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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. 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.

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

    PubMed Central

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

    2009-01-01

    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 (SF2) was modeled in 48 human cancer cell lines. We apply a linear regression algorithm that integrates gene expression with biological variables including: ras status (mut/wt), tissue of origin (TO) and p53 status (mut/wt). Results The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression. 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 link to the network, demonstrating clinical relevance. Furthermore, the model identifies four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis along with TO 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. Conclusions We developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We propose this platform will play a central role in the integration of biology into clinical radiation oncology practice. PMID:19735874

  4. 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.

  5. Automated Sample Preparation Platform for Mass Spectrometry-Based Plasma Proteomics and Biomarker Discovery

    PubMed Central

    Guryča, Vilém; Roeder, Daniel; Piraino, Paolo; Lamerz, Jens; Ducret, Axel; Langen, Hanno; Cutler, Paul

    2014-01-01

    The identification of novel biomarkers from human plasma remains a critical need in order to develop and monitor drug therapies for nearly all disease areas. The discovery of novel plasma biomarkers is, however, significantly hampered by the complexity and dynamic range of proteins within plasma, as well as the inherent variability in composition from patient to patient. In addition, it is widely accepted that most soluble plasma biomarkers for diseases such as cancer will be represented by tissue leakage products, circulating in plasma at low levels. It is therefore necessary to find approaches with the prerequisite level of sensitivity in such a complex biological matrix. Strategies for fractionating the plasma proteome have been suggested, but improvements in sensitivity are often negated by the resultant process variability. Here we describe an approach using multidimensional chromatography and on-line protein derivatization, which allows for higher sensitivity, whilst minimizing the process variability. In order to evaluate this automated process fully, we demonstrate three levels of processing and compare sensitivity, throughput and reproducibility. We demonstrate that high sensitivity analysis of the human plasma proteome is possible down to the low ng/mL or even high pg/mL level with a high degree of technical reproducibility. PMID:24833342

  6. PAPAyA: a platform for breast cancer biomarker signature discovery, evaluation and assessment

    PubMed Central

    Janevski, Angel; Kamalakaran, Sitharthan; Banerjee, Nilanjana; Varadan, Vinay; Dimitrova, Nevenka

    2009-01-01

    Background The decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, therefore, a need for translational clinical tools based on molecular bioinformatics, particularly in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions. Results We present a platform for molecular signature discovery and clinical decision support that relies on genomic and epigenomic measurement modalities as well as clinical parameters such as histopathological results and survival information. Our Physician Accessible Preclinical Analytics Application (PAPAyA) integrates a powerful set of statistical and machine learning tools that leverage the connections among the different modalities. It is easily extendable and reconfigurable to support integration of existing research methods and tools into powerful data analysis and interpretation pipelines. A current configuration of PAPAyA with examples of its performance on breast cancer molecular profiles is used to present the platform in action. Conclusion PAPAyA enables analysis of data from (pre)clinical studies, formulation of new clinical hypotheses, and facilitates clinical decision support by abstracting molecular profiles for clinicians. PMID:19761577

  7. Biomarker Discovery for Heterogeneous Diseases

    PubMed Central

    Wallstrom, Garrick; Anderson, Karen S.; LaBaer, Joshua

    2013-01-01

    Background Modern genomic and proteomic studies reveal that many diseases are heterogeneous, comprising multiple different subtypes. The common notion that one biomarker can be predictive for all patients may need to be replaced by an understanding that each subtype has its own set of unique biomarkers, affecting how discovery studies are designed and analyzed. Methods We used Monte Carlo simulation to measure and compare the performance of eight selection methods with homogeneous and heterogeneous diseases using both single-stage and two-stage designs. We also applied the selection methods in an actual proteomic biomarker screening study of heterogeneous breast cancer cases. Results Different selection methods were optimal and more than 2-fold larger sample sizes were needed for heterogeneous diseases compared with homogeneous diseases. We also found that for larger studies, two-stage designs can achieve nearly the same statistical power as single-stage designs at significantly reduced cost. Conclusions We found that disease heterogeneity profoundly affected biomarker performance. We report sample size requirements and provide guidance on the design and analysis of biomarker discovery studies for both homogeneous and heterogeneous diseases. Impact We have shown that studies to identify biomarkers for the early detection of heterogeneous disease require different statistical selection methods and larger sample sizes than if the disease were homogeneous. These findings provide a methodological platform for biomarker discovery of heterogeneous diseases. PMID:23462916

  8. 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

  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. Mass cytometry as a platform for the discovery of cellular biomarkers to guide effective rheumatic disease therapy.

    PubMed

    Nair, Nitya; Mei, Henrik E; Chen, Shih-Yu; Hale, Matthew; Nolan, Garry P; Maecker, Holden T; Genovese, Mark; Fathman, C Garrison; Whiting, Chan C

    2015-01-01

    The development of biomarkers for autoimmune diseases has been hampered by a lack of understanding of disease etiopathogenesis and of the mechanisms underlying the induction and maintenance of inflammation, which involves complex activation dynamics of diverse cell types. The heterogeneous nature and suboptimal clinical response to treatment observed in many autoimmune syndromes highlight the need to develop improved strategies to predict patient outcome to therapy and personalize patient care. Mass cytometry, using CyTOF®, is an advanced technology that facilitates multiparametric, phenotypic analysis of immune cells at single-cell resolution. In this review, we outline the capabilities of mass cytometry and illustrate the potential of this technology to enhance the discovery of cellular biomarkers for rheumatoid arthritis, a prototypical autoimmune disease. PMID:25981462

  11. 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

  12. Translation of neurological biomarkers to clinically relevant platforms.

    PubMed

    Hayes, Ronald L; Robinson, Gillian; Muller, Uwe; Wang, Kevin K W

    2009-01-01

    Like proteomics more generally, neuroproteomics has recently been linked to the discovery of biochemical markers of central nervous system (CNS) injury and disease. Although neuroproteomics has enjoyed considerable success in discovery of candidate biomarkers, there are a number of challenges facing investigators interested in developing clinically useful platforms to assess biomarkers for damage to the CNS. These challenges include intrinsic physiological complications such as the blood-brain barrier. Effective translation of biomarkers to clinical practice also requires development of entirely novel pathways and product development strategies. Drawing from lessons learned from applications of biomarkers to traumatic brain injury, this study outlines major elements of such a pathway. As with other indications, biomarkers can have three major areas of application: (1) drug development; (2) diagnosis and prognosis; (3) patient management. Translation of CNS biomarkers to practical clinical platforms raises a number of integrated elements. Biomarker discovery and initial selection needs to be integrated at the earliest stages with components that will allow systematic prioritization and triage of biomarker candidates. A number of important criteria need to be considered in selecting clinical biomarker candidates. Development of proof of concept assays and their optimization and validation represent an often overlooked feature of biomarker translational research. Initial assay optimization should confirm that assays can detect biomarkers in relevant clinical samples. Since access to human clinical samples is critical to identification of biomarkers relevant to injury and disease as well as for assay development, design of human clinical validation studies is an important component of translational biomarker research platforms. Although these clinical studies share much in common with clinical trials for assessment of drug therapeutic efficacy, there are a number of

  13. Metagenomic biomarker discovery and explanation

    PubMed Central

    2011-01-01

    This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/. PMID:21702898

  14. 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.

  15. Contribution of oncoproteomics to cancer biomarker discovery

    PubMed Central

    Cho, William CS

    2007-01-01

    Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. Proteomic research first came to the fore with the introduction of two-dimensional gel electrophoresis. At the turn of the century, proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip. There is an intense interest in applying proteomics to foster an improved understanding of cancer pathogenesis, develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. Oncoproteomics has the potential to revolutionize clinical practice, including cancer diagnosis and screening based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Besides, oncoproteomics is also applied to the discovery of new therapeutic targets and to the study of drug effects. In pace with the successful completion of the Human Genome Project, the wave of proteomics has raised the curtain on the postgenome era. The study of oncoproteomics provides mankind with a better understanding of neoplasia. In this article, the discovery of cancer biomarkers in recent years is reviewed. The challenges ahead and perspectives of oncoproteomics for biomarkers development are also addressed. With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this review serves as a reference for biomarker researchers, scientists working in proteomics and bioinformatics, oncologists, pharmaceutical scientists, biochemists, biologists, and chemists. PMID:17407558

  16. Cancer biomarker discovery using DNA aptamers.

    PubMed

    Jin, Cheng; Qiu, Liping; Li, Jin; Fu, Ting; Zhang, Xiaobing; Tan, Weihong

    2016-01-21

    Biomarkers are signature molecules able to indicate specific physiological states of cells. Identification of reliable biomarkers is essential for early diagnosis and adaptive treatment of diseases, especially cancer. Aptamers are single-stranded oligonucleotides generated by an in vitro screening method called Systematic Evolution of Ligands by Exponential Enrichment (SELEX). They can recognize their cognate targets with selectivity and affinity comparable to protein antibodies. In addition, aptamers have superiorities including easy synthesis, high chemical stability, convenient modification and flexible design. As such, these DNA molecules show great promise as powerful molecular probes for biomarker discovery and biomarker-based clinical applications. Using complex samples as targets, a panel of aptamers can be systematically generated for comprehensive recognition of disease-specific proteins, which can potentially serve as biomarkers. This review describes the current methods for biomarker discovery using aptamers. PMID:26567694

  17. Biological Networks for Cancer Candidate Biomarkers Discovery.

    PubMed

    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

  18. 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

  19. Biomarker Discovery and Translation in Metabolomics

    PubMed Central

    Nagana Gowda, G.A.; Raftery, D.

    2016-01-01

    The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions. PMID:27134822

  20. Applications of protein microarrays for biomarker discovery

    PubMed Central

    Ramachandran, Niroshan; Srivastava, Sanjeeva; LaBaer, Joshua

    2011-01-01

    The search for new biomarkers for diagnosis, prognosis and therapeutic monitoring of diseases continues in earnest despite dwindling success at finding novel reliable markers. Some of the current markers in clinical use do not provide optimal sensitivity and specificity, with the prostate cancer antigen (PSA) being one of many such examples. The emergence of proteomic techniques and systems approaches to study disease pathophysiology has rekindled the quest for new biomarkers. In particular the use of protein microarrays has surged as a powerful tool for large scale testing of biological samples. Approximately half the reports on protein microarrays have been published in the last two years especially in the area of biomarker discovery. In this review, we will discuss the application of protein microarray technologies that offer unique opportunities to find novel biomarkers. PMID:21136793

  1. 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. PMID:26519185

  2. Using Aptamers for Cancer Biomarker Discovery

    PubMed Central

    Chang, Yun Min; Donovan, Michael J.; Tan, Weihong

    2013-01-01

    Aptamers are single-stranded synthetic DNA- or RNA-based oligonucleotides that fold into various shapes to bind to a specific target, which includes proteins, metals, and molecules. Aptamers have high affinity and high specificity that are comparable to that of antibodies. They are obtained using iterative method, called (Systematic Evolution of Ligands by Exponential Enrichment) SELEX and cell-based SELEX (cell-SELEX). Aptamers can be paired with recent advances in nanotechnology, microarray, microfluidics, and other technologies for applications in clinical medicine. One particular area that aptamers can shed a light on is biomarker discovery. Biomarkers are important in diagnosis and treatment of cancer. In this paper, we will describe ways in which aptamers can be used to discover biomarkers for cancer diagnosis and therapeutics. PMID:23401749

  3. Better Cancer Biomarker Discovery Through Better Study Design

    PubMed Central

    Rundle, Andrew; Ahsan, Habibul; Vineis, Paolo

    2013-01-01

    Background High through-put laboratory technologies coupled with sophisticated bioinformatics algorithms have tremendous potential for discovering novel biomarkers, or profiles of biomarkers, that could serve as predictors of disease risk, response to treatment or prognosis. We discuss methodological issues in wedding high through-put approaches for biomarker discovery with the case-control study designs typically used in biomarker discovery studies, especially focusing on nested case-control designs. Methods We review principles for nested case-control study design in relation to biomarker discovery studies and describe how the efficiency of biomarker discovery can be effected by study design choices. We develop a simulated prostate cancer cohort data set and a series of biomarker discovery case-control studies nested within the cohort to illustrate how study design choices can influence biomarker discovery process. Result Common elements of nested case-control design, incidence density sampling and matching of controls to cases, are not typically factored correctly into biomarker discovery analyses, inducing bias in the discovery process. We illustrate how incidence density sampling and matching of controls to cases reduces the apparent specificity of truly valid biomarkers “discovered” in a nested case-control study. We also propose and demonstrate a new case-control matching protocol, we call “anti-matching”, that improves the efficiency of biomarker discovery studies. Conclusions For a valid, but as yet undiscovered, biomarker(s) disjunctions between correctly designed epidemiologic studies and the practice of biomarker discovery reduce the likelihood that true biomarker(s) will be discovered and increases the false positive discovery rate. PMID:22998109

  4. Biomarker Signature Discovery from Mass Spectrometry Data.

    PubMed

    Kong, Ao; Gupta, Chinmaya; Ferrari, Mauro; Agostini, Marco; Bedin, Chiara; Bouamrani, Ali; Tasciotti, Ennio; Azencott, Robert

    2014-01-01

    Mass spectrometry based high throughput proteomics are used for protein analysis and clinical diagnosis. Many machine learning methods have been used to construct classifiers based on mass spectrometry data, for discrimination between cancer stages. However, the classifiers generated by machine learning such as SVM techniques typically lack biological interpretability. We present an innovative technique for automated discovery of signatures optimized to characterize various cancer stages. We validate our signature discovery algorithm on one new colorectal cancer MALDI-TOF data set, and two well-known ovarian cancer SELDI-TOF data sets. In all of these cases, our signature based classifiers performed either better or at least as well as four benchmark machine learning algorithms including SVM and KNN. Moreover, our optimized signatures automatically select smaller sets of key biomarkers than the black-boxes generated by machine learning, and are much easier to interpret. PMID:26356346

  5. Evaluation of reverse phase protein array (RPPA)-based pathway-activation profiling in 84 non-small cell lung cancer (NSCLC) cell lines as platform for cancer proteomics and biomarker discovery.

    PubMed

    Ummanni, Ramesh; Mannsperger, Heiko A; Sonntag, Johanna; Oswald, Marcus; Sharma, Ashwini K; König, Rainer; Korf, Ulrike

    2014-05-01

    The reverse phase protein array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant proteins and phosphoproteins in 84 non-small cell lung cancer (NSCLC) cell lines and by monitoring the activation state of selected receptor tyrosine kinases, PI3K/AKT and MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT- and RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, and PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA were identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge. PMID:24361481

  6. 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

  7. Cancer Biomarker Discovery: Lectin-Based Strategies Targeting Glycoproteins

    PubMed Central

    Clark, David; Mao, Li

    2012-01-01

    Biomarker discovery can identify molecular markers in various cancers that can be used for detection, screening, diagnosis, and monitoring of disease progression. Lectin-affinity is a technique that can be used for the enrichment of glycoproteins from a complex sample, facilitating the discovery of novel cancer biomarkers associated with a disease state. PMID:22710864

  8. 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

  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: 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. PMID:27426622

  11. Front view of the Orbiter Discovery from an elevated platform ...

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

    Front view of the Orbiter Discovery from an elevated platform in the Vehicle Assembly Building at NASA's Kennedy Space Center. - Space Transportation System, Orbiter Discovery (OV-103), Lyndon B. Johnson Space Center, 2101 NASA Parkway, Houston, Harris County, TX

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

    PubMed

    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

  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. 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

  15. 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

  16. Ovarian Cancer Biomarker Discovery Based on Genomic Approaches

    PubMed Central

    Lee, Jung-Yun; Kim, Hee Seung; Suh, Dong Hoon; Kim, Mi-Kyung; Chung, Hyun Hoon; Song, Yong-Sang

    2013-01-01

    Ovarian cancer presents at an advanced stage in more than 75% of patients. Early detection has great promise to improve clinical outcomes. Although the advancing proteomic technologies led to the discovery of numerous ovarian cancer biomarkers, no screening method has been recommended for early detection of ovarian cancer. Complexity and heterogeneity of ovarian carcinogenesis is a major obstacle to discover biomarkers. As cancer arises due to accumulation of genetic change, understanding the close connection between genetic changes and ovarian carcinogenesis would provide the opportunity to find novel gene-level ovarian cancer biomarkers. In this review, we summarize the various gene-based biomarkers by genomic technologies, including inherited gene mutations, epigenetic changes, and differential gene expression. In addition, we suggest the strategy to discover novel gene-based biomarkers with recently introduced next generation sequencing. PMID:25337559

  17. Proteomics of gliomas: Initial biomarker discovery and evolution of technology

    PubMed Central

    Kalinina, Juliya; Peng, Junmin; Ritchie, James C.; Van Meir, Erwin G.

    2011-01-01

    Gliomas are a group of aggressive brain tumors that diffusely infiltrate adjacent brain tissues, rendering them largely incurable, even with multiple treatment modalities and agents. Mostly asymptomatic at early stages, they present in several subtypes with astrocytic or oligodendrocytic features and invariably progress to malignant forms. Gliomas are difficult to classify precisely because of interobserver variability during histopathologic grading. Identifying biological signatures of each glioma subtype through protein biomarker profiling of tumor or tumor-proximal fluids is therefore of high priority. Such profiling not only may provide clues regarding tumor classification but may identify clinical biomarkers and pathologic targets for the development of personalized treatments. In the past decade, differential proteomic profiling techniques have utilized tumor, cerebrospinal fluid, and plasma from glioma patients to identify the first candidate diagnostic, prognostic, predictive, and therapeutic response markers, highlighting the potential for glioma biomarker discovery. The number of markers identified, however, has been limited, their reproducibility between studies is unclear, and none have been validated for clinical use. Recent technological advancements in methodologies for high-throughput profiling, which provide easy access, rapid screening, low sample consumption, and accurate protein identification, are anticipated to accelerate brain tumor biomarker discovery. Reliable tools for biomarker verification forecast translation of the biomarkers into clinical diagnostics in the foreseeable future. Herein we update the reader on the recent trends and directions in glioma proteomics, including key findings and established and emerging technologies for analysis, together with challenges we are still facing in identifying and verifying potential glioma biomarkers. PMID:21852429

  18. Innovative proteomic approaches for cancer biomarker discovery.

    PubMed

    Faca, Vitor; Krasnoselsky, Alexei; Hanash, Samir

    2007-09-01

    Substantial technological advances in proteomics and related computational science have been made in the past few years. These advances overcome in part the complexity and heterogeneity of the human proteome, permitting quantitative analysis and identification of protein changes associated with tumor development. Here, we discuss some of these advances that are uncovering new cancer biomarkers that have potential to detect cancer at early and curable stages and address remaining challenges. PMID:17907570

  19. Utilizing human blood plasma for proteomic biomarker discovery

    SciTech Connect

    Jacobs, Jon M.; Adkins, Joshua N.; Qian, Weijun; Liu, Tao; Shen, Yufeng; Camp, David G.; Smith, Richard D.

    2005-08-01

    Application of proteomic biomarker discovery efforts towards human plasma entails both incredible clinical potential as well as significant challenges to overcome the intrinsic characteristics of plasma. The dynamic range of proteins within plasma, coupled with the likely presence of potential biomarkers in the more difficult to detect lower abundance range has driven the development of various methodologies and strategies to maximize the possible detective dynamic range within this biofluid. Discussed is the array of the available approaches currently used by our laboratory and others to utilized human plasma as a viable medium for biomarker discovery efforts. Various separation, depletion, enrichment, and quantitative efforts have resulted in a measurable improvement in the detectability of the low abundance fraction of proteins but more advances are needed to bridge the gap between the current range of detection and what remains unobservable to fully maximize the potential of this sample.

