Application of quantitative proteomics technologies to the biomarker discovery pipeline for multiple sclerosis

Multiple sclerosis is an inflammatory-mediated demyelinating disorder most prevalent in young Caucasian adults. The various clinical manifestations of the disease present several challenges in the clinic in terms of diagnosis, monitoring disease progression and response to treatment. Advances in MS-based proteomic technologies have revolutionized the field of biomarker research and paved the way for the identification and validation of disease-specific markers. This review focuses on the novel candidates discovered by the application of quantitative proteomics to relevant disease-affected tissues in both the human context and within the animal model of the disease known as experimental autoimmune encephalomyelitis. The role of targeted MS approaches for biomarker validation studies, such as multiple reaction monitoring will also be discussed.     

Molecular interaction networks in the analyses of sequence variation and proteomics data

Protein–protein interaction networks are typically generated in standard cell lines or model organisms as it is prohibitively difficult to record large interaction datasets from specific tissues or disease models at a reasonable pace. Although the interaction data are of high confidence, they thus do not reflect in vivo relationships as such. A wealth of physiologically relevant protein information, obtained under different conditions and from different systems, is available including information on genetic variation, protein levels, and PTMs. However, these data are difficult to assess comprehensively because the relationships between the entities remain elusive from the measurements. Here, we exemplarily highlight recent studies that gained deeper insight from genetic variation, protein, and PTM measurements using interaction information pointing toward the importance and potential of interaction networks for the interpretation of sequencing and proteomics data.     

Agent-based modeling of host-pathogen systems: The successes and challenges

Agent-based models have been employed to describe numerous processes in immunology. Simulations based on these types of models have been used to enhance our understanding of immunology and disease pathology. We review various agent-based models relevant to host-pathogen systems and discuss their contributions to our understanding of biological processes. We then point out some limitations and challenges of agent-based models and encourage efforts towards reproducibility and model validation.     

Proteomics as a research tool in clinical and experimental ophthalmology

It is estimated that 37 million people worldwide suffer from blindness and 124 million people have impaired vision. While the relatively recently developed therapies, antivascular endothelial growth factor inhibitors for the treatment of age-related macular degeneration, and prostaglandin analogues for the treatment of glaucoma are beneficial for some patients, there are many individuals with sight-threatening diseases for whom no effective pharmacological therapy is available. For many of these diseases, the molecular mechanisms remain to be comprehensively elucidated, thus precluding the design of successful therapies against specific pathological targets. The current review summarises recent attempts to elucidate molecular mechanisms of ocular diseases, including diabetic retinal disease, age-related macular degeneration and inherited blindness using proteomic methodologies. A novel hypothesis can be generated from global protein expression analysis of disease tissue, which can then be addressed with cellular and in vivo functional studies. For example, the identification of extracellular carbonic anhydrase from the vitreous of diabetic retinopathy patients using MS based proteomics led to the elucidation of a new pathway involved in intraretinal edema, which could be inhibited by a number of agents targeting different proteins in this pathway in relevant animal models. The potential of protein biomarkers for diagnosis and the identification of novel disease mechanisms are also discussed.     

Proteomics of pulmonary hypertension: Could personalized profiles lead to personalized medicine?

Pulmonary hypertension (PH) is a fatal syndrome that arises from a multifactorial and complex background, is characterized by increased pulmonary vascular resistance and right heart afterload, and often leads to cor pulmonale. Over the past decades, remarkable progress has been made in reducing patient symptoms and delaying the progression of the disease. Unfortunately, PH remains a disease with no cure. The substantial heterogeneity of PH continues to be a major limitation to the development of newer and more efficacious therapies. New advances in our understanding of the biological pathways leading to such a complex pathogenesis will require the identification of the important proteins and protein networks that differ between a healthy lung (or right ventricle) and a remodeled lung in an individual with PH. In this article, we present the case for the increased use of proteomics—the study of proteins and protein networks—as a discovery tool for key proteins and protein networks operational in the PH lung. We review recent applications of proteomics in PH, and summarize the biological pathways identified. Finally, we attempt to presage what the future will bring with regard to proteomics in PH and offer our perspectives on the prospects of developing personalized proteomics and custom-tailored therapies.     

