Targeted proteomics strategy applied to biomarker evaluation

The evaluation of biomarker candidates, involving quantitative measurement of a large number of proteins in bodily fluids, remains the main obstruction in the development of a biomarker validation pipeline. Although immunoassays are commonly used, high-throughput and multiplex-capable methods are required for expediting the evaluation process. MS-based approaches employing targeted proteomic strategies provide not only a sensitive, but in addition a precise quantification tool, which is versatile, systematic, and scalable. Its capability of multiplexing hundreds of targets facilitate a cost-effective and rapid evaluation and is especially useful during the early stage of the process where a large list of candidate biomarkers must be triaged before entering validation studies. The robustness requirement for the methods also mandates a high degree of selectivity to analyze complex clinical samples. Improvement in the selectivity of LC-MS methods has been achieved by adopting high-resolution and high-accuracy mass analyzers to perform quantitative analyses with a novel method called parallel reaction monitoring. This article discusses the design and performance of biomarker evaluation studies using targeted proteomics strategies and the implementation of recent technology developments.     

Cardiovascular proteomics: translational studies to develop novel biomarkers in heart failure and left ventricular remodeling

Heart failure (HF) remains a severe disease with a poor prognosis. HF biomarkers may include demographic features, cardiac imaging, or genetic polymorphisms but this term is commonly applied to circulating serum or plasma analytes. Biomarkers may have at least three clinical uses in the context of HF: diagnosis, risk stratification, and guidance in the selection of therapy. Proteomic studies on HF biomarkers can be designed as case/control using clinical endpoints; alternatively, left ventricular remodeling can be used as a surrogate endpoint. The type of samples (tissue, cells, serum or plasma) used for proteomic analysis is a key factor in the research of biomarkers. Since the final aim is the discovery of circulating biomarkers, and since plasma and serum samples are easily accessible, proteomic analysis is frequently used for blood samples. However, standardization of sampling and access to low-abundance proteins remains problematic. Although, proteomics is playing a major role in the discovery phase of biomarkers, validation in independent populations is necessary by using more specific methods. The knowledge of new HF biomarkers may allow a more personalized medicine in the future.     

Lysosomal proteomics and disease

A recent trend in proteomic studies has been to analyze macromolecular complexes such as subcellular organelles instead of complete cells or tissues. This "divide and conquer" approach circumvents some of the formidable problems associated with whole proteome analyses and allows focus on a subset of proteins that may be involved in a particular process or disease of interest. One organelle that has been the focus of considerable attention in proteomic studies is the lysosome, an acidic, membrane-delimited compartment that plays an essential role in the degradation and recycling of biological macromolecules. Lysosomal proteomics have been driven in part by the well-established involvement of this organelle in numerous human diseases, but also by the availability of approaches to selectively visualize and/or isolate subsets of lysosomal proteins. In terms of clinical application, proteomic studies of the lysosome have led to the identification of gene defects in three human hereditary diseases. This review summarizes past progress, current limitations and future directions in the field of lysosomal proteomics.     

Modeling of liver metastatic disease with applied drug therapy

Colorectal carcinoma is acknowledged as the second leading cause of total cancer-related death in the European Region. The majority of deaths related to colorectal carcinoma are connected with liver metastatic disease. Approximately, in 25% of all patients, liver metastatic disease is diagnosed at the same time as the primary diagnosis, while up to a quarter of others would develop liver metastases in the course of the illness. In this study, we developed reaction-diffusion model and analyzed the effect of drug therapy on liver metastatic disease for a specific patient. Tumor volumes in specific time points were obtained using CT scan images. The nonlinear function for cell proliferation rate as well as data about clinically applied drug therapy was included in the model. Fitting procedure was used for parameter estimation. Good agreement of numerical and experimental results shows the feasibility and efficacy of the proposed system.     

Degradative proteomics and disease mechanisms

Protein degradation is a fundamental biological process, which is essential for the maintenance and regulation of normal cellular function. In humans and animals, proteins can be degraded by a number of mechanisms: the ubiquitin-proteasome system, autophagy and intracellular proteases. The advances in contemporary protein analysis means that proteomics is increasingly being used to explore these key pathways and as a means of monitoring protein degradation. The dysfunction of protein degradative pathways has been associated with the development of a number of important diseases including cancer, muscle wasting disorders and neurodegenerative diseases. This review will focus on the role of proteomics to study cellular degradative processes and how these strategies are being applied to understand the molecular basis of diseases arising from disturbances in protein degradation.     