  20. 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

  1. System-wide peripheral biomarker discovery using information theory.

    PubMed

    Alterovitz, Gil; Xiang, Michael; Liu, Jonathan; Chang, Amelia; Ramoni, Marco F

    2008-01-01

    The identification of reliable peripheral biomarkers for clinical diagnosis, patient prognosis, and biological functional studies would allow for access to biological information currently available only through invasive methods. Traditional approaches have so far considered aspects of tissues and biofluid markers independently. Here we introduce an information theoretic framework for biomarker discovery, integrating biofluid and tissue information. This allows us to identify tissue information in peripheral biofluids. We treat tissue-biofluid interactions as an information channel through functional space using 26 proteomes from 45 different sources to determine quantitatively the correspondence of each biofluid for specific tissues via relative entropy calculation of proteomes mapped onto phenotype, function, and drug space. Next, we identify candidate biofluids and biomarkers responsible for functional information transfer (p < 0.01). A total of 851 unique candidate biomarkers proxies were identified. The biomarkers were found to be significant functional tissue proxies compared to random proteins (p < 0.001). This proxy link is found to be further enhanced by filtering the biofluid proteins to include only significant tissue-biofluid information channels and is further validated by gene expression. Furthermore, many of the candidate biomarkers are novel and have yet to be explored. In addition to characterizing proteins and their interactions with a systemic perspective, our work can be used as a roadmap to guide biomedical investigation, from suggesting biofluids for study to constraining the search for biomarkers. This work has applications in disease screening, diagnosis, and protein function studies. PMID:18229689

  2. CE-MS analysis of the human urinary proteome for biomarker discovery and disease diagnostics

    PubMed Central

    Coon, Joshua J.; Zürbig, Petra; Dakna, Mohammed; Dominiczak, Anna F.; Decramer, Stéphane; Fliser, Danilo; Frommberger, Moritz; Golovko, Igor; Good, David M.; Herget-Rosenthal, Stefan; Jankowski, Joachim; Julian, Bruce A.; Kellmann, Markus; Kolch, Walter; Massy, Ziad; Novak, Jan; Rossing, Kasper; Schanstra, Joost P.; Schiffer, Eric; Theodorescu, Dan; Vanholder, Raymond; Weissinger, Eva M.; Mischak, Harald; Schmitt-Kopplin, Philippe

    2010-01-01

    Owing to its availability, ease of collection, and correlation with pathophysiology of diseases, urine is an attractive source for clinical proteomics. However, many proteomic studies have had only limited clinical impact, due to factors such as modest numbers of subjects, absence of disease controls, small numbers of defined biomarkers, and diversity of analytical platforms. Therefore, it is difficult to merge biomarkers from different studies into a broadly applicable human urinary proteome database. Ideally, the methodology for defining the biomarkers should combine a reasonable analysis time with high resolution, thereby enabling the profiling of adequate samples and recognition of sufficient features to yield robust diagnostic panels. Capillary electrophoresis coupled to mass spectrometry (CE-MS), which was used to analyze urine samples from healthy subjects and patients with various diseases, is a suitable approach for this task. The database of these datasets compiled from the urinary peptides enabled the diagnosis, classification, and monitoring of a wide range of diseases. CE-MS exhibits excellent performance for biomarker discovery and allows subsequent biomarker sequencing independent of the separation platform. This approach may elucidate the pathogenesis of many diseases, and better define especially renal and urological disorders at the molecular level. PMID:20130789

  3. Metabolomics for Biomarker Discovery in Gastroenterological Cancer

    PubMed Central

    Nishiumi, Shin; Suzuki, Makoto; Kobayashi, Takashi; Matsubara, Atsuki; Azuma, Takeshi; Yoshida, Masaru

    2014-01-01

    The study of the omics cascade, which involves comprehensive investigations based on genomics, transcriptomics, proteomics, metabolomics, etc., has developed rapidly and now plays an important role in life science research. Among such analyses, metabolome analysis, in which the concentrations of low molecular weight metabolites are comprehensively analyzed, has rapidly developed along with improvements in analytical technology, and hence, has been applied to a variety of research fields including the clinical, cell biology, and plant/food science fields. The metabolome represents the endpoint of the omics cascade and is also the closest point in the cascade to the phenotype. Moreover, it is affected by variations in not only the expression but also the enzymatic activity of several proteins. Therefore, metabolome analysis can be a useful approach for finding effective diagnostic markers and examining unknown pathological conditions. The number of studies involving metabolome analysis has recently been increasing year-on-year. Here, we describe the findings of studies that used metabolome analysis to attempt to discover biomarker candidates for gastroenterological cancer and discuss metabolome analysis-based disease diagnosis. PMID:25003943

  4. PROFILEing idiopathic pulmonary fibrosis: rethinking biomarker discovery.

    PubMed

    Maher, Toby M

    2013-06-01

    Despite major advances in the understanding of the pathogenesis of idiopathic pulmonary fibrosis (IPF), diagnosis and management of the condition continue to pose significant challenges. Clinical management of IPF remains unsatisfactory due to limited availability of effective drug therapies, a lack of accurate indicators of disease progression, and an absence of simple short-term measures of therapeutic response. The identification of more accurate predictors of prognosis and survival in IPF would facilitate counseling of patients and their families, aid communication among clinicians, and would guide optimal timing of referral for transplantation. Improvements in molecular techniques have led to the identification of new disease pathways and a more targeted approach to the development of novel anti-fibrotic agents. However, despite an increased interest in biomarkers of IPF disease progression there are a lack of measures that can be used in early phase clinical trials. Careful longitudinal phenotyping of individuals with IPF together with the application of novel omics-based technology should provide important insights into disease pathogenesis and should address some of the major issues holding back drug development in IPF. The PROFILE (Prospective Observation of Fibrosis in the Lung Clinical Endpoints) study is a currently enrolling, prospective cohort study designed to tackle these issues. PMID:23728868

  5. Secretome proteomics for discovery of cancer biomarkers.

    PubMed

    Makridakis, Manousos; Vlahou, Antonia

    2010-11-10

    "Secretome" is referred to as the rich, complex set of molecules secreted from living cells. In a less strict definition frequently followed in "secretome" studies, the term also includes molecules shed from the surface of living cells. Proteins of secretome (will be referred to as secreted) play a key role in cell signaling, communication and migration. The need for developing more effective cancer biomarkers and therapeutic modalities has led to the study of cancer cell secretome as a means to identify and characterize diagnostic and prognostic markers and potential drug and therapeutic targets. Significant technological advances in the field of proteomics during the last two decades have greatly facilitated research towards this direction. Nevertheless, secretome analysis still faces some difficulties mainly related to sample collection and preparation. The goal of this article is to provide an overview of the main findings from the analysis of cancer cell secretome. Specifically, we focus on the presentation of main methodological approaches that have been developed for the study of secreted proteins and the results thereof from the analysis of secretome in different types of malignancies; special emphasis is given on correlation of findings with protein expression in body fluids. PMID:20637910

  6. 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

  7. Analytical considerations for mass spectrometry profiling in serum biomarker discovery.

    PubMed

    Whiteley, Gordon R; Colantonio, Simona; Sacconi, Andrea; Saul, Richard G

    2009-03-01

    The potential of using mass spectrometry profiling as a diagnostic tool has been demonstrated for a wide variety of diseases. Various cancers and cancer-related diseases have been the focus of much of this work because of both the paucity of good diagnostic markers and the knowledge that early diagnosis is the most powerful weapon in treating cancer. The implementation of mass spectrometry as a routine diagnostic tool has proved to be difficult, however, primarily because of the stringent controls that are required for the method to be reproducible. The method is evolving as a powerful guide to the discovery of biomarkers that could, in turn, be used either individually or in an array or panel of tests for early disease detection. Using proteomic patterns to guide biomarker discovery and the possibility of deployment in the clinical laboratory environment on current instrumentation or in a hybrid technology has the possibility of being the early diagnosis tool that is needed. PMID:19389551

  8. 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

  9. 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

  10. Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets.

    PubMed

    Swanton, Charles; Larkin, James M; Gerlinger, Marco; Eklund, Aron C; Howell, Michael; Stamp, Gordon; Downward, Julian; Gore, Martin; Futreal, P Andrew; Escudier, Bernard; Andre, Fabrice; Albiges, Laurence; Beuselinck, Benoit; Oudard, Stephane; Hoffmann, Jens; Gyorffy, Balázs; Torrance, Chris J; Boehme, Karen A; Volkmer, Hansjuergen; Toschi, Luisella; Nicke, Barbara; Beck, Marlene; Szallasi, Zoltan

    2010-01-01

    The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, reducing ineffective therapy in drug-resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding

  11. In-depth quantitative proteomics for pancreatic cancer biomarker discovery.

    PubMed

    Faca, Vitor; Hanash, Samir

    2007-09-01

    Pancreatic adenocarcinoma is one of the leading causes of cancer-related death due to common presentation at an advanced stage. Although early detection and screening are likely to improve outcome, effective strategies are lacking. Proteomics holds substantial promise for the identification of blood-based biomarkers. Discovery strategies that have been investigated include analysis of tumor tissue and tumor cells, biologic fluids and serum and plasma for the identification of circulating biomarkers. A promising, complementary strategy consists of harnessing the immune response to tumor antigens for detecting pancreatic cancer at an early stage through a seropositive response to pancreatic cancer antigens. In addition, mouse models of pancreatic cancer may represent a rich source of candidate biomarkers applicable to humans. Although much work remains to be done, the findings so far are encouraging with respect to prospects for early detection and effective diagnosis. PMID:23489270

  12. 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

  13. Metabolomics for Biomarker Discovery: Moving to the Clinic

    PubMed Central

    Zhang, Aihua; Sun, Hui; Yan, Guangli; Wang, Ping; Wang, Xijun

    2015-01-01

    To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases. PMID:26090402

  14. 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

  15. Analytical Validation Considerations of Multiplex Mass-Spectrometry-Based Proteomic Platforms for Measuring Protein Biomarkers

    PubMed Central

    2015-01-01

    Protein biomarker discovery and validation in current omics era are vital for healthcare professionals to improve diagnosis, detect cancers at an early stage, identify the likelihood of cancer recurrence, stratify stages with differential survival outcomes, and monitor therapeutic responses. The success of such biomarkers would have a huge impact on how we improve the diagnosis and treatment of patients and alleviate the financial burden of healthcare systems. In the past, the genomics community (mostly through large-scale, deep genomic sequencing technologies) has been steadily improving our understanding of the molecular basis of disease, with a number of biomarker panels already authorized by the U.S. Food and Drug Administration (FDA) for clinical use (e.g., MammaPrint, two recently cleared devices using next-generation sequencing platforms to detect DNA changes in the cystic fibrosis transmembrane conductance regulator (CFTR) gene). Clinical proteomics, on the other hand, albeit its ability to delineate the functional units of a cell, more likely driving the phenotypic differences of a disease (i.e., proteins and protein–protein interaction networks and signaling pathways underlying the disease), “staggers” to make a significant impact with only an average ∼1.5 protein biomarkers per year approved by the FDA over the past 15–20 years. This statistic itself raises the concern that major roadblocks have been impeding an efficient transition of protein marker candidates in biomarker development despite major technological advances in proteomics in recent years. PMID:25171765

  16. Analytical validation considerations of multiplex mass-spectrometry-based proteomic platforms for measuring protein biomarkers.

    PubMed

    Boja, Emily S; Fehniger, Thomas E; Baker, Mark S; Marko-Varga, György; Rodriguez, Henry

    2014-12-01

    Protein biomarker discovery and validation in current omics era are vital for healthcare professionals to improve diagnosis, detect cancers at an early stage, identify the likelihood of cancer recurrence, stratify stages with differential survival outcomes, and monitor therapeutic responses. The success of such biomarkers would have a huge impact on how we improve the diagnosis and treatment of patients and alleviate the financial burden of healthcare systems. In the past, the genomics community (mostly through large-scale, deep genomic sequencing technologies) has been steadily improving our understanding of the molecular basis of disease, with a number of biomarker panels already authorized by the U.S. Food and Drug Administration (FDA) for clinical use (e.g., MammaPrint, two recently cleared devices using next-generation sequencing platforms to detect DNA changes in the cystic fibrosis transmembrane conductance regulator (CFTR) gene). Clinical proteomics, on the other hand, albeit its ability to delineate the functional units of a cell, more likely driving the phenotypic differences of a disease (i.e., proteins and protein-protein interaction networks and signaling pathways underlying the disease), "staggers" to make a significant impact with only an average ∼ 1.5 protein biomarkers per year approved by the FDA over the past 15-20 years. This statistic itself raises the concern that major roadblocks have been impeding an efficient transition of protein marker candidates in biomarker development despite major technological advances in proteomics in recent years. PMID:25171765

  17. Mass spectrometric protein maps for biomarker discovery and clinical research

    PubMed Central

    Liu, Yansheng; Hüttenhain, Ruth; Collins, Ben; Aebersold, Ruedi

    2013-01-01

    Among the wide range of proteomic technologies, targeted mass spectrometry (MS) has shown great potential for biomarker studies. To extend the degree of multiplexing achieved by selected reaction monitoring (SRM), we recently developed SWATH MS. SWATH MS is a variant of the emerging class of data-independent acquisition (DIA) methods and essentially converts the molecules in a physical sample into perpetually re-usable digital maps. The thus generated SWATH maps are then mined using a targeted data extraction strategy, allowing us to profile disease-related proteomes at a high degree of reproducibility. The successful application of both SRM and SWATH MS requires the a priori generation of reference spectral maps that provide coordinates for quantification. Herein, we demonstrate that the application of the mass spectrometric reference maps and the acquisition of personalized SWATH maps hold a particular promise for accelerating the current process of biomarker discovery. PMID:24138574

  18. A glycomics approach to the discovery of potential cancer biomarkers.

    PubMed

    An, Hyun Joo; Lebrilla, Carlito B

    2010-01-01

    Glycosylation is highly sensitive to the biochemical environment and plays a key role in development and disease manifestation. Moreover, glycan biosynthesis depends on several highly competitive processes; thus, variations in the concentration of specific glycosyltransferases produce different products. For this reason, monitoring changes in glycosylation may be a more specific and sensitive approach to biomarker discovery and possibly disease diagnosis. Glycans in serum are of particular interest as approximately half of all proteins are glycosylated. We have developed the methods for profiling the glycans in human serum to identify glycan biomarker. Global release methods were used including chemical and enzymatic to access O-linked and N-linked glycans, respectively. Glycans were released from the culture medium of various cancer cell lines, in control sera, and in cancer patients and isolated using solid phase extraction (SPE) with a porous graphitized carbon. The SPE fractions were analyzed by matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI FTICR MS). Glycan compositions were determined based on accurate masses and tandem mass spectrometry. Glycosylation changes between control and patient group were monitored. Several glycans were identified as potential markers for ovarian, breast, and prostate cancer. In short, direct glycan analysis of human serum without any protein identification represents a new and innovative approach to disease marker discovery. PMID:19882130

  19. 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

  20. Protein biomarker discovery and validation: the long and uncertain path to clinical utility.

    PubMed

    Rifai, Nader; Gillette, Michael A; Carr, Steven A

    2006-08-01

    Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests. PMID:16900146

  1. As if Biomarker Discovery Isn't Hard Enough: the Consequences of Poorly Characterized Reagents

    SciTech Connect

    Rodland, Karin D.

    2014-02-04

    The advent of high throughput omic technologies over the past two decades has driven a vast expansion in the search for clinical biomarkers, as manifested by the plethora of publications on biomarker discovery (over 8,600) listed on PubMed since 2000. Unfortunately, the same time period has seen a relative dearth of clinically validated biomarkers that have received FDA approval; only 10 new cancer biomarkers have been approved by the FDA in the same time period [1].

  2. Biomarkers for serum diagnosis of infectious diseases and their potential application in novel sensor platforms.

    PubMed

    Goulart, Luiz R; Vieira, Carlos U; Freschi, Ana Paula P; Capparelli, Fausto E; Fujimura, Patricia T; Almeida, Juliana F; Ferreira, Lucas F; Goulart, Isabela M B; Brito-Madurro, Ana Graci; Madurro, Joao M

    2010-01-01

    Nanotechnological tools and biomarkers for diagnosis and prognosis, as well as strategies for disease control and monitoring populations at higher risk, are continuous worldwide challenges for infectious diseases. Phage display and monoclonal antibody combinatorial libraries are important sources for biomarker discovery and for improved diagnostic strategies. Mimetic peptides were selected against polyclonal antibodies from patients with dengue fever, leprosy, and leishmaniasis as model diseases, and from immunized chickens with total antigens from all three pathogens. Selected single or combined multi-epitope peptide biomarkers were further associated with four different sensor platforms, classified as affinity biosensors, that may be suitable as general protocols for field diagnosis. We have also developed two methods for nanoparticle agglutination assays (a particle gel agglutination test and a magnetic microparticle [MMP]-enzyme-linked immunosorbent assay [ELISA]) and two electrochemical biosensors (impedimetric and amperometric) for DNA and antibody detection. For the agglutination tests, micro- and nanoparticles were coupled with filamentous bacteriophages displaying the selected mimotopes on their surfaces, which has favored the formation of the antigen-antibody or peptide-protein complexes, amplifying the optical detection in ELISA assays or after the chromatographic separation of the microagglutinates. We have also demonstrated a proof-of-concept for the electrochemical biosensors by using electrodes modified with novel functionalized polymers. These electrochemical biosensors have proven to be fast, very sensitive, and specific for the detection of pathogen DNA and circulating antibodies of patients, which may become important in a wide range of diagnostic devices for many infectious agents. PMID:20370630

  3. 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

  4. 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. PMID:27193679

  5. 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.

  6. Disease Classification and Biomarker Discovery Using ECG Data

    PubMed Central

    Huang, Rong; Zhou, Yingchun

    2015-01-01

    In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. Motivated by the observation that the distributions of various measures on ECGs of the diseased group are often skewed, heavy-tailed, or multimodal, we characterize the distributions by sample quantiles which outperform sample means. Three classifiers are compared in application both to all features and to dimension-reduced features by PCA: stepwise discriminant analysis (SDA), SVM, and LASSO logistic regression. It is found that SDA applied to dimension-reduced features by PCA is the most stable and effective procedure, with sensitivity, specificity, and accuracy being 89.68%, 84.62%, and 88.52%, respectively. PMID:26688816

  7. Discovery of putative pancreatic cancer biomarkers using subcellular proteomics.

    PubMed

    McKinney, Kimberly Q; Lee, Yong-Yook; Choi, Hyun-Su; Groseclose, Gale; Iannitti, David A; Martinie, John B; Russo, Mark W; Lundgren, Deborah H; Han, David K; Bonkovsky, Herbert L; Hwang, Sun-Il

    2011-01-01

    Pancreatic cancer (PC) is a highly aggressive disease that frequently remains undetected until it has progressed to an advanced, systemic stage. Successful treatment of PC is hindered by the lack of early detection. The application of proteomic analysis to PC combined with subcellular fractionation has introduced new possibilities in the field of biomarker discovery. We utilized matched pairs of pancreas tumor and non-tumor pancreas from patients undergoing tumor resection. The tissues were treated to obtain cellular protein fractions corresponding to cytosol, membrane, nucleus and cytoskeleton. The fractions were then separated by molecular weight and digested with trypsin, followed by liquid chromatography and tandem mass spectrometry. The spectra obtained were searched using Sequest engine and combined into a single analysis file to obtain a semi-quantitative number, spectral count, using Scaffold software. We identified 2393 unique proteins in non-tumor and cancer pancreas. Utilizing PLGEM statistical analysis we determined 104 proteins were significantly changed in cancer. From these, we further validated four secreted proteins that are up-regulated in cancer and have potential for development as minimally-invasive diagnostic markers. We conclude that subcellular fractionation followed by gel electrophoresis and tandem mass spectrometry is a powerful strategy for identification of differentially expressed proteins in pancreatic cancer. PMID:20807598

  8. 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.

