Proteogenomics approaches for studying cancer biology and their potential in the identification of acute myeloid leukemia biomarkers

Introduction: Mass spectrometry (MS)-based proteomics has become an indispensable tool for the characterization of the proteome and its post-translational modifications (PTM). In addition to standard protein sequence databases, proteogenomics strategies search the spectral data against the theoretical spectra obtained from customized protein sequence databases. Up to date, there are no published proteogenomics studies on acute myeloid leukemia (AML) samples.

Areas covered: Proteogenomics involves the understanding of genomic and proteomic data. The intersection of both datatypes requires advanced bioinformatics skills. A standard proteogenomics workflow that could be used for the study of AML samples is described. The generation of customized protein sequence databases as well as bioinformatics tools and pipelines commonly used in proteogenomics are discussed in detail.

Expert commentary: Drawing on evidence from recent cancer proteogenomics studies and taking into account the public availability of AML genomic data, the interpretation of present and future MS-based AML proteomic data using AML-specific protein sequence databases could discover new biological mechanisms and targets in AML. However, proteogenomics workflows including bioinformatics guidelines can be challenging for the wide AML research community. It is expected that further automation and simplification of the bioinformatics procedures might attract AML investigators to adopt the proteogenomics strategy.     

Protein–protein interaction networks improve the proteomics data interpretation in induced apoptosis

Evaluation of: Deighton RF, Kerr LE, Short DM et al. Network generation enhances interpretation of proteomics data from induced apoptosis. Proteomics DOI: 10.1002/pmic.200900112 (2010) (Epub ahead of print).

The huge ongoing improvements in proteomics technologies, including the development of high-throughput mass spectrometry, are resulting in ever increasing information on protein behavior during cellular processes. The exponential accumulation of proteomics data has the promise to advance biomedical sciences by shedding light on the most important events that regulate mammalian cells under normal and pathophysiological conditions. This may provide practical insights that will impact medical practice and therapy, and may permit the development of a new generation of personalized therapeutics. Proteomics, as a powerful tool, creates numerous opportunities as well as challenges. At the different stages, data interpretation requires proteomics analysis, various tools to help deal with large proteomics data banks and the extraction of more functional information. Network analysis tools facilitate proteomics data interpretation and predict protein functions, functional interactions and in silica identification of intracellular pathways. The work reported by Deighton and colleagues illustrates an example of improving proteomics data interpretation by network generation. The authors used ingenuity pathway analysis to generate a protein network predicting direct and indirect interaction between 13 proteins found to be affected by staurosporine treatment. Importantly, the authors highlight the caution required when interpreting the results from a small number of proteins analyzed using network analysis tools.     

卵巢高/低级别浆液性癌的定量蛋白质组学比较研究

目的:探讨卵巢高级别浆液性癌和低级别浆液性癌的差异表达蛋白,为阐明卵巢癌发生机制及寻找诊断和预后标志物的提供线索。方法:收集卵巢癌新鲜组织标本冻存于液氮中,经病理学确诊为高级别浆液性癌和低级别浆液性癌,两种类型各收集15例。应用i TRAQ定量蛋白质组学技术筛选及鉴定高/低级别浆液性癌的差异表达蛋白,并进行生物信息学分析。结果:卵巢高级别和低级别浆液性癌组织的定量蛋白质组学比较研究鉴定出差异表达蛋白314个,其中与低级别浆液性癌组比较,高级别浆液性癌组上调蛋白有97种,下调蛋白有217种。GO分析显示这些差异蛋白在分子功能、生物学功能、细胞成分方面均具有一定分布特点。KEGG分析显示这些差异蛋白涉及复杂的信号通路。结论:高/低级别浆液性癌之间存在差异表达蛋白,这些蛋白涉及复杂的功能和信号通路可能在两型卵巢癌发生机制及肿瘤生物学行为差异中具有重要意义。     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

Proteomic analysis of mitochondria: biological and clinical progresses in cancer

Introduction: Mitochondria play important roles in regulating multiple biological processes and signalling pathways in eukaryotic cells, and mitochondrial dysfunction may result in a wide range of serious diseases, including cancer. With improvements in the identification of mitochondrial proteins, mitochondrial proteomics has made great achievements. In particular, this approach has been widely used to compare tumour cells at different stages of malignancy. Therefore, there is an urgent need to identify and characterize the function of mitochondrial proteins in cancer progression and to determine the involved mechanisms.

Areas covered: We provide an overview of recent progress related to mitochondrial proteomics in cancer and the application of comparative mitochondrial proteomics in various biological processes, including apoptosis, necroptosis, autophagy and metastasis, as well as clinical progress in cancer. Proteomics-related reports were found using PubMed and Google Scholar databases.

Expert commentary: Understanding both post-translational modification and post-translational processing is important in the comprehensive characterization of protein function. The application of comparative mitochondrial proteomics to investigate clinical samples and cancer cells will contribute to our understanding of the molecular interplay of mitochondrial proteins in the development of cancer. This approach will mine more biomarkers for diagnosis and prognosis and improve therapeutic outcomes among cancer patients.     

