitines, and increased aromatic and decreased branched-chain amino acid levels compared to healthy controls. The novelty of this work is that it provides an approach to acquire MRM ion pairs from real samples, is not limited to metabolite standards, and it provides a foundation to achieve pseudotargeted metabolomic analysis on the widely used LC/QQQ MS platform.

  17. Prognostic Biomarkers in Ovarian Cancer

    PubMed Central

    Huang, Jie; Hu, Wei; Sood, Anil K

    2014-01-01

    Epithelial ovarian cancer (EOC) remains the most lethal gynecological malignancy despite several decades of progress in diagnosis and treatment. Taking advantage of the robust development of discovery and utility of prognostic biomarkers, clinicians and researchers are developing personalized and targeted treatment strategies. This review encompasses recently discovered biomarkers of ovarian cancer, the utility of published prognostic biomarkers for EOC (especially biomarkers related to angiogenesis and key signaling pathways), and their integration into clinical practice. PMID:22045356

  18. A high-quality secretome of A549 cells aided the discovery of C4b-binding protein as a novel serum biomarker for non-small cell lung cancer.

    PubMed

    Luo, Xiaoyang; Liu, Yansheng; Wang, Rui; Hu, Haichuan; Zeng, Rong; Chen, Haiquan

    2011-04-01

    Cancer secretomes are a promising source for biomarker discovery. The analysis of cancer secretomes still faces some difficulties mainly related to the intracellular contamination, which hinders the qualification and follow-up validations. This study aimed to establish a high-quality secretome of A549 cells by using the cellular proteome as a reference and to test the merits of this refined secretome for biomarker discovery for non-small cell lung cancer (NSCLC). Using one-dimensional gel electrophoresis followed by liquid-chromatography tandem mass spectrometry, we comprehensively investigated the secretome and the concurrent cellular proteome of A549 cells. A high-quality secretome consisting of 382 proteins was refined from 889 initial secretory proteins. More than 85.3% of proteins were annotated as secreted and 76.8% as extracellular or membrane-bound. The discriminative power of the lung-cancer associated secretome was confirmed by gene expression and serum proteomic data. The elevated level of C4b-binding Protein (C4BP) in NSCLC blood was verified by enzyme-linked immunosorbent assays (ELISA, p = 6.07e-6). Moreover, the serum C4BP level in 89 patients showed a strong association with the clinical staging of NSCLC. Our reference-experiment-driven strategy is simple and widely applicable, and may facilitate the identification of novel promising biomarkers of lung cancer.

  19. The Hexosamine Template – A Platform for Modulating Gene Expression and for Sugar-based Drug Discovery

    PubMed Central

    Elmouelhi, Noha; Aich, Udayanath; Paruchuri, Venkata D.P.; Meledeo, M. Adam; Campbell, Christopher T.; Wang, Jean J.; Srinivas, Raja; Khanna, Hargun S.; Yarema, Kevin J.

    2009-01-01

    This study investigates the breadth of cellular responses engendered by short chain fatty acid (SCFA)-hexosamine hybrid molecules, a class of compounds long used in ‘metabolic glycoengineering’ that are now emerging as drug candidates. First, a ‘mix-and-match’ strategy showed that different SCFA (n-butyrate and acetate) appended to the same core sugar altered biological activity, complementing previous results [Campbell et al., (2008) J. Med. Chem. 51, 8135–8147] where a single type of SCFA elicited distinct responses. Microarray profiling then compared transcriptional responses engendered by regioisomerically-modified ManNAc, GlcNAc, and GalNAc analogs in MDA-MB-231 cells. These data – which were validated by qRT-PCR or Western analysis for ID1, TP53, HPSE, NQO1, EGR1 and VEGFA – showed a two-pronged response where a core set of genes was coordinately regulated by all analogs while each analog simultaneously uniquely regulated a larger number of genes. Finally, AutoDock modeling supported a mechanism where the analogs directly interact with elements of the NF-κB pathway. Together, these results establish the SCFA-hexosamine template as a versatile platform for modulating biological activity and developing new therapeutics. PMID:19326913

  20. The BioFIND study: Characteristics of a clinically typical Parkinson's disease biomarker cohort

    PubMed Central

    Goldman, Jennifer G.; Alcalay, Roy N.; Xie, Tao; Tuite, Paul; Henchcliffe, Claire; Hogarth, Penelope; Amara, Amy W.; Frank, Samuel; Rudolph, Alice; Casaceli, Cynthia; Andrews, Howard; Gwinn, Katrina; Sutherland, Margaret; Kopil, Catherine; Vincent, Lona; Frasier, Mark

    2016-01-01

    ABSTRACT Background Identifying PD‐specific biomarkers in biofluids will greatly aid in diagnosis, monitoring progression, and therapeutic interventions. PD biomarkers have been limited by poor discriminatory power, partly driven by heterogeneity of the disease, variability of collection protocols, and focus on de novo, unmedicated patients. Thus, a platform for biomarker discovery and validation in well‐characterized, clinically typical, moderate to advanced PD cohorts is critically needed. Methods BioFIND (Fox Investigation for New Discovery of Biomarkers in Parkinson's Disease) is a cross‐sectional, multicenter biomarker study that established a repository of clinical data, blood, DNA, RNA, CSF, saliva, and urine samples from 118 moderate to advanced PD and 88 healthy control subjects. Inclusion criteria were designed to maximize diagnostic specificity by selecting participants with clinically typical PD symptoms, and clinical data and biospecimen collection utilized standardized procedures to minimize variability across sites. Results We present the study methodology and data on the cohort's clinical characteristics. Motor scores and biospecimen samples including plasma are available for practically defined off and on states and thus enable testing the effects of PD medications on biomarkers. Other biospecimens are available from off state PD assessments and from controls. Conclusion Our cohort provides a valuable resource for biomarker discovery and validation in PD. Clinical data and biospecimens, available through The Michael J. Fox Foundation for Parkinson's Research and the National Institute of Neurological Disorders and Stroke, can serve as a platform for discovering biomarkers in clinically typical PD and comparisons across PD's broad and heterogeneous spectrum. © 2016 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society PMID:27113479

  1. Full-length novel MHC class I allele discovery by next-generation sequencing: two platforms are better than one.

    PubMed

    Dudley, Dawn M; Karl, Julie A; Creager, Hannah M; Bohn, Patrick S; Wiseman, Roger W; O'Connor, David H

    2014-01-01

    Deep sequencing has revolutionized major histocompatibility complex (MHC) class I analysis of nonhuman primates by enabling high-throughput, economical, and comprehensive genotyping. Full-length MHC class I cDNA sequences, which are required to generate reagents such as MHC-peptide tetramers, cannot be directly obtained by short read deep sequencing. We combined data from two next-generation sequencing platforms to discover novel full-length MHC class I mRNA/cDNA transcripts in Chinese rhesus macaques. We first genotyped macaques by Roche/454 pyrosequencing using a 530-bp amplicon spanning the densely polymorphic exons 2 through 4 of the MHC class I loci that encode the peptide-binding region. We then mapped short paired-end 250 bp Illumina sequence reads spanning the full-length transcript to each 530-bp amplicon at high stringency and used paired-end information to reconstruct full-length allele sequences. We characterized 65 full-length sequences from six Chinese rhesus macaques. Overall, approximately 70 % of the alleles distinguished in these six animals contained new sequence information, including 29 novel transcripts. The flexibility of this approach should make full-length MHC class I allele genotyping accessible for any nonhuman primate population of interest. We are currently optimizing this method for full-length characterization of other highly polymorphic, duplicated loci such as the MHC class II DRB and killer immunoglobulin-like receptors. We anticipate that this method will facilitate rapid expansion and near completion of sequence libraries of polymorphic loci, such as MHC class I, within a few years.

  2. Determining conserved metabolic biomarkers from a million database queries

    PubMed Central

    Kurczy, Michael E.; Ivanisevic, Julijana; Johnson, Caroline H.; Uritboonthai, Winnie; Hoang, Linh; Fang, Mingliang; Hicks, Matthew; Aldebot, Anthony; Rinehart, Duane; Mellander, Lisa J.; Tautenhahn, Ralf; Patti, Gary J.; Spilker, Mary E.; Benton, H. Paul; Siuzdak, Gary

    2015-01-01

    Motivation: Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. Results: With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. Availability and implementation. METLIN can be accessed by logging on to: https://metlin.scripps.edu Contact: siuzdak@scripps.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26275895

  3. Discovery, screening and evaluation of a plasma biomarker panel for subjects with psychological suboptimal health state using 1H-NMR-based metabolomics profiles

    PubMed Central

    Tian, Jun-sheng; Xia, Xiao-tao; Wu, Yan-fei; Zhao, Lei; Xiang, Huan; Du, Guan-hua; Zhang, Xiang; Qin, Xue-mei

    2016-01-01

    Individuals in the state of psychological suboptimal health keep increasing, only scales and questionnaires were used to diagnose in clinic under current conditions, and symptoms of high reliability and accuracy are destitute. Therefore, the noninvasive and precise laboratory diagnostic methods are needed. This study aimed to develop an objective method through screen potential biomarkers or a biomarker panel to facilitate the diagnosis in clinic using plasma metabolomics. Profiles were based on H-nuclear magnetic resonance (1H-NMR) metabolomics techniques combing with multivariate statistical analysis. Furthermore, methods of correlation analysis with Metaboanalyst 3.0 for selecting a biomarker panel, traditional Chinese medicine (TCM) drug intervention for validating the close relations between the biomarker panel and the state and the receiver operating characteristic curves (ROC curves) analysis for evaluation of clinical diagnosis ability were carried out. 9 endogenous metabolites containing trimethylamine oxide (TMAO), glutamine, N-acetyl-glycoproteins, citrate, tyrosine, phenylalanine, isoleucine, valine and glucose were identified and considered as potential biomarkers. Then a biomarker panel consisting of phenylalanine, glutamine, tyrosine, citrate, N-acetyl-glycoproteins and TMAO was selected, which exhibited the highest area under the curve (AUC = 0.971). This study provided critical insight into the pathological mechanism of psychological suboptimal health and would supply a novel and valuable diagnostic method. PMID:27650680

  4. A Short Survey on the State of the Art in Architectures and Platforms for Large Scale Data Analysis and Knowledge Discovery from Data

    SciTech Connect

    Begoli, Edmon

    2012-01-01

    Intended as a survey for practicing architects and researchers seeking an overview of the state-of-the-art architectures for data analysis, this paper provides an overview of the emerg- ing data management and analytic platforms including par- allel databases, Hadoop-based systems, High Performance Computing (HPC) platforms and platforms popularly re- ferred to as NoSQL platforms. Platforms are presented based on their relevance, analysis they support and the data organization model they support.

