Thyroid tumors: novel insights from proteomic studies

Several genomics-based techniques have been applied in the last decade to the molecular characterization of cancer, which has led to a variety of applications suitable for improved diagnosis, prognosis and prediction of outcome to treatment. Proteomics-based approaches have also been seen as crucial to the discovery of biomarkers for early diagnosis and prognosis of tumors, as well as for a better understanding of the molecular bases of cancer. Accordingly, proteomic techniques have been used extensively for a better molecular characterization of thyroid tumors. In this field, three main directions have been preceded: first, proteomic studies of model systems; second, proteomics of thyroid tumor specimens; and third, serum proteomics. In this review, we describe the most relevant results that have been obtained for tumors derived from thyroid follicular cells using various proteomic approaches.     

Proteomics in thyroid cancer and other thyroid-related diseases: A review of the literature

Misdiagnosis, over-diagnosis, or inappropriate treatment strategies are frequently seen in thyroid disease management. Currently, there is an urgent interest to identify biomarkers for specific disease states in the thyroid gland. Proteomics-based approaches have been widely used for the discovery of potential biomarkers in thyroid research. Specifically, proteomic techniques have been used to better understand thyroid tumors of different origin and classification. Besides, there is remarkable thyroid proteomics research in thyroid-related diseases, including thyroid orbitopathy. In this review, we aim to describe proteomic studies and how the results obtained from thyroid cancer, up to now, as well as those from, other thyroid-related diseases pose challenges, which might be solved via high-resolution protein analysis.     

Update on: proteome analysis in thyroid pathology – part II: overview of technical and clinical enhancement of proteomic investigation of the thyroid lesions

ABSTRACT

Introduction: An accurate diagnostic classification of thyroid lesions remains an important clinical aspect that needs to be addressed in order to avoid ‘diagnostic’ thyroidectomies. Among the several ‘omics’ techniques, proteomics is playing a pivotal role in the search for diagnostic markers. In recent years, different approaches have been used, taking advantage of the technical improvements related to mass spectrometry that have occurred.

Areas covered: The review provides an update of the recent findings in diagnostic classification, in genetic definition and in the investigation of thyroid lesions based on different proteomics approaches and on different type of specimens: cytological, surgical and biofluid samples. A brief section will discuss how these findings can be integrated with those obtained by metabolomics investigations.

Expert commentary: Among the several proteomics approaches able to deepen our knowledge of the molecular alterations of the different thyroid lesions, MALDI-MSI is strongly emerging above all. In fact, MS-imaging has also been demonstrated to be capable of distinguishing thyroid lesions, based on their different molecular signatures, using cytological specimens. The possibility to use the material obtained by the fine needle aspiration makes MALDI-MSI a highly promising technology that could be implemented into the clinical and pathological units.     

The use of biophysical proteomic techniques in advancing our understanding of diseases

The use of proteomic approaches in investigating diseases is continuing to expand and has started to provide answers to substantial gaps in our understanding of disease pathogenesis as well as in the development of effective strategies for the early diagnosis and treatment of diseases. Biophysical techniques form a crucial part of the advanced proteomic techniques currently used and include mass spectrometry and protein separation techniques, such as two-dimensional gel electrophoresis and liquid chromatography. The application of biophysical proteomic techniques in the study of disease includes delineation of altered protein expression, not only at the whole-cell or tissue levels, but also in subcellular structures, protein complexes, and biological fluids. These techniques are also being used for the discovery of novel disease biomarkers, exploration of the pathogenesis of diseases, development of new diagnostic methodologies, and identification of new targets for therapeutics. Proteomic techniques also have the potential for accelerating drug development through more effective strategies for evaluating a specific drug’s therapeutic effects and toxicity. This article discusses the application of biophysical proteomic techniques in delineating cardiovascular disease and other diseases, as well as the limitations and future research directions required for these techniques to gain greater acceptance and have a larger impact.     

Cardiovascular proteomic analysis

Here, we report on our proteomic studies in the field of cardiovascular medicine. Our research has been focused on understanding the role of proteins in cardiovascular disease with a particular focus on epigenetic regulation and biomarker discovery, with the objective of better understanding cardiovascular pathophysiology to lead to the development of new and better diagnostic and therapeutic methods. We have used mass spectrometry for over 5 years as a viable method to investigate protein-protein interactions and post-translational modifications in cellular proteins as well as a method to investigate the role of extra-cellular proteins. Use of mass spectrometry not only as a research tool but also as a potential diagnostic tool is a topic of interest. In addition to these functional proteomics studies, structural proteomic studies are also done with expectations to allow for pinpoint drug design and therapeutic intervention. Collectively, our proteomics studies are focused on understanding the functional role and potential therapeutically exploitable property of proteins in cardiovascular disease from both intra-cellular and extra-cellular aspects with both functional as well as structural proteomics approaches to allow for comprehensive analysis.     

