The biology of the post-genomic era: the proteomics

Deciphering core proteins of osteoporosis with iron accumulation by proteomics in human bone

Iron accumulation is an independent risk factor for postmenopausal osteoporosis, but mechanistic studies of this phenomenon are still focusing on molecular and genetic researches in model animal. Osteoporosis with iron accumulation is a distinct endocrine disease with complicated pathogenesis regulated by several proteins. However, the comprehensive proteome-wide analysis of human bone is lacking. Using multiplex quantitative tandem mass tag-based proteomics, we detected 2900 and quantified 1150 proteins from bone of 10 postmenopausal patients undergoing hip replacement. Comparing with non-osteoporosis patients, a total of 75 differentially expressed proteins were identified, comprising 53 downregulated proteins and 22 upregulated proteins. These proteins primarily affect oxidoreductase activity, GTPase activity, GTP binding, and neural nucleus development, were mainly enriched in neural, angiogenesis and energy-related pathways, and formed complex regulatory networks with strong interconnections. We ultimately identified 4 core proteins (GSTP1, LAMP2, COPB1, RAB5B) that were significantly differentially expressed in the bone of osteoporosis patients with iron accumulation, and validated the changed protein level in the serum of the medical examination population. Our systemic analysis uncovers molecular insights for revealing underlying mechanism and clinical therapeutics in osteoporosis with iron accumulation.

Proteomics in uveal melanoma

Uveal melanoma is the most common primary intraocular malignancy in adults, with an incidence of 5-7 per million per year. It is associated with the development of metastasis in about 50% of cases, and 40% of patients with uveal melanoma die of metastatic disease despite successful treatment of the primary tumour. The survival rates at 5, 10 and 15 years are 65%, 50% and 45% respectively. Unlike progress made in many other areas of cancer, uveal melanoma is still poorly understood and survival rates have remained similar over the past 25 years. Recently, advances made in molecular genetics have improved our understanding of this disease and stratification of patients into low risk and high risk for developing metastasis. However, only a limited number of studies have been performed using proteomic methods. This review will give an overview of various proteomic technologies currently employed in life sciences research, and discuss proteomic studies of uveal melanoma.

Proteomics in evolutionary ecology: linking the genotype with the phenotype

The study of the proteome (proteomics), which includes the dynamics of protein expression, regulation, interactions and its function, has played a less prominent role in evolutionary and ecological investigations in comparison with the study of the genome and transcriptome. There are, however, a number of arguments suggesting that this situation should change. First, the proteome is closer to the phenotype than the genome or the transcriptome, and as such may be more directly responsive to natural selection, and thus closely linked to adaptation. Second, there is evidence of a low correlation between protein and transcript expression levels across genes in many different organisms. Finally, there have been some recent important technological improvements in proteomics methods that make them feasible, practical and useful to address a wide range of evolutionary questions even in nonmodel organisms. The different proteomic methods, their limitations and problems when interpreting empirical data are described and discussed. In addition, the proteomic literature pertaining to evolutionary ecology is reviewed with examples, and potential applications of proteomics in a variety of evolutionary contexts are outlined. New proteomic research trends such as the study of posttranslational modifications and protein-protein interactions, as well as the combined use of the different -omics approaches, are discussed in relation to the development of a more functional and integrated perspective, needed for achieving a more comprehensive knowledge of evolutionary change.

Proteomic analysis of tumor necrosis factor-alpha-induced secretome of human adipose tissue-derived mesenchymal stem cells