  5. Methodological and analytic considerations for blood biomarkers.

    PubMed

    Christenson, Robert H; Duh, Show-Hong

    2012-01-01

    Biomarkers typically evolve from a research setting to use in clinical care as evidence for their independent contribution to patient management accumulates. This evidence relies heavily on knowledge of the preanalytical, analytical, and postanalytical characteristics of the biomarker's measurement. For the preanalytical phase, considerations such specimen type, acceptable anticoagulants for blood samples, biologic variation and stability of the biomarker under various conditions are key. The analytical phase entails critical details for development and maintenance of assays having performance characteristics that are "fit for service" for the clinical application at hand. Often, these characteristics describe the ability to measure minute quantities in the biologic matrix used for measurement. Although techniques such as mass spectrometry are used effectively for biomarker discovery, routine quantification often relies on use of immunoassays; early in development, the most common immunoassay used is the enzyme-linked immunosorbent assay format. As biomarkers evolve successfully, they will be adapted to large main laboratory platforms or, depending on the need for speed, point-of-care devices. Users must pay particular attention to performance parameters of assays they are considering for clinical implementation. These parameters include the limit of blank, a term used to describe the limit of analytical noise for an assay; limit of detection, which describes the lowest concentration that can reliably be discriminated from analytical noise; and perhaps most importantly, the limit of quantitation, which is the lowest concentration at which a biomarker can be reliably measured within some predefined specifications for total analytical error that is based on clinical requirements of the test. The postanalytical phase involves reporting biomarker values, which includes reporting units, any normalization factors, and interpretation. Standardization, a process that

  6. IgY14 and SuperMix immunoaffinity separations coupled with liquid chromatography-mass spectrometry for human plasma proteomic biomarker discovery

    SciTech Connect

    Shi, Tujin; Zhou, Jianying; Gritsenko, Marina A.; Hossain, Mahmud; Camp, David G.; Smith, Richard D.; Qian, Weijun

    2012-02-01

    Interest in the application of advanced proteomics technologies to human blood plasma- or serum-based clinical samples for the purpose of discovering disease biomarkers continues to grow; however, the enormous dynamic range of protein concentrations in these types of samples (often >10 orders of magnitude) represents a significant analytical challenge, particularly for detecting low-abundance candidate biomarkers. In response, immunoaffinity separation methods for depleting multiple high- and moderate-abundance proteins have become key tools for enriching low-abundance proteins and enhancing detection of these proteins in plasma proteomics. Herein, we describe IgY14 and tandem IgY14-Supermix separation methods for removing 14 high-abundance and up to 60 moderate-abundance proteins, respectively, from human blood plasma and highlight their utility when combined with liquid chromatography-tandem mass spectrometry for interrogating the human plasma proteome.

  7. Patient-derived tumour xenografts for breast cancer drug discovery

    PubMed Central

    Batra, Ankita S; Greenwood, Wendy

    2016-01-01

    Despite remarkable advances in our understanding of the drivers of human malignancies, new targeted therapies often fail to show sufficient efficacy in clinical trials. Indeed, the cost of bringing a new agent to market has risen substantially in the last several decades, in part fuelled by extensive reliance on preclinical models that fail to accurately reflect tumour heterogeneity. To halt unsustainable rates of attrition in the drug discovery process, we must develop a new generation of preclinical models capable of reflecting the heterogeneity of varying degrees of complexity found in human cancers. Patient-derived tumour xenograft (PDTX) models prevail as arguably the most powerful in this regard because they capture cancer’s heterogeneous nature. Herein, we review current breast cancer models and their use in the drug discovery process, before discussing best practices for developing a highly annotated cohort of PDTX models. We describe the importance of extensive multidimensional molecular and functional characterisation of models and combination drug–drug screens to identify complex biomarkers of drug resistance and response. We reflect on our own experiences and propose the use of a cost-effective intermediate pharmacogenomic platform (the PDTX-PDTC platform) for breast cancer drug and biomarker discovery. We discuss the limitations and unanswered questions of PDTX models; yet, still strongly envision that their use in basic and translational research will dramatically change our understanding of breast cancer biology and how to more effectively treat it. PMID:27702751

  8. Nanostructured optical microchips for cancer biomarker detection.

    PubMed

    Zhang, Tianhua; He, Yuan; Wei, Jianjun; Que, Long

    2012-01-01

    Herein we report the label-free detection of a cancer biomarker using newly developed arrayed nanostructured Fabry-Perot interferometer (FPI) microchips. Specifically, the prostate cancer biomarker free prostate-specific antigen (f-PSA) has been detected with a mouse anti-human PSA monoclonal antibody (mAb) as the receptor. Experiments found that the limit-of-detection of current nanostructured FPI microchip for f-PSA is about 10 pg/mL and the upper detection range for f-PSA can be dynamically changed by varying the amount of the PSA mAb immobilized on the sensing surface. The control experiments have also demonstrated that the immunoassay protocol used in the experiments shows excellent specificity and selectivity, suggesting the great potential to detect the cancer biomarkers at trace levels in complex biofluids. In addition, given its nature of low cost, simple-to-operation and batch fabrication capability, the arrayed nanostructured FPI microchip-based platform could provide an ideal technical tool for point-of-care diagnostics application and anticancer drug screen and discovery.

  9. Radiation metabolomics. 3. Biomarker discovery in the urine of gamma-irradiated rats using a simplified metabolomics protocol of gas chromatography-mass spectrometry combined with random forests machine learning algorithm.

    PubMed

    Lanz, Christian; Patterson, Andrew D; Slavík, Josef; Krausz, Kristopher W; Ledermann, Monika; Gonzalez, Frank J; Idle, Jeffrey R

    2009-08-01

    Abstract Radiation metabolomics employing mass spectral technologies represents a plausible means of high-throughput minimally invasive radiation biodosimetry. A simplified metabolomics protocol is described that employs ubiquitous gas chromatography-mass spectrometry and open source software including random forests machine learning algorithm to uncover latent biomarkers of 3 Gy gamma radiation in rats. Urine was collected from six male Wistar rats and six sham-irradiated controls for 7 days, 4 prior to irradiation and 3 after irradiation. Water and food consumption, urine volume, body weight, and sodium, potassium, calcium, chloride, phosphate and urea excretion showed major effects from exposure to gamma radiation. The metabolomics protocol uncovered several urinary metabolites that were significantly up-regulated (glyoxylate, threonate, thymine, uracil, p-cresol) and down-regulated (citrate, 2-oxoglutarate, adipate, pimelate, suberate, azelaate) as a result of radiation exposure. Thymine and uracil were shown to derive largely from thymidine and 2'-deoxyuridine, which are known radiation biomarkers in the mouse. The radiation metabolomic phenotype in rats appeared to derive from oxidative stress and effects on kidney function. Gas chromatography-mass spectrometry is a promising platform on which to develop the field of radiation metabolomics further and to assist in the design of instrumentation for use in detecting biological consequences of environmental radiation release.

  10. A multi-platform metabolomics approach identifies highly specific biomarkers of bacterial diversity in the vagina of pregnant and non-pregnant women

    PubMed Central

    McMillan, Amy; Rulisa, Stephen; Sumarah, Mark; Macklaim, Jean M.; Renaud, Justin; Bisanz, Jordan E.; Gloor, Gregory B.; Reid, Gregor

    2015-01-01

    Bacterial vaginosis (BV) increases transmission of HIV, enhances the risk of preterm labour, and is associated with malodour. Clinical diagnosis often relies on microscopy, which may not reflect the microbiota composition accurately. We use an untargeted metabolomics approach, whereby we normalize the weight of samples prior to analysis, to obtained precise measurements of metabolites in vaginal fluid. We identify biomarkers for BV with high sensitivity and specificity (AUC = 0.99) in a cohort of 131 pregnant and non-pregnant Rwandan women, and demonstrate that the vaginal metabolome is strongly associated with bacterial diversity. Metabolites associated with high diversity and clinical BV include 2-hydroxyisovalerate and γ-hydroxybutyrate (GHB), but not succinate, which is produced by both Lactobacillus crispatus and BV-associated anaerobes in vitro. Biomarkers associated with high diversity and clinical BV are independent of pregnancy status, and were validated in a blinded replication cohort from Tanzania (n = 45), where we predicted clinical BV with 91% accuracy. Correlations between the metabolome and microbiota identified Gardnerella vaginalis as a putative producer of GHB, and we demonstrate production by this species in vitro. This work illustrates how changes in community structure alter the chemical composition of the vagina, and identifies highly specific biomarkers for a common condition. PMID:26387596

  11. Large-Scale Glycomics of Livestock: Discovery of Highly Sensitive Serum Biomarkers Indicating an Environmental Stress Affecting Immune Responses and Productivity of Holstein Dairy Cows.

    PubMed

    Rehan, Ibrahim F; Ueda, Koichiro; Mitani, Tomohiro; Amano, Maho; Hinou, Hiroshi; Ohashi, Tetsu; Kondo, Seiji; Nishimura, Shin-Ichiro

    2015-12-09

    Because various stresses strongly influence the food productivity of livestock, biomarkers to indicate unmeasurable environmental stress in domestic animals are of increasing importance. Thermal comfort is one of the basic principles of dairy cow welfare that enhances productivity. To discover sensitive biomarkers that monitor such environmental stresses in dairy cows, we herein performed, for the first time, large-scale glycomics on 336 lactating Holstein cow serum samples over 9 months between February and October. Glycoblotting combined with MALDI-TOF/MS and DMB/HPLC allowed for comprehensive glycomics of whole serum glycoproteins. The results obtained revealed seasonal alterations in serum N-glycan levels and their structural characteristics, such as an increase in high-mannose type N-glycans in spring, the occurrence of di/triantennary complex type N-glycans terminating with two or three Neu5Gc residues in summer and autumn, and N-glycans in winter dominantly displaying Neu5Ac. A multivariate analysis revealed a correlation between the serum expression levels of these season-specific glycoforms and productivity.

  12. Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned.

    PubMed

    Li, Gerald; Bankhead, Peter; Dunne, Philip D; O'Reilly, Paul G; James, Jacqueline A; Salto-Tellez, Manuel; Hamilton, Peter W; McArt, Darragh G

    2016-06-02

    Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating 'big data' across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

  13. Blood-based biomarkers for Parkinson's disease.

    PubMed

    Chahine, Lama M; Stern, Matthew B; Chen-Plotkin, Alice

    2014-01-01

    There is a pressing need for biomarkers to diagnose Parkinson's disease (PD), assess disease severity, and prognosticate course. Various types of biologic specimens are potential candidates for identifying biomarkers--defined here as surrogate indicators of physiological or pathophysiological states--but blood has the advantage of being minimally invasive to obtain. There are, however, several challenges to identifying biomarkers in blood. Several candidate biomarkers identified in other diseases or in other types of biological fluids are being pursued as blood-based biomarkers in PD. In addition, unbiased discovery is underway using techniques including metabolomics, proteomics, and gene expression profiling. In this review, we summarize these techniques and discuss the challenges and successes of blood-based biomarker discovery in PD. Blood-based biomarkers that are discussed include α-synuclein, DJ-1, uric acid, epidermal growth factor, apolipoprotein-A1, and peripheral inflammatory markers.

