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.     

Joint discriminative and collaborative representation for fatty liver disease diagnosis

Many people suffer from the Fatty Liver disease due to the changes in diet and lifestyle, and the convenient diagnosis of it has attracted many attentions in recent years. The computerized tongue or facial diagnosis as an important diagnostic tool provides a possible way to detect the disease in the daily life. Most of existing approaches only takes a single modality (e.g., tongue or face) into account, although various modalities would contribute complementary information which is beneficial for the improvement of the diagnosis accuracy. To circumvent this issue, a novel multi-modal fusion method is presented in this paper. Particularly, a noninvasive capture device is first used to captured the tongue and facial images, followed by the feature extraction. Our so-called joint discriminative and collaborative representation approach is then proposed to not only reveal the correlation between the tongue and facial images, but also keep the discriminative representation of each class simultaneously. To optimize the proposed method, an efficient algorithm is proposed, obtaining a closed-form solution and greatly reducing the computation. In identification of the Fatty Liver Disease for healthy controls, the proposed multi-modal fusion approach achieves 85.10% in average accuracy and 0.9363 in the area under ROC curve, which obviously outperform the case of using a single modality and some state-of-the-art methods.     

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.     

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.     

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.     

Type 2 diabetes: Gaining insight into the disease process using proteomics

The incidence of diabetes mellitus is growing rapidly, with an increasing disease related morbidity and mortality. This is caused by macro- and microvascular complications, as a consequence of the often late diagnosis of type 2 diabetes (T2D), but especially by the difficulties to control glucose homeostasis due to the progressive nature of the disease. T2D is moreover a dual disease, with components of beta-cell failure and components of insulin resistance in peripheral organs, such as liver, fat, and muscle. Understanding the pathogenesis of the disease by gaining insight into the molecular pathways involved in both phenomena is one of the major assets of proteomic approaches. Moreover, proteomics and peptidomics may provide us with robust biomarkers for beta-cell failure, insulin resistance in pheripheral organs, but also for the development of diabetic complications. This review focuses on the knowledge gained by use of proteomic and peptidomic techniques in the study of the pathophysiology of T2D and in the attempts to discover new therapeutic targets.     

Vascular proteomics

The characterization of patients with acute coronary syndromes (ACS) at the molecular and cellular levels provides a novel vision for understanding the pathological and clinical expression of the disease. Recent advances in proteomic technologies permit the evaluation of systematic changes in protein expression in many biological systems and have been extensively applied to cardiovascular diseases (CVD). The cardiovascular system is in permanent intimate contact with blood, making blood-based biomarker discovery a particularly worthwhile approach. Thus, proteomics can potentially yield novel biomarkers reflecting CVD, establish earlier detection strategies, and monitor response to therapy. Here we review the different proteomic strategies used in the study of atherosclerosis and the novel proteins differentially expressed and secreted by atherosclerotic lesions which constitute novel potential biomarkers (HSP-27, Cathepsin D). Special attention is paid to MS-Imaging of atheroma plaque and the generation, for the first time, of 2-D images of lipids, showing the distribution of these molecules in the different areas of the atherosclerotic lesions. In addition new potential biomarkers have been identified in plasma (amyloid A1α, transtherytin), circulating cells (protein profile in monocytes from ACS patients) and individual cells constituents of atheroma plaques (endothelial, VSMC, macrophages) which provide novel insights into vascular pathophysiology.     

Urine proteomics for profiling of human disease using high accuracy mass spectrometry

Knowledge of the biologically relevant components of human tissues has enabled the invention of numerous clinically useful diagnostic tests, as well as non-invasive ways of monitoring disease and its response to treatment. Recent use of advanced MS-based proteomics revealed that the composition of human urine is more complex than anticipated. Here, we extend the current characterization of the human urinary proteome by extensively fractionating urine using ultra-centrifugation, gel electrophoresis, ion exchange and reverse-phase chromatography, effectively reducing mixture complexity while minimizing loss of material. By using high-accuracy mass measurements of the linear ion trap-Orbitrap mass spectrometer and LC-MS/MS of peptides generated from such extensively fractionated specimens, we identified 2362 proteins in routinely collected individual urine specimens, including more than 1000 proteins not described in previous studies. Many of these are biomedically significant molecules, including glomerularly filtered cytokines and shed cell surface molecules, as well as renally and urogenitally produced transporters and structural proteins. Annotation of the identified proteome reveals distinct patterns of enrichment, consistent with previously described specific physiologic mechanisms, including 336 proteins that appear to be expressed by a variety of distal organs and glomerularly filtered from serum. Comparison of the proteomes identified from 12 individual specimens revealed a subset of generally invariant proteins, as well as individually variable ones, suggesting that our approach may be used to study individual differences in age, physiologic state and clinical condition. Consistent with this, annotation of the identified proteome by using machine learning and text mining exposed possible associations with 27 common and more than 500 rare human diseases, establishing a widely useful resource for the study of human pathophysiology and biomarker discovery.     

Rule extraction for fatty liver detection using neural networks

Neural Computing and Applications - Non-alcoholic fatty liver disease (NAFLD) is one of the most common diseases in the world. Recently the FibroScan device is used as a noninvasive, yet costly...     