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

    PubMed

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

    2013-06-01

    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. PMID:23614579

  10. 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

  11. Human platelets as a platform to monitor metabolic biomarkers using stable isotopes and LC–MS

    PubMed Central

    Basu, Sankha S; Deutsch, Eric C; Schmaier, Alec A; Lynch, David R; Blair, Ian A

    2014-01-01

    Background Intracellular metabolites such as CoA thioesters are modulated in a number of clinical settings. Their accurate measurement from surrogate tissues such as platelets may provide additional information to current serum and urinary biomarkers. Methods Freshly isolated platelets from healthy volunteers were treated with rotenone, propionate or isotopically labeled metabolic tracers. Using a recently developed LC–MS-based methodology, absolute changes in short-chain acyl-CoA thioesters were monitored, as well as relative metabolic labeling using isotopomer distribution analysis. Results Consistent with in vitro experiments, isolated platelets treated with rotenone showed decreased intracellular succinyl-CoA and increased β-hydroxybutyryl-CoA, while propionate treatment resulted in increased propionyl-CoA. In addition, isotopomers of the CoAs were readily detected in platelets treated with the [13C]- or [13C15N]-labeled metabolic precursors. Conclusion Here, we show that human platelets can provide a powerful ex vivo challenge platform with potential clinical diagnostic and biomarker discovery applications. PMID:24320127

  12. 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

  13. atBioNet– an integrated network analysis tool for genomics and biomarker discovery

    PubMed Central

    2012-01-01

    Background Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. Results atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. Conclusion atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http

  14. 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.

  15. Role of Systems Biology in Brain Injury Biomarker Discovery: Neuroproteomics Application.

    PubMed

    Jaber, Zaynab; Aouad, Patrick; Al Medawar, Mohamad; Bahmad, Hisham; Abou-Abbass, Hussein; Ghandour, Hiba; Mondello, Stefania; Kobeissy, Firas

    2016-01-01

    Years of research in the field of neurotrauma have led to the concept of applying systems biology as a tool for biomarker discovery in traumatic brain injury (TBI). Biomarkers may lead to understanding mechanisms of injury and recovery in TBI and can be potential targets for wound healing, recovery, and increased survival with enhanced quality of life. The literature available on neurotrauma studies from both animal and clinical studies has provided rich insight on the molecular pathways and complex networks of TBI, elucidating the proteomics of this disease for the discovery of biomarkers. With such a plethora of information available, the data from the studies require databases with tools to analyze and infer new patterns and associations. The role of different systems biology tools and their use in biomarker discovery in TBI are discussed in this chapter. PMID:27604718

  16. Proteomics of pediatric heart failure: from traditional biomarkers to new discovery strategies.

    PubMed

    Xu, Mingguo; Ramirez-Correa, Genaro A; Murphy, Anne M

    2015-08-01

    Heart failure in children is a complex clinical syndrome with multiple aetiologies. The underlying disorders that lead to heart failure in children differ significantly from those in adults. Some clinical biomarkers for heart failure status and prognosis appear to be useful in both age groups. This review outlines the use and the present status of biomarkers for heart failure in paediatric cardiology. Furthermore, clinical scenarios in which development of new biomarkers might address management or prognosis are discussed. Finally, strategies for proteomic discovery of novel biomarkers and application to practice are described. PMID:26377710

  17. 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

  18. Yeast display of antibody fragments: a discovery and characterization platform

    SciTech Connect

    Feldhaus, Michael; Siegel, Robert W.

    2004-07-01

    This review will focus on some of the novel attributes of the yeast surface display platform for the discovery and characterization of novel affinity reagents, optimization of those reagents, and novel uses of the platform. This is not intended to serve as an exhaustive review on the broader topic of general scFv technologies (see Winter et al., 1994; Smith and Petrenko, 1997; Bradbury et al., 2003) Furthermore, the scFv format of antibodies are easily manipulated through molecular cloning into a number of other formats such IgG, Fab, diabodies and such, for use in down steam applications and the reader is encouraged to read ?IgG?, ?Fab?, or your favorite format whenever scFv is seen in this review. This review is presented in 5 parts; (1) description of yeast display and its components, (2) library types and construction methods, (3) screening approaches for non-immune libraries and benefits, (4) screening approaches for directed evolution, kinetic on and off rates and (5) epitope complementation binning of clones.

  19. 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

  20. 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

  1. 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.

  2. Discovery of Novel Biomarkers for Alzheimer's Disease from Blood

    PubMed Central

    Long, Jintao; Pan, Genhua; Ifeachor, Emmanuel; Belshaw, Robert; Li, Xinzhong

    2016-01-01

    Blood-based biomarkers for Alzheimer's disease would be very valuable because blood is a more accessible biofluid and is suitable for repeated sampling. However, currently there are no robust and reliable blood-based biomarkers for practical diagnosis. In this study we used a knowledge-based protein feature pool and two novel support vector machine embedded feature selection methods to find panels consisting of two and three biomarkers. We validated these biomarker sets using another serum cohort and an RNA profile cohort from the brain. Our panels included the proteins ECH1, NHLRC2, HOXB7, FN1, ERBB2, and SLC6A13 and demonstrated promising sensitivity (>87%), specificity (>91%), and accuracy (>89%). PMID:27418712

  3. Recent advances in biomarker discovery in solid organ transplant by proteomics

    PubMed Central

    Sigdel, Tara K; Sarwal, Minnie M

    2012-01-01

    The identification and clinical use of more sensitive and specific biomarkers in the field of solid organ transplantation is an urgent need in medicine. Solid organ transplantation has seen improvements in the short-term survival of transplanted organs due to recent advancements in immunosuppressive therapy. However, the currently available methods of allograft monitoring are not optimal. Recent advancements in assaying methods for biomolecules such as genes, mRNA and proteins have helped to identify surrogate biomarkers that can be used to monitor the transplanted organ. These high-throughput ‘omic’ methods can help researchers to significantly speed up the identification and the validation steps, which are crucial factors for biomarker discovery efforts. Still, the progress towards identifying more sensitive and specific biomarkers remains a great deal slower than expected. In this article, we have evaluated the current status of biomarker discovery using proteomics tools in different solid organ transplants in recent years. This article summarizes recent reports and current status, along with the hurdles in efficient biomarker discovery of protein biomarkers using proteomics approaches. Finally, we will touch upon personalized medicine as a future direction for better management of transplanted organs, and provide what we think could be a recipe for success in this field. PMID:22087656

  4. Multiplexing of miniaturized planar antibody arrays for serum protein profiling--a biomarker discovery in SLE nephritis.

    PubMed

    Petersson, Linn; Dexlin-Mellby, Linda; Bengtsson, Anders A; Sturfelt, Gunnar; Borrebaeck, Carl A K; Wingren, Christer

    2014-06-01

    In the quest to decipher disease-associated biomarkers, miniaturized and multiplexed antibody arrays may play a central role in generating protein expression profiles, or protein maps, of crude serum samples. In this conceptual study, we explored a novel, 4-times larger pen design, enabling us to, in a unique manner, simultaneously print 48 different reagents (antibodies) as individual 78.5 μm(2) (10 μm in diameter) sized spots at a density of 38,000 spots cm(-2) using dip-pen nanolithography technology. The antibody array set-up was interfaced with a high-resolution fluorescent-based scanner for sensitive sensing. The performance and applicability of this novel 48-plex recombinant antibody array platform design was demonstrated in a first clinical application targeting SLE nephritis, a severe chronic autoimmune connective tissue disorder, as the model disease. To this end, crude, directly biotinylated serum samples were targeted. The results showed that the miniaturized and multiplexed array platform displayed adequate performance, and that SLE-associated serum biomarker panels reflecting the disease process could be deciphered, outlining the use of miniaturized antibody arrays for disease proteomics and biomarker discovery. PMID:24763547

  5. Mass spectrometry-based N-glycoproteomics for cancer biomarker discovery

    PubMed Central

    2014-01-01

    Glycosylation is estimated to be found in over 50% of human proteins. Aberrant protein glycosylation and alteration of glycans are closely related to many diseases. More than half of the cancer biomarkers are glycosylated-proteins, and specific glycoforms of glycosylated-proteins may serve as biomarkers for either the early detection of disease or the evaluation of therapeutic efficacy for treatment of diseases. Glycoproteomics, therefore, becomes an emerging field that can make unique contributions to the discovery of biomarkers of cancers. The recent advances in mass spectrometry (MS)-based glycoproteomics, which can analyze thousands of glycosylated-proteins in a single experiment, have shown great promise for this purpose. Herein, we described the MS-based strategies that are available for glycoproteomics, and discussed the sensitivity and high throughput in both qualitative and quantitative manners. The discovery of glycosylated-proteins as biomarkers in some representative diseases by employing glycoproteomics was also summarized. PMID:24872809

  6. The Clinical Impact of Recent Advances in LC-MS for Cancer Biomarker Discovery and Verification

    SciTech Connect

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

    2016-01-01

    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.

  7. 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. PMID:27168482

  8. Secreted proteins as a fundamental source for biomarker discovery

    PubMed Central

    Stastna, Miroslava; Van Eyk, Jennifer E.

    2012-01-01

    The proteins secreted by various cells (the secretomes) are a potential rich source of biomarkers since they reflect various states of the cells at real time and at given conditions. To have accessible, sufficient and reliable protein markers is desirable since they mark various stages of disease development and their presence/absence can be used for diagnosis, prognosis, risk stratification and therapeutic monitoring. As direct analysis of blood/plasma, a common and noninvasive patient screening method, can be difficult for candidate protein biomarker identification, the alternative/complementary approaches are required, one of them is the analysis of secretomes in cell conditioned media in vitro. Since the proteins secreted by cells as a response to various stimuli are most likely secreted into blood/plasma, the identification and preselection of candidate protein biomarkers from cell secretomes with subsequent validation of their presence at higher levels in serum/plasma is a promising approach. In this review, we discuss the proteins secreted by three progenitor cell types (smooth muscle, endothelial and cardiac progenitor cells) and two adult cell types (neonatal rat ventrical myocytes and smooth muscle cells) which can be relevant to cardiovascular research and which have been recently published in the literature. We found, at least for secretome studies included in this review, that secretomes of progenitor and adult cells overlap by 48% but the secretomes are very distinct among progenitor cell themselves as well as between adult cells. In addition, we compared secreted proteins to protein identifications listed in the Human Plasma PeptideAtlas and in two reports with cardiovascular-related proteins and we performed the extensive literature search to find if any of these secreted proteins were identified in a biomarker study. As expected, many proteins have been identified as biomarkers in cancer but 18 proteins (out of 62) have been tested as biomarkers in

  9. 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

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

    PubMed

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

    2013-03-15

    "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

  11. 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. PMID:21881773

  12. 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

  13. BIOMARKERS: HOPES AND CHALLENGES IN THE PATH FROM DISCOVERY TO CLINICAL PRACTICE

    PubMed Central

    Frangogiannis, Nikolaos G

    2015-01-01

    Biomarkers are objectively measured indicators of normal or pathological processes that may be helpful in diagnosis, staging, monitoring treatment, or prognostic evaluation of a disease. Although development of genomic, metabolomic and proteomic technologies has contributed to an explosion in identification of candidate analytes, validation remains expensive and challenging, and successful introduction of new biomarkers to clinical practice occurs at a very slow pace. The goal of this introductory overview is to provide the context for a series of review manuscripts published in the special issue on biomarkers. The promises and challenges of biomarker discovery are highlighted. Discovery and implementation of transformative new biomarkers in clinical practice requires close collaborations between scientists, clinicians and industry. High throughput technologies can identify a myriad of promising candidates but cannot predict their clinical value. In addition to rapid effective and systematic approaches for clinical validation, there is a need to study and establish links between the purported biomarker and the pathophysiologic basis of the disease of interest. Biomarkers are most informative when they provide insights into activation of specific pathways, thus serving as windows into the molecular basis of the disease. PMID:22424424

  14. 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

  15. 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

    2015-11-01

    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. PMID:26171467

  16. 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

  17. Banking on the future: biobanking for "omics" approaches to biomarker discovery for Opisthorchis-induced cholangiocarcinoma in Thailand.

    PubMed

    Mulvenna, Jason; Yonglitthipagon, Ponlapat; Sripa, Banchob; Brindley, Paul J; Loukas, Alex; Bethony, Jeffrey M

    2012-03-01

    Cholangiocarcinoma (CCA)--bile duct cancer--is associated with late presentation, poses challenges for diagnosis, and has high mortality. These features t highlight the desperate need for biomarkers than can be measured early and in accessible body fluids such as plasma of people at risk for developing this lethal cancer. In this manuscript, we address previous limitations in the discovery stage of biomarker(s) for CCA and indicate how new generation of "omics" technologies could be used for biomarker discovery in Thailand. A key factor in the success of this biomarker program for CCA is the combination of cutting edge technology with strategic sample acquisition by a biorepositories. PMID:21855650

  18. Biomarker discovery for inflammatory bowel disease, using proteomic serum profiling.

    PubMed

    Meuwis, Marie-Alice; Fillet, Marianne; Geurts, Pierre; de Seny, Dominique; Lutteri, Laurence; Chapelle, Jean-Paul; Bours, Vincent; Wehenkel, Louis; Belaiche, Jacques; Malaise, Michel; Louis, Edouard; Merville, Marie-Paule

    2007-05-01

    Crohn's disease and ulcerative colitis known as inflammatory bowel diseases (IBD) are chronic immuno-inflammatory pathologies of the gastrointestinal tract. These diseases are multifactorial, polygenic and of unknown etiology. Clinical presentation is non-specific and diagnosis is based on clinical, endoscopic, radiological and histological criteria. Novel markers are needed to improve early diagnosis and classification of these pathologies. We performed a study with 120 serum samples collected from patients classified in 4 groups (30 Crohn, 30 ulcerative colitis, 30 inflammatory controls and 30 healthy controls) according to accredited criteria. We compared protein sera profiles obtained with a Surface Enhanced Laser Desorption Ionization-Time of Flight-Mass Spectrometer (SELDI-TOF-MS). Data analysis with univariate process and a multivariate statistical method based on multiple decision trees algorithms allowed us to select some potential biomarkers. Four of them were identified by mass spectrometry and antibody based methods. Multivariate analysis generated models that could classify samples with good sensitivity and specificity (minimum 80%) discriminating groups of patients. This analysis was used as a tool to classify peaks according to differences in level on spectra through the four categories of patients. Four biomarkers showing important diagnostic value were purified, identified (PF4, MRP8, FIBA and Hpalpha2) and two of these: PF4 and Hpalpha2 were detected in sera by classical methods. SELDI-TOF-MS technology and use of the multiple decision trees method led to protein biomarker patterns analysis and allowed the selection of potential individual biomarkers. Their downstream identification may reveal to be helpful for IBD classification and etiology understanding. PMID:17258689

  19. Manifold Learning for Biomarker Discovery in MR Imaging

    NASA Astrophysics Data System (ADS)

    Wolz, Robin; Aljabar, Paul; Hajnal, Joseph V.; Rueckert, Daniel

    We propose a framework for the extraction of biomarkers from low-dimensional manifolds representing inter- and intra-subject brain variation in MR image data. The coordinates of each image in such a low-dimensional space captures information about structural shape and appearance and, when a phenotype exists, about the subject's clinical state. A key contribution is that we propose a method for incorporating longitudinal image information in the learned manifold. In particular, we compare simultaneously embedding baseline and follow-up scans into a single manifold with the combination of separate manifold representations for inter-subject and intra-subject variation. We apply the proposed methods to 362 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and classify healthy controls, subjects with Alzheimer's disease (AD) and subjects with mild cognitive impairment (MCI). Learning manifolds based on both the appearance and temporal change of the hippocampus, leads to correct classification rates comparable with those provided by state-of-the-art automatic segmentation estimates of hippocampal volume and atrophy. The biomarkers identified with the proposed method are data-driven and represent a potential alternative to a-priori defined biomarkers derived from manual or automated segmentations.

  20. Statistical Design for Biospecimen Cohort Size in Proteomics-based Biomarker Discovery and Verification Studies

    PubMed Central

    Skates, Steven J.; Gillette, Michael A.; LaBaer, Joshua; Carr, Steven A.; Anderson, N. Leigh; Liebler, Daniel C.; Ransohoff, David; Rifai, Nader; Kondratovich, Marina; Težak, Živana; Mansfield, Elizabeth; Oberg, Ann L.; Wright, Ian; Barnes, Grady; Gail, Mitchell; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Boja, Emily S.

    2014-01-01

    Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC), with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance, and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step towards building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research. PMID:24063748

  1. Discovery of Predictive Biomarkers for Litter Size in Boar Spermatozoa*

    PubMed Central

    Kwon, Woo-Sung; Rahman, Md Saidur; Lee, June-Sub; Yoon, Sung-Jae; Park, Yoo-Jin; Pang, Myung-Geol

    2015-01-01

    Conventional semen analysis has been used for prognosis and diagnosis of male fertility. Although this tool is essential for providing initial quantitative information about semen, it remains a subject of debate. Therefore, development of new methods for the prognosis and diagnosis of male fertility should be seriously considered for animal species of economic importance as well as for humans. In the present study, we applied a comprehensive proteomic approach to identify global protein biomarkers in boar spermatozoa in order to increase the precision of male fertility prognoses and diagnoses. We determined that l-amino acid oxidase, mitochondrial malate dehydrogenase 2, NAD (MDH2), cytosolic 5′-nucleotidase 1B, lysozyme-like protein 4, and calmodulin (CALM) were significantly and abundantly expressed in high-litter size spermatozoa. We also found that equatorin, spermadhesin AWN, triosephosphate isomerase (TPI), Ras-related protein Rab-2A (RAB2A), spermadhesin AQN-3, and NADH dehydrogenase [ubiquinone] iron-sulfur protein 2 (NDUFS2) were significantly and abundantly expressed in low-litter size spermatozoa (>3-fold). Moreover, RAB2A, TPI, and NDUFS2 were negatively correlated with litter size, whereas CALM and MDH2 were positively correlated. This study provides novel biomarkers for the prediction of male fertility. To the best of our knowledge, this is the first work that shows significantly increased litter size using male fertility biomarkers in a field trial. Moreover, these protein markers may provide new developmental tools for the selection of superior sires as well as for the prognosis and diagnosis of male fertility. PMID:25693803

  2. 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

  3. Identification and Confirmation of biomarkers using an integrated platform for quantitative analysis of glycoproteins and their glycosylations

    PubMed Central

    Liu, Yashu; He, Jintang; Li, Chen; Benitez, Ricardo; Fu, Sherry; Marrero, Jorge; Lubman, David M.

    2009-01-01

    Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver. However, accurate diagnosis can be difficult as most of the patients who develop this tumor have symptoms similar to those caused by longstanding liver disease. Herein we developed an integrated platform to discover the glycoprotein biomarkers in early HCC. At first, lectin arrays were applied to investigate the differences in glycan structures on serum glycoproteins from HCC and cirrhosis patients. The intensity for AAL and LCA was significantly higher in HCC, indicating an elevation of fucosylation level. Then serum from 10 HCC samples and 10 cirrhosis samples were used to screen the altered fucosylated proteins by a combination of Exactag labeling, lectin extraction and LC-MS/MS. Finally, 27 HCC and 27 cirrhosis serum samples were used for lectin-antibody arrays to confirm the change of these fucosylated proteins. C3, CE, HRG, CD14 and HGF were found to be biomarker candidates for distinguishing early HCC from cirrhosis, with a sensitivity of 72% and specificity of 79%. Our work gives insight to the detection of early HCC, and the application of this comprehensive strategy has the potential to facilitate biomarker discovery on a large scale. PMID:19961239

  4. 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.

  5. Data Fusion in Metabolomics and Proteomics for Biomarker Discovery.

    PubMed

    Blanchet, Lionel; Smolinska, Agnieszka

    2016-01-01

    Proteomics and metabolomics provide key insights into status and dynamics of biological systems. These molecular studies reveal the complex mechanisms involved in disease or aging processes. Invaluable information can be obtained using various analytical techniques such as nuclear magnetic resonance, liquid chromatography, or gas chromatography coupled to mass spectrometry. Each method has inherent advantages and drawbacks, but they are complementary in terms of biological information.The fusion of different measurements is a complex topic. We describe here a framework allowing combining multiple data sets, provided by different analytical platforms. For each platform, the relevant information is extracted in the first step. The obtained latent variables are then fused and further analyzed. The influence of the original variables is then calculated back and interpreted. PMID:26519180

  6. Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery.