Human adipose tissue-derived mesenchymal stem cells (hASCs) are useful for regeneration of inflamed or injured tissues. To identify secreted hASC proteins during inflammation, hASCs were exposed to tumor necrosis factor-alpha (TNF-alpha) and conditioned media derived from hASCs were analyzed by liquid chromatography coupled with tandem mass spectrometry. We identified 187 individual proteins as secreted proteins (secretome) in hASC-conditioned media; 118 proteins were secreted at higher levels upon TNF-alpha treatment. The TNF-alpha-induced secretome included a variety of cytokines and chemokines such as interleukin-6 (IL-6), IL-8, chemokine (C-X-C motif) ligand 6, and monocyte chemotactic protein-1 (MCP-1). TNF-alpha also increased expression of various proteases including cathepsin L, matrix metalloproteases and protease inhibitors, and induced secretion of long pentraxin 3, a key inflammatory mediator implicated in innate immunity. TNF-alpha-conditioned media stimulated migration of human monocytes, which play a key role in inflammatory responses. This migration was abrogated by pretreatment with neutralizing anti-IL-6, anti-IL-8, and anti-MCP-1 antibodies, suggesting that IL-6, IL-8, and MCP-1 are involved in migration of monocytes. Taken together, these results suggest that TNF-alpha-induced secretome may play a pivotal role in inflammatory responses and that shotgun proteomic analysis will be useful for elucidation of the paracrine functions of mesenchymal stem cells.

Proteomics in uveal melanoma research: opportunities and challenges in biomarker discovery

Uveal melanoma (UM) is the most frequent primary intraocular tumor in adult humans. Despite the significant advances in diagnosis and treatment of UM in the last decades, the prognosis of UM sufferers is still poor. Metastatic liver disease is the leading cause of death in UM and can develop after a long disease-free interval, suggesting the presence of occult micrometastasis. Proteomics technology has opened new opportunities for elucidating the molecular mechanism of complex diseases, such as cancer. This article will review the recent developments in biomarker discovery for UM research by proteomics. In the last few years, the first UM proteomics-based analyses have been launched, yielding promising results. An update on recent developments on this field is presented.

Expression profiling in granulomatous lung disease

Granulomatous lung diseases, such as sarcoidosis, hypersensitivity pneumonitis, Wegener's granulomatosis, and chronic beryllium disease, along with granulomatous diseases of known infectious etiologies, such as tuberculosis, are major causes of morbidity and mortality throughout the world. Clinical manifestations of these diseases are highly heterogeneous, and the determinants of disease susceptibility and clinical course (e.g., resolution vs. chronic, progressive fibrosis) are largely unknown. The underlying pathogenic mechanisms of these diseases also remain poorly understood. Within this context, these diseases have been approached using genomic and proteomic technologies to allow us to identify patterns of gene/protein expression that track with clinical disease or to identify new pathways involved in disease pathogenesis. The results from these initial studies highlight the potential for these "-omics" approaches to reveal novel insights into the pathogenesis of granulomatous lung disease and provide new tools to improve diagnosis, clinical classification, course prediction, and response to therapy. Realizing this potential will require collaboration among multidisciplinary groups with expertise in the respective technologies, bioinformatics, and clinical medicine for these complex diseases.

A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples

Currently, the best way to reduce the mortality of cancer is to detect and treat it in the earliest stages. Technological advances in genomics and proteomics have opened a new realm of methods for early detection that show potential to overcome the drawbacks of current strategies. In particular, pattern analysis of mass spectra of blood samples has attracted attention as an approach to early detection of cancer. Mass spectrometry provides rapid and precise measurements of the sizes and relative abundances of the proteins present in a complex biological/chemical mixture. This article presents a review of the development of clinical decision support systems using mass spectrometry from a machine learning perspective. The literature is reviewed in an explicit machine learning framework, the components of which are preprocessing, feature extraction, feature selection, classifier training, and evaluation.

Cancer proteomics: Serum diagnostics for tumor marker discovery

Cancer proteomics is an exciting field that is witnessing many new developments in recent years. It is hoped that these advances will result in decreased cancer death rates, which have not declined dramatically in the last several decades. Some of the problems with current tumor markers include the lack of sensitivity and specificity, factors that prevent their use in population-based screening of disease. Thus, there is an urgent need to identify novel biomarkers that can faithfully detect the disease state. As we are now in the post-genome era, many opportunities have been created. Genomic sequence data are available for human, as well as several other species. We are now poised to mine these data and to determine the functions of the encoded proteins constituting the human genome. Proteomics affords this opportunity by providing enhanced procedures and tools for discovery and also a framework for understanding these components in terms of pathogenesis. New technologies and improvements in existing methodologies will allow for the rapid growth in the identification and characterization of peptides and proteins that are unique to various clinical states. This technology can be successfully applied to clinical specimens for the identification of new tumor markers.