  14. Biomarkers for neuromyelitis optica.

    PubMed

    Chang, Kuo-Hsuan; Ro, Long-Sun; Lyu, Rong-Kuo; Chen, Chiung-Mei

    2015-02-02

    Neuromyelitis optica (NMO) is an acquired, heterogeneous inflammatory disorder, which is characterized by recurrent optic neuritis and longitudinally extensive spinal cord lesions. The discovery of the serum autoantibody marker, anti-aquaporin 4 (anti-AQP4) antibody, revolutionizes our understanding of pathogenesis of NMO. In addition to anti-AQP4 antibody, other biomarkers for NMO are also reported. These candidate biomarkers are particularly involved in T helper (Th)17 and astrocytic damages, which play a critical role in the development of NMO lesions. Among them, IL-6 in the peripheral blood is associated with anti-AQP4 antibody production. Glial fibrillary acidic protein (GFAP) in CSF demonstrates good correlations with clinical severity of NMO relapses. Detecting these useful biomarkers may be useful in the diagnosis and evaluation of disease activity of NMO. Development of compounds targeting these biomarkers may provide novel therapeutic strategies for NMO. This article will review the related biomarker studies in NMO and discuss the potential therapeutics targeting these biomarkers.

  15. Computational drug discovery

    PubMed Central

    Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

    2012-01-01

    Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

  16. Discovery of a Biomarker and Lead Small Molecules to Target r(GGGGCC)-Associated Defects in c9FTD/ALS

    PubMed Central

    Su, Zhaoming; Zhang, Yongjie; Gendron, Tania F.; Bauer, Peter O.; Chew, Jeannie; Yang, Wang-Yong; Fostvedt, Erik; Jansen-West, Karen; Belzil, Veronique V.; Desaro, Pamela; Johnston, Amelia; Overstreet, Karen; Oh, Seok-Yoon; Todd, Peter K.; Berry, James D.; Cudkowicz, Merit E.; Boeve, Bradley F.; Dickson, Dennis; Floeter, Mary Kay; Traynor, Bryan J.; Morelli, Claudia; Ratti, Antonia; Silani, Vincenzo; Rademakers, Rosa; Brown, Robert H.; Rothstein, Jeffrey D.; Boylan, Kevin B.; Petrucelli, Leonard; Disney, Matthew D.

    2014-01-01

    Summary A repeat expansion in C9ORF72 causes frontotemporal dementia and amyotrophic lateral sclerosis (c9FTD/ALS). RNA of the expanded repeat (r(GGGGCC)exp) forms nuclear foci or undergoes repeat-associated non-ATG (RAN) translation producing “c9RAN proteins”. Since neutralizing r(GGGGCC)exp could inhibit these potentially toxic events, we sought to identify small molecule binders of r(GGGGCC)exp. Chemical and enzymatic probing of r(GGGGCC)8 indicate it adopts a hairpin structure in equilibrium with a quadruplex structure. Using this model, bioactive small molecules targeting r(GGGGCC)exp were designed and found to significantly inhibit RAN translation and foci formation in cultured cells expressing r(GGGGCC)66 and neurons trans-differentiated from fibroblasts of repeat expansion carriers. Finally, we show that poly(GP) c9RAN proteins are specifically detected in c9ALS patient cerebrospinal fluid. Our findings highlight r(GGGGCC)exp-binding small molecules as a possible c9FTD/ALS therapeutic, and suggest c9RAN proteins could potentially serve as a pharmacodynamic biomarker to assess efficacy of therapies that target r(GGGGCC)exp. PMID:25132468

  17. Chip-based Reversed-phase Liquid Chromatography-Mass Spectrometry of Permethylated N-linked Glycans: a Potential Methodology for Cancer-biomarker Discovery

    PubMed Central

    Alley, William R.; Madera, Milan; Mechref, Yehia; Novotny, Milos V.

    2010-01-01

    The study of protein glycosylation in biological fluids and tissues has substantial medical importance, as changes in glycan structures have now been associated with a number of diseases. Quantification of glycomic-profile changes is becoming increasingly important in the search for disease biomarkers. Here, we report a highly reproducible combination of a glycomic sample preparation/solid-phase derivatization of glycoprotein-derived N-linked glycans with their subsequent microchip-based separation and mass-spectrometric (MS) measurements. Following our previously-described reductive β-elimination for O-linked glycans with ammonia-borane complex to reduce N-linked structures, the N-linked alditol structures are effectively methylated in dimethylformamide medium to avoid artefacts in MS measurements. Reversed-phase microfluidic liquid chromatography (LC) of methylated N-linked oligosaccharide alditols resolved some closely related structures into regular retention increments, aiding in their structural assignments. Optimized LC gradients, together with nanospray MS, have been applied here in the quantitative measurements of N-linked glycans in blood serum, distinguishing breast cancer patients from control individuals. PMID:20491449

  18. Discovery of a biomarker and lead small molecules to target r(GGGGCC)-associated defects in c9FTD/ALS.

    PubMed

    Su, Zhaoming; Zhang, Yongjie; Gendron, Tania F; Bauer, Peter O; Chew, Jeannie; Yang, Wang-Yong; Fostvedt, Erik; Jansen-West, Karen; Belzil, Veronique V; Desaro, Pamela; Johnston, Amelia; Overstreet, Karen; Oh, Seok-Yoon; Todd, Peter K; Berry, James D; Cudkowicz, Merit E; Boeve, Bradley F; Dickson, Dennis; Floeter, Mary Kay; Traynor, Bryan J; Morelli, Claudia; Ratti, Antonia; Silani, Vincenzo; Rademakers, Rosa; Brown, Robert H; Rothstein, Jeffrey D; Boylan, Kevin B; Petrucelli, Leonard; Disney, Matthew D

    2014-09-03

    A repeat expansion in C9ORF72 causes frontotemporal dementia and amyotrophic lateral sclerosis (c9FTD/ALS). RNA of the expanded repeat (r(GGGGCC)exp) forms nuclear foci or undergoes repeat-associated non-ATG (RAN) translation, producing "c9RAN proteins." Since neutralizing r(GGGGCC)exp could inhibit these potentially toxic events, we sought to identify small-molecule binders of r(GGGGCC)exp. Chemical and enzymatic probing of r(GGGGCC)8 indicate that it adopts a hairpin structure in equilibrium with a quadruplex structure. Using this model, bioactive small molecules targeting r(GGGGCC)exp were designed and found to significantly inhibit RAN translation and foci formation in cultured cells expressing r(GGGGCC)66 and neurons transdifferentiated from fibroblasts of repeat expansion carriers. Finally, we show that poly(GP) c9RAN proteins are specifically detected in c9ALS patient cerebrospinal fluid. Our findings highlight r(GGGGCC)exp-binding small molecules as a possible c9FTD/ALS therapeutic and suggest that c9RAN proteins could potentially serve as a pharmacodynamic biomarker to assess efficacy of therapies that target r(GGGGCC)exp.

  19. Mass spectrometry-driven drug discovery for development of herbal medicine.

    PubMed

    Zhang, Aihua; Sun, Hui; Wang, Xijun

    2016-12-23

    Herbal medicine (HM) has made a major contribution to the drug discovery process with regard to identifying products compounds. Currently, more attention has been focused on drug discovery from natural compounds of HM. Despite the rapid advancement of modern analytical techniques, drug discovery is still a difficult and lengthy process. Fortunately, mass spectrometry (MS) can provide us with useful structural information for drug discovery, has been recognized as a sensitive, rapid, and high-throughput technology for advancing drug discovery from HM in the post-genomic era. It is essential to develop an efficient, high-quality, high-throughput screening method integrated with an MS platform for early screening of candidate drug molecules from natural products. We have developed a new chinmedomics strategy reliant on MS that is capable of capturing the candidate molecules, facilitating their identification of novel chemical structures in the early phase; chinmedomics-guided natural product discovery based on MS may provide an effective tool that addresses challenges in early screening of effective constituents of herbs against disease. This critical review covers the use of MS with related techniques and methodologies for natural product discovery, biomarker identification, and determination of mechanisms of action. It also highlights high-throughput chinmedomics screening methods suitable for lead compound discovery illustrated by recent successes.

  20. Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset

    PubMed Central

    2011-01-01

    Background Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics. Results We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods. Conclusions The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author. PMID:21554713

  1. Nanoparticles: potential biomarker harvesters.

    PubMed

    Geho, David H; Jones, Clinton D; Petricoin, Emanuel F; Liotta, Lance A

    2006-02-01

    A previously untapped bank of information resides within the low molecular weight proteomic fraction of blood. Intensive efforts are underway to harness this information so that it can be used for early diagnosis of diseases such as cancer. The physicochemical malleability and high surface areas of nanoparticle surfaces make them ideal candidates for developing biomarker harvesting platforms. Given the variety of engineering strategies afforded through nanoparticle technologies, a significant goal is to tailor nanoparticle surfaces to selectively bind a subset of biomarkers, sequestering them for later study using high sensitivity proteomic tests. To date, applications of nanoparticles have largely focused on imaging systems and drug delivery vectors. As such, biomarker harvesting is an underutilized application of nanoparticle technology and is an area of nanotechnology research that will likely undergo substantial growth.

  2. Microfluidic immunosensor based on mesoporous silica platform and CMK-3/poly-acrylamide-co-methacrylate of dihydrolipoic acid modified gold electrode for cancer biomarker detection.

    PubMed

    Regiart, Matías; Fernández-Baldo, Martin A; Villarroel-Rocha, Jhonny; Messina, Germán A; Bertolino, Franco A; Sapag, Karim; Timperman, Aaron T; Raba, Julio

    2017-04-22

    We report a hybrid glass-poly (dimethylsiloxane) microfluidic immunosensor for epidermal growth factor receptor (EGFR) determination, based on the covalent immobilization of anti-EGFR antibody (anti-EGFR) on amino-functionalized mesoporous silica (AMS) retained in the central channel of a microfluidic device. The synthetized AMS was characterized by N2 adsorption-desorption isotherm, scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) and infrared spectroscopy. The cancer biomarker was quantified in human serum samples by a direct sandwich immunoassay measuring through a horseradish peroxidase-conjugated anti-EGFR. The enzymatic product was detected at -100 mV by amperometry on a sputtering gold electrode, modified with an ordered mesoporous carbon (CMK-3) in a matrix of poly-acrylamide-co-methacrylate of dihydrolipoic acid (poly(AC-co-MDHLA)) through in situ copolymerization. CMK-3/poly(AC-co-MDHLA)/gold was characterized by cyclic voltammetry, EDS and SEM. The measured current was directly proportional to the level of EGFR in human serum samples. The linear range was from 0.01 ng mL(-1) to 50 ng mL(-1). The detection limit was 3.03 pg mL(-1), and the within- and between-assay coefficients of variation were below 5.20%. The microfluidic immunosensor is a very promising device for the diagnosis of several kinds of epithelial origin carcinomas.