    PubMed

    Zhou, Cong; Simpson, Kathryn L; Lancashire, Lee J; Walker, Michael J; Dawson, Martin J; Unwin, Richard D; Rembielak, Agata; Price, Patricia; West, Catharine; Dive, Caroline; Whetton, Anthony D

    2012-04-01

    A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes>2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation. PMID:22338609

  7. Aptamer-Based Detection of Disease Biomarkers in Mouse Models for Chagas Drug Discovery

    PubMed Central

    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. PMID:25569299

  8. 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. PMID:25569299

  9. 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

  10. 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

  11. A TMA De-Arraying Method for High Throughput Biomarker Discovery in Tissue Research

    PubMed Central

    Wang, Yinhai; Savage, Kienan; Grills, Claire; McCavigan, Andrena; James, Jacqueline A.; Fennell, Dean A.; Hamilton, Peter W.

    2011-01-01

    Background Tissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide. Methodology This study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap. Conclusion This study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly. PMID:22016800

  12. 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

  13. Status and Prospects for Discovery and Verification of New Biomarkers of Cardiovascular Disease by Proteomics

    PubMed Central

    Gerszten, Robert E.; Asnani, Aarti; Carr, Steven A.

    2012-01-01

    Despite unmet needs for cardiovascular biomarkers, few new protein markers have been FDA approved for the diagnosis or screening of cardiovascular diseases. Mass spectrometry (MS)-based proteomics technologies are capable of identifying hundreds to thousands of proteins in cells, tissues and biofluids. Proteomics may therefore provide the opportunity to elucidate new biomarkers and pathways without a prior known association with cardiovascular disease. However, important obstacles remain. In this review we focus on emerging techniques that may form a coherently integrated pipeline to overcome present limitations to both the discovery and validation processes. PMID:21817166

  14. DNA microarrays and likelihood ratio bioinformatic methods: discovery of human melanocyte biomarkers.

    PubMed

    Dooley, Thomas P; Curto, Ernest V; Davis, Richard L; Grammatico, Paola; Robinson, Edward S; Wilborn, Teresa W

    2003-06-01

    In this article, some of the advantages and limitations of DNA microarray technologies for gene expression profiling are summarized. As a model experiment, DermArray DNA microarrays were utilized to identify potential biomarkers of cultured normal human melanocytes in two different experimental comparisons. In the first case, melanocyte RNA was compared with vastly dissimilar non-melanocytic RNA samples of normal skin keratinocytes and fibroblasts. In the second case, melanocyte RNA was compared with a primary cutaneous melanoma line (MS7) and a metastatic melanoma cell line (SKMel-28). The alternative approaches provide dramatically different lists of 'normal melanocyte' biomarkers. The most robust biomarkers were identified using principal component analysis bioinformatic methods related to likelihood ratios. Only three of 25 robust biomarkers in the melanocyte-proximal study (i.e. melanocytes vs. melanoma cells) were coincidentally identified in the melanocyte-distal study (i.e. melanocytes vs. non-melanocytic cells). Selected up-regulated biomarkers of melanocytes (i.e. TRP-1, melan-A/MART-1, silver/Pmel17, and nidogen-2) were validated by qRT-PCR. Some of the melanocytic biomarkers identified here may be useful in molecular diagnostics, as potential molecular targets for drug discovery, and for understanding the biochemistry of melanocytic cells. PMID:12753397

  15. 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

  16. 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). PMID:21468941

  17. Hyaluronidase treatment of synovial fluid to improve assay precision for biomarker research using multiplex immunoassay platforms.

    PubMed

    Jayadev, Chethan; Rout, Raj; Price, Andrew; Hulley, Philippa; Mahoney, David

    2012-12-14

    Synovial fluid (SF) is a difficult biological matrix to analyse due to its complex non-Newtonian nature. This can result in poor assay repeatability and potentially inefficient use of precious samples. This study assessed the impact of SF treatment by hyaluronidase and/or dilution on intra-assay precision using the Luminex and Meso Scale Discovery (MSD) multiplex platforms. SF was obtained from patients with knee osteoarthritis at the time of joint replacement surgery. Aliquots derived from the same sample were left untreated (neat), 2-fold diluted, 4-fold diluted or treated with 2mg/ml testicular hyaluronidase (with 2-fold dilution). Preparation methods were compared in a polysterene-bead Luminex 10-plex (N=16), magnetic-bead Luminex singleplex (N=7) and MSD 4-plex (N=7). Each method was assessed for coefficient of variation (CV) of replicate measurements, number of bead events (for Luminex assays) and dilution-adjusted analyte concentration. Percentage recovery was calculated for dilutions and HAse treatment. Hyaluronidase treatment significantly increased the number of wells with satisfactory bead events/region (95%) compared to neat (48%, p<0.001) in the polystyrene-bead Luminex assay, but the magnetic-bead Luminex assay achieved ≥50 bead events irrespective of treatment method. Hyaluronidase treatment resulted in lower intra-assay CVs for detectable ligands (group average CV<10%) than neat, 2-fold and 4-fold dilution (CV~25% for all, p<0.05) in both polystyrene- and magnetic-bead Luminex assays. In addition, measured sample concentrations were higher and recovery was poor (elevated) after hyaluronidase treatment. In the MSD 4-plex, within-group comparison of the intra-assay CV or concentration was not conclusively influenced by SF preparation. However, only hyaluronidase treatment resulted in CV<25% for all samples for TNF-α. There was no effect on analyte concentrations or recovery. Hyaluronidase treatment can improve intra-assay precision and assay signal

  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. PMID:27567960

  19. 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

  20. Potential Approaches and Recent Advances in Biomarker Discovery in Clear-Cell Renal Cell Carcinoma

    PubMed Central

    Majer, Weronika; Kluzek, Katarzyna; Bluyssen, Hans; Wesoły, Joanna

    2015-01-01

    The early diagnosis and monitoring of clear-cell Renal Cell Carcinoma (ccRCC), which is the most common renal malignancy, remains challenging. The late diagnosis and lack of tools that can be used to assess the progression of the disease and metastasis significantly influence the chance of survival of ccRCC patients. Molecular biomarkers have been shown to aid the diagnosis and disease monitoring for other cancers, but such markers are not currently available for ccRCC. Recently, plasma and serum circulating nucleic acids, nucleic acids present in urine, and plasma and urine proteins gained interest in the field of cancer biomarker discovery. Here, we describe the applicability of plasma and urine nucleic acids as cancer biomarkers with a particular focus on DNA, small RNA, and protein markers for ccRCC. PMID:26516358

  1. Hair metabolomics: identification of fetal compromise provides proof of concept for biomarker discovery.

    PubMed

    Sulek, Karolina; Han, Ting-Li; Villas-Boas, Silas Granato; Wishart, David Scott; Soh, Shu-E; Kwek, Kenneth; Gluckman, Peter David; Chong, Yap-Seng; Kenny, Louise Claire; Baker, Philip Newton

    2014-01-01

    Analysis of the human metabolome has yielded valuable insights into health, disease and toxicity. However, the metabolic profile of complex biological fluids such as blood is highly dynamic and this has limited the discovery of robust biomarkers. Hair grows relatively slowly, and both endogenous compounds and environmental exposures are incorporated from blood into hair during growth, which reflects the average chemical composition over several months. We used hair samples to study the metabolite profiles of women with pregnancies complicated by fetal growth restriction (FGR) and healthy matched controls. We report the use of GC-MS metabolite profiling of hair samples for biomarker discovery. Unsupervised statistical analysis showed complete discrimination of FGR from controls based on hair composition alone. A predictive model combining 5 metabolites produced an area under the receiver-operating curve of 0.998. This is the first study of the metabolome of human hair and demonstrates that this biological material contains robust biomarkers, which may lead to the development of a sensitive diagnostic tool for FGR, and perhaps more importantly, to stable biomarkers for a range of other diseases. PMID:25057319

  2. Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

    NASA Astrophysics Data System (ADS)

    Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

    2014-07-01

    Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain- (IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.

  3. 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...

  4. Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine

    PubMed Central

    2013-01-01

    Since the emergence of the so-called omics technology, thousands of putative biomarkers have been identified and published, which have dramatically increased the opportunities for developing more effective therapeutics. These opportunities can have profound benefits for patients and for the economics of healthcare. However, the transfer of biomarkers from discovery to clinical practice is still a process filled with lots of pitfalls and limitations, mostly limited by structural and scientific factors. To become a clinically approved test, a potential biomarker should be confirmed and validated using hundreds of specimens and should be reproducible, specific and sensitive. Besides the lack of quality in biomarker validation, a number of other key issues can be identified and should be addressed. Therefore, the aim of this article is to discuss a series of interpretative and practical issues that need to be understood and resolved before potential biomarkers become a clinically approved test or are already on the diagnostic market. Some of these issues are shortly discussed here. PMID:23442211

  5. 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. PMID:26616366

  6. The Matchmaker Exchange: a platform for rare disease gene discovery.

    PubMed

    Philippakis, Anthony A; Azzariti, Danielle R; Beltran, Sergi; Brookes, Anthony J; Brownstein, Catherine A; Brudno, Michael; Brunner, Han G; Buske, Orion J; Carey, Knox; Doll, Cassie; Dumitriu, Sergiu; Dyke, Stephanie O M; den Dunnen, Johan T; Firth, Helen V; Gibbs, Richard A; Girdea, Marta; Gonzalez, Michael; Haendel, Melissa A; Hamosh, Ada; Holm, Ingrid A; Huang, Lijia; Hurles, Matthew E; Hutton, Ben; Krier, Joel B; Misyura, Andriy; Mungall, Christopher J; Paschall, Justin; Paten, Benedict; Robinson, Peter N; Schiettecatte, François; Sobreira, Nara L; Swaminathan, Ganesh J; Taschner, Peter E; Terry, Sharon F; Washington, Nicole L; Züchner, Stephan; Boycott, Kym M; Rehm, Heidi L

    2015-10-01

    There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow. PMID:26295439

  7. Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry.

    PubMed

    Cominetti, Ornella; Núñez Galindo, Antonio; Corthésy, John; Oller Moreno, Sergio; Irincheeva, Irina; Valsesia, Armand; Astrup, Arne; Saris, Wim H M; Hager, Jörg; Kussmann, Martin; Dayon, Loïc

    2016-02-01

    The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results. PMID:26620284

  8. Effect of Size and Heterogeneity of Samples on Biomarker Discovery: Synthetic and Real Data Assessment

    PubMed Central

    Di Camillo, Barbara; Sanavia, Tiziana; Martini, Matteo; Jurman, Giuseppe; Sambo, Francesco; Barla, Annalisa; Squillario, Margherita; Furlanello, Cesare; Toffolo, Gianna; Cobelli, Claudio

    2012-01-01

    Motivation The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods. Methods We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state. Results The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results. PMID:22403633

  9. Proteomic profiling in MPTP monkey model for early Parkinson disease biomarker discovery.

    PubMed

    Lin, Xiangmin; Shi, Min; Masilamoni, Jeyaraj Gunasingh; Dator, Romel; Movius, James; Aro, Patrick; Smith, Yoland; Zhang, Jing

    2015-07-01

    Identification of reliable and robust biomarkers is crucial to enable early diagnosis of Parkinson disease (PD) and monitoring disease progression. While imperfect, the slow, chronic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced non-human primate animal model system of parkinsonism is an abundant source of pre-motor or early stage PD biomarker discovery. Here, we present a study of a MPTP rhesus monkey model of PD that utilizes complementary quantitative iTRAQ-based proteomic, glycoproteomics and phosphoproteomics approaches. We compared the glycoprotein, non-glycoprotein, and phosphoprotein profiles in the putamen of asymptomatic and symptomatic MPTP-treated monkeys as well as saline injected controls. We identified 86 glycoproteins, 163 non-glycoproteins, and 71 phosphoproteins differentially expressed in the MPTP-treated groups. Functional analysis of the data sets inferred the biological processes and pathways that link to neurodegeneration in PD and related disorders. Several potential biomarkers identified in this study have already been translated for their usefulness in PD diagnosis in human subjects and further validation investigations are currently under way. In addition to providing potential early PD biomarkers, this comprehensive quantitative proteomic study may also shed insights regarding the mechanisms underlying early PD development. This article is part of a Special Issue entitled: Neuroproteomics: Applications in neuroscience and neurology. PMID:25617661

  10. A Combined Shotgun and Targeted Mass Spectrometry Strategy for Breast Cancer Biomarker Discovery.

    PubMed

    Sjöström, Martin; Ossola, Reto; Breslin, Thomas; Rinner, Oliver; Malmström, Lars; Schmidt, Alexander; Aebersold, Ruedi; Malmström, Johan; Niméus, Emma

    2015-07-01

    It is of highest importance to find proteins responsible for breast cancer dissemination, for use as biomarkers or treatment targets. We established and performed a combined nontargeted LC-MS/MS and a targeted LC-SRM workflow for discovery and validation of protein biomarkers. Eighty breast tumors, stratified for estrogen receptor status and development of distant recurrence (DR ± ), were collected. After enrichment of N-glycosylated peptides, label-free LC-MS/MS was performed on each individual tumor in triplicate. In total, 1515 glycopeptides from 778 proteins were identified and used to create a map of the breast cancer N-glycosylated proteome. Based on this specific proteome map, we constructed a 92-plex targeted label-free LC-SRM panel. These proteins were quantified across samples by LC-SRM, resulting in 10 proteins consistently differentially regulated between DR+/DR- tumors. Five proteins were further validated in a separate cohort as prognostic biomarkers at the gene expression level. We also compared the LC-SRM results to clinically reported HER2 status, demonstrating its clinical accuracy. In conclusion, we demonstrate a combined mass spectrometry strategy, at large scale on clinical samples, leading to the identification and validation of five proteins as potential biomarkers for breast cancer recurrence. All MS data are available via ProteomeXchange and PASSEL with identifiers PXD001685 and PASS00643. PMID:25944384

  11. Proteomic profiling in MPTP monkey model for early Parkinson disease biomarker discovery

    PubMed Central

    Lin, Xiangmin; Shi, Min; Gunasingh Masilamoni, Jeyaraj; Dator, Romel; Movius, James; Aro, Patrick; Smith, Yoland; Zhang, Jing

    2015-01-01

    Identification of reliable and robust biomarkers is crucial to enable early diagnosis of Parkinson disease (PD) and monitoring disease progression. While imperfect, the slow, chronic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced non-human primate animal model system of parkinsonism is an abundant source of pre-motor or early stage PD biomarker discovery. Here, we present a study of a MPTP rhesus monkey model of PD that utilizes complementary quantitative iTRAQ-based proteomic, glycoproteomics and phosphoproteomics approaches. We compared the glycoprotein, non-glycoprotein, and phosphoprotein profiles in the putamen of asymptomatic and symptomatic MPTP-treated monkeys as well as saline injected controls. We identified 86 glycoproteins, 163 non-glycoproteins, and 71 phosphoproteins differentially expressed in the MPTP-treated groups. Functional analysis of the data sets inferred the biological processes and pathways that link to neurodegeneration in PD and related disorders. Several potential biomarkers identified in this study have already been translated for their usefulness in PD diagnosis in human subjects and further validation investigations are currently under way. In addition to providing potential early PD biomarkers, this comprehensive quantitative proteomic study may also shed insights regarding the mechanisms underlying early PD development. This article is part of a Special Issue entitled: Neuroproteomics: Applications in neuroscience and neurology. PMID:25617661

  12. Nuclear magnetic resonance: a key metabolomics platform in the drug discovery process.

    PubMed

    Leenders, Justine; Frédérich, Michel; de Tullio, Pascal

    2015-06-01

    Metabolomics is an innovative tool that is now emerging in the drug discovery process. Indeed, its ability to follow the dynamic perturbations in the metabolome resulting from pathologies but also from drug treatment and or/toxicity is of value for the development of new therapeutic approaches. Nuclear magnetic resonance (NMR) spectroscopy, which is an important analytical technique for several steps of the lead discovery, validation and optimization processes, has been described, together with mass spectrometry (MS) as one of the major platform that could be used for metabolomics studies. This review highlights why NMR could be considered a key tool for the application of metabolomics in drug discovery. PMID:26190682

  13. 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

  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. 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. PMID:26558960

  16. Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction

    PubMed Central

    Rossing, Kasper; Bosselmann, Helle Skovmand; Gustafsson, Finn; Zhang, Zhen-Yu; Gu, Yu-Mei; Kuznetsova, Tatiana; Nkuipou-Kenfack, Esther; Mischak, Harald; Staessen, Jan A.

    2016-01-01

    Background Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF. Methods and Results Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972) discriminated between HFrEF patients (N = 94, sensitivity = 93.6%) and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%). Interestingly, HFrEF103 showed low sensitivity (12.6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin. Conclusion CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure. PMID:27308822

  17. 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

  18. HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery

    PubMed Central

    Leung, Alvin; Bader, Gary D.; Reimand, Jüri

    2014-01-01

    Summary: Correlating disease mutations with clinical and phenotypic information such as drug response or patient survival is an important goal of personalized cancer genomics and a first step in biomarker discovery. HyperModules is a network search algorithm that finds frequently mutated gene modules with significant clinical or phenotypic signatures from biomolecular interaction networks. Availability and implementation: HyperModules is available in Cytoscape App Store and as a command line tool at www.baderlab.org/Sofware/HyperModules. Contact: Juri.Reimand@utoronto.ca or Gary.Bader@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online PMID:24713437

  19. Discovery of urinary metabolomic biomarkers for early detection of acute kidney injury.

    PubMed

    Won, A Jin; Kim, Siwon; Kim, Yoon Gyoon; Kim, Kyu-Bong; Choi, Wahn Soo; Kacew, Sam; Kim, Kyeong Seok; Jung, Jee H; Lee, Byung Mu; Kim, Suhkmann; Kim, Hyung Sik

    2016-01-01

    The discovery of new biomarkers for early detection of drug-induced acute kidney injury (AKI) is clinically important. In this study, sensitive metabolomic biomarkers identified in the urine of rats were used to detect cisplatin-induced AKI. Cisplatin (10 mg kg(-1), i.p.) was administered to Sprague-Dawley rats, which were subsequently euthanized after 1, 3 or 5 days. In cisplatin-treated rats, mild histopathological alterations were noted at day 1, and these changes were severe at days 3 and 5. Blood urea nitrogen (BUN) and serum creatinine (SCr) levels were significantly increased at days 3 and 5. The levels of new urinary protein-based biomarkers, including kidney injury molecule-1 (KIM-1), glutathione S-transferase-α (GST-α), tissue inhibitor of metalloproteinase-1 (TIMP-1), vascular endothelial growth factor (VEGF), calbindin, clusterin, neutrophil, neutrophil gelatinase-associated lipocalin (NGAL), and osteopontin, were significantly elevated at days 3 and 5. Among urinary metabolites, trigonelline and 3-indoxylsulfate (3-IS) levels were significantly decreased in urine collected from cisplatin-treated rats prior to histological kidney damage. However, carbon tetrachloride (CCl4), a hepatotoxicant, did not affect these urinary biomarkers. Trigonelline is closely associated with GSH depletion and results in insufficient antioxidant capacity against cisplatin-induced AKI. The predominant cisplatin-induced AKI marker appeared to be reduced in urinary 3-IS levels. Because 3-IS is predominantly excreted via active secretion in proximal tubules, a decrease is indicative of tubular damage. Further, urinary excretion of 3-IS levels was markedly reduced in patients with AKI compared to normal subjects. The area under the curve receiver operating characteristics (AUC-ROC) for 3-IS was higher than for SCr, BUN, lactate dehydrogenase (LDH), total protein, and glucose. Therefore, low urinary or high serum 3-IS levels may be more useful for early detection of AKI than

  20. Probing the O-Glycoproteome of Gastric Cancer Cell Lines for Biomarker Discovery*

    PubMed Central

    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.

    2015-01-01

    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. PMID:25813380

  1. 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

  2. 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...

  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. Use of a Single-Chain Antibody Library for Ovarian Cancer Biomarker Discovery*

    PubMed Central

    Ramirez, Arturo B.; Loch, Christian M.; Zhang, Yuzheng; Liu, Yan; Wang, Xiaohong; Wayner, Elizabeth A.; Sargent, Jonathon E.; Sibani, Sahar; Hainsworth, Eugenie; Mendoza, Eliseo A.; Eugene, Ralph; LaBaer, Joshua; Urban, Nicole D.; McIntosh, Martin W.; Lampe, Paul D.