The proteomics of cancer stem cells: potential clinical applications for innovative research in oncology

Cancer stem cells (CSCs) or tumour-maintaining cells are becoming an important new reality in oncology. The intriguing molecular pathophysiology of CSCs may justify some of the obscure pathogenetic, diagnostic, prognostic, and above all, therapeutic aspects of cancer and, eventually, lead to new solutions in oncology. CSC is a cell within the tumour that possesses the capacity to self-renew and, in doing so, gives rise to the heterogeneous lineages that comprise the tumour. The precise identification of this peculiar subpopulation of cancer cells, which has some intriguing similarities to normal stem cells, is becoming an important and urgent topic in oncology. In fact, some debated CSC markers have been already adopted by pharmacological research as targets of new and/or old anticancer drugs, showing an intriguing therapeutic index. These discussed identification markers include cell surface proteins, different activated signalling pathways, several molecules of the stem cell niche, various drug resistance mechanisms (ABCG2 and ALDH), telomerase, oncogenes and oncosuppressors (p16INK4 - Rb) and lastly, various microRNAs. In this new promising area of cancer research, proteomics, in general, and oncoproteomics, in particular, can and must play a significant role if the methodological approaches and the experimental protocols are correctly designed and interpreted.     

Quantitative proteomics in cardiovascular research: Global and targeted strategies

Extensive technical advances in the past decade have substantially expanded quantitative proteomics in cardiovascular research. This has great promise for elucidating the mechanisms of cardiovascular diseases and the discovery of cardiac biomarkers used for diagnosis and treatment evaluation. Global and targeted proteomics are the two major avenues of quantitative proteomics. While global approaches enable unbiased discovery of altered proteins via relative quantification at the proteome level, targeted techniques provide higher sensitivity and accuracy, and are capable of multiplexed absolute quantification in numerous clinical/biological samples. While promising, technical challenges need to be overcome to enable full utilization of these techniques in cardiovascular medicine. Here, we discuss recent advances in quantitative proteomics and summarize applications in cardiovascular research with an emphasis on biomarker discovery and elucidating molecular mechanisms of disease. We propose the integration of global and targeted strategies as a high-throughput pipeline for cardiovascular proteomics. Targeted approaches enable rapid, extensive validation of biomarker candidates discovered by global proteomics. These approaches provide a promising alternative to immunoassays and other low-throughput means currently used for limited validation.     

虚拟专用网及其应用研究

本文主要介绍虚拟专用网的基本概念、特点,虚拟专用网的相关技术及解决方案,以及企业构建虚拟专用网的基本思路。     

虚拟专用网及其应用研究

本文主要介绍虚拟专用网的基本概念、特点,虚拟专用网的相关技术及解决方案,以及企业构建虚拟专用网的基本思路。     

Automated speech analysis applied to laryngeal disease categorization

The long-term goal of the work is a decision support system for diagnostics of laryngeal diseases. Colour images of vocal folds, a voice signal, and questionnaire data are the information sources to be used in the analysis. This paper is concerned with automated analysis of a voice signal applied to screening of laryngeal diseases. The effectiveness of 11 different feature sets in classification of voice recordings of the sustained phonation of the vowel sound /a/ into a healthy and two pathological classes, diffuse and nodular, is investigated. A k-NN classifier, SVM, and a committee build using various aggregation options are used for the classification. The study was made using the mixed gender database containing 312 voice recordings. The correct classification rate of 84.6% was achieved when using an SVM committee consisting of four members. The pitch and amplitude perturbation measures, cepstral energy features, autocorrelation features as well as linear prediction cosine transform coefficients were amongst the feature sets providing the best performance. In the case of two class classification, using recordings from 79 subjects representing the pathological and 69 the healthy class, the correct classification rate of 95.5% was obtained from a five member committee. Again the pitch and amplitude perturbation measures provided the best performance.     

Challenges and opportunities for cell line secretomes in cancer proteomics

Cancer cell lines are the most widely used experimental models in cancer research. Their advantages of easy growth and manipulation are unfortunately paralleled by their limitations derived from long-term growth in isolation from the rest of the tumor, and hence, lack of tumor microenvironment. We are however currently witnessing novel and transformative advances that are making cell lines more reflective of the human biology and therefore, better experimental models for cancer research. Beyond the experimental model used, the choice of cellular proteome is key in proteomics-based biomarker discovery. Over the last decade, cell line secretomes have been proposed as an alternative for tumor biomarker discovery due to the difficulties posed by plasma in terms of complexity and low abundance of tumor-specific biomarkers. Cell line secretomes are enriched with proteins already linked to tumorigenesis, which also have a good chance of being present in biological fluids. In this review, we will provide an overview of the main technical and biological issues related to cell line secretome analysis, and briefly discuss both the challenges and opportunities in its use for tumor biomarker discovery.     