An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images

Alcoholic and non-alcoholic fatty liver disease is one of the leading causes of chronic liver diseases and mortality in Western countries and Asia. Ultrasound image assessment is most commonly and widely used to identify the Non-Alcoholic Fatty Liver Disease (NAFLD). It is one of the faster and safer non-invasive methods of NAFLD diagnosis available in imaging modalities. The diagnosis of NAFLD using biopsies is expensive, invasive, and causes anxiety to the patients. The advent of advanced image processing and data mining techniques have helped to develop faster, efficient, objective, and accurate decision support system for fatty liver disease using ultrasound images. This paper proposes a novel feature extraction models based on Radon Transform (RT) and Discrete Cosine Transform (DCT). First, Radon Transform (RT) is performed on the ultrasound images for every 1 degree to capture the low frequency details. Then 2D-DCT is applied on the Radon transformed image to obtain the frequency features (DCT coefficients). Further the 2D-DCT frequency coefficients (features) obtained are converted to 1D coefficients vector in zigzag fashion. This 1D array of DCT coefficients are subjected to Locality Sensitive Discriminant Analysis (LSDA) to reduce the number of features. Then these features are ranked using minimum Redundancy and Maximum Relevance (mRMR) ranking method. Finally, highly ranked minimum numbers of features are fused using Decision Tree (DT), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Support Vector Machine (SVM), Fuzzy Sugeno (FS) and AdaBoost classifiers to get the highest classification performance. In this work, we have obtained an average accuracy, sensitivity and specificity of 100% in the detection of NAFLD using FS classifier. Also, we have devised an integrated index named as Fatty Liver Disease Index (FLDI) by fusing two significant LSDA components to distinguish normal and FLD class with single number.     

Gastrointestinal fluids proteomics

Seventy million people suffer from diseases of the gastrointestinal tract annually in US, translating to US$85.5 billion in direct healthcare costs. The debilitating effects of these gastrointestinal (GI) diseases can be circumvented with good biomarkers for early detection of these disorders, which will greatly increase the success of curative treatments. GI fluids represent a potential reservoir of biomarkers for early diagnosis of various GI and systemic diseases since these fluids are the most proximal fluid bathing diseased cells. They are anticipated to have proteomes that closely reflect the ensemble of proteins secreted from the respective GI tissues. Most importantly, the disease markers present in GI fluids should be present in higher concentrations than in sera, thus offering greater sensitivity in their detection. However, proteome analysis of GI fluids can be complex mainly due to the dynamic range of protein content and the numerous PTMs of proteins in each specialized GI compartment. This review attempts to discuss the physiology of the various GI fluids, the special technical considerations required for proteome analysis of each fluid, as well as to summarize the current state of knowledge of biomarker discoveries and clinical utility of GI fluids such as salivary, gastric, pancreatic, and biliary secretions.     

Translational vitreous proteomics

The vitreous is an extracellular matrix that is still poorly understood. Although many constituents and characteristics have been previously determined, there are many attributes still being discovered. Currently, using protein arrays, MS, and bioinformatics, the vitreous provides a wealth of knowledge regarding ocular diseases and potential targets for personalized therapeutics.     

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.     

Recent progress in urinary proteomics

Urinary proteomics has become one of the most attractive subdisciplines in clinical proteomics as the urine is an ideal source for the discovery of noninvasive biomarkers for kidney and nonkidney diseases. This field has been growing rapidly as indicated by >80 original research articles on urinary proteome analyses appearing since 2001, of which 28 (approximately 1/3) had been published within the year 2006. The most common technologies used in recent urinary proteome studies remain gel-based methods (1-DE, 2-DE and 2-D DIGE), whereas LC-MS/MS, SELDI-TOF MS, and CE-MS are other commonly used techniques. In addition, mass spectrometric immunoassay (MSIA) and array technology have also been applied. This review provides an extensive but concise summary of recent applications of urinary proteomics. Proteomic analyses of dialysate and ultrafiltrate fluids derived from renal replacement therapy (or artificial kidney) are also discussed.     

Clinical proteomics in neurodegenerative diseases

Investigation of the human specimens is an essential element for understanding the pathogenesis of neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. The studies hold promise for identifying biomarkers for diagnosis and prognosis, elucidating disease mechanisms, and accelerating the development of new strategies for therapeutic intervention. Here, we review proteomics studies of human brain samples in light of recent advances of mass spectrometry, focusing on the general strategies for experimental design and analysis (e.g., sample pooling and replication, selection of proteomics platforms, and false discovery rate in data processing), because quantitative analysis of clinical samples is confounded by a number of variables, including genetic differences, antemortem and postmortem factors, and experimental errors. Diverse proteomics platforms are also discussed with respect to sensitivity, throughput, and accuracy. Regarding the enormous complexity of the human brain and the limitation of current proteomics technologies, it may be more practical to analyze a subset of proteome in a functional context, in order to facilitate the identification of important disease-related proteins in the substantial noise reflecting biological and technical variances.