    2010-01-01

    The discovery of novel early detection biomarkers of disease could offer one of the best approaches to decrease the morbidity and mortality of ovarian and other cancers. We report on the use of a single-chain variable fragment antibody library for screening ovarian serum to find novel biomarkers for the detection of cancer. We alternately panned the library with ovarian cancer and disease-free control sera to make a sublibrary of antibodies that bind proteins differentially expressed in cancer. This sublibrary was printed on antibody microarrays that were incubated with labeled serum from multiple sets of cancer patients and controls. The antibodies that performed best at discriminating disease status were selected, and their cognate antigens were identified using a functional protein microarray. Overexpression of some of these antigens was observed in cancer serum, tumor proximal fluid, and cancer tissue via dot blot and immunohistochemical staining. Thus, our use of recombinant antibody microarrays for unbiased discovery found targets for ovarian cancer detection in multiple sample sets, supporting their further study for disease diagnosis. PMID:20467042

  5. Proteomic-driven biomarker discovery in gestational diabetes mellitus: a review.

    PubMed

    Singh, Apoorva; Subramani, Elavarasan; Datta Ray, Chaitali; Rapole, Srikanth; Chaudhury, Koel

    2015-09-01

    Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy and it affects 18% of pregnant women worldwide. GDM is considered a high-risk state which may lead to type II diabetes which is associated with an increase in a number of interrelated adverse perinatal outcomes. Given the fact that the progress of a successful pregnancy is dependent on the intricate communication between several biological molecules, identification of the proteomic profile perturbations in women with GDM is expected to help in understanding the disease pathogenesis and also discovery of clinical biomarker(s). In recent years, both gel-free and gel-based proteomics have been extensively investigated for improving maternal and child health. Although there are several reports integrating various aspects of proteomics in pregnancy related diseases such as preeclampsia, extensive Pubmed search shows no review so far on the application of proteomics in gestational diabetes. In this review, we focus on various high-throughput proteomic technologies for the identification of unique biosignatures and biomarkers responsible for the early prediction of GDM. Further, different analytical strategies and biological samples involved in proteomic analysis of this pregnancy-related disease are discussed.This article is part of a Special Issue entitled: Proteomics in India. PMID:26216595

  6. Discovery of lung cancer biomarkers by profiling the plasma proteome with monoclonal antibody libraries.

    PubMed

    Guergova-Kuras, Mariana; Kurucz, István; Hempel, William; Tardieu, Nadège; Kádas, János; Malderez-Bloes, Carole; Jullien, Anne; Kieffer, Yann; Hincapie, Marina; Guttman, András; Csánky, Eszter; Dezso, Balázs; Karger, Barry L; Takács, László

    2011-12-01

    A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ. The approach is capable of generating independent antibodies against different epitopes of the same proteins, allowing fast translation to multiplexed sandwich assays. Based on these results, we have verified in two independent clinical collections a panel of five biomarkers for classifying patient disease status with a diagnostics performance of 77% sensitivity and 87% specificity. Combining CYFRA, an established cancer marker, with the panel resulted in a performance of 83% sensitivity at 95% specificity for stage I NSCLC. PMID:21947365

  7. Literature-Based Discovery of Salivary Biomarkers for Type 2 Diabetes Mellitus

    PubMed Central

    Srinivasan, Mythily; Blackburn, Corinne; Mohamed, Mohamed; Sivagami, AV; Blum, Janice

    2015-01-01

    The alarming increase in type 2 diabetes mellitus (T2DM) underscores the need for efficient screening and preventive strategies. Select protein biomarker profiles emerge over time during T2DM development. Periodic evaluation of these markers will increase the predictive ability of diabetes risk scores. Noninvasive methods for frequent measurements of biomarkers are increasingly being investigated. Application of salivary diagnostics has gained importance with the establishment of significant similarities between the salivary and serum proteomes. The objective of this study is to identify T2DM-specific salivary biomarkers by literature-based discovery. A serial interrogation of the PubMed database was performed using MeSH terms of specific T2DM pathological processes in primary and secondary iterations to compile cohorts of T2DM-specific serum markers. Subsequent search consisted of mining for the identified serum markers in human saliva. More than 60% of T2DM-associated serum proteins have been measured in saliva. Nearly half of these proteins have been reported in diabetic saliva. Measurements of salivary lipids and oxidative stress markers that can exhibit correlated saliva plasma ratio could constitute reliable factors for T2DM risk assessment. We conclude that a high percentage of T2DM-associated serum proteins can be measured in saliva, which offers an attractive and economical strategy for T2DM screening. PMID:26005324

  8. ProfileDB: a resource for proteomics and cross-omics biomarker discovery.

    PubMed

    Bauer, Chris; Glintschert, Alexander; Schuchhardt, Johannes

    2014-05-01

    The increasing size and complexity of high-throughput datasets pose a growing challenge for researchers. Often very different (cross-omics) techniques with individual data analysis pipelines are employed making a unified biomarker discovery strategy and a direct comparison of different experiments difficult and time consuming. Here we present the comprehensive web-based application ProfileDB. The application is designed to integrate data from different high-throughput 'omics' data types (Transcriptomics, Proteomics, Metabolomics) with clinical parameters and prior knowledge on pathways and ontologies. Beyond data storage, ProfileDB provides a set of dedicated tools for study inspection and data visualization. The user can gain insights into a complex experiment with just a few mouse clicks. We will demonstrate the application by presenting typical use cases for the identification of proteomics biomarkers. All presented analyses can be reproduced using the public ProfileDB web server. The ProfileDB application is available by standard browser (Firefox 18+, Internet Explorer Version 9+) technology via http://profileDB.-microdiscovery.de/ (login and pass-word: profileDB). The installation contains several public datasets including different cross-'omics' experiments. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge. PMID:24270047

  9. Discovery of Lung Cancer Biomarkers by Profiling the Plasma Proteome with Monoclonal Antibody Libraries*

    PubMed Central

    Guergova-Kuras, Mariana; Kurucz, István; Hempel, William; Tardieu, Nadège; Kádas, János; Malderez-Bloes, Carole; Jullien, Anne; Kieffer, Yann; Hincapie, Marina; Guttman, András; Csánky, Eszter; Dezső, Balázs; Karger, Barry L.; Takács, László

    2011-01-01

    A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ. The approach is capable of generating independent antibodies against different epitopes of the same proteins, allowing fast translation to multiplexed sandwich assays. Based on these results, we have verified in two independent clinical collections a panel of five biomarkers for classifying patient disease status with a diagnostics performance of 77% sensitivity and 87% specificity. Combining CYFRA, an established cancer marker, with the panel resulted in a performance of 83% sensitivity at 95% specificity for stage I NSCLC. PMID:21947365

  10. 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

  11. Recent patents and advances in genomic biomarker discovery for colorectal cancers.

    PubMed

    Quyun, Chen; Ye, Zhiyun; Lin, Sheng-Cai; Lin, Biaoyang

    2010-06-01

    Colorectal cancer (CRC) is the third most common cancer in the world. Early diagnosis of colorectal cancer is the key to reducing the death rate of CRC patients. Predicting the response to current therapeutic modalities of CRC will also have a great impact on patient care. This review summarizes recent advances and patents in biomarker discovery in CRC under five major categories; including genomic changes, expression changes, mutations, epigenetic changes and microRNAs. The interesting patents include: 1) a patent for a method to differentiate normal exfoliated cells from cancer cells based on whether they were subjected to apoptosis and DNA degradation; 2) A model (PM-33 multiple molecular marker model) based on expression changes of up-regulation of the MDM2, DUSP6, and NFl genes down-regulation of the RNF4, MMD and EIF2S3 genes, which achieved an 88% sensitivity, and an 82% specificity for CRC diagnosis; 3) gene mutations in PTEN, KRAS, PIK3CA for predicting the response to anti-EGFR therapies, a common drug used for CRC treatment; 4) patents on epigenetic changes of ITGA4, SEPT9, ALX4, TFAP2E FOXL2, SARM1, ID4 etc. and many key miRNAs. Finally, future directions in the fields were commented on or suggested, including the combination of multiple categories of biomarkers and pathway central or network-based biomarker panels. PMID:20426761

  12. Development of a pharmaceutical hepatotoxicity biomarker panel using a discovery to targeted proteomics approach.

    PubMed

    Collins, Ben C; Miller, Christine A; Sposny, Alexandra; Hewitt, Phillip; Wells, Martin; Gallagher, William M; Pennington, Stephen R

    2012-08-01

    There is a pressing and continued need for improved predictive power in preclinical pharmaceutical toxicology assessment as substantial numbers of drugs are still removed from the market, or from late-stage development, because of unanticipated issues of toxicity. In recent years a number of consortia have been formed with a view to integrating -omics molecular profiling strategies to increase the sensitivity and predictive power of preclinical toxicology evaluation. In this study we report on the LC-MS based proteomic analysis of the effects of the hepatotoxic compound EMD 335823 on liver from rats using an integrated discovery to targeted proteomics approach. This compound was one of a larger panel studied by a variety of molecular profiling techniques as part of the InnoMed PredTox Consortium. Label-free LC-MS analysis of hepatotoxicant EMD 335823 treated animals revealed only moderate correlation of individual protein expression with changes in mRNA expression observed by transcriptomic analysis of the same liver samples. Significantly however, analysis of the protein and transcript changes at the pathway level revealed they were in good agreement. This higher level analysis was also consistent with the previously suspected PPARα activity of the compound. Subsequently, a panel of potential biomarkers of liver toxicity was assembled from the label-free LC-MS proteomics discovery data, the previously acquired transcriptomics data and selected candidates identified from the literature. We developed and then deployed optimized selected reaction monitoring assays to undertake multiplexed measurement of 48 putative toxicity biomarkers in liver tissue. The development of the selected reaction monitoring assays was facilitated by the construction of a peptide MS/MS spectral library from pooled control and treated rat liver lysate using peptide fractionation by strong cation exchange and off-gel electrophoresis coupled to LC-MS/MS. After iterative optimization and

  13. Development of a Pharmaceutical Hepatotoxicity Biomarker Panel Using a Discovery to Targeted Proteomics Approach*

    PubMed Central

    Collins, Ben C.; Miller, Christine A.; Sposny, Alexandra; Hewitt, Phillip; Wells, Martin; Gallagher, William M.; Pennington, Stephen R.

    2012-01-01

    There is a pressing and continued need for improved predictive power in preclinical pharmaceutical toxicology assessment as substantial numbers of drugs are still removed from the market, or from late-stage development, because of unanticipated issues of toxicity. In recent years a number of consortia have been formed with a view to integrating -omics molecular profiling strategies to increase the sensitivity and predictive power of preclinical toxicology evaluation. In this study we report on the LC-MS based proteomic analysis of the effects of the hepatotoxic compound EMD 335823 on liver from rats using an integrated discovery to targeted proteomics approach. This compound was one of a larger panel studied by a variety of molecular profiling techniques as part of the InnoMed PredTox Consortium. Label-free LC-MS analysis of hepatotoxicant EMD 335823 treated animals revealed only moderate correlation of individual protein expression with changes in mRNA expression observed by transcriptomic analysis of the same liver samples. Significantly however, analysis of the protein and transcript changes at the pathway level revealed they were in good agreement. This higher level analysis was also consistent with the previously suspected PPARα activity of the compound. Subsequently, a panel of potential biomarkers of liver toxicity was assembled from the label-free LC-MS proteomics discovery data, the previously acquired transcriptomics data and selected candidates identified from the literature. We developed and then deployed optimized selected reaction monitoring assays to undertake multiplexed measurement of 48 putative toxicity biomarkers in liver tissue. The development of the selected reaction monitoring assays was facilitated by the construction of a peptide MS/MS spectral library from pooled control and treated rat liver lysate using peptide fractionation by strong cation exchange and off-gel electrophoresis coupled to LC-MS/MS. After iterative optimization and

  14. Biomarker discovery and applications for foods and beverages: proteomics to nanoproteomics.

    PubMed

    Agrawal, Ganesh Kumar; Timperio, Anna Maria; Zolla, Lello; Bansal, Vipul; Shukla, Ravi; Rakwal, Randeep

    2013-11-20

    Foods and beverages have been at the heart of our society for centuries, sustaining humankind - health, life, and the pleasures that go with it. The more we grow and develop as a civilization, the more we feel the need to know about the food we eat and beverages we drink. Moreover, with an ever increasing demand for food due to the growing human population food security remains a major concern. Food safety is another growing concern as the consumers prefer varied foods and beverages that are not only traded nationally but also globally. The 21st century science and technology is at a new high, especially in the field of biological sciences. The availability of genome sequences and associated high-throughput sensitive technologies means that foods are being analyzed at various levels. For example and in particular, high-throughput omics approaches are being applied to develop suitable biomarkers for foods and beverages and their applications in addressing quality, technology, authenticity, and safety issues. Proteomics are one of those technologies that are increasingly being utilized to profile expressed proteins in different foods and beverages. Acquired knowledge and protein information have now been translated to address safety of foods and beverages. Very recently, the power of proteomic technology has been integrated with another highly sensitive and miniaturized technology called nanotechnology, yielding a new term nanoproteomics. Nanoproteomics offer a real-time multiplexed analysis performed in a miniaturized assay, with low-sample consumption and high sensitivity. To name a few, nanomaterials - quantum dots, gold nanoparticles, carbon nanotubes, and nanowires - have demonstrated potential to overcome the challenges of sensitivity faced by proteomics for biomarker detection, discovery, and application. In this review, we will discuss the importance of biomarker discovery and applications for foods and beverages, the contribution of proteomic technology in

  15. A plasmonic chip for biomarker discovery and diagnosis of type 1 diabetes

    PubMed Central

    Zhang, Bo; Kumar, Rajiv B; Dai, Hongjie; Feldman, Brian J

    2016-01-01

    Type 1 diabetes (T1D) is an autoimmune disease, whereas type 2 diabetes (T2D) results from insulin resistance and beta cell dysfunction. Previously, the onset of these two separate diseases was easily distinguished, with children being most at risk for T1D and T2D occurring in overweight adults. However, the dramatic rise in obesity, coupled with the notable increase in T1D, has created a large overlap in these previously discrete patient populations. Delayed diagnosis of T1D can result in severe illness or death, and rapid diagnosis of T1D is critical for the efficacy of emerging therapies. However, attempts to apply next-generation platforms have been unsuccessful for detecting diabetes biomarkers. Here we describe the development of a plasmonic gold chip for near-infrared fluorescence–enhanced (NIR-FE) detection of islet cell–targeting autoantibodies. We demonstrate that this platform has high sensitivity and specificity for the diagnosis of T1D and can be used to discover previously unknown biomarkers of T1D. PMID:25038825

  16. LOBSTAHS: An Adduct-Based Lipidomics Strategy for Discovery and Identification of Oxidative Stress Biomarkers.

    PubMed

    Collins, James R; Edwards, Bethanie R; Fredricks, Helen F; Van Mooy, Benjamin A S

    2016-07-19

    Discovery and identification of molecular biomarkers in large LC/MS data sets requires significant automation without loss of accuracy in the compound screening and annotation process. Here, we describe a lipidomics workflow and open-source software package for high-throughput annotation and putative identification of lipid, oxidized lipid, and oxylipin biomarkers in high-mass-accuracy HPLC-MS data. Lipid and oxylipin biomarker screening through adduct hierarchy sequences, or LOBSTAHS, uses orthogonal screening criteria based on adduct ion formation patterns and other properties to identify thousands of compounds while providing the user with a confidence score for each assignment. Assignments are made from one of two customizable databases; the default databases contain 14 068 unique entries. To demonstrate the software's functionality, we screened more than 340 000 mass spectral features from an experiment in which hydrogen peroxide was used to induce oxidative stress in the marine diatom Phaeodactylum tricornutum. LOBSTAHS putatively identified 1969 unique parent compounds in 21 869 features that survived the multistage screening process. While P. tricornutum maintained more than 92% of its core lipidome under oxidative stress, patterns in biomarker distribution and abundance indicated remodeling was both subtle and pervasive. Treatment with 150 μM H2O2 promoted statistically significant carbon-chain elongation across lipid classes, with the strongest elongation accompanying oxidation in moieties of monogalactosyldiacylglycerol, a lipid typically localized to the chloroplast. Oxidative stress also induced a pronounced reallocation of lipidome peak area to triacylglycerols. LOBSTAHS can be used with environmental or experimental data from a variety of systems and is freely available at https://github.com/vanmooylipidomics/LOBSTAHS . PMID:27322848

  17. Lung Cancer Serum Biomarker Discovery Using Label Free LC-MS/MS

    PubMed Central

    Zeng, Xuemei; Hood, Brian L.; Zhao, Ting; Conrads, Thomas P.; Sun, Mai; Gopalakrishnan, Vanathi; Grover, Himanshu; Day, Roger S.; Weissfeld, Joel L.; Wilson, David O.; Siegfried, Jill M.; Bigbee, William L.

    2011-01-01

    Introduction Lung cancer remains the leading cause of cancer-related death with poor survival due to the late stage at which lung cancer is typically diagnosed. Given the clinical burden from lung cancer, and the relatively favorable survival associated with early stage lung cancer, biomarkers for early detection of lung cancer are of important potential clinical benefit. Methods We performed a global lung cancer serum biomarker discovery study using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a set of pooled non-small cell lung cancer (NSCLC) case sera and matched controls. Immunoaffinity subtraction was used to deplete the top most abundant serum proteins; the remaining serum proteins were subjected to trypsin digestion and analyzed in triplicate by LC-MS/MS. The tandem mass spectrum data were searched against the human proteome database and the resultant spectral counting data were used to estimate the relative abundance of proteins across the case/control serum pools. The spectral counting derived abundances of some candidate biomarker proteins were confirmed with multiple reaction monitoring MS assays. Results A list of 49 differentially abundant candidate proteins was compiled by applying a negative binomial regression model to the spectral counting data (p<0.01). Functional analysis with Ingenuity Pathway Analysis tools showed significant enrichment of inflammatory response proteins, key molecules in cell-cell signaling and interaction network and differential physiological responses for the two common NSCLC subtypes. Conclusions We identified a set of candidate serum biomarkers with statistically significant differential abundance across the lung cancer case/control pools which, when validated, could improve lung cancer early detection. PMID:21304412

  18. 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. PMID:25929723

  19. Top-down proteomics with mass spectrometry imaging: a pilot study towards discovery of biomarkers for neurodevelopmental disorders.

    PubMed

    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

  20. 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

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

    PubMed

    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

  2. Identification of Candidate Serum Biomarkers for Schistosoma mansoni Infected Mice Using Multiple Proteomic Platforms

    PubMed Central

    Kardoush, Manal I.