  3. A platform for discovery and quantification of modified ribonucleosides in RNA: Application to stress-induced reprogramming of tRNA modifications

    PubMed Central

    Cai, Weiling Maggie; Chionh, Yok Hian; Hia, Fabian; Gu, Chen; Kellner, Stefanie; McBee, Megan E.; Ng, Chee Sheng; Pang, Yan Ling Joy; Prestwich, Erin G.; Lim, Kok Seong; Babu, I. Ramesh; Begley, Thomas J.; Dedon, Peter C.

    2016-01-01

    Here we describe an analytical platform for systems-level quantitative analysis of modified ribonucleosides in any RNA species, with a focus on stress-induced reprogramming of tRNA as part of a system of translational control of cell stress response. The chapter emphasizes strategies and caveats for each of the seven steps of the platform workflow: 1) RNA isolation, 2) RNA purification, 3) RNA hydrolysis to individual ribonucleosides, 4) chromatographic resolution of ribonucleosides, 5) identification of the full set of modified ribonucleosides, 6) mass spectrometric quantification of ribonucleosides, 6) interrogation of ribonucleoside datasets, and 7) mapping the location of stress-sensitive modifications in individual tRNA molecules. We have focused on the critical determinants of analytical sensitivity, specificity, precision and accuracy in an effort to ensure the most biologically meaningful data on mechanisms of translational control of cell stress response. The methods described here should find wide use in virtually any analysis involving RNA modifications. PMID:26253965

  4. Proteomic response of mussels Mytilus galloprovincialis exposed to CuO NPs and Cu²⁺: an exploratory biomarker discovery.

    PubMed

    Gomes, Tânia; Chora, Suze; Pereira, Catarina G; Cardoso, Cátia; Bebianno, Maria João

    2014-10-01

    absence of the mussel genome precluded the identification of other proteins relevant to clarify the effects of CuO NPs in mussels' tissues, proteomics analysis provided additional knowledge of their potential effects at the protein level that after confirmation and validation can be used as putative new biomarkers in nanotoxicology.

  5. Biomarkers for antipsychotic therapies.

    PubMed

    Pich, Emilio Merlo; Vargas, Gabriel; Domenici, Enrico

    2012-01-01

    Molecular biomarkers for antipsychotic treatments have been conceptually linked to the measurements of dopamine functions, mostly D(2) receptor occupancy, either by imaging using selective PET/SPECT radioactive tracers or by assessing plasma prolactin levels. A quest for novel biomarkers was recently proposed by various academic, health service, and industrial institutions driven by the need for better treatments of psychoses. In this review we conceptualize biomarkers within the Translational Medicine paradigm whose goal was to provide support to critical decision-making in drug discovery. At first we focused on biomarkers as outcome measure of clinical studies by searching into the database clinicaltrial.gov. The results were somewhat disappointing, showing that out of 1,659 antipsychotic trials only 18 used a biomarker as an outcome measure. Several of these trials targeted plasma lipids as sentinel marker for metabolic adverse effects associated with the use of atypical antipsychotics, while only few studies were aimed to new disease specific biological markers. As an example of a mechanistic biomarker, we described the work done to progress the novel class of glycine transporter inhibitors as putative treatment for negative symptoms of schizophrenia. We also review how large-scale multiplex biological assays were applied to samples from tissues of psychiatric patients, so to learn from changes of numerous analytes (metabolic products, lipids, proteins, RNA transcripts) about the substrates involved in the disease. We concluded that a stringent implementation of these techniques could contribute to the endophenotypic characterization of patients, helping in the identification of key biomarkers to drive personalized medicine and new treatment development.

  6. Discovery of a new genus and new species of Indo-West Pacific pilumnoidid crab from a semisubmersible oil platform (Crustacea: Brachyura: Pseudozioidea).

    PubMed

    Ng, Peter K L; Ahyong, Shane T

    2013-01-01

    A new genus and new species of pseudozioid crab of the family Pilumnoididae is described from a fouling community on a semisubmersible oil platform in Singapore that had been operating in the Timor Sea and South China Sea. Setozius incertus gen. et sp. nov. superficially resembles species of Pilumnus (Pilumnidae, Pilumnoidea) but has male first and second gonopod structures characteristic of the Pseudozioidea. The form of the carapace, male anterior thoracic sternum and male abdomen indicates that it should be classified in the Pilumnoididae. Setozius is the first member of the family to be recorded from the Indo-West Pacific; all other known pilumnoidids occur in the Atlantic and eastern Pacific.

  7. Discovering Biomarkers within the Genomic Landscape of Renal Cell Carcinoma.

    PubMed

    A, Sankin

    2016-02-01

    Recent advances in molecular sequencing technology have led to the discovery of numerous biomarkers in renal cell carcinoma (RCC). These biomarkers have the potential to predict clinical outcomes and aid in clinical management decisions. The following commentary is a review of the preliminary data on some of the most promising genetic biomarker candidates.

  8. Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization.

    PubMed

    Amur, S; LaVange, L; Zineh, I; Buckman-Garner, S; Woodcock, J

    2015-07-01

    The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration.

  9. The national biomarker development alliance: confronting the poor productivity of biomarker research and development.

    PubMed

    Poste, George; Compton, Carolyn C; Barker, Anna D

    2015-02-01

    Making precision (personalized) medicine a routine clinical reality will require a comprehensive inventory of validated biomarkers and molecular diagnostic tests to stratify disease subtypes and improve accuracy in diagnosis and treatment selection. Realization of this promise has been hindered by the poor productivity of biomarker identification and validation. This situation reflects deficiencies that are pervasive across the entire spectrum of biomarker R&D, from discovery to clinical validation and in the failure of regulatory and reimbursement policies to accommodate new classes of biomarkers. The launch of the National Biomarker Development Alliance is the culmination of a 2-year review and consultation process involving diverse stakeholders to advance standards, best practices and guidelines to enhance biomarker discovery and validation by adoption of systems-based approaches and trans-sector collaboration between academia, clinical medicine and relevant private and public sector stakeholders.

  10. The Utility of Metabolomics in Natural Product and Biomarker Characterization

    PubMed Central

    Cox, Daniel G.; Oh, Joonseok; Keasling, Adam; Colson, Kim

    2014-01-01

    Background Metabolomics is a well-established rapidly developing research field involving quantitative and qualitative metabolite assessment within biological systems. Recent improvements in metabolomics technologies reveal the unequivocal value of metabolomics tools in natural products discovery, gene-function analysis, systems biology and diagnostic platforms. Scope of review We review of some of the prominent metabolomics methodologies employed in data acquisition and analysis of natural products and disease-related biomarkers. Major conclusions This review demonstrates that metabolomics represents a highly adaptable technology with diverse applications ranging from environmental toxicology to disease diagnosis. Metabolomic analysis is shown to provide a unique snapshot of the functional genetic status of an organism by examining its biochemical profile, with relevance toward resolving phylogenetic associations involving horizontal gene transfer and distinguishing subgroups of genera possessing high genetic homology, as well as an increasing role in both elucidating biosynthetic transformations of natural products and detecting preclinical biomarkers of numerous disease states. General significance This review expands the interest in multiplatform combinatorial metabolomic analysis. The applications reviewed range from phylogenetic assignment, biosynthetic transformations of natural products, and the detection of preclinical biomarkers. PMID:25151044

  11. Urinary extracellular vesicles and the kidney: biomarkers and beyond.

    PubMed

    Salih, Mahdi; Zietse, Robert; Hoorn, Ewout J

    2014-06-01

    Extracellular vesicles have been isolated in various body fluids, including urine. The cargo of urinary extracellular vesicles (uEVs) is composed of proteins and nucleic acids reflecting the physiological and possibly pathophysiological state of cells lining the nephron. Because urine is a noninvasive and readily available biofluid, the discovery of uEVs has opened a new field of biomarker research. Their potential use as diagnostic, prognostic, or therapeutic biomarkers for various kidney diseases, including glomerulonephritis, acute kidney injury, tubular disorders, and polycystic kidney disease, is currently being explored. Some challenges, however, remain. These challenges include the need to standardize isolation methods, normalization between samples, and validation of candidate biomarkers. Also, the development of a high-throughput platform to isolate and analyze uEVs, for example, an enzyme-linked immunosorbent assay, is desirable. Here, we review recent studies on uEVs dealing with kidney physiology and pathophysiology. Furthermore, we discuss new and exciting developments regarding vesicles, including their role in cell-to-cell communication and the possibility of using vesicles as a therapy for kidney disorders.

  12. Discovery of a Protective Rickettsia prowazekii Antigen Recognized by CD8+ T Cells, RP884, Using an In Vivo Screening Platform

    PubMed Central

    Goez, Yenny; Cespedes, Maria A.; Hidalgo, Marylin; Correa, Paula; Valbuena, Gustavo

    2013-01-01

    Rickettsia prowazekii has been tested for biological warfare due to the high mortality that it produces after aerosol transmission of very low numbers of rickettsiae. Epidemic typhus, the infection caused by these obligately intracellular bacteria, continues to be a threat because it is difficult to diagnose due to initial non-specific symptoms and the lack of commercial diagnostic tests that are sensitive and specific during the initial clinical presentation. A vaccine to prevent epidemic typhus would constitute an effective deterrent to the weaponization of R. prowazekii; however, an effective and safe vaccine is not currently available. Due to the cytoplasmic niche of Rickettsia, CD8+ T-cells are critical effectors of immunity; however, the identification of antigens recognized by these cells has not been systematically addressed. To help close this gap, we designed an antigen discovery strategy that uses cell-based vaccination with antigen presenting cells expressing microbe's proteins targeted to the MHC class I presentation pathway. We report the use of this method to discover a protective T-cell rickettsial antigen, RP884, among a test subset of rickettsial proteins. PMID:24146844

  13. Biomarkers in Parkinson's disease: a funder's perspective.

    PubMed

    Frasier, Mark; Chowdhury, Sohini; Eberling, Jamie; Sherer, Todd

    2010-10-01

    Therapeutic development in Parkinson's disease is hampered by the paucity of well-validated biomarkers that can assist with diagnosis and/or tracking the progression of the disease. Since its inception, the Michael J Fox Foundation for Parkinson's Research has invested heavily in biomarker research and continues to prioritize discovery and development efforts. This article summarizes the history and evolution of the Michael J Fox Foundation's role in supporting biomarker research and lays out the current challenges in successfully developing markers that can be used to test therapies, while also providing a vision of future funding efforts in Parkinson's disease biomarkers.

  14. Discovery of Novel Small Molecule Inhibitors of VEGF Expression in Tumor Cells Using a Cell-Based High Throughput Screening Platform

    PubMed Central

    Weetall, Marla; Bombard, Jenelle; Qi, Hongyan; Arasu, Tamil; Lennox, William; Hedrick, Jean; Sheedy, Josephine; Risher, Nicole; Brooks, Peter C.; Trifillis, Panayiota; Trotta, Christopher; Moon, Young-Choon; Babiak, John; Almstead, Neil G.; Colacino, Joseph M.; Davis, Thomas W.; Peltz, Stuart W.