Proteomics applied to exercise physiology: a cutting-edge technology

Exercise research has always drawn the attention of the scientific community because it can be widely applied to sport training, health improvement, and disease prevention. For many years numerous tools have been used to investigate the several physiological adaptations induced by exercise stimuli. Nowadays a closer look at the molecular mechanisms underlying metabolic pathways and muscular and cardiovascular adaptation to exercise are among the new trends in exercise physiology research. Considering this, to further understand these adaptations as well as pathology attenuation by exercise, several studies have been conducted using molecular investigations, and this trend looks set to continue. Through enormous biotechnological advances, proteomic tools have facilitated protein analysis within complex biological samples such as plasma and tissue, commonly used in exercise research. Until now, classic proteomic tools such as one- and two-dimensional polyacrylamide gel electrophoresis have been used as standard approaches to investigate proteome modulation by exercise. Furthermore, other recently developed in gel tools such as differential gel electrophoresis (DIGE) and gel-free techniques such as the protein labeling methods (ICAT, SILAC, and iTRAQ) have empowered proteomic quantitative analysis, which may successfully benefit exercise proteomic research. However, despite the three decades of 2-DE development, neither classic nor novel proteomic tools have been convincingly explored by exercise researchers. To this end, this review gives an overview of the directions in which exercise-proteome research is moving and examines the main tools that can be used as a novel strategy in exercise physiology investigation.     

Role of proteomics in biomarker discovery and psychiatric disorders: current status,potentials, limitations and future challenges

Proteomic approaches have advanced clinical research towards more reliable, sensitive and specific biological diagnostic markers for diseases. Mood disorders are most difficult to diagnose and very much prevalent in society; hence, their proper diagnosis becomes essential. Despite tremendous research efforts to dissect the neurobiological basis of psychiatric disorders, the diagnosis and evaluation for such diseases is still poor. Biomarker discovery in psychiatry research has been accelerated by proteomic technologies, accepting the challenges in order to meet disease state-related investigations. Proteomics-based research for disease-specific protein signatures is expected to give a new direction in psychiatry research. Therefore, this may become a more powerful tool to predict the development, course and outcome of the disease towards personalized psychiatric ailments. The review discusses the role of proteomics in elucidating mechanisms of psychiatric disorders, current status, prospects, limitations and new possibilities towards a strong diagnostic tool in the clinical laboratory.     

Clinical Proteomics and OMICS clues useful in Translational Medicine Research

ABSTRACT: Since the advent of the new proteomics era more than a decade ago, large-scale studies of protein profiling have been used to identify distinctive molecular signatures in a wide array of biological systems, spanning areas of basic biological research, clinical diagnostics, and biomarker discovery directed toward therapeutic applications. Recent advances in protein separation and identification techniques have significantly improved proteomic approaches, leading to enhancement of the depth and breadth of proteome coverage. Proteomic signatures, specific for multiple diseases, including cancer and pre-invasive lesions, are emerging. This article combines, in a simple manner, relevant proteomic and OMICS clues used in the discovery and development of diagnostic and prognostic biomarkers that are applicable to all clinical fields, thus helping to improve applications of clinical proteomic strategies for translational medicine research.     

Challenges in cancer research and multifaceted approaches for cancer biomarker quest

Recent advances in cancer biology have subsequently led to the development of new molecularly targeted anti-cancer agents that can effectively hit cancer-related proteins and pathways. Despite better insight into genomic aberrations and diversity of cancer phenotypes, it is apparent that proteomics too deserves attention in cancer research. Currently, a wide range of proteomic technologies are being used in quest for new cancer biomarkers with effective use. These, together with newer technologies such as multiplex assays could significantly contribute to the discovery and development of selective and specific cancer biomarkers with diagnostic or prognostic values for monitoring the disease state. This review attempts to illustrate recent advances in the field of cancer biomarkers and multifaceted approaches undertaken in combating cancer.     

Biomarkers: mining the biofluid proteome

Proteomics has brought with it the hope of identifying novel biomarkers for diseases such as cancer. This hope is built on the ability of proteomic technologies, such as mass spectrometry (MS), to identify hundreds of proteins in complex biofluids such as plasma and serum. There are many factors that make this research very challenging beginning with the lack of standardization of sample collection and continuing through the entire analytical process. Fortunately the advances made in the characterization of biofluids using proteomic techniques have been rapid and suggest that these mainly discovery driven approaches will lead to the development of highly specific platforms for diagnosing diseases and monitoring responses to different treatments in the near future.     