Contribution of proteomics to the study of the role of cytokeratins in disease and physiopathology

Cytokeratins (CKs), the most abundant group of cytoskeletal intermediate filaments, and proteomics are strongly connected. On the one hand, proteomics has been extremely useful to uncover new features and functions of CKs, on the other, the highly abundant CKs serve as an exceptional tool to test new technological developments in proteomics. As a result, proteomics has contributed to finding valuable associations of CKs with diseases as diverse as cancer, cystic fibrosis, steatohepatitis, viral and bacterial infection, keratoconus, vitreoretinopathy, preeclampsia or the chronic fatigue syndrome, as well as to characterizing their participation in a number of physiopathological processes, including drug resistance, response to toxicants, inflammation, stem cell differentiation, embryo development, and tissue repair. In some cases, like in cystic fibrosis, CKs have been described as potential therapeutic targets. The development of a specific field of proteomics where CKs become the main subject of research aims and hypotheses is suggested.     

MVTU software package in scientific research and applied developments

The features and basic functionality of the MVTU software package are considered. The software is intended for the research and design of a wide range of systems described by differential, algebraic, and difference equations. Examples of the solutions of the research and applied problems are given.     

Urinary proteomics in cardiovascular disease: Achievements, limits and hopes

Cardiovascular disease (CVD) is the major cause of mortality and morbidity worldwide. Diagnosis of CVD and risk stratification of patients with CVD remains challenging despite the availability of a wealth of non-invasive and invasive tests. Clinical proteomics analyses a large number of peptides and proteins in biofluids. For clinical applications, the urinary proteome appears particularly attractive due to the relative low complexity compared with the plasma proteome and the noninvasive collection of urine. In this article, we review the results from pilot studies into urinary proteomics of coronary artery disease and discuss the potential of urinary proteomics in the context of pathogenesis of CVD.     

Colorectal cancer proteomics, molecular characterization and biomarker discovery

Colorectal cancer (CRC) is a widespread disease, whose major genetic changes and mutations have been well characterized in the sporadic form. Much less is known at the protein and proteome level. Still, CRC has been the subject of multiple proteomic studies due to the urgent necessity of finding clinically relevant markers and to elucidate the molecular mechanisms underlying the progression of the disease. These proteomic approaches have been limited by different technical issues, mainly related with sensitivity and reproducibility. However, recent advances in proteomic techniques and MS systems have rekindled the quest for new biomarkers in CRC and an improved molecular characterization. In this review, we will discuss the application of different proteomic approaches to the identification of differentially expressed proteins in CRC. In particular, we will make a critical assessment about the use of 2-D DIGE, MS and protein microarray technologies, in their different formats, to identify up- or downregulated proteins and/or autoantibodies profiles that could be useful for CRC characterization and diagnosis. Despite a wide list of potential biomarkers, it is clear that more scientific efforts and technical advances are still needed to cover the range of low-abundant proteins, which may play a key role in CRC diagnostics and progression.     

计算机视觉技术在齿轮测量中的应用研究

齿轮作为传递运动和动力的基础零件,在工业发展的历程中,一直发挥着十分重要的作用。通过研究,在齿轮测量中适当地引入了计算机视觉技术,探讨和分析了相应的计算机视觉测量系统及其实现。该方法具有较高的测量精度和测量效率,操作简便,测量项目多,是一种具有发展前途和应用前景的非接触测量技术,必将推动齿轮测量技术和齿轮工业的进步和发展。     

Coupling enrichment methods with proteomics for understanding and treating disease

Owing to recent advances in proteomics analytical methods and bioinformatics capabilities there is a growing trend toward using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of drug action, and biomarker discovery. Currently, the genetic sequences of many major organisms are available, which have helped greatly in characterizing proteomes in model animal systems and humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as changes in PTMs such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the drug targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular disease, and neurological conditions, and in the design and development of better medicines for treating these afflictions.     

Hybrid dendritic computing with kernel-LICA applied to Alzheimer's disease detection in MRI

Dendritic computing has been proved to produce perfect approximation of any data distribution. This result guarantees perfect accuracy training. However, we have found great performance degradation when tested on conventional k-fold cross-validation schemes. In this paper we propose to use Lattice Independent Component Analysis (LICA) and the Kernel transformation of the data as an appropriate feature extraction that improves the generalization of dendritic computing classifiers. We obtain a big increase in classification performance applying with this schema over a database of features extracted from Magnetic Resonance Imaging (MRI) including Alzheimer's disease (AD) patients and control subjects.