    2016-01-01

    Current anti-VEGF (Vascular Endothelial Growth Factor A) therapies to treat various cancers indiscriminately block VEGF function in the patient resulting in the global loss of VEGF signaling which has been linked to dose-limiting toxicities as well as treatment failures due to acquired resistance. Accumulating evidence suggests that this resistance is at least partially due to increased production of compensatory tumor angiogenic factors/cytokines. VEGF protein production is differentially controlled depending on whether cells are in the normal “homeostatic” state or in a stressed state, such as hypoxia, by post-transcriptional regulation imparted by elements in the 5’ and 3’ untranslated regions (UTR) of the VEGF mRNA. Using the Gene Expression Modulation by Small molecules (GEMS™) phenotypic assay system, we performed a high throughput screen to identify low molecular weight compounds that target the VEGF mRNA UTR-mediated regulation of stress-induced VEGF production in tumor cells. We identified a number of compounds that potently and selectively reduce endogenous VEGF production under hypoxia in HeLa cells. Medicinal chemistry efforts improved the potency and pharmaceutical properties of one series of compounds resulting in the discovery of PTC-510 which inhibits hypoxia-induced VEGF expression in HeLa cells at low nanomolar concentration. In mouse xenograft studies, oral administration of PTC-510 results in marked reduction of intratumor VEGF production and single agent control of tumor growth without any evident toxicity. Here, we show that selective suppression of stress-induced VEGF production within tumor cells effectively controls tumor growth. Therefore, this approach may minimize the liabilities of current global anti-VEGF therapies. PMID:27992500

  15. A dual platform approach to transcript discovery for the planarian Schmidtea mediterranea to establish RNAseq for stem cell and regeneration biology.

    PubMed

    Blythe, Martin J; Kao, Damian; Malla, Sunir; Rowsell, Joanna; Wilson, Ray; Evans, Deborah; Jowett, Jamie; Hall, Amy; Lemay, Virginie; Lam, Sabrina; Aboobaker, A Aziz

    2010-12-14

    The use of planarians as a model system is expanding and the mechanisms that control planarian regeneration are being elucidated. The planarian Schmidtea mediterranea in particular has become a species of choice. Currently the planarian research community has access to this whole genome sequencing project and over 70,000 expressed sequence tags. However, the establishment of massively parallel sequencing technologies has provided the opportunity to define genetic content, and in particular transcriptomes, in unprecedented detail. Here we apply this approach to the planarian model system. We have sequenced, mapped and assembled 581,365 long and 507,719,814 short reads from RNA of intact and mixed stages of the first 7 days of planarian regeneration. We used an iterative mapping approach to identify and define de novo splice sites with short reads and increase confidence in our transcript predictions. We more than double the number of transcripts currently defined by publicly available ESTs, resulting in a collection of 25,053 transcripts described by combining platforms. We also demonstrate the utility of this collection for an RNAseq approach to identify potential transcripts that are enriched in neoblast stem cells and their progeny by comparing transcriptome wide expression levels between irradiated and intact planarians. Our experiments have defined an extensive planarian transcriptome that can be used as a template for RNAseq and can also help to annotate the S. mediterranea genome. We anticipate that suites of other 'omic approaches will also be facilitated by building on this comprehensive data set including RNAseq across many planarian regenerative stages, scenarios, tissues and phenotypes generated by RNAi.

  16. Highlights of the Third Annual Biomarker World Congress: biomarkers for molecular diagnostics.

    PubMed

    Burczynski, Michael E

    2007-09-01

    With the proliferation of high-content profiling technologies available for analyzing genetic, transcriptomic, proteomic and metabolomic markers in tissues, both novel analytes and increasingly complex signatures are emerging as biomarkers supporting drug development. If prospective studies during the course of clinical development support the clinical utility of a biomarker, consideration may eventually be given to converting such research-use only biomarker assays into medical diagnostics. The present report briefly summarizes a selected set of presentations geared towards the issues and principles involved in the validation of biomarkers for use as molecular diagnostics, as presented at the Third Annual Biomarker World Congress held in Philadelphia, Pennsylvania on 15 - 18 May 2007. More than 400 delegates attended the Third Annual Biomarker World Congress to discuss the role of biomarkers in all phases of drug discovery and development. The main conference consisted of four tracks addressing issues involving: i) biomarkers in early drug development; ii) biomarkers in clinical development; iii) biomarkers for molecular diagnostics; and iv) biomarker assay development. The present review focuses on selected presentations of relevance to the third track. Topics covered by the speakers included biomarker assays that seem to have diagnostic and/or theranostic potential in a variety of therapeutic settings and disease indications. Themes included both the general and specific issues that can be encountered when attempting to convert a biomarker assay into a molecular diagnostic for use in the clinical setting.

  17. MetPP: a computational platform for comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry-based metabolomics

    PubMed Central

    Wei, Xiaoli; Shi, Xue; Koo, Imhoi; Kim, Seongho; Schmidt, Robin H.; Arteel, Gavin E.; Watson, Walter H.; McClain, Craig; Zhang, Xiang

    2013-01-01

    Motivation: Due to the high complexity of metabolome, the comprehensive 2D gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) is considered as a powerful analytical platform for metabolomics study. However, the applications of GC×GC-TOF MS in metabolomics are not popular owing to the lack of bioinformatics system for data analysis. Results: We developed a computational platform entitled metabolomics profiling pipeline (MetPP) for analysis of metabolomics data acquired on a GC×GC-TOF MS system. MetPP can process peak filtering and merging, retention index matching, peak list alignment, normalization, statistical significance tests and pattern recognition, using the peak lists deconvoluted from the instrument data as its input. The performance of MetPP software was tested with two sets of experimental data acquired in a spike-in experiment and a biomarker discovery experiment, respectively. MetPP not only correctly aligned the spiked-in metabolite standards from the experimental data, but also correctly recognized their concentration difference between sample groups. For analysis of the biomarker discovery data, 15 metabolites were recognized with significant concentration difference between the sample groups and these results agree with the literature results of histological analysis, demonstrating the effectiveness of applying MetPP software for disease biomarker discovery. Availability: The source code of MetPP is available at http://metaopen.sourceforge.net Contact: xiang.zhang@louisville.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23665844

  18. Blood-Based Biomarkers for Parkinson’s Disease

    PubMed Central

    Chahine, LM; Stern, MB; Chen-Plotkin, A

    2014-01-01

    There is a pressing need for biomarkers to diagnose Parkinson’s disease (PD), assess disease severity, and prognosticate course. Various types of biologic specimens are potential candidates for identifying biomarkers – defined here as surrogate indicators of physiological or pathophysiological states – but blood has the advantage of being minimally invasive to obtain. There are, however, several challenges to identifying biomarkers in blood. Several candidate biomarkers identified in other diseases or in other types of biological fluids are being pursued as blood-based biomarkers in PD. In addition, unbiased discovery is underway using techniques including metabolomics, proteomics, and gene expression profiling. In this review, we summarize these techniques and discuss the challenges and successes of blood-based biomarker discovery in PD. Blood-based biomarkers that are discussed include α-synuclein, DJ-1, uric acid, epidermal growth factor, apolipoprotein-A1, and peripheral inflammatory markers. PMID:24262199

  19. Opportunities and challenges of disease biomarkers: a new section in the Journal of Translational Medicine.

    PubMed

    Wang, Xiangdong; Ward, Peter A

    2012-12-05

    Disease biomarkers are defined to diagnose various phases of diseases, monitor severities of diseases and responses to therapies, or predict prognosis of patients. Disease-specific biomarkers should benefit drug discovery and development, integrate multidisciplinary sciences, be validated by molecular imaging. The opportunities and challenges in biomarker development are emphasized and considered. The Journal of Translational Medicine opens a new Section of Disease Biomarkers to bridge identification and validation of gene or protein-based biomarkers, network biomarkers, dynamic network biomarkers in human diseases, patient phenotypes, and clinical applications. Disease biomarkers are also important for determining drug effects, target specificities and binding, dynamic metabolism and pharmacological kinetics, or toxicity profiles.

  20. Guided Discoveries.

    ERIC Educational Resources Information Center

    Ehrlich, Amos

    1991-01-01

    Presented are four mathematical discoveries made by students on an arithmetical function using the Fibonacci sequence. Discussed is the nature of the role of the teacher in directing the students' discovery activities. (KR)

  1. Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets.

    PubMed

    Amar, David; Hait, Tom; Izraeli, Shai; Shamir, Ron

    2015-09-18

    Genome-wide expression profiling has revolutionized biomedical research; vast amounts of expression data from numerous studies of many diseases are now available. Making the best use of this resource in order to better understand disease processes and treatment remains an open challenge. In particular, disease biomarkers detected in case-control studies suffer from low reliability and are only weakly reproducible. Here, we present a systematic integrative analysis methodology to overcome these shortcomings. We assembled and manually curated more than 14,000 expression profiles spanning 48 diseases and 18 expression platforms. We show that when studying a particular disease, judicious utilization of profiles from other diseases and information on disease hierarchy improves classification quality, avoids overoptimistic evaluation of that quality, and enhances disease-specific biomarker discovery. This approach yielded specific biomarkers for 24 of the analyzed diseases. We demonstrate how to combine these biomarkers with large-scale interaction, mutation and drug target data, forming a highly valuable disease summary that suggests novel directions in disease understanding and drug repurposing. Our analysis also estimates the number of samples required to reach a desired level of biomarker stability. This methodology can greatly improve the exploitation of the mountain of expression profiles for better disease analysis.

  2. Impact of non-profit organizations on drug discovery: opportunities, gaps, solutions.

    PubMed

    Matter, Alex; Keller, Thomas H

    2008-04-01

    Non-profit organizations (NPO) play an increasingly important role in drug discovery and development for diseases that are neglected by the pharmaceutical industry because of low or absent commercial incentives. Governments and major private foundations such as the Wellcome Trust and the Bill & Melinda Gates Foundation increasingly step in to provide strategic direction, communication platforms and major resources, motivated by the fact that major healthcare problems remain unsolved. Drug discovery in the field of neglected diseases is fraught with complexities since, in many cases, important tools are lacking including readily available diagnostics, molecular epidemiology, appropriate model systems, representative strain collections, biomarkers, up-to-date trial methodologies and regulatory strategies. On top of this, the high hurdles addressing novel drug targets must be cleared.