Clinical proteomics: towards early detection of cancers

A key challenge in clinical proteomic of cancer is the identification of biomarkers that would allow early detection, diagnosis and monitor progression of the disease to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and surfaced-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry is one of the most interesting new approaches for cancer diagnostic using proteomic profiling. This review aims to summarize the results of studies that have used this new technology method for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems and still requires some efforts to be standardised and validated.     

Liver tumors in wild flatfish: a histopathological, proteomic, and metabolomic study

Fish play host to viral, bacterial, and parasitic diseases in addition to non-infectious conditions such as cancer. The National Marine Monitoring Programme (NMMP) provides information to the U.K. Government on the health status of marine fish stocks. An aspect of this work relates to the presence of tumors and other pathologies in the liver of the offshore sentinel flatfish species, dab (Limanda limanda). Using internationally agreed quality assurance criteria, tumors and pre-tumors are diagnosed using histopathology. The current study has expanded upon this work by integrating these traditional diagnostic approaches with ones utilizing modern technologies for analysis of proteomic and metabolomic profiles of selected lesions. We have applied SELDI and FT-ICR technologies (for proteomic and metabolomic analyses, respectively) to tumor and non-tumor samples resected from the liver of dab. This combined approach has demonstrated how these technologies are able to identify protein and metabolite profiles that are specific to liver tumors. Using histopathology to classify "analysis groups" is key to the success of such an approach since it allows for elimination of spurious samples (e.g., those containing parasite infections) that may confuse interpretation of "omic" data. As such, the pathology laboratory plays a central role in collating information relating to particular specimens and in establishing sampling groups relative to specific diagnostic questions. In this study, we present pilot data, which illustrates that proteomics and metabolomics can be used to discriminate fish liver tumors and suggest future directions for work of this type.     

Tissue microarrays as a platform for proteomic investigation

Tissue microarrays have become an essential tool in translational pathology. They are used to confirm results from other experimental platforms, such as expression microarrays, as well as a primary tool to explore the expression profile of proteins by immunohistochemical analysis. Tissue microarrays are routinely used molecular epidemiology, drug development and determining the diagnostic, prognostic and predictive value of new biomarkers. By applying traditional protein based assays, as well as novel assays to the platform, tissue microarrays have gained a new utility as a proteomic tool for both basic science as well as clinical investigation. This article will explore the new approaches that are being applied to tissue microarrays to, characterize the human proteome, and new technologies that allow tissue microarrays to function as a protein array. The U.S. Government's right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged     

Utility of mass spectrometry for proteome analysis: part II. Ion-activation methods,statistics, bioinformatics and annotation

This is the second article in a series, intended as a tutorial to provide the interested reader with an overview of the concepts not covered in part I, such as: the principles of ion-activation methods, the ability of mass-spectrometric methods to interface with various proteomic strategies, analysis techniques, bioinformatics and data interpretation and annotation. Although these are different topics, it is important that a reader has a basic and collective understanding of all of them for an overall appreciation of how to carry out and analyze a proteomic experiment. Different ion-activation methods for MS/MS, such as collision-induced dissociation (including postsource decay) and surface-induced dissociation, electron capture and electron-transfer dissociation, infrared multiphoton and blackbody infrared radiative dissociation have been discussed since they are used in proteomic research. The high dimensionality of data generated from proteomic studies requires an understanding of the underlying analytical procedures used to obtain these data, as well as the development of improved bioinformatics tools and data-mining approaches for efficient and accurate statistical analyses of biological samples from healthy and diseased individuals, in addition to determining the utility of the interpreted data. Currently available strategies for the analysis of the proteome by mass spectrometry, such as those employed for the analysis of substantially purified proteins and complex peptide mixtures, as well as hypothesis-driven strategies, have been elaborated upon. Processing steps prior to the analysis of mass spectrometry data, statistics and the several informatics steps currently used for the analysis of shotgun proteomic experiments, as well as proteomics ontology, are also discussed.     