Proteomic profiling of animal models mimicking skeletal muscle disorders

Over the last few decades of biomedical research, animal models of neuromuscular diseases have been widely used for determining pathological mechanisms and for testing new therapeutic strategies. With the emergence of high-throughput proteomics technology, the identification of novel protein factors involved in disease processes has been decisively improved. This review outlines the usefulness of the proteomic profiling of animal disease models for the discovery of new reliable biomarkers, for the optimization of diagnostic procedures and the development of new treatment options for skeletal muscle disorders. Since inbred animal strains show genetically much less interindividual differences as compared to human patients, considerably lower experimental repeats are capable of producing meaningful proteomic data. Thus, animal model proteomics can be conveniently employed for both studying basic mechanisms of molecular pathogenesis and the effects of drugs, genetic modifications or cell-based therapies on disease progression. Based on the results from comparative animal proteomics, a more informed decision on the design of clinical proteomics studies could be reached. Since no one animal model represents a perfect pathobiochemical replica of all of the symptoms seen in complex human disorders, the proteomic screening of novel animal models can also be employed for swift and enhanced protein biochemical phenotyping.     

Proteomics of brain synapses and molecular dissection of synaptic subdomains

Synapses form the nuts and bolts of the brain. Synaptic transmission involves an intricate network of synaptic proteins that forms the molecular machinery underlying transmitter release, activation of transmitter receptors, and signal transduction cascades. It is generally believed that neuronal activity-dependent change of synaptic efficacy is at the basis of learning and memory and is encoded by sequential molecular events at the synapse. In the past 2-3?years, a number of proteomics studies have been performed on synaptic subdomains, including synaptic vesicles, postsynaptic density, synaptic lipid raft, synapse protein complexes, as well as on synaptic protein PTMs, notably phosphorylation. The activity-dependent dynamics of protein constituents of the synapse are starting to be examined by quantitative proteomics. It is anticipated that these analyses will yield novel insights into the organization of the synapse, and will generate testable hypotheses of synapse function and regulation both in health and disease.     

Proteomic approaches for discovery of new targets for vaccine and therapeutics against visceral leishmaniasis

Visceral leishmaniasis (VL) is the most devastating type caused by Leishmania donovani, Leishmania infantum, and Leishmania chagasi. The therapeutic mainstay is still based on the antiquated pentavalent antimonial against which resistance is now increasing. Unfortunately, due to the digenetic life cycle of parasite, there is significant antigenic diversity. There is an urgent need to develop novel drug/vaccine targets against VL for which the primary goal should be to identify and characterize the structural and functional proteins. Proteomics, being widely employed in the study of Leishmania seems to be a suitable strategy as the availability of annotated sequenced genome of Leishmania major has opened the door for dissection of both protein expression/regulation and function. Advances in clinical proteomic technologies have enable to enhance our mechanistic understanding of virulence/pathogenicity/host-pathogen interactions, drug resistance thereby defining novel therapeutic/vaccine targets. Expression proteomics exploits the differential expression of leishmanial proteins as biomarkers for application towards early diagnosis. Further using immunoproteomics efforts were also focused on evaluating responses to define parasite T-cell epitopes as vaccine/diagnostic targets. This review has highlighted some of the relevant developments in the rapidly emerging field of leishmanial proteomics and focus on its future applications in drug and vaccine discovery against VL.     

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.     

Urinary Proteomics and Precision Medicine for Chronic Kidney Disease: Current Status and Future Perspectives

Precision medicine is since long an ongoing refinement of classical medicine, integrating improved and more detailed pathophysiological understanding with rapid technological advances. In the heterogenous area of chronic kidney disease there seems to be a high potential for the improvement in treatment and prognosis for several causes, with new technologies under development, that are yet to be introduced in clinical practice. As in other medical disciplines, investigation of abundant peptide patterns (proteomics) has gained recent interest. Especially relevant for kidney disease, urinary proteomics may provide both improved diagnosis and, as reviewed here, also holds promise for personalized treatment in the future. So far, capillary electrophoresis coupled to mass spectrometry (CE‐MS) is the most widely applied technique, and in addition to several cross‐sectional and cohort studies, there is even an ongoing randomized controlled trial that will soon report on the concept used as a method of personalizing treatment. In addition, there is hope that urinary proteomics can turn into a “liquid biopsy,” replacing the invasive diagnostic procedure. The next couple of years will provide more answers on the topic.     

A better understanding of molecular mechanisms underlying human disease

This review summarises and discusses the degree to which proteomics is contributing to medical care, providing examples and signspots for future directions. Why do genomic approaches provide a limited view of gene expression? Because of the multifactorial nature of many diseases, proteomics enables us to understand the molecular basis of disease, not only at the organism, whole-cell or tissue levels, but also in subcellular structures, protein complexes and biological fluids. The application of proteomics in medicine is expected to have a major impact by providing an integrated view of individual disease processes. This review describes several proteomic platforms and examines the role of proteomics as a tool for clinical biomarker discovery, the identification of prognostic and earlier diagnostic markers, their use in monitoring the effects of drug treatments and eventually find more efficient and safer therapeutics for a wide range of pathologies.     