  3. Label-free proteomics identifies Calreticulin and GRP75/Mortalin as peripherally accessible protein biomarkers for spinal muscular atrophy

    PubMed Central

    2013-01-01

    Background Spinal muscular atrophy (SMA) is a neuromuscular disease resulting from mutations in the survival motor neuron 1 (SMN1) gene. Recent breakthroughs in preclinical research have highlighted several potential novel therapies for SMA, increasing the need for robust and sensitive clinical trial platforms for evaluating their effectiveness in human patient cohorts. Given that most clinical trials for SMA are likely to involve young children, there is a need for validated molecular biomarkers to assist with monitoring disease progression and establishing the effectiveness of therapies being tested. Proteomics technologies have recently been highlighted as a potentially powerful tool for such biomarker discovery. Methods We utilized label-free proteomics to identify individual proteins in pathologically-affected skeletal muscle from SMA mice that report directly on disease status. Quantitative fluorescent western blotting was then used to assess whether protein biomarkers were robustly changed in muscle, skin and blood from another mouse model of SMA, as well as in a small cohort of human SMA patient muscle biopsies. Results By comparing the protein composition of skeletal muscle in SMA mice at a pre-symptomatic time-point with the muscle proteome at a late-symptomatic time-point we identified increased expression of both Calreticulin and GRP75/Mortalin as robust indicators of disease progression in SMA mice. We report that these protein biomarkers were consistently modified in different mouse models of SMA, as well as across multiple skeletal muscles, and were also measurable in skin biopsies. Furthermore, Calreticulin and GRP75/Mortalin were measurable in muscle biopsy samples from human SMA patients. Conclusions We conclude that label-free proteomics technology provides a powerful platform for biomarker identification in SMA, revealing Calreticulin and GRP75/Mortalin as peripherally accessible protein biomarkers capable of reporting on disease progression in

  4. Automated, high-throughput IgG-antibody glycoprofiling platform.

    PubMed

    Stöckmann, Henning; Adamczyk, Barbara; Hayes, Jerrard; Rudd, Pauline M

    2013-09-17

    One of today's key challenges is the ability to decode the functions of complex carbohydrates in various biological contexts. To generate high-quality glycomics data in a high-throughput fashion, we developed a robotized and low-cost N-glycan analysis platform for glycoprofiling of immunoglobulin G antibodies (IgG), which are central players of the immune system and of vital importance in the biopharmaceutical industry. The key features include (a) rapid IgG affinity purification and sample concentration, (b) protein denaturation and glycan release on a multiwell filtration device, (c) glycan purification on solid-supported hydrazide, and (d) glycan quantification by ultra performance liquid chromatography. The sample preparation workflow was automated using a robotic liquid-handling workstation, allowing the preparation of 96 samples (or multiples thereof) in 22 h with excellent reproducibility and, thus, should greatly facilitate biomarker discovery and glycosylation monitoring of therapeutic IgGs.

  5. Biomarkers of renal cell carcinoma.

    PubMed

    Ngo, Tin C; Wood, Christopher G; Karam, Jose A

    2014-04-01

    The incidence of renal cell carcinoma (RCC) has increased steadily in past few decades and is partially attributable to the increased utilization of cross-sectional imaging. Many of these carcinomas are small incidental discoveries, although a subset leads to locally advanced or distant disease. Although its molecular pathophysiology is not completely understood, knowledge of hereditary RCCs has shed light on some of the pathways involved. More recently, the rapid advances in genomics, proteomics, and metabolomics have allowed for a deeper and more nuanced understanding of the genetic aberrations that lead up to and result from the transformation of a renal tubular epithelial cell into a carcinoma. These discoveries have allowed for the development of novel therapeutics that target these pathways. They have also led to the development of diagnostic, prognostic, and predictive biomarkers that could radically change the way RCC is diagnosed and treated. Although some of the current investigations are nascent and it remains to be seen which biomarkers will become clinically available, many candidate biomarkers show promise and require external validation. Ultimately, biomarkers may allow for cost-effective screening of high-risk patients, the identification of aggressive cancers among small renal masses, the identification of high-risk patients, the detection of recurrences postoperatively with minimal imaging, and the ability to choose appropriate targeted therapies for patients with metastatic disease.

  6. Imaging Biomarkers or Biomarker Imaging?

    PubMed Central

    Mitterhauser, Markus; Wadsak, Wolfgang

    2014-01-01

    Since biomarker imaging is traditionally understood as imaging of molecular probes, we highly recommend to avoid any confusion with the previously defined term “imaging biomarkers” and, therefore, only use “molecular probe imaging (MPI)” in that context. Molecular probes (MPs) comprise all kinds of molecules administered to an organism which inherently carry a signalling moiety. This review highlights the basic concepts and differences of molecular probe imaging using specific biomarkers. In particular, PET radiopharmaceuticals are discussed in more detail. Specific radiochemical and radiopharmacological aspects as well as some legal issues are presented. PMID:24967536

  7. How-to guide on biomarkers: biomarker definitions, validation and applications with examples from cardiovascular disease.

    PubMed

    Puntmann, V O

    2009-10-01

    A biomarker is a characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. Many commonly used tests in clinical practice can serve as biomarkers. The majority have been identified on the basis of insight or underlying physiology or biological mechanisms. With increasing knowledge and practical experience, some of these tests have evolved into a measurable end point in clinical research, applied as an indicator of change, for the better or worse. The traditional identification of biomarkers as an observational side product of clinical practice is increasingly turning into an industrialised process of biomarker discovery, supported by standardised paradigms of biomarker validation and translation from bench to bedside. The potential utility of biomarkers in clinical studies, investigating either new treatments or new strategies of clinical management, is capitalizing on recent advances in technology, from molecular sciences to powerful imaging, bearing the promise of expediting the discovery of new treatments. In the active search for new biomarkers, many potential candidates can be considered side by side, allowing many failures but a few great winners. Biomarker discovery is an ongoing process, with translation being tested de novo in every single study, providing us with the opportunity to revise our knowledge of the complex scheme of human physiology and pathophysiology. In predicting what Nature has set in place, advances in technology may be only the first step. This review provides an introduction to the field of biomarker discovery and translation. It deals with evolving nomenclature, basic principles of the validation process, and, drawing on examples in cardiovascular medicine, their significance for clinical application.

  8. Drug discovery FAQs: workflows for answering multidomain drug discovery questions.

    PubMed

    Chichester, Christine; Digles, Daniela; Siebes, Ronald; Loizou, Antonis; Groth, Paul; Harland, Lee

    2015-04-01

    Modern data-driven drug discovery requires integrated resources to support decision-making and enable new discoveries. The Open PHACTS Discovery Platform (http://dev.openphacts.org) was built to address this requirement by focusing on drug discovery questions that are of high priority to the pharmaceutical industry. Although complex, most of these frequently asked questions (FAQs) revolve around the combination of data concerning compounds, targets, pathways and diseases. Computational drug discovery using workflow tools and the integrated resources of Open PHACTS can deliver answers to most of these questions. Here, we report on a selection of workflows used for solving these use cases and discuss some of the research challenges. The workflows are accessible online from myExperiment (http://www.myexperiment.org) and are available for reuse by the scientific community.

  9. Biomarkers of Parkinson's disease: current status and future perspectives.

    PubMed

    Wang, Jian; Hoekstra, Jake G; Zuo, Chuantao; Cook, Travis J; Zhang, Jing

    2013-02-01

    This review summarizes major advances in biomarker discovery for diagnosis, differential diagnosis and progression of Parkinson's disease (PD), with emphasis on neuroimaging and biochemical markers. Potential strategies to develop biomarkers capable of predicting PD in the prodromal stage before the appearance of motor symptoms or correlating with nonmotor symptoms, an active area of research, are also discussed.

  10. Biomarkers of Parkinson’s disease: current status and future

    PubMed Central

    Wang, Jian; Hoekstra, Jake G.; Zuo, Chuantao; Cook, Travis J.; Zhang, Jing

    2012-01-01

    This review summarizes major advances in biomarker discovery for diagnosis, differential diagnosis, and progression of Parkinson’s disease (PD), with emphasis on neuroimaging and biochemical markers. Potential strategies to develop biomarkers capable of predicting PD in the prodromal stage before the appearance of motor symptoms or correlating with nonmotor symptoms, an active area of research, are also discussed. PMID:22982303

  11. Cross-Disciplinary Biomarkers Research: Lessons Learned by the CKD Biomarkers Consortium.

    PubMed

    Hsu, Chi-Yuan; Ballard, Shawn; Batlle, Daniel; Bonventre, Joseph V; Böttinger, Erwin P; Feldman, Harold I; Klein, Jon B; Coresh, Josef; Eckfeldt, John H; Inker, Lesley A; Kimmel, Paul L; Kusek, John W; Liu, Kathleen D; Mauer, Michael; Mifflin, Theodore E; Molitch, Mark E; Nelsestuen, Gary L; Rebholz, Casey M; Rovin, Brad H; Sabbisetti, Venkata S; Van Eyk, Jennifer E; Vasan, Ramachandran S; Waikar, Sushrut S; Whitehead, Krista M; Nelson, Robert G

    2015-05-07

    Significant advances are needed to improve the diagnosis, prognosis, and management of persons with CKD. Discovery of new biomarkers and improvements in currently available biomarkers for CKD hold great promise to achieve these necessary advances. Interest in identification and evaluation of biomarkers for CKD has increased substantially over the past decade. In 2009, the National Institute of Diabetes and Digestive and Kidney Diseases established the CKD Biomarkers Consortium (http://www.ckdbiomarkersconsortium.org/), a multidisciplinary, collaborative study group located at over a dozen academic medical centers. The main objective of the consortium was to evaluate new biomarkers for purposes related to CKD in established prospective cohorts, including those enriched for CKD. During the first 5 years of the consortium, many insights into collaborative biomarker research were gained that may be useful to other investigators involved in biomarkers research. These lessons learned are outlined in this Special Feature and include a wide range of issues related to biospecimen collection, storage, and retrieval, and the internal and external quality assessment of laboratories that performed the assays. The authors propose that investigations involving biomarker discovery and validation are greatly enhanced by establishing and following explicit quality control metrics, including the use of blind replicate and proficiency samples, by carefully considering the conditions under which specimens are collected, handled, and stored, and by conducting pilot and feasibility studies when there are concerns about the condition of the specimens or the accuracy or reproducibility of the assays.

  12. Diabetes mellitus: channeling care through cellular discovery.

    PubMed

    Maiese, Kenneth; Shang, Yan Chen; Chong, Zhao Zhong; Hou, Jinling

    2010-02-01

    Diabetes mellitus (DM) impacts a significant portion of the world's population and care for this disorder places an economic burden on the gross domestic product for any particular country. Furthermore, both Type 1 and Type 2 DM are becoming increasingly prevalent and there is increased incidence of impaired glucose tolerance in the young. The complications of DM are protean and can involve multiple systems throughout the body that are susceptible to the detrimental effects of oxidative stress and apoptotic cell injury. For these reasons, innovative strategies are necessary for the implementation of new treatments for DM that are generated through the further understanding of cellular pathways that govern the pathological consequences of DM. In particular, both the precursor for the coenzyme beta-nicotinamide adenine dinucleotide (NAD(+)), nicotinamide, and the growth factor erythropoietin offer novel platforms for drug discovery that involve cellular metabolic homeostasis and inflammatory cell control. Interestingly, these agents and their tightly associated pathways that consist of cell cycle regulation, protein kinase B, forkhead transcription factors, and Wnt signaling also function in a broader sense as biomarkers for disease onset and progression.