    2016-01-01

    Background Schistosomiasis is an important helminth infection of humans. There are few reliable diagnostic biomarkers for early infection, for recurrent infection or to document successful treatment. In this study, we compared serum protein profiles in uninfected and infected mice to identify disease stage-specific biomarkers. Methods Serum collected from CD1 mice infected with 50–200 Schistosoma mansoni cercariae were analyzed before infection and at 3, 6 and 12 weeks post-infection using three mass spectrometric (MS) platforms. Results Using SELDI-TOF MS, 66 discriminating m/z peaks were detected between S. mansoni infected mice and healthy controls. Used in various combinations, these peaks could 1) reliably diagnose early-stage disease, 2) distinguish between acute and chronic infection and 3) diagnose S. mansoni infection regardless the parasite burden. The most important contributors to these diagnostic algorithms were peaks at 3.7, 13 and 46 kDa. Employing sample fractionation and differential gel electrophoresis, we analyzed gel slices either by MALDI-TOF MS or Velos Orbitrap MS. The former yielded eight differentially-expressed host proteins in the serum at different disease stages including transferrin and alpha 1- antitrypsin. The latter suggested the presence of a surprising number of parasite-origin proteins in the serum during both the acute (n = 200) and chronic (n = 105) stages. The Orbitrap platform also identified many differentially-expressed host-origin serum proteins during the acute and chronic stages (296 and 220 respectively). The presence of one of the schistosome proteins, glutathione S transferase (GST: 25 KDa), was confirmed by Western Blot. This study provides proof-of-principle for an approach that can yield a large number of novel candidate biomarkers for Schistosoma infection. PMID:27138990

  3. Functionalized nanoparticles for measurement of biomarkers using a SERS nanochannel platform

    NASA Astrophysics Data System (ADS)

    Benford, Melodie; Wang, Miao; Kameoka, Jun; Good, Theresa; Cote, Gerard

    2010-02-01

    The overall goal of this research is to develop a new point-of-care system for early detection and characterization of cardiac markers to aid in diagnosis of acute coronary syndrome. The envisioned final technology platform incorporates functionalized gold colloidal nanoparticles trapped at the entrance to a nanofluidic device providing a robust means for analyte detection at trace levels using surface enhanced Raman spectroscopy (SERS). To discriminate a specific biomarker, we designed an assay format analogous to a competitive ELISA. Notably, the biomarker would be captured by an antibody and in turn displace a peptide fragment, containing the binding epitope of the antibody labeled with a Raman reporter molecule that would not interfere with blood serum proteins. To demonstrate the feasibility of this approach, we used C-reactive protein (CRP) as a surrogate biomarker. We functionalized agarose beads with anti-CRP that were placed outside the nanochannel, then added either Rhodamine-6-G (R6G) labeled-CRP and gold (as a surrogate of a sample without analyte present), or R6G labeled CRP, gold, and unlabeled CRP (as a surrogate of a sample with analyte present). Analyzing the spectra we see an increase in peak intensity in the presence of analyte at characteristic peaks for R6G specifically, 1284 and1567 cm- 1. Further, our results illustrate the reproducibility of the Raman spectra collected for R6G-labeled CRP in the nanochannel. Overall, we believe that this method will provide the advantage of sensitivity and narrow line widths characteristic of SERS as well as the specificity toward the biomarker of interest.

  4. A Standardized and Reproducible Urine Preparation Protocol for Cancer Biomarkers Discovery

    PubMed Central

    Beretov, Julia; Wasinger, Valerie C; Schwartz, Peter; Graham, Peter H; Li, Yong

    2014-01-01

    A suitable and standardized protein purification technique is essential to maintain consistency and to allow data comparison between proteomic studies for urine biomarker discovery. Ultimately, efforts should be made to standardize urine preparation protocols. The aim of this study was to develop an optimal analytical protocol to achieve maximal protein yield and to ensure that this method was applicable to examine urine protein patterns that distinguish disease and disease-free states. In this pilot study, we compared seven different urine sample preparation methods to remove salts, and to precipitate and isolate urinary proteins. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) profiles showed that the sequential preparation of urinary proteins by combining acetone and trichloroacetic acid (TCA) alongside high speed centrifugation (HSC) provided the best separation, and retained the most urinary proteins. Therefore, this approach is the preferred method for all further urine protein analysis. PMID:25452700

  5. Isolation and quantification of microRNAs from urinary exosomes/microvesicles for biomarker discovery.

    PubMed

    Lv, Lin-Li; Cao, Yuhan; Liu, Dan; Xu, Min; Liu, Hong; Tang, Ri-Ning; Ma, Kun-Ling; Liu, Bi-Cheng

    2013-01-01

    Recent studies indicate that microRNA (miRNA) is contained within exosome. Here we sought to optimize the methodologies for the isolation and quantification of urinary exosomal microRNA as a prelude to biomarker discovery studies. Exosomes were isolated through ultracentrifugation and characterized by immunoelectron microscopy. To determine the RNA was confined inside exosomes, the pellet was treated with RNase before RNA isolation. The minimum urine volume, storage conditions for exosomes and exosomal miRNA was evaluated. The presence of miRNAs in patients with various kidney diseases was validated with real-time PCR. The result shows that miRNAs extracted from the exosomal fraction were resistant to RNase digestion and with high quality confirmed by agarose electrophoresis. 16 ml of urine was sufficient for miRNA isolation by absolute quantification with 4.15×10(5) copies/ul for miR-200c. Exosomes was stable at 4℃ 24h for shipping before stored at -80℃ and was stable in urine when stored at -80°C for 12 months. Exosomal miRNA was detectable despite 5 repeat freeze-thaw cycles. The detection of miRNA by quantitative PCR showed high reproducibility (>94% for intra-assay and >76% for inter-assay), high sensitivity (positive call 100% for CKD patients), broad dynamic range (8-log wide) and good linearity for quantification (R(2)>0.99). miR-29c and miR-200c showed different expression in different types of kidney disease. In summary, the presence of urinary exosomal miRNA was confirmed for patients with a diversity of chronic kidney disease. The conditions of urine collection, storage and miRNA detection determined in this study may be useful for future biomarker discovery efforts. PMID:24250247

  6. Isolation and Quantification of MicroRNAs from Urinary Exosomes/Microvesicles for Biomarker Discovery

    PubMed Central

    Lv, Lin-Li; Cao, Yuhan; Liu, Dan; Xu, Min; Liu, Hong; Tang, Ri-Ning; Ma, Kun-Ling; Liu, Bi-Cheng

    2013-01-01

    Recent studies indicate that microRNA (miRNA) is contained within exosome. Here we sought to optimize the methodologies for the isolation and quantification of urinary exosomal microRNA as a prelude to biomarker discovery studies. Exosomes were isolated through ultracentrifugation and characterized by immunoelectron microscopy. To determine the RNA was confined inside exosomes, the pellet was treated with RNase before RNA isolation. The minimum urine volume, storage conditions for exosomes and exosomal miRNA was evaluated. The presence of miRNAs in patients with various kidney diseases was validated with real-time PCR. The result shows that miRNAs extracted from the exosomal fraction were resistant to RNase digestion and with high quality confirmed by agarose electrophoresis. 16ml of urine was sufficient for miRNA isolation by absolute quantification with 4.15×105 copies/ul for miR-200c. Exosomes was stable at 4℃ 24h for shipping before stored at -80℃ and was stable in urine when stored at -80°C for 12months. Exosomal miRNA was detectable despite 5 repeat freeze-thaw cycles. The detection of miRNA by quantitative PCR showed high reproducibility (>94% for intra-assay and >76% for inter-assay), high sensitivity (positive call 100% for CKD patients), broad dynamic range (8-log wide) and good linearity for quantification (R2>0.99). miR-29c and miR-200c showed different expression in different types of kidney disease. In summary, the presence of urinary exosomal miRNA was confirmed for patients with a diversity of chronic kidney disease. The conditions of urine collection, storage and miRNA detection determined in this study may be useful for future biomarker discovery efforts. PMID:24250247

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

    PubMed

    Saigusa, Daisuke; 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

  8. Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches*

    PubMed Central

    Morris, Jeffrey S.

    2012-01-01

    In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry (Cromwell) and 2D gel electrophoresis (Pinnacle) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods

  9. Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.

    PubMed

    Morris, Jeffrey S

    2012-01-01

    In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry (Cromwell) and 2D gel electrophoresis (Pinnacle) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods

  10. Next generation SPR technology of membrane-bound proteins for ligand screening and biomarker discovery

    PubMed Central

    Maynard, Jennifer A.; Lindquist, Nathan C.; Sutherland, Jamie N.; Lesuffleur, Antoine; Warrington, Arthur E.; Rodriguez, Moses; Oh, Sang-Hyun

    2009-01-01

    Technology based on surface plasmon resonance (SPR) has allowed rapid, label-free characterization of protein-protein and protein-small molecule interactions, from quantitative measurements of binding kinetics and thermodynamics and concentrations in complex samples to epitope analysis. SPR has become the gold standard in industrial and academic settings, in which typically the interaction between a pair of soluble binding partners is characterized in detail or a library of molecules is screened for binding against a single soluble protein. In spite of these successes, the technology is only beginning to be adapted to the needs of membrane-bound proteins. Including G protein-coupled receptors (GPCR), ion channels and other growth, immune and cellular receptors, these proteins are difficult to study in situ but represent promising targets for drug and biomarker development. Existing technologies, such as BIAcore™, have been adapted for membrane protein analysis by building supported lipid layers or vesicle capture on existing chips. Newer technologies, still in development, will allow membrane proteins to be presented in native or near-native formats. These include SPR nanopore arrays, in which lipid bilayers containing membrane proteins stably span small pores that are addressable from both sides of the bilayer. Here, we discuss successes with current SPR instrumentation and the potential for SPR nanopore arrays to enable quantitative, high-throughput screening of GPCR ligands, biomarker discovery involving membrane bound proteins and basic cellular biology. PMID:19918786

  11. Prostate cancer serum biomarker discovery through proteomic analysis of alpha-2 macroglobulin protein complexes

    PubMed Central

    Burgess, Earle F.; Ham, Amy-Joan L.; Tabb, David L.; Billheimer, Dean; Roth, Bruce J.; Chang, Sam S.; Cookson, Michael S.; Hinton, Timothy J.; Cheek, Kristin L.; Hill, Salisha; Pietenpol, Jennifer A.

    2010-01-01

    Alpha-2 macroglobulin (A2M) functions as a universal protease inhibitor in serum and is capable of binding various cytokines and growth factors. In this study, we investigated if immunoaffinity enrichment and proteomic analysis of A2M protein complexes from human serum could improve detection of biologically relevant and novel candidate protein biomarkers in prostate cancer. Serum samples from six patients with androgen-independent, metastatic prostate cancer and six control patients without malignancy were analyzed by immunoaffinity enrichment of A2M protein complexes and MS identification of associated proteins. Known A2M substrates were reproducibly identified from patient serum in both cohorts, as well as proteins previously undetected in human serum. One example is heat shock protein 90 alpha (HSP90α), which was identified only in the serum of cancer patients in this study. Using an ELISA, the presence of HSP90α in human serum was validated on expanded test cohorts and found to exist in higher median serum concentrations in prostate cancer (n = 18) relative to control (n = 13) patients (median concentrations 50.7 versus 27.6 ng/mL, respectively, p = 0.001). Our results demonstrate the technical feasibility of this approach and support the analysis of A2M protein complexes for proteomic-based serum biomarker discovery. PMID:20107526

  12. Comprehensive Native Glycan Profiling with Isomer Separation and Quantitation for the Discovery of Cancer Biomarkers

    PubMed Central

    Hua, Serenus; An, Hyun Joo; Ozcan, Sureyya; Ro, Grace S.; Soares, Stephanie; DeVere-White, Ralph; Lebrilla, Carlito B.

    2012-01-01

    Glycosylation is highly sensitive to the biochemical environment and has been implicated in many diseases including cancer. Glycan compositional profiling of human serum with mass spectrometry has already identified potential biomarkers for several types of cancer and diseases; however, composition alone does not fully describe glycan stereo- and regioisomeric diversity. The vast structural heterogeneity of glycans presents a formidable analytical challenge. We have developed a method to identify and quantify isomeric native glycans using nanoflow liquid chromatography (nano-LC)/mass spectrometry. A microfluidic chip packed with graphitized carbon was used to chromatographically separate the glycans. To determine the utility of this method for structure-specific biomarker discovery, we analyzed serum samples from two groups of prostate cancer patients with different prognoses. More than 300 N-glycan species (including isomeric structures) were identified, corresponding to over 100 N-glycan compositions. Statistical tests established significant differences in glycan abundances between patient groups. This method provides comprehensive, selective, and quantitative glycan profiling. PMID:21776491

  13. 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...

  14. 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

  15. 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. PMID:22298275

  16. 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

  17. 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

    2016-01-01

    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

  18. 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.

  19. A Tool for Biomarker Discovery in the Urinary Proteome: A Manually Curated Human and Animal Urine Protein Biomarker Database*

    PubMed Central

    Shao, Chen; Li, Menglin; Li, Xundou; Wei, Lilong; Zhu, Lisi; Yang, Fan; Jia, Lulu; Mu, Yi; Wang, Jiangning; Guo, Zhengguang; Zhang, Dan; Yin, Jianrui; Wang, Zhigang; Sun, Wei; Zhang, Zhengguo; Gao, Youhe

    2011-01-01

    Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics

  20. 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

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

    PubMed

    Wang, Xiaojun; Su, Xiaoquan; Cui, Xinping; Ning, Kang

    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

  2. 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

  3. 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

  4. A Proteomic Analysis of Eccrine Sweat: Implications for the Discovery of Schizophrenia Biomarker Proteins

    PubMed Central

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

    2012-01-01

    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 two-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. PMID:22256890

  5. Including network knowledge into Cox regression models for biomarker signature discovery.

    PubMed

    Fröhlich, Holger

    2014-03-01

    Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step toward a better personalized medicine. During the last decade various methods 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. Most of these methods focus on classification problems, that is learn a model from data that discriminates patients into distinct clinical groups. Far less has been published on approaches that predict a patient's event risk. In this paper, we investigate eight methods that integrate network information into multivariable Cox proportional hazard models for risk prediction in breast cancer. We compare the prediction performance of our tested algorithms via cross-validation as well as across different datasets. In addition, we highlight the stability and interpretability of obtained gene signatures. In conclusion, we find GeneRank-based filtering to be a simple, computationally cheap and highly predictive technique to integrate network information into event time prediction models. Signatures derived via this method are highly reproducible. PMID:24430933

  6. High-Sensitivity and Low-Toxicity Fucose Probe for Glycan Imaging and Biomarker Discovery.

    PubMed

    Kizuka, Yasuhiko; Funayama, Sho; Shogomori, Hidehiko; Nakano, Miyako; Nakajima, Kazuki; Oka, Ritsuko; Kitazume, Shinobu; Yamaguchi, Yoshiki; Sano, Masahiro; Korekane, Hiroaki; Hsu, Tsui-Ling; Lee, Hsiu-Yu; Wong, Chi-Huey; Taniguchi, Naoyuki

    2016-07-21

    Fucose, a terminal sugar in glycoconjugates, critically regulates various physiological and pathological phenomena, including cancer development and inflammation. However, there are currently no probes for efficient labeling and detection of this sugar. We chemically synthesized a novel series of alkynyl-fucose analogs as probe candidates and found that 7-alkynyl-fucose gave the highest labeling efficiency and low cytotoxicity. Among the fucose analogs, 7-alkynyl-fucose was the best substrate against all five fucosyltransferases examined. We confirmed its conversion to the corresponding guanosine diphosphate derivative in cells and found that cellular glycoproteins were labeled much more efficiently with 7-alkynyl-fucose than with an existing probe. 7-Alkynyl-fucose was detected in the N-glycan core by mass spectrometry, and 7-alkynyl-fucose-modified proteins mostly disappeared in core-fucose-deficient mouse embryonic fibroblasts, suggesting that this analog mainly labeled core fucose in these cells. These results indicate that 7-alkynyl-fucose is a highly sensitive and powerful tool for basic glycobiology research and clinical application for biomarker discovery. PMID:27447047

  7. 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

    2012-04-01

    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. PMID:22256890

  8. Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling

    PubMed Central

    Atrih, A; Mudaliar, M A V; Zakikhani, P; Lamont, D J; Huang, J T-J; Bray, S E; Barton, G; Fleming, S; Nabi, G

    2014-01-01

    Background: Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues. Methods: Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis. Results: A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways. Conclusions: Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients. PMID:24548857

  9. Capillary electrophoresis–mass spectrometry as a powerful tool in biomarker discovery and clinical diagnosis: an update of recent developments

    PubMed Central

    Mischak, Harald; Coon, Joshua J.; Novak, Jan; Weissinger, Eva M.; Schanstra, Joost; Dominiczak, Anna F.

    2009-01-01

    Proteome analysis has emerged as a powerful technology to decipher biological processes. One of the main goals is to discover biomarkers for diseases from tissues and body fluids. However, the complexity and wide dynamic range of protein expression present an enormous challenge to separation technologies and mass spectrometry (MS). In this review, we examine the limitations of proteomics, and aim towards the definition of the current key prerequisites. We focus on capillary electrophoresis coupled to mass spectrometry (CE-MS), because this technique continues to show great promise. We discuss CE-MS from an application point of view, and evaluate its merits and vices for biomarker discovery and clinical applications. Finally, we present several examples on the use of CE-MS to determine urinary biomarkers and implications for disease diagnosis, prognosis, and therapy evaluation. PMID:18973238

  10. The Glycolyzer: Automated Glycan Annotation Software for High Performance Mass Spectrometry and Its Application to Ovarian Cancer Glycan Biomarker Discovery

    PubMed Central

    Kronewitter, Scott R.; De Leoz, Maria Lorna A.; Strum, John S.; An, Hyun Joo; Dimapasoc, Lauren M.; Guerrero, Andrés; Miyamoto, Suzanne; Lebrilla, Carlito B.; Leiserowitz, Gary S.

    2013-01-01

    Human serum glycomics is a promising method for finding cancer biomarkers but often lacks the tools for streamlined data analysis. The Glycolyzer software incorporates a suite of analytic tools capable of identifying informative glycan peaks out of raw mass spectrometry data. As a demonstration of its utility, the program was used to identify putative biomarkers for epithelial ovarian cancer from a human serum sample set. A randomized, blocked and blinded experimental design was used on a discovery set consisting of 46 cases and 48 controls. Retrosynthetic glycan libraries were used for data analysis and several significant candidate glycan biomarkers were discovered via hypothesis testing. The significant glycans were attributed to a glycan family based on glycan composition relationships and incorporated into a linear classifier motif test. The motif test was then applied to the discovery set to evaluate the disease state discrimination performance. The test provided strongly predictive results based on receiver operator characteristic curve analysis. The area under the receiver operator characteristic curve was 0.93. Using the Glycolyzer software, we were able to identify a set of glycan biomarkers that highly discriminate between cases and controls, and are ready to be formally validated in subsequent studies. PMID:22903841

  11. High-throughput and targeted in-depth mass spectrometry-based approaches for biofluid profiling and biomarker discovery.

    PubMed

    Jimenez, Connie R; Piersma, Sander; Pham, Thang V

    2007-12-01

    Proteomics aims to create a link between genomic information, biological function and disease through global studies of protein expression, modification and protein-protein interactions. Recent advances in key proteomics tools, such as mass spectrometry (MS) and (bio)informatics, provide tremendous opportunities for biomarker-related clinical applications. In this review, we focus on two complementary MS-based approaches with high potential for the discovery of biomarker patterns and low-abundant candidate biomarkers in biofluids: high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy-based methods for peptidome profiling and label-free liquid chromatography-based methods coupled to MS for in-depth profiling of biofluids with a focus on subproteomes, including the low-molecular-weight proteome, carrier-bound proteome and N-linked glycoproteome. The two approaches differ in their aims, throughput and sensitivity. We discuss recent progress and challenges in the analysis of plasma/serum and proximal fluids using these strategies and highlight the potential of liquid chromatography-MS-based proteomics of cancer cell and tumor secretomes for the discovery of candidate blood-based biomarkers. Strategies for candidate validation are also described. PMID:20477373

  12. Biomarker Discovery Using New Metabolomics Software for Automated Processing of High Resolution LC-MS Data

    PubMed Central

    Hnatyshyn, S.; Reily, M.; Shipkova, P.; McClure, T.; Sanders, M.; Peake, D.

    2011-01-01

    Robust biomarkers of target engagement and efficacy are required in different stages of drug discovery. Liquid chromatography coupled to high resolution mass spectrometry provides sensitivity, accuracy and wide dynamic range required for identification of endogenous metabolites in biological matrices. LCMS is widely-used tool for biomarker identification and validation. Typical high resolution LCMS profiles from biological samples may contain greater than a million mass spectral peaks corresponding to several thousand endogenous metabolites. Reduction of the total number of peaks, component identification and statistical comparison across sample groups remains to be a difficult and time consuming challenge. Blood samples from four groups of rats (male vs. female, fully satiated and food deprived) were analyzed using high resolution accurate mass (HRAM) LCMS. All samples were separated using a 15 minute reversed-phase C18 LC gradient and analyzed in both positive and negative ion modes. Data was acquired using 15K resolution and 5ppm mass measurement accuracy. The entire data set was analyzed using software developed in collaboration between Bristol Meyers Squibb and Thermo Fisher Scientific to determine the metabolic effects of food deprivation on rats. Metabolomic LC-MS data files are extraordinarily complex and appropriate reduction of the number of spectral peaks via identification of related peaks and background removal is essential. A single component such as hippuric acid generates more than 20 related peaks including isotopic clusters, adducts and dimers. Plasma and urine may contain 500-1500 unique quantifiable metabolites. Noise filtering approaches including blank subtraction were used to reduce the number of irrelevant peaks. By grouping related signals such as isotopic peaks and alkali adducts, data processing was greatly simplified by reducing the total number of components by 10-fold. The software processes 48 samples in under 60minutes. Principle

  13. 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

  14. In-depth Proteomic Analysis of Six Types of Exudative Pleural Effusions for Nonsmall Cell Lung Cancer Biomarker Discovery*

    PubMed Central

    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

    2015-01-01

    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

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

    PubMed Central

    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.