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

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

Application of proteomic techniques to the study of urine and renal tissue

The increasing application of proteomic methods to biomedical research is providing us with important new information; it holds particular promise in advancing basic and clinical renal research, but whether proteomics can ever become a routine diagnostic tool in nephrology is still uncertain. Currently, proteomic techniques are used by many groups in the search for "biomarkers" of disease, especially kidney disease, because of the ready availability of urine as an "end-product" of renal function. However, the question as to whether any disease-specific biomarkers exist or can be identified by proteomics is also uncertain. A growing application of proteomics in biomedical research is to understand the mechanism(s) of disease. This brief review is selective; in it we consider examples of proteomic studies of human urine for biomarkers, others that have explored renal physiology, and still others that have begun to probe the proteome of organelles. No single approach is sufficiently comprehensive, and the pooled application of proteomics to renal research will undoubtedly improve our understanding of renal function and enable us to explore in more detail subcellular structures, and to characterize cellular processes at the molecular level. When combined with other techniques in renal research, proteomics, and related analytical methods could prove indispensable in modeling renal function, and perhaps also in diagnosis and management of renal disease.     

Combinatorial vascular targeting in translational medicine

In the post-genomic era, the phage display technology surfaces as an alternative approach for large-scale study of tissue-specific protein interactions with direct clinical and therapeutic applications. The unbiased identification of molecular complexes expressed on the surface of cells and blood vessels of organs and tissues may eventually lead to a considerably improved understanding of cellular and vascular proteomics. The ultimate value of this technology consists in the conception of a new ligand-directed pharmacology, with broad implications for both treatment and molecular imaging of cancer patients. In this review, we describe the use and applications of phage display for efficient development of targeted drug discovery and design.     

Clinical application of proteomics approaches in vascular diseases

Cardiovascular diseases are leading causes of morbidity and mortality in developed countries. Despite modern advances in science and medicine, our understanding of the factors that contribute to the development of vascular dysfunction is incomplete. Progress in treatment will depend on elucidating the complex interactions of hormones, growth factors, inflammatory agents, and oxidative stress on cell proliferation, migration, and death both in the vasculature and in circulation. Recent developments in proteomic techniques provide a promising approach for determining protein changes associated with complex diseases and permitting thorough evaluation of molecular changes associated with vascular dysfunction. Proteomic studies have revealed novel, dynamic, complex, and subtle changes of intracellular processes that are associated with the abnormal regulation of vascular function. This review provides an overview on the progress of applying proteomics to vascular diseases. We will describe the application of proteomics to clinically important vascular conditions and highlight the potential of using proteomics to advance our understanding on the mechanisms that underlie vascular diseases.     

Proteomics in human cancer research

Proteomics is now widely employed in the study of cancer. Many laboratories are applying the rapidly emerging technologies to elucidate the underlying mechanisms associated with cancer development, progression, and severity in addition to developing drugs and identifying patients who will benefit most from molecular targeted compounds. Various proteomic approaches are now available for protein separation and identification, and for characterization of the function and structure of candidate proteins. In spite of significant challenges that still exist, proteomics has rapidly expanded to include the discovery of novel biomarkers for early detection, diagnosis and prognostication (clinical application), and for the identification of novel drug targets (pharmaceutical application). To achieve these goals, several innovative technologies including 2-D-difference gel electrophoresis, SELDI, multidimensional protein identification technology, isotope-coded affinity tag, solid-state and suspension protein array technologies, X-ray crystallography, NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulation have evolved, and are being used in different combinations. This review provides an overview of the field of proteomics and discusses the key proteomic technologies available to researchers. It also describes some of the important challenges and highlights the current pharmaceutical and clinical applications of proteomics in human cancer research.     

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.     

Building inferential prediction models of batch processes using subspace identification

In this paper, a general method for converting available batch plant data into useful prediction models is proposed. A subspace identification method, which was proposed for identification of continuous systems, is used to develop a batch-to-batch correlation model from plant data. In this context, the state of the model is a holder of relevant information contained in the past batch data for predicting the behavior of current and future batches. The modeling framework naturally allows the user to capture correlations among the variables within each individual batch as well as those of successive batches, as reflected in the modeling data, and take advantage of them in prediction and control. It will be shown that the batch-to-batch model can be converted into a time transition model that can be used to predict the future behavior of the relevant variables, including the end-quality variables, in real time based on incoming measurements. Various practical issues will be addressed, such as the reduction of state dimension and incorporation of delayed laboratory measurements of quality variables.