  13. Novel diagnostic biomarkers for prostate cancer

    PubMed Central

    Madu, Chikezie O.; Lu, Yi

    2010-01-01

    Prostate cancer is the most frequently diagnosed malignancy in American men, and a more aggressive form of the disease is particularly prevalent among African Americans. The therapeutic success rate for prostate cancer can be tremendously improved if the disease is diagnosed early. Thus, a successful therapy for this disease depends heavily on the clinical indicators (biomarkers) for early detection of the presence and progression of the disease, as well as the prediction after the clinical intervention. However, the current clinical biomarkers for prostate cancer are not ideal as there remains a lack of reliable biomarkers that can specifically distinguish between those patients who should be treated adequately to stop the aggressive form of the disease and those who should avoid overtreatment of the indolent form. A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. A biomarker reveals further information to presently existing clinical and pathological analysis. It facilitates screening and detecting the cancer, monitoring the progression of the disease, and predicting the prognosis and survival after clinical intervention. A biomarker can also be used to evaluate the process of drug development, and, optimally, to improve the efficacy and safety of cancer treatment by enabling physicians to tailor treatment for individual patients. The form of the prostate cancer biomarkers can vary from metabolites and chemical products present in body fluid to genes and proteins in the prostate tissues. Current advances in molecular techniques have provided new tools facilitating the discovery of new biomarkers for prostate cancer. These emerging biomarkers will be beneficial and critical in developing new and clinically reliable indicators that will have a high specificity for the diagnosis and prognosis of prostate cancer. The

  14. DNA Methylation Biomarkers: Cancer and Beyond

    PubMed Central

    Mikeska, Thomas; Craig, Jeffrey M.

    2014-01-01

    Biomarkers are naturally-occurring characteristics by which a particular pathological process or disease can be identified or monitored. They can reflect past environmental exposures, predict disease onset or course, or determine a patient’s response to therapy. Epigenetic changes are such characteristics, with most epigenetic biomarkers discovered to date based on the epigenetic mark of DNA methylation. Many tissue types are suitable for the discovery of DNA methylation biomarkers including cell-based samples such as blood and tumor material and cell-free DNA samples such as plasma. DNA methylation biomarkers with diagnostic, prognostic and predictive power are already in clinical trials or in a clinical setting for cancer. Outside cancer, strong evidence that complex disease originates in early life is opening up exciting new avenues for the detection of DNA methylation biomarkers for adverse early life environment and for estimation of future disease risk. However, there are a number of limitations to overcome before such biomarkers reach the clinic. Nevertheless, DNA methylation biomarkers have great potential to contribute to personalized medicine throughout life. We review the current state of play for DNA methylation biomarkers, discuss the barriers that must be crossed on the way to implementation in a clinical setting, and predict their future use for human disease. PMID:25229548

  15. DNA methylation biomarkers: cancer and beyond.

    PubMed

    Mikeska, Thomas; Craig, Jeffrey M

    2014-09-16

    Biomarkers are naturally-occurring characteristics by which a particular pathological process or disease can be identified or monitored. They can reflect past environmental exposures, predict disease onset or course, or determine a patient's response to therapy. Epigenetic changes are such characteristics, with most epigenetic biomarkers discovered to date based on the epigenetic mark of DNA methylation. Many tissue types are suitable for the discovery of DNA methylation biomarkers including cell-based samples such as blood and tumor material and cell-free DNA samples such as plasma. DNA methylation biomarkers with diagnostic, prognostic and predictive power are already in clinical trials or in a clinical setting for cancer. Outside cancer, strong evidence that complex disease originates in early life is opening up exciting new avenues for the detection of DNA methylation biomarkers for adverse early life environment and for estimation of future disease risk. However, there are a number of limitations to overcome before such biomarkers reach the clinic. Nevertheless, DNA methylation biomarkers have great potential to contribute to personalized medicine throughout life. We review the current state of play for DNA methylation biomarkers, discuss the barriers that must be crossed on the way to implementation in a clinical setting, and predict their future use for human disease.

  16. Toward an integrated pipeline for protein biomarker development.

    PubMed

    Drabovich, Andrei P; Martínez-Morillo, Eduardo; Diamandis, Eleftherios P

    2015-06-01

    Protein biomarker development is a multidisciplinary task involving basic, translational and clinical research. Integration of multidisciplinary efforts in a single pipeline is challenging, but crucial to facilitate rational discovery of protein biomarkers and alleviate existing disappointments in the field. In this review, we discuss in detail individual phases of biomarker development pipeline, such as biomarker candidate identification, verification and validation. We focus on mass spectrometry as a principal technique for protein identification and quantification, and discuss complementary -omics approaches for selection of biomarker candidates. Proteomic samples, protein-based clinical laboratory tests and limitations of biomarker development are reviewed in detail, and critical assessment of all phases of biomarker development pipeline is provided. This article is part of a Special Issue entitled: Medical Proteomics.

  17. Validation of analytic methods for biomarkers used in drug development.

    PubMed

    Chau, Cindy H; Rixe, Olivier; McLeod, Howard; Figg, William D

    2008-10-01

    The role of biomarkers in drug discovery and development has gained precedence over the years. As biomarkers become integrated into drug development and clinical trials, quality assurance and, in particular, assay validation become essential with the need to establish standardized guidelines for analytic methods used in biomarker measurements. New biomarkers can revolutionize both the development and use of therapeutics but are contingent on the establishment of a concrete validation process that addresses technology integration and method validation as well as regulatory pathways for efficient biomarker development. This perspective focuses on the general principles of the biomarker validation process with an emphasis on assay validation and the collaborative efforts undertaken by various sectors to promote the standardization of this procedure for efficient biomarker development.

  18. Crawler transporter nears the Mobile Launch Platform

    NASA Technical Reports Server (NTRS)

    1998-01-01

    The crawler transporter nears the Mobile Launch Platform and Space Shuttle Discovery to await possible orders for a rollback. KSC managers developed a precautionary plan to roll back Discovery to the Vehicle Assembly Building in the event that Hurricane Georges threatens Central Florida. The decision was made to minimize risk and provide protection to the Space Shuttle national asset

  19. Inflammation biomarkers in vaginal fluid and preterm delivery

    PubMed Central

    Taylor, Brandie D.; Holzman, Claudia B.; Fichorova, Raina N.; Tian, Yan; Jones, Nicole M.; Fu, Wenjiang; Senagore, Patricia K.

    2013-01-01

    STUDY QUESTION Which inflammation biomarkers detected in the vaginal fluid are most informative for identifying preterm delivery (PTD) risk? SUMMARY ANSWER Elevated interleukin (IL)-6 at mid-trimester was associated with increased odds of spontaneous PTD at <35 weeks and with PTD plus histologic chorioamnionitis (HCA), and had the greatest sensitivity for detecting these two PTD subtypes. WHAT IS KNOWN ALREADY Maternal and/or fetal inflammation play a role in some preterm deliveries, therefore inflammation biomarkers might help to identify women at greater risk. STUDY DESIGN, SIZE, DURATION We examined 1115 women from the Pregnancy Outcomes and Community Health Study, a cohort study conducted from September 1998 through June 2004, for whom data were available on mid-pregnancy inflammatory biomarkers. PARTICIPANTS/MATERIALS, SETTING, METHODS At enrollment at 16–27 weeks gestation, vaginal fluid samples were collected from a swab and 15 eluted biomarkers were measured using the Meso Scale Discovery multiplex electrochemiluminescence platform. Associations of biomarkers with PTD were examined, according to clinical circumstance, week at delivery and presence/absence of HCA. Weighted logistic regression was used to determine odds ratios (OR) and 95% confidence intervals (CI) adjusted for race. Sensitivity and specificity were compared between individual and multiple biomarkers, identified by a bootstrapping method. MAIN RESULTS AND THE ROLE OF CHANCE Elevated IL-6 (>75th percentile) displayed the strongest association with spontaneous PTD <35 weeks (OR 2.3; CI 1.3–4.0) and PTD with HCA (OR 2.8; CI 1.4–6.0). The sensitivity of IL-6 to detect spontaneous PTD <35 weeks or PTD with HCA was 0.43 and 0.51, respectively, while specificity was 0.74 and 0.75, respectively. IL-6 plus IL1β, IL-6r, tumor necrosis factor-alpha or granulocyte-macrophage colony-stimulating factor increased specificity (range 0.84–0.88), but decreased sensitivity (range 0.28–0.34) to detect

  20. microRNAs as neuroregulators, biomarkers and therapeutic agents in neurodegenerative diseases.

    PubMed

    Basak, Indranil; Patil, Ketan S; Alves, Guido; Larsen, Jan Petter; Møller, Simon Geir

    2016-02-01

    The last decade has experienced the emergence of microRNAs as a key molecular tool for the diagnosis and prognosis of human diseases. Although the focus has mostly been on cancer, neurodegenerative diseases present an exciting, yet less explored, platform for microRNA research. Several studies have highlighted the significance of microRNAs in neurogenesis and neurodegeneration, and pre-clinical studies have shown the potential of microRNAs as biomarkers. Despite this, no bona fide microRNAs have been identified as true diagnostic or prognostic biomarkers for neurodegenerative disease. This is mainly due to the lack of precisely defined patient cohorts and the variability within and between individual cohorts. However, the discovery that microRNAs exist as stable molecules at detectable levels in body fluids has opened up new avenues for microRNAs as potential biomarker candidates. Furthermore, technological developments in microRNA biology have contributed to the possible design of microRNA-mediated disease intervention strategies. The combination of these advancements, with the availability of well-defined longitudinal patient cohort, promises to not only assist in developing invaluable diagnostic tools for clinicians, but also to increase our overall understanding of the underlying heterogeneity of neurodegenerative diseases. In this review, we present a comprehensive overview of the existing knowledge of microRNAs in neurodegeneration and provide a perspective of the applicability of microRNAs as a basis for future therapeutic intervention strategies.

  1. Parkinson's disease plasma biomarkers: an automated literature analysis followed by experimental validation.

    PubMed

    Alberio, Tiziana; Bucci, Enrico M; Natale, Massimo; Bonino, Dario; Di Giovanni, Marco; Bottacchi, Edo; Fasano, Mauro

    2013-09-02

    Diagnosis of Parkinson's disease (PD) is currently assessed by the clinical evaluation of extrapyramidal signs. The identification of specific biomarkers would be advisable, however most studies stop at the discovery phase, with no biomarkers reaching clinical exploitation. To this purpose, we developed an automated literature analysis procedure to retrieve all the background knowledge available in public databases. The bioinformatic platform allowed us to analyze more than 51,000 scientific papers dealing with PD, containing information on 4121 proteins. Out of these, we could track back 35 PD-related proteins as present in at least two published 2-DE maps of human plasma. Then, 9 different proteins (haptoglobin, transthyretin, apolipoprotein A-1, serum amyloid P component, apolipoprotein E, complement factor H, fibrinogen γ, thrombin, complement C3) split into 32 spots were identified as a potential diagnostic pattern. Eventually, we compared the collected literature data to experimental gels from 90 subjects (45 PD patients, 45 non-neurodegenerative control subjects) to experimentally verify their potential as plasma biomarkers of PD.