    2015-01-01

    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. PMID:26425690

  16. From yeast to patient neurons and back again: powerful new discovery platform.

    PubMed

    Tardiff, Daniel F; Khurana, Vikram; Chung, Chee Yeun; Lindquist, Susan

    2014-09-01

    No disease-modifying therapies are available for synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple systems atrophy (MSA). The lack of therapies has been impeded by a paucity of validated drug targets and problematic cell-based model systems. New approaches are therefore needed to identify genes and compounds that directly target the underlying cellular pathologies elicited by the pathological protein, α-synuclein (α-syn). This small, lipid-binding protein impinges on evolutionarily conserved processes such as vesicle trafficking and mitochondrial function. For decades, the genetically tractable, single-cell eukaryote, budding yeast, has been used to study nearly all aspects of cell biology. More recently, yeast has revealed key insights into the underlying cellular pathologies caused by α-syn. The robust cellular toxicity caused by α-syn expression facilitates unbiased high-throughput small-molecule screening. Critically, one must validate the discoveries made in yeast in disease-relevant neuronal models. Here, we describe two recent reports that together establish yeast-to-human discovery platforms for synucleinopathies. In this exemplar, genes and small molecules identified in yeast were validated in patient-derived neurons that present the same cellular phenotypes initially discovered in yeast. On validation, we returned to yeast, where unparalleled genetic approaches facilitated the elucidation of a small molecule's mode of action. This approach enabled the identification and neuronal validation of a previously unknown "druggable" node that interfaces with the underlying, precipitating pathologies caused by α-syn. Such platforms can provide sorely needed leads and fresh ideas for disease-modifying therapy for these devastating diseases. PMID:25131316

  17. 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.

  18. Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer.

    PubMed

    Muntel, Jan; Xuan, Yue; Berger, Sebastian T; Reiter, Lukas; Bachur, Richard; Kentsis, Alex; Steen, Hanno

    2015-11-01

    The promises of data-independent acquisition (DIA) strategies are a comprehensive and reproducible digital qualitative and quantitative record of the proteins present in a sample. We developed a fast and robust DIA method for comprehensive mapping of the urinary proteome that enables large scale urine proteomics studies. Compared to a data-dependent acquisition (DDA) experiments, our DIA assay doubled the number of identified peptides and proteins per sample at half the coefficients of variation observed for DDA data (DIA = ∼8%; DDA = ∼16%). We also tested different spectral libraries and their effects on overall protein and peptide identifications and their reproducibilities, which provided clear evidence that sample type-specific spectral libraries are preferred for reliable data analysis. To show applicability for biomarker discovery experiments, we analyzed a sample set of 87 urine samples from children seen in the emergency department with abdominal pain. The whole set was analyzed with high proteome coverage (∼1300 proteins/sample) in less than 4 days. The data set revealed excellent biomarker candidates for ovarian cyst and urinary tract infection. The improved throughput and quantitative performance of our optimized DIA workflow allow for the efficient simultaneous discovery and verification of biomarker candidates without the requirement for an early bias toward selected proteins. PMID:26423119

  19. Biomarker discovery study design for type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study

    PubMed Central

    Lee, Hye-Seung; Burkhardt, Brant R.; McLeod, Wendy; Smith, Susan; Eberhard, Chris; Lynch, Kristian; Hadley, David; Rewers, Marian; Simell, Olli; She, Jin-Xiong; Hagopian, Bill; Lernmark, Ake; Akolkar, Beena; Ziegler, Anette-G.; Krischer, Jeffrey P.

    2014-01-01

    Aims: The Environmental Determinants of Diabetes in the Young (TEDDY) planned biomarker discovery studies on longitudinal samples for persistent confirmed islet cell autoantibodies and type 1 diabetes (T1D) using dietary biomarkers, metabolomics, microbiome/viral metagenomics and gene expression. Methods: This paper describes the details of planning the TEDDY biomarker discovery studies using a nested case-control design that was chosen as an alternative to the full cohort analysis. In the frame of a nested case-control design, it guides the choice of matching factors, selection of controls, preparation of external quality control samples, and reduction of batch effects along with proper sample allocation. Results and Conclusion: Our design is to reduce potential bias and retain study power while reduce the costs by limiting the numbers of samples requiring laboratory analyses. It also covers two primary end points (the occurrence of diabetes-related autoantibodies and the diagnosis of T1D). The resulting list of case-control matched samples for each laboratory was augmented with external quality control (QC) samples. PMID:24339168

  20. Study on Certain Biomarkers of Inflammation in Psoriasis Through “OMICS” Platforms

    PubMed Central

    Rodríguez-Cerdeira, C.; Molares-Vila, A.; Sánchez-Blanco, E.; Sánchez-Blanco, B.

    2014-01-01

    Background: In recent years, research on psoriasis has focused on the identification of biomarkers for the diagnosis, pathogenesis, prognosis, or therapeutic response of the disease. These studies could provide insights into the susceptibility and natural history of psoriasis. The identification of biomarkers related to comorbidities in psoriasis, such as arthritis, cardiovascular disease, and the metabolic syndrome, is of special clinical interest. Materials and Methods: We performed an extensive review on psoriasis biomarkers, including cytokine and growth factors, in the literature published between 1997 and 2013, including cross-references of any retrieved articles. We also included some data from our own studies. Results: This review presents current knowledge of soluble biomarkers in psoriasis, including cytokines, chemokines, proangiogenic mediators, growth factors, antimicrobial proteins, neuropeptides, and oxidative stress markers. Conclusion: In conclusion, a number of studies have been conducted with the aim of establishing soluble biomarkers for psoriasis. Most of the biomarkers that have been studied do not meet the criteria for a clinically useful biomarker. Further work is needed to establish a role for soluble biomarkers in the diagnosis and treatment of psoriasis, with a special focus on biomarkers for psoriasis comorbidities, such as arthritis, cardiovascular disease, and the metabolic syndrome. PMID:24688608

  1. 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

  2. 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

  3. Antibody validation of immunohistochemistry for biomarker discovery: Recommendations of a consortium of academic and pharmaceutical based histopathology researchers

    PubMed Central

    Howat, William J.; Lewis, Arthur; Jones, Phillipa; Kampf, Caroline; Pontén, Fredrik; van der Loos, Chris M.; Gray, Neil; Womack, Chris; Warford, Anthony

    2014-01-01

    As biomarker discovery takes centre-stage, the role of immunohistochemistry within that process is increasing. At the same time, the number of antibodies being produced for “research use” continues to rise and it is important that antibodies to be used as biomarkers are validated for specificity and sensitivity before use. This guideline seeks to provide a stepwise approach for the validation of an antibody for immunohistochemical assays, reflecting the views of a consortium of academic and pharmaceutical based histopathology researchers. We propose that antibodies are placed into a tier system, level 1–3, based on evidence of their usage in immunohistochemistry, and that the degree of validation required is proportionate to their place on that tier. PMID:24525140

  4. Antibody validation of immunohistochemistry for biomarker discovery: recommendations of a consortium of academic and pharmaceutical based histopathology researchers.

    PubMed

    Howat, William J; Lewis, Arthur; Jones, Phillipa; Kampf, Caroline; Pontén, Fredrik; van der Loos, Chris M; Gray, Neil; Womack, Chris; Warford, Anthony

    2014-11-01

    As biomarker discovery takes centre-stage, the role of immunohistochemistry within that process is increasing. At the same time, the number of antibodies being produced for "research use" continues to rise and it is important that antibodies to be used as biomarkers are validated for specificity and sensitivity before use. This guideline seeks to provide a stepwise approach for the validation of an antibody for immunohistochemical assays, reflecting the views of a consortium of academic and pharmaceutical based histopathology researchers. We propose that antibodies are placed into a tier system, level 1-3, based on evidence of their usage in immunohistochemistry, and that the degree of validation required is proportionate to their place on that tier. PMID:24525140

  5. 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

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

    PubMed

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

    2016-07-01

    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

  7. Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children

    PubMed Central

    West, Paul R.; Amaral, David G.; Bais, Preeti; Smith, Alan M.; Egnash, Laura A.; Ross, Mark E.; Palmer, Jessica A.; Fontaine, Burr R.; Conard, Kevin R.; Corbett, Blythe A.; Cezar, Gabriela G.; Donley, Elizabeth L. R.; Burrier, Robert E.

    2014-01-01

    Background The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. Objectives To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. Methods Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. Results A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. Conclusions This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment

  8. Proteomic profiling of human sera for discovery of potential biomarkers to monitor abstinence from alcohol abuse

    PubMed Central

    Lai, Xianyin; Liangpunsakul, Suthat; Li, Kaigang; Witzmann, Frank A.

    2015-01-01

    Although numerous biomarkers or biomarker candidates have been discovered to detect levels of drinking and intervals of time after last drinking episode, only a few biomarkers have been applied to monitor abstinence in a longer interval (≥ 6 weeks) from alcohol abuse. Considering sample sources, sensitivity, and specificity, new biomarkers from blood with better accuracy are needed. To address this, serum proteomic profiles were compared between pre- and post- treatment samples from subjects seeking treatment for alcohol abuse and dependence in an intensive 6-week daily outpatient program using high-abundance plasma protein immunodepletion and LC-MS/MS techniques. Protein identification, quantification, candidate biomarker selection, and prioritization analyses were carried out. Among the 246 quantified serum proteins, abundance of 13 and 45 proteins in female and male subjects were significantly changed (p ≤ 0.05), respectively. Of these biomarker candidate proteins, 2 (female) and 8 (male) proteins were listed in category 1, with high area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and fold change. In summary, several new biomarker candidates have been identified to monitor abstinence from alcohol abuse. PMID:25475211

  9. The future of liquid chromatography-mass spectrometry in metabolic profiling and metabolomic studies for biomarker discovery.

    SciTech Connect

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

    2007-06-01

    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 and 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.

  10. 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

  11. Open Support Platform for Environmental Research (OSPER) - tools for the discovery and exploitation of environmental data

    NASA Astrophysics Data System (ADS)

    Dawes, N. M.; Lehning, M.; Bavay, M.; Sarni, S.; Iosifescu, I.; Gwadera, R.; Scipion, D. E.; Blanchet, J.; Davison, A.; Berne, A.; Hurni, L.; Parlange, M. B.; Aberer, K.

    2012-12-01

    The Open Support Platform for Environmental Research (OSPER) has been launched to take forward key data management components developed under the Swiss Experiment platform project to achieve improved usability and a wider scope. With this project, we aim to connect users to data and their context, an area identified during SwissEx as having the greatest potential impact on the research community. OSPER has a clear focus on providing the technology for data storage, management and exploitation with a special focus on data interoperability and documentation. In this presentation, we will demonstrate the key aims of OSPER for the period 2012 - 2015. Inheriting the basic SwissEx functionality, OSPER provides an excellent method of making data accessible via their metadata. One of the biggest differences between the OSPER infrastructure and other data platforms is the level of interaction that one can have with the data and the level of integration with the analysis tools used in science. We wish to capitalise on this advantage by increasing this integration and working with environmental research projects to develop the tools that make a difference to their daily research. The new data infrastructure will serve the following purposes: ● Open documentation, archiving and discovery of datasets. ● Facilitation of data sharing and collaboration (especially inter-disciplinary) with data owner controlled access rights, particularly concentrating on providing as much contextual information as possible. ● Improvements in ease of data access and combination of data sources. ● Tools for data visualisation and statistical and numerical data analysis with a focus on spatial data and trends. Key areas identified for development during OSPER are: ● New infrastructure and content for current WebGIS-based data visualisation system to create a publicly available platform. ● Provision of data in standard formats using standard methods as well as the consumption of such data

  12. A systematic approach to biomarker discovery; Preamble to "the iSBTc-FDA taskforce on immunotherapy biomarkers"

    PubMed Central

    Butterfield, Lisa H; Disis, Mary L; Fox, Bernard A; Lee, Peter P; Khleif, Samir N; Thurin, Magdalena; Trinchieri, Giorgio; Wang, Ena; Wigginton, Jon; Chaussabel, Damien; Coukos, George; Dhodapkar, Madhav; Håkansson, Leif; Janetzki, Sylvia; Kleen, Thomas O; Kirkwood, John M; Maccalli, Cristina; Maecker, Holden; Maio, Michele; Malyguine, Anatoli; Masucci, Giuseppe; Palucka, A Karolina; Potter, Douglas M; Ribas, Antoni; Rivoltini, Licia; Schendel, Dolores; Seliger, Barbara; Selvan, Senthamil; Slingluff, Craig L; Stroncek, David F; Streicher, Howard; Wu, Xifeng; Zeskind, Benjamin; Zhao, Yingdong; Zocca, Mai-Britt; Zwierzina, Heinz; Marincola, Francesco M

    2008-01-01

    The International Society for the Biological Therapy of Cancer (iSBTc) has initiated in collaboration with the United States Food and Drug Administration (FDA) a programmatic look at innovative avenues for the identification of relevant parameters to assist clinical and basic scientists who study the natural course of host/tumor interactions or their response to immune manipulation. The task force has two primary goals: 1) identify best practices of standardized and validated immune monitoring procedures and assays to promote inter-trial comparisons and 2) develop strategies for the identification of novel biomarkers that may enhance our understating of principles governing human cancer immune biology and, consequently, implement their clinical application. Two working groups were created that will report the developed best practices at an NCI/FDA/iSBTc sponsored workshop tied to the annual meeting of the iSBTc to be held in Washington DC in the Fall of 2009. This foreword provides an overview of the task force and invites feedback from readers that might be incorporated in the discussions and in the final document. PMID:19105846

  13. 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

  14. A platform for discovery: The University of Pennsylvania Integrated Neurodegenerative Disease Biobank.

    PubMed

    Toledo, Jon B; Van Deerlin, Vivianna M; Lee, Edward B; Suh, EunRan; Baek, Young; Robinson, John L; Xie, Sharon X; McBride, Jennifer; Wood, Elisabeth M; Schuck, Theresa; Irwin, David J; Gross, Rachel G; Hurtig, Howard; McCluskey, Leo; Elman, Lauren; Karlawish, Jason; Schellenberg, Gerard; Chen-Plotkin, Alice; Wolk, David; Grossman, Murray; Arnold, Steven E; Shaw, Leslie M; Lee, Virginia M-Y; Trojanowski, John Q

    2014-07-01

    Neurodegenerative diseases (NDs) are defined by the accumulation of abnormal protein deposits in the central nervous system (CNS), and only neuropathological examination enables a definitive diagnosis. Brain banks and their associated scientific programs have shaped the actual knowledge of NDs, identifying and characterizing the CNS deposits that define new diseases, formulating staging schemes, and establishing correlations between neuropathological changes and clinical features. However, brain banks have evolved to accommodate the banking of biofluids as well as DNA and RNA samples. Moreover, the value of biobanks is greatly enhanced if they link all the multidimensional clinical and laboratory information of each case, which is accomplished, optimally, using systematic and standardized operating procedures, and in the framework of multidisciplinary teams with the support of a flexible and user-friendly database system that facilitates the sharing of information of all the teams in the network. We describe a biobanking system that is a platform for discovery research at the Center for Neurodegenerative Disease Research at the University of Pennsylvania. PMID:23978324

  15. Improved small-molecule macroarray platform for the rapid synthesis and discovery of antibacterial chalcones.

    PubMed

    Stringer, Joseph R; Bowman, Matthew D; Weisblum, Bernard; Blackwell, Helen E

    2011-03-14

    Bacterial resistance to current antibiotics is a major global health threat. Consequently, there is an urgent need for the identification of new antibacterial agents. We are applying the small-molecule macroarray platform to rapidly synthesize and screen compounds for activity against methicillin-resistant Staphylococcus aureus (MRSA). Herein, we report the synthesis of a 1,3-diphenyl-2-propen-1-one (chalcone) macroarray using a Rink-amide linker-derivatized cellulose support. The Rink linker allowed for the incorporation of a broader array of library building blocks relative to our previous syntheses because milder reaction conditions could be utilized; significantly higher compound loadings were also achieved (~80% vs ~15%). Analysis of the 174-member chalcone macroarray in off-support antibacterial screening assays revealed three chalcones with minimum inhibitory concentration (MIC) values against MRSA comparable to currently used antibacterial drugs and low hemolytic activities. These results serve to further showcase and extend the utility of the small molecule macroarray for antibacterial discovery. PMID:21210707

  16. A platform for discovery: The University of Pennsylvania Integrated Neurodegenerative Disease Biobank

    PubMed Central

    Toledo, Jon B.; Van Deerlin, Vivianna M.; Lee, Edward B.; Suh, EunRan; Baek, Young; Robinson, John L.; Xie, Sharon X.; McBride, Jennifer; Wood, Elisabeth M.; Schuck, Theresa; Irwin, David J.; Gross, Rachel G.; Hurtig, Howard; McCluskey, Leo; Elman, Lauren; Karlawish, Jason; Schellenberg, Gerard; Chen-Plotkin, Alice; Wolk, David; Grossman, Murray; Arnold, Steven E.; Shaw, Leslie M.; Lee, Virginia M.-Y.; Trojanowski, John Q.

    2014-01-01

    Neurodegenerative diseases (NDs) are defined by the accumulation of abnormal protein deposits in the central nervous system (CNS), and only neuropathological examination enables a definitive diagnosis. Brain banks and their associated scientific programs have shaped the actual knowledge of NDs, identifying and characterizing the CNS deposits that define new diseases, formulating staging schemes, and establishing correlations between neuropathological changes and clinical features. However, brain banks have evolved to accommodate the banking of biofluids as well as DNA and RNA samples. Moreover, the value of biobanks is greatly enhanced if they link all the multidimensional clinical and laboratory information of each case, which is accomplished, optimally, using systematic and standardized operating procedures, and in the framework of multidisciplinary teams with the support of a flexible and user-friendly database system that facilitates the sharing of information of all the teams in the network. We describe a biobanking system that is a platform for discovery research at the Center for Neurodegenerative Disease Research at the University of Pennsylvania. PMID:23978324

  17. Current application of proteomics in biomarker discovery for inflammatory bowel disease.

    PubMed

    Chan, Patrick Py; Wasinger, Valerie C; Leong, Rupert W

    2016-02-15

    Recently, the field of proteomics has rapidly expanded in its application towards clinical research with objectives ranging from elucidating disease pathogenesis to discovering clinical biomarkers. As proteins govern and/or reflect underlying cellular processes, the study of proteomics provides an attractive avenue for research as it allows for the rapid identification of protein profiles in a biological sample. Inflammatory bowel disease (IBD) encompasses several heterogeneous and chronic conditions of the gastrointestinal tract. Proteomic technology provides a powerful means of addressing major challenges in IBD today, especially for identifying biomarkers to improve its diagnosis and management. This review will examine the current state of IBD proteomics research and its use in biomarker research. Furthermore, we also discuss the challenges of translating proteomic research into clinically relevant tools. The potential application of this growing field is enormous and is likely to provide significant insights towards improving our future understanding and management of IBD. PMID:26909226

  18. Current application of proteomics in biomarker discovery for inflammatory bowel disease

    PubMed Central

    Chan, Patrick PY; Wasinger, Valerie C; Leong, Rupert W

    2016-01-01

    Recently, the field of proteomics has rapidly expanded in its application towards clinical research with objectives ranging from elucidating disease pathogenesis to discovering clinical biomarkers. As proteins govern and/or reflect underlying cellular processes, the study of proteomics provides an attractive avenue for research as it allows for the rapid identification of protein profiles in a biological sample. Inflammatory bowel disease (IBD) encompasses several heterogeneous and chronic conditions of the gastrointestinal tract. Proteomic technology provides a powerful means of addressing major challenges in IBD today, especially for identifying biomarkers to improve its diagnosis and management. This review will examine the current state of IBD proteomics research and its use in biomarker research. Furthermore, we also discuss the challenges of translating proteomic research into clinically relevant tools. The potential application of this growing field is enormous and is likely to provide significant insights towards improving our future understanding and management of IBD. PMID:26909226

  19. A comparative genomics approach for biomarker candidate discovery among shiga toxin-producing Escherichia coli

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Shiga toxin-producing Escherichia coli (STEC) O157:H7 and non-O157 serogroups are a common cause of outbreaks of human illness; however, few studies have systematically collected and verified reliable biomarkers to enable detection and differentiation of highly pathogenic STEC. The goal of this stu...