Clinical proteomics of breast cancer

Despite the lifetimes that increased in breast cancers due to the the early screening programs and new therapeutic strategies, many cases still are being lost due to the metastatic relapses. For this reason, new approaches such as the proteomic techniques have currently become the prime objectives of breast cancer researches. Various omic-based techniques have been applied with increasing success to the molecular characterisation of breast tumours, which have resulted in a more detailed classification scheme and have produced clinical diagnostic tests that have been applied to both the prognosis and the prediction of outcome to the treatment. Implementation of the proteomics-based techniques is also seen as crucial if we are to develop a systems biology approach in the discovery of biomarkers of the early diagnosis, prognosis and prediction of the outcome of the breast cancer therapies. In this review, we discuss the studies that have been conducted thus far, for the discovery of diagnostic, prognostic and predictive biomarkers, and evaluate the potential of the discriminating proteins identified in this research for clinical use as breast cancer biomarkers.     

A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry

Human saliva has great potential for clinical disease diagnostics. Constructing a comprehensive catalogue of saliva proteins using proteomic approaches is a necessary first step to identifying potential protein biomarkers of disease. However, because of the challenge presented in cataloguing saliva proteins with widely varying abundance, new proteomic approaches are needed. To this end, we used a newly developed approach coupling peptide separation using free flow electrophoresis with linear ion trap tandem mass spectrometry to identify proteins in whole human saliva. We identified 437 proteins with high confidence (false positive rate below 1%), producing the largest catalogue of proteins from a single saliva sample to date and providing new information on the composition and potential diagnostic utility of this fluid. The statistically validated, transparently presented, and annotated dataset provides a model for presenting large scale proteomic data of this type, which should facilitate better dissemination and easier comparisons of proteomic datasets from future studies in saliva.     

To label or not to label: applications of quantitative proteomics in neuroscience research

Proteomics has provided researchers with a sophisticated toolbox of labeling-based and label-free quantitative methods. These are now being applied in neuroscience research where they have already contributed to the elucidation of fundamental mechanisms and the discovery of candidate biomarkers. In this review, we evaluate and compare labeling-based and label-free quantitative proteomic techniques for applications in neuroscience research. We discuss the considerations required for the analysis of brain and central nervous system specimens, the experimental design of quantitative proteomic workflows as well as the feasibility, advantages, and disadvantages of the available techniques for neuroscience-oriented questions. Furthermore, we assess the use of labeled standards as internal controls for comparative studies in humans and review applications of labeling-based and label-free mass spectrometry approaches in relevant model organisms and human subjects. Providing a comprehensive guide of feasible and meaningful quantitative proteomic methodologies for neuroscience research is crucial not only for overcoming current limitations but also for gaining useful insights into brain function and translating proteomics from bench to bedside.     

The proteomes of feedstocks used for the production of second‐generation ethanol: a lacuna in the biofuel era

Maize, sorghum, sugarcane, switchgrass and miscanthus are the main crops suggested as potential sources of lignocellulosic biomass for the production of second‐generation ethanol. The attention these crops have received has been concentrated in the field of genomics, and very little research has been performed in the field of proteomics, particularly in the cell wall proteomic, despite the importance of these crops in biofuel production. New mass spectrometry‐based proteomic methods allow the identification and quantification of thousands of proteins in complex mixtures, as well as the detection of post‐translational changes in complex proteomes, providing important insight into the downstream consequences of gene expression. Together with other ‘omic’ approaches, proteomic might be decisive to bring new information in the study of cell wall formation. Here, we briefly highlight proteomic techniques and review the research that has been completed on the proteomes of these five crops.     

Advances in proteomic technologies and their scope of application in understanding plant–pathogen interactions

Proteomics, one of the major tools of ‘omics’ is evolving phenomenally since the development and application of two-dimensional gel electrophoresis coupled with mass spectrometry at the end of twentieth century. However, the adoption and application of advanced proteomic technologies in understanding plant–pathogen interactions are far less, when compared to their application in other related fields of systems biology. Hence, this review is diligently focused on the advances in various proteomic approaches and their gamut of applications in different facets of phyto-pathoproteomics. Especially, the scope and application of proteomics in understanding fundamental concepts of plant–pathogen interactions such as identification of pathogenicity determinants (effector proteins), disease resistance proteins (resistance and pathogenesis-related proteins) and their regulation by post-translational modifications have been portrayed. This review, for the first time, presents a critical appraisal of various proteomic applications by assessing all phyto-pathoproteomics-related research publications that were published in peer-reviewed journals, during the period 2000–2016. This assessment has revealed the present status and contribution of proteomic applications in different categories of phyto-pathoproteomics, namely, cellular components, host–pathogen interactions, model and non-model plants, and utilization of different proteomic approaches. Comprehensively, the analysis highlights the burgeoning application of global proteome approaches in various crop diseases, and demand for acceleration in deploying advanced proteomic technologies to thoroughly comprehend the intricacies of complex and rapidly evolving plant–pathogen interactions.