  2. IMMUNOASSAYS FOR BIOMARKERS AND NEUTRACEUTICALS/PHARMACEUTICALS

    EPA Science Inventory

    Product is an abstract for an invited oral platform presentation to be given at the Pittsburgh Conference to be held February 25 - March 2, 2007 in Chicago, Ilinois. The presentation will describe methods research for the development of bioanalytical methods to measure biomarker...

  3. Biomarkers of An Autoimmune Skin Disease—Psoriasis

    PubMed Central

    Jiang, Shan; Hinchliffe, Taylor E.; Wu, Tianfu

    2015-01-01

    Psoriasis is one of the most prevalent autoimmune skin diseases. However, its etiology and pathogenesis are still unclear. Over the last decade, omics-based technologies have been extensively utilized for biomarker discovery. As a result, some promising markers for psoriasis have been identified at the genome, transcriptome, proteome, and metabolome level. These discoveries have provided new insights into the underlying molecular mechanisms and signaling pathways in psoriasis pathogenesis. More importantly, some of these markers may prove useful in the diagnosis of psoriasis and in the prediction of disease progression once they have been validated. In this review, we summarize the most recent findings in psoriasis biomarker discovery. In addition, we will discuss several emerging technologies and their potential for novel biomarker discovery and diagnostics for psoriasis. PMID:26362816

  4. Lysimeter Platform

    NASA Astrophysics Data System (ADS)

    Klammler, Gernot; Murer, Erwin; Plieschnegger, Markus

    2014-05-01

    The existing European Lysimeter Platform (www.lysimeter.at/HP_EuLP) provides an overview of lysimeter types used in Europe and show details on equipment, research results and future perspectives of lysimeter facilities. However, this platform is not user-editable and has not been updated since 2008. Thus, the Lysimeter Research Group (www.lysimeter.at) intends to serve a new database based website called Lysimeter Platform, where existing information of the former European Lysimeter Platform will be transferred to the new Lysimeter Platform and, furthermore, registered users are able to create and edit sites where lysimeters, soil water samplers and soil hydrologic measuring profiles are operated. The Lysimeter Research Group is a scientific association and, therefore, the membership is free of charge. The new Lysimeter Platform contains general information of lysimeter sites worldwide (e.g., what is measured at which site) in a standardized form to get a quick but informative overview of the sites and can be linked to more detailed, already existing information provided by the site operators. Due to the standardized information in the database the Lysimeter Platform serves also as search-engine for soil water measurements and helps to find sites of interest and corresponding contact information worldwide. The Session "Estimation of soil-atmosphere and vadose zone water fluxes by use of precision lysimeter measurements" at the EGU General Assembly 2014 would be an excellent chance to present the idea and the concept of this new Lysimeter Platform to international site operators and scientists.

  5. Genomics and transcriptomics in drug discovery.

    PubMed

    Dopazo, Joaquin

    2014-02-01

    The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery.

  6. Ensuring continued progress in biomarkers for amyotrophic lateral sclerosis

    PubMed Central

    Turner, Martin R; Benatar, Michael

    2015-01-01

    Multiple candidate biomarkers for amyotrophic lateral sclerosis (ALS) have emerged across a range of platforms. Replication of results, however, has been absent in all but a few cases, and the range of control samples has been limited. If progress toward clinical translation is to continue, the specific biomarker needs of ALS, which differ from those of other neurodegenerative disorders, as well as the challenges inherent to longitudinal ALS biomarker cohorts, must be understood. Appropriate application of multimodal approaches, international collaboration, presymptomatic studies, and biomarker integration into future therapeutic trials are among the essential priorities going forward. PMID:25288265

  7. The virtual heart as a platform for screening drug cardiotoxicity

    PubMed Central

    Yuan, Yongfeng; Bai, Xiangyun; Luo, Cunjin; Wang, Kuanquan

    2015-01-01

    To predict the safety of a drug at an early stage in its development is a major challenge as there is a lack of in vitro heart models that correlate data from preclinical toxicity screening assays with clinical results. A biophysically detailed computer model of the heart, the virtual heart, provides a powerful tool for simulating drug–ion channel interactions and cardiac functions during normal and disease conditions and, therefore, provides a powerful platform for drug cardiotoxicity screening. In this article, we first review recent progress in the development of theory on drug–ion channel interactions and mathematical modelling. Then we propose a family of biomarkers that can quantitatively characterize the actions of a drug on the electrical activity of the heart at multi‐physical scales including cellular and tissue levels. We also conducted some simulations to demonstrate the application of the virtual heart to assess the pro‐arrhythmic effects of cisapride and amiodarone. Using the model we investigated the mechanisms responsible for the differences between the two drugs on pro‐arrhythmogenesis, even though both prolong the QT interval of ECGs. Several challenges for further development of a virtual heart as a platform for screening drug cardiotoxicity are discussed. Linked Articles This article is part of a themed section on Chinese Innovation in Cardiovascular Drug Discovery. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-23 PMID:25363597

  8. Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers.

    PubMed

    Buckler, Andrew J; Liu, Tiffany Ting; Savig, Erica; Suzek, Baris E; Ouellette, M; Danagoulian, J; Wernsing, G; Rubin, Daniel L; Paik, David

    2013-08-01

    A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image post-processing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

  9. Biomarkers of (osteo)arthritis

    PubMed Central

    Mobasheri, Ali; Henrotin, Yves

    2015-01-01

    Abstract Arthritic diseases are a major cause of disability and morbidity, and cause an enormous burden for health and social care systems globally. Osteoarthritis (OA) is the most common form of arthritis. The key risk factors for the development of OA are age, obesity, joint trauma or instability. Metabolic and endocrine diseases can also contribute to the pathogenesis of OA. There is accumulating evidence to suggest that OA is a whole-organ disease that is influenced by systemic mediators, inflammaging, innate immunity and the low-grade inflammation induced by metabolic syndrome. Although all joint tissues are implicated in disease progression in OA, articular cartilage has received the most attention in the context of aging, injury and disease. There is increasing emphasis on the early detection of OA as it has the capacity to target and treat the disease more effectively. Indeed it has been suggested that this is the era of “personalized prevention” for OA. However, the development of strategies for the prevention of OA require new and sensitive biomarker tools that can detect the disease in its molecular and pre-radiographic stage, before structural and functional alterations in cartilage integrity have occurred. There is also evidence to support a role for biomarkers in OA drug discovery, specifically the development of disease modifying osteoarthritis drugs. This Special Issue of Biomarkers is dedicated to recent progress in the field of OA biomarkers. The papers in this Special Issue review the current state-of-the-art and discuss the utility of OA biomarkers as diagnostic and prognostic tools. PMID:26954784

  10. Biomarkers of (osteo)arthritis.

    PubMed

    Mobasheri, Ali; Henrotin, Yves

    2015-01-01

    Arthritic diseases are a major cause of disability and morbidity, and cause an enormous burden for health and social care systems globally. Osteoarthritis (OA) is the most common form of arthritis. The key risk factors for the development of OA are age, obesity, joint trauma or instability. Metabolic and endocrine diseases can also contribute to the pathogenesis of OA. There is accumulating evidence to suggest that OA is a whole-organ disease that is influenced by systemic mediators, inflammaging, innate immunity and the low-grade inflammation induced by metabolic syndrome. Although all joint tissues are implicated in disease progression in OA, articular cartilage has received the most attention in the context of aging, injury and disease. There is increasing emphasis on the early detection of OA as it has the capacity to target and treat the disease more effectively. Indeed it has been suggested that this is the era of "personalized prevention" for OA. However, the development of strategies for the prevention of OA require new and sensitive biomarker tools that can detect the disease in its molecular and pre-radiographic stage, before structural and functional alterations in cartilage integrity have occurred. There is also evidence to support a role for biomarkers in OA drug discovery, specifically the development of disease modifying osteoarthritis drugs. This Special Issue of Biomarkers is dedicated to recent progress in the field of OA biomarkers. The papers in this Special Issue review the current state-of-the-art and discuss the utility of OA biomarkers as diagnostic and prognostic tools.

  11. Space Discovery.

    ERIC Educational Resources Information Center

    Blackman, Joan

    1998-01-01

    Describes one teacher's experience taking Space Discovery courses that were sponsored by the United States Space Foundation (USSF). These courses examine the history of space science, theory of orbits and rocketry, the effects of living in outer space on humans, and space weather. (DDR)

  12. Warehousing re-annotated cancer genes for biomarker meta-analysis.

    PubMed

    Orsini, M; Travaglione, A; Capobianco, E

    2013-07-01

    Translational research in cancer genomics assigns a fundamental role to bioinformatics in support of candidate gene prioritization with regard to both biomarker discovery and target identification for drug development. Efforts in both such directions rely on the existence and constant update of large repositories of gene expression data and omics records obtained from a variety of experiments. Users who interactively interrogate such repositories may have problems in retrieving sample fields that present limited associated information, due for instance to incomplete entries or sometimes unusable files. Cancer-specific data sources present similar problems. Given that source integration usually improves data quality, one of the objectives is keeping the computational complexity sufficiently low to allow an optimal assimilation and mining of all the information. In particular, the scope of integrating intraomics data can be to improve the exploration of gene co-expression landscapes, while the scope of integrating interomics sources can be that of establishing genotype-phenotype associations. Both integrations are relevant to cancer biomarker meta-analysis, as the proposed study demonstrates. Our approach is based on re-annotating cancer-specific data available at the EBI's ArrayExpress repository and building a data warehouse aimed to biomarker discovery and validation studies. Cancer genes are organized by tissue with biomedical and clinical evidences combined to increase reproducibility and consistency of results. For better comparative evaluation, multiple queries have been designed to efficiently address all types of experiments and platforms, and allow for retrieval of sample-related information, such as cell line, disease state and clinical aspects.

  13. [Lens platform].

    PubMed

    Łukaszewska-Smyk, Agnieszka; Kałuzny, Józef

    2010-01-01

    The lens platform defines lens structure and lens material. Evolution of lens comprises change in their shape, angulation of haptens and transition of three-piece lens into one-piece lens. The lens fall into two categories: rigid (PMMA) and soft (siliconic, acrylic, colameric). The main lens maaterials are polymers (hydrophilic and hydrophobic). The lens platform has an effect on biocompatibility, bioadhesion, stability of lens in capsule, degree of PCO evolution and sensitiveness to laser damages.