  20. Prostate cancer biomarker discovery using high performance mass spectral serum profiling.

    PubMed

    Oh, Jung Hun; Lotan, Yair; Gurnani, Prem; Rosenblatt, Kevin P; Gao, Jean

    2009-10-01

    Prostate-specific antigen (PSA) is the most widely used serum biomarker for early detection of prostate cancer (PCA). Nevertheless, PSA level can be falsely elevated due to prostatic enlargement, inflammation or infection, which limits the PSA test specificity. The objective of this study is to use a machine learning approach for the analysis of mass spectrometry data to discover more reliable biomarkers that distinguish PCA from benign specimens. Serum samples from 179 prostate cancer patients and 74 benign patients were analyzed. These samples were processed using ProXPRESSION Biomarker Enrichment Kits (PerkinElmer). Mass spectra were acquired using a prOTOF 2000 matrix-assisted laser desorption/ionization orthogonal time-of-flight (MALDI-O-TOF) mass spectrometer. In this study, we search for potential biomarkers using our feature selection method, the Extended Markov Blanket (EMB). From the new marker selection algorithm, a panel of 26 peaks achieved an accuracy of 80.7%, a sensitivity of 83.5%, a specificity of 74.4%, a positive predictive value (PPV) of 87.9%, and a negative predictive value (NPV) of 68.2%. On the other hand, when PSA alone was used (with a cutoff of 4.0ng/ml), a sensitivity of 66.7%, a specificity of 53.6%, a PPV of 73.5%, and a NPV of 45.4% were obtained. PMID:19423179

  1. Rapid Assimilation Platform for Insight and Discovery (RAPID) with Application to Space Weather Research

    NASA Astrophysics Data System (ADS)

    Galkin, I. A.; Bilitza, D.; Reinisch, B. W.; Grinstein, G.; Huang, X.

    2010-12-01

    Classic tasks of sensor data retrieval and assimilation into forecast models do not come easy in the Space Weather arena - the world of great distances, sparse and sporadic observations, system latencies, exigent data quality issues, and complex, cutting-edge instrumentation requiring expert operational and data analysis support. Success of the space weather endeavor critically depends on collaborative, low-latency global observations and their rapid integration with global assimilative models to provide an hour-by-hour specification of the Earth environment. The proposed Rapid Assimilation Platform for Insight and Discovery (RAPID) will make an important step forward by integrating near real-time sensor data from the Global Ionospheric Radio Observatory (GIRO) and total electron content products from the Global Navigation Satellite System (GNSS) receiver network with a global climatological model of the electron density distribution, the International Reference Ionosphere (IRI). GIRO sites are equipped with high frequency Digisonde ionospheric sounders, remote sounding instruments that specify the vertical profile of plasma density in the ionosphere at nominal 15 minute cadence. Integration of the low latency sensor data from 40+ worldwide GIRO locations and 100+ GNSS sites with the IRI model will be accomplished via a novel assimilation process and an open source Web-based Analysis and Visualization Environment (WEAVE) developed for collaborative data presentation to large audiences. Such integration accomplishes an important task of abstracting from the GIRO/GNSS observations, whose complexity requires an expert interpretation, an intuitive, global, rapid insight into the space weather conditions facilitating discovery of events in Earth’s ionospheric plasma environment. We will discuss expected challenges of assimilating GIRO and GNSS data into IRI and automated techniques suitable for RAPID applications. The topics will include development of an intelligent

  2. Implementation of proteomic biomarkers: making it work

    PubMed Central

    Mischak, Harald; Ioannidis, John PA; Argiles, Angel; Attwood, Teresa K; Bongcam-Rudloff, Erik; Broenstrup, Mark; Charonis, Aristidis; Chrousos, George P; Delles, Christian; Dominiczak, Anna; Dylag, Tomasz; Ehrich, Jochen; Egido, Jesus; Findeisen, Peter; Jankowski, Joachim; Johnson, Robert W; Julien, Bruce A; Lankisch, Tim; Leung, Hing Y; Maahs, David; Magni, Fulvio; Manns, Michael P; Manolis, Efthymios; Mayer, Gert; Navis, Gerjan; Novak, Jan; Ortiz, Alberto; Persson, Frederik; Peter, Karlheinz; Riese, Hans H; Rossing, Peter; Sattar, Naveed; Spasovski, Goce; Thongboonkerd, Visith; Vanholder, Raymond; Schanstra, Joost P; Vlahou, Antonia

    2012-01-01

    While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare. PMID:22519700

  3. 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

  4. 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 %22flexible%22 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 %242

  5. 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. PMID:25727321

  6. TOFwave: reproducibility in biomarker discovery from time-of-flight mass spectrometry data.

    PubMed

    Chierici, Marco; Albanese, Davide; Franceschi, Pietro; Furlanello, Cesare

    2012-11-01

    Many are the sources of variability that can affect reproducibility of disease biomarkers from time-of-flight (TOF) Mass Spectrometry (MS) data. Here we present TOFwave, a complete software pipeline for TOF-MS biomarker identification, that limits the impact of parameter tuning along the whole chain of preprocessing and model selection modules. Peak profiles are obtained by a preprocessing based on Continuous Wavelet Transform (CWT), coupled with a machine learning protocol aimed at avoiding selection bias effects. Only two parameters (minimum peak width and a signal to noise cutoff) have to be explicitly set. The TOFwave pipeline is built on top of the mlpy Python package. Examples on Matrix-Assisted Laser Desorption and Ionization (MALDI) TOF datasets are presented. Software prototype, datasets and details to replicate results in this paper can be found at http://mlpy.sf.net/tofwave/. PMID:22875362

  7. Synovial tissue analysis for the discovery of diagnostic and prognostic biomarkers in patients with early arthritis.

    PubMed

    de Hair, Maria J H; Harty, Leonard C; Gerlag, Danielle M; Pitzalis, Costantino; Veale, Douglas J; Tak, Paul P

    2011-09-01

    Rheumatoid arthritis (RA) is a chronic disease of unspecified etiology that is manifest by persistent inflammation of the synovium. Considerable efforts have been undertaken globally to study the microenvironment of the inflamed synovium, with many encouraging and enlightening results that bring us closer to unmasking the precise etiologies of RA. Subsequent to these efforts, it has been discovered that CD68-positive macrophages present in abundance in the synovial sublining of the inflamed synovium rescind with treatments that induce clinical improvement in RA. Examination of serial synovial biopsies is now commonly used for screening purposes during early drug development, and the number of centers able to perform synovial tissue biopsy sampling according to standardized methods is increasing. Having implemented the use of serial synovial tissue biopsies to evaluate the effects of new treatments on the group level in early proof of principle studies, it is the ambition of the OMERACT Synovial Tissue Group to identify synovial diagnostic and prognostic biomarkers that could be used in individual patients. Therefore, we started a prospective study termed the Synoviomics Project aimed at the identification of novel diagnostic and prognostic synovial biomarkers. We will use straightforward and powerful technologies to analyze patient material and assess clinical parameters to identify such biomarkers. These markers may be used in the future to identify patients who are at risk of having persistent and destructive disease and to start tailor-made targeted therapies in an early phase to prevent autonomous disease progression and irreversible joint damage. PMID:21885519

  8. Discovery of biochemical biomarkers for aggression: A role for metabolomics in psychiatry.

    PubMed

    Hagenbeek, Fiona A; Kluft, Cornelis; Hankemeier, Thomas; Bartels, Meike; Draisma, Harmen H M; Middeldorp, Christel M; Berger, Ruud; Noto, Antonio; Lussu, Milena; Pool, René; Fanos, Vassilios; Boomsma, Dorret I

    2016-07-01

    Human aggression encompasses a wide range of behaviors and is related to many psychiatric disorders. We introduce the different classification systems of aggression and related disorders as a basis for discussing biochemical biomarkers and then present an overview of studies in humans (published between 1990 and 2015) that reported statistically significant associations of biochemical biomarkers with aggression, DSM-IV disorders involving aggression, and their subtypes. The markers are of different types, including inflammation markers, neurotransmitters, lipoproteins, and hormones from various classes. Most studies focused on only a limited portfolio of biomarkers, frequently a specific class only. When integrating the data, it is clear that compounds from several biological pathways have been found to be associated with aggressive behavior, indicating complexity and the need for a broad approach. In the second part of the paper, using examples from the aggression literature and psychiatric metabolomics studies, we argue that a better understanding of aggression would benefit from a more holistic approach such as provided by metabolomics. © 2016 Wiley Periodicals, Inc. PMID:26913573

  9. Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents.

    PubMed

    Prados, Julien; Kalousis, Alexandros; Sanchez, Jean-Charles; Allard, Laure; Carrette, Odile; Hilario, Melanie

    2004-08-01

    In this paper we try to identify potential biomarkers for early stroke diagnosis using surface-enhanced laser desorption/ionization mass spectrometry coupled with analysis tools from machine learning and data mining. Data consist of 42 specimen samples, i.e., mass spectra divided in two big categories, stroke and control specimens. Among the stroke specimens two further categories exist that correspond to ischemic and hemorrhagic stroke; in this paper we limit our data analysis to discriminating between control and stroke specimens. We performed two suites of experiments. In the first one we simply applied a number of different machine learning algorithms; in the second one we have chosen the best performing algorithm as it was determined from the first phase and coupled it with a number of different feature selection methods. The reason for this was 2-fold, first to establish whether feature selection can indeed improve performance, which in our case it did not seem to confirm, but more importantly to acquire a small list of potentially interesting biomarkers. Of the different methods explored the most promising one was support vector machines which gave us high levels of sensitivity and specificity. Finally, by analyzing the models constructed by support vector machines we produced a small set of 13 features that could be used as potential biomarkers, and which exhibited good performance both in terms of sensitivity, specificity and model stability. PMID:15274126

  10. A lectin chromatography/mass spectrometry discovery workflow identifies putative biomarkers of aggressive breast cancers

    PubMed Central

    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.

    2012-01-01

    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 upregulated 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. PMID:22309216

  11. Ensuring Sample Quality for Biomarker Discovery Studies - Use of ICT Tools to Trace Biosample Life-cycle.

    PubMed

    Riondino, Silvia; Ferroni, Patrizia; Spila, Antonella; Alessandroni, Jhessica; D'Alessandro, Roberta; Formica, Vincenzo; Della-Morte, David; Palmirotta, Raffaele; Nanni, Umberto; Roselli, Mario; Guadagni, Fiorella

    2015-01-01

    The growing demand of personalized medicine marked the transition from an empirical medicine to a molecular one, aimed at predicting safer and more effective medical treatment for every patient, while minimizing adverse effects. This passage has emphasized the importance of biomarker discovery studies, and has led sample availability to assume a crucial role in biomedical research. Accordingly, a great interest in Biological Bank science has grown concomitantly. In biobanks, biological material and its accompanying data are collected, handled and stored in accordance with standard operating procedures (SOPs) and existing legislation. Sample quality is ensured by adherence to SOPs and sample whole life-cycle can be recorded by innovative tracking systems employing information technology (IT) tools for monitoring storage conditions and characterization of vast amount of data. All the above will ensure proper sample exchangeability among research facilities and will represent the starting point of all future personalized medicine-based clinical trials. PMID:26543078

  12. Systematic biomarker discovery and coordinative validation for different primary nephrotic syndromes using gas chromatography-mass spectrometry.

    PubMed

    Lee, Jung-Eun; Lee, Yu Ho; Kim, Se-Yun; Kim, Yang Gyun; Moon, Ju-Young; Jeong, Kyung-Hwan; Lee, Tae Won; Ihm, Chun-Gyoo; Kim, Sooah; Kim, Kyoung Heon; Kim, Dong Ki; Kim, Yon Su; Kim, Chan-Duck; Park, Cheol Whee; Lee, Do Yup; Lee, Sang-Ho

    2016-07-01

    The goal of this study is to identify systematic biomarker panel for primary nephrotic syndromes from urine samples by applying a non-target metabolite profiling, and to validate their utility in independent sampling and analysis by multiplex statistical approaches. Nephrotic syndrome (NS) is a nonspecific kidney disorder, which is mostly represented by minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and membranous glomerulonephritis (MGN). Since urine metabolites may mirror disease-specific functional perturbations in kidney injury, we examined urine samples for distinctive metabolic changes to identify biomarkers for clinical applications. We developed unbiased multi-component covarianced models from a discovery set with 48 samples (12 healthy controls, 12 MCD, 12 FSGS, and 12 MGN). To extensively validate their diagnostic potential, new batch from 54 patients with primary NS were independently examined a year after. In the independent validation set, the model including citric acid, pyruvic acid, fructose, ethanolamine, and cysteine effectively discriminated each NS using receiver operating characteristic (ROC) analysis except MCD-MGN comparison; nonetheless an additional metabolite multi-composite greatly improved the discrimination power between MCD and MGN. Finally, we proposed the re-constructed metabolic network distinctively dysregulated by the different NSs that may deepen comprehensive understanding of the disease mechanistic, and help the enhanced identification of NS and therapeutic plans for future. PMID:27247212

  13. Discovery of human urinary biomarkers of aronia-citrus juice intake by HPLC-q-TOF-based metabolomic approach.

    PubMed

    Llorach, Rafael; Medina, Sonia; García-Viguera, Cristina; Zafrilla, Pilar; Abellán, José; Jauregui, Olga; Tomás-Barberán, Francisco A; Gil-Izquierdo, Angel; Andrés-Lacueva, Cristina

    2014-06-01

    Metabolomics has emerged in the field of food and nutrition sciences as a powerful tool for doing profiling approaches. In this context, HPLC-q-TOF-based metabolomics approach was applied to unveil changes in the urinary metabolome in human subjects (n = 51, 23 men and 28 women) after regular aronia-citrus juice (AC-juice) intake (250 mL/day) during 16 weeks compared to individuals given a placebo beverage. Samples were analyzed by HPLC-q-TOF followed by multivariate data analysis (orthogonal signal filtering-partial least square discriminant analysis) that discriminated relevant mass features related to AC-juice intake. The results showed that biomarkers of AC-juice intake including metabolites coming from metabolism of food components as proline betaine, ferulic acid, and two unknown mercapturate derivatives were identified. Discovery of new biomarkers of food intake will help in the building up of the food metabolome and facilitate future insights into the mechanisms of action of dietary components in population health. PMID:24616048

  14. Nanostructured Aptamer-Functionalized Black Phosphorus Sensing Platform for Label-Free Detection of Myoglobin, a Cardiovascular Disease Biomarker.

    PubMed

    Kumar, Vinod; Brent, Jack R; Shorie, Munish; Kaur, Harmanjit; Chadha, Gaganpreet; Thomas, Andrew G; Lewis, Edward A; Rooney, Aidan P; Nguyen, Lan; Zhong, Xiang Li; Burke, M Grace; Haigh, Sarah J; Walton, Alex; McNaughter, Paul D; Tedstone, Aleksander A; Savjani, Nicky; Muryn, Christopher A; O'Brien, Paul; Ganguli, Ashok K; Lewis, David J; Sabherwal, Priyanka

    2016-09-01

    We report the electrochemical detection of the redox active cardiac biomarker myoglobin (Mb) using aptamer-functionalized black phosphorus nanostructured electrodes by measuring direct electron transfer. The as-synthesized few-layer black phosphorus nanosheets have been functionalized with poly-l-lysine (PLL) to facilitate binding with generated anti-Mb DNA aptamers on nanostructured electrodes. This aptasensor platform has a record-low detection limit (∼0.524 pg mL(-1)) and sensitivity (36 μA pg(-1) mL cm(-2)) toward Mb with a dynamic response range from 1 pg mL(-1) to 16 μg mL(-1) for Mb in serum samples. This strategy opens up avenues to bedside technologies for multiplexed diagnosis of cardiovascular diseases in complex human samples. PMID:27508925

  15. 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

  16. Proteomic Analysis of Temporally Stimulated Ovarian Cancer Cells for Biomarker Discovery*

    PubMed Central

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

    2013-01-01

    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 EGF1, 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 monoclonal

  17. 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

  18. The use of metabolomics for the discovery of new biomarkers of effect.

    PubMed

    van Ravenzwaay, B; Cunha, G Coelho-Palermo; Leibold, E; Looser, R; Mellert, W; Prokoudine, A; Walk, T; Wiemer, J

    2007-07-30

    Will metabolomics have a greater chance of success in toxicology and biomarker assessment than genomics and proteomics? Metabolomics has the advantage that (1) it analyses the last step in a series of changes following a toxic insult, (2) many of the metabolites have a known function and (3) changes are detectable in blood. If the analysis of a great number of individual organs can be replaced by one matrix then this will provide significant advantages (less invasive method, no need to kill animals, time course analysis possible). We have chosen to perform the analysis of blood metabolites in such a way as to minimize the risk of artifacts and to have a high number of known metabolites. We have also reduced the amount of variation in the biological system as well as during analysis. In a series of proof of concept studies it could be demonstrated that (1) the metabolome of control animals was stable of a period of nearly 1 year, with a remarkable differentiation between males and females, (2) a dose response relationship in metabolome changes was induced by phenobarbital and that (3) different modes of action could be distinguished by blood metabolome analysis. To investigate the potential of metabolomics to find biomarkers or specific patterns of change we have analyzed the blood metabolome of rats treated with HPPD inhibitors, a novel class of herbicides. The results demonstrated that a single metabolite, tyrosine, can be used as a biomarker. In addition to tyrosine we also found a specific pattern of change that involved nine metabolites. Though the extent of change was less than for tyrosine the consistent change of these metabolites is diagnostic for this (toxicological) mode of action. PMID:17614222

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

    PubMed

    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

  20. 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

  1. Integration of metabolomics and proteomics in multiple sclerosis: From biomarkers discovery to personalized medicine.

    PubMed

    Del Boccio, Piero; Rossi, Claudia; di Ioia, Maria; Cicalini, Ilaria; Sacchetta, Paolo; Pieragostino, Damiana

    2016-04-01

    Personalized medicine is the science of individualized prevention and therapy. In the last decade, advances in high-throughput approaches allowed the development of proteomic and metabolomic studies in evaluating the association of genetic and phenotypic variability with disease sensitivity and analgesic response. These considerations have more value in case of multiple sclerosis (MuS), a multifactorial disease with high heterogeneity in clinical course and treatment response. In this review, we reported and updated about proteomic and metabolomic studies for the research of new candidate biomarkers in MuS, and difficulties in their clinical applications. We focused especially on the description of both "omics" approaches that, once integrated, may synergically describe pathophysiology conditions. To prove this assumption, we rebuilt interaction between proteins and metabolites described in the literature as potential biomarkers for MuS, and a pathway analysis of these molecules was performed. The result of such speculation demonstrated a strong convergence of proteomic and metabolomic results in this field, showing also a poorness of available tools for incorporating "omics" approaches. In conclusion, the integration of Metabolomics and Proteomics may allow a more complete characterization of such a heterogeneous disease, providing further insights into personalized healthcare. PMID:27061322

  2. 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

  3. 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. PMID:27082433

  4. 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

  5. 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

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

    2012-02-01

    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 (r(2) = 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

  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. 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

  8. 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. PMID:25388014

  9. 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

    2016-02-15

    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. PMID:26884103

  10. 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.