The application of gene silencing in proteomics: from laboratory to clinic

Introduction: Since the completion of genome sequencing, gene silencing technologies have emerged as powerful tools to study gene functions in various biological processes, both in vivo and in vitro. Moreover, they have also been proposed as therapeutic agents to inhibit selected genes in a variety of pathological conditions, such as cancer, neurodegenerative, and cardiovascular diseases.

Area covered: This review summarizes the mechanisms of action and applications of genome editing tools, from RNA interference to clustered regularly interspaced short palindromic repeats-based systems, in research and in clinics. We describe their essential role in high-throughput genetic screens and, in particular, in functional proteomics studies, to identify diagnostic markers and therapeutic targets. Indeed, gene silencing and proteomics have been extensively integrated to study global proteome changes, posttranslational modifications, and protein–protein interactions.

Expert commentary: Functional proteomics approaches that leverage gene silencing tools have been successfully applied to examine the role of several genes in various contexts, leading to a deeper knowledge of biological pathways and disease mechanisms. Recent developments of gene silencing tools have improved their performance, also in terms of off-targets effects reduction, paving the way for a wider therapeutic application of these systems.     

Application of proteomics in research on traditional Chinese medicine

Introduction: Traditional Chinese medicine (TCM) is a widely used complementary alternative medicine approach. Although many aspects of its effectiveness have been approved clinically, rigorous scientific techniques are highly required to translate the promises from TCM into powerful modern therapies. In this respect, proteomics is useful because of its ability to unveil the underlying target proteins and/or protein biomarkers.

Areas covered: In this review, we summarize the recent interplay between proteomics and research on TCM, ranging from exploration of the medicinal materials to the biological basis of TCM concepts, and from pathological studies to pharmacological investigations. We show that proteomic analyses provide preliminary biological evidence of the promises in TCM, and the integration of proteomics with other omics and bioinformatics offers a comprehensive methodology to address the complications of TCM.

Expert commentary: Currently, only limited information can be obtained regarding TCM issues and thus more work is required to resolve the ambiguity. As such, more collaborations between proteomics and other techniques (other omics, network pharmacology, etc.) are essential for deciphering the underlying biological basis in TCM topics.     

Roles for the two-hybrid system in exploration of the yeast protein interactome

Comprehensive analysis of protein-protein interactions is a challenging endeavor of functional proteomics and has been best explored in the budding yeast. The yeast protein interactome analysis was achieved first by using the yeast two-hybrid system in a proteome-wide scale and next by large-scale mass spectrometric analysis of affinity-purified protein complexes. While these interaction data have led to a number of novel findings and the emergence of a single huge network containing thousands of proteins, they suffer many false signals and fall short of grasping the entire interactome. Thus, continuous efforts are necessary in both bioinformatics and experimentation to fully exploit these data and to proceed another step forward to the goal. Computational tools to integrate existing biological knowledge buried in literature and various functional genomic data with the interactome data are required for biological interpretation of the huge protein interaction network. Novel experimental methods have to be developed to detect weak, transient interactions involving low abundance proteins as well as to obtain clues to the biological role for each interaction. Since the yeast two-hybrid system can be used for the mapping of the interaction domains and the isolation of interaction-defective mutants, it would serve as a technical basis for the latter purpose, thereby playing another important role in the next phase of protein interactome research.     

Emerging single-cell technologies for functional proteomics in oncology

Introduction: Cellular heterogeneity has challenged current cancer therapeutics and hindered the discovery and development of cancer drugs. The heterogeneity in functional proteome is of particular interest because many cancer drugs are developed to target signaling proteins. The complex nature of tumor systems calls for more advanced multiplexed single-cell tools to address the heterogeneity issue.

Area covered: Over the past five years, there are a few single-cell functional proteomics tools introduced with unprecedented multiplexity and performance that are transforming the oncology field. Those tools are generally categorized as cytometry-based tools and microfluidics-based tools, and we discuss the representatives in both categories.

Expert commentary: The single-cell tools have provided an avenue to understand the multifaceted differences of cancer cells, the complex signaling networks, and the relationship of intercellular interaction and tumor architecture. We also provide an outlook of single-cell tools in five years and the challenges to address before a greater impact on oncology can be made.     

Discovering semantic features in the literature: a foundation for building functional associations

Background  

Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.     

Recent insights into human bronchial proteomics – how are we progressing and what is next?

Introduction: The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries.

Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed.

Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.     

Proteome informatics for cancer research: from molecules to clinic

Proteomics offers the most direct approach to understand disease and its molecular biomarkers. Biomarkers denote the biological states of tissues, cells, or body fluids that are useful for disease detection and classification. Clinical proteomics is used for early disease detection, molecular diagnosis of disease, identification and formulation of therapies, and disease monitoring and prognostics. Bioinformatics tools are essential for converting raw proteomics data into knowledge and subsequently into useful applications. These tools are used for the collection, processing, analysis, and interpretation of the vast amounts of proteomics data. Management, analysis, and interpretation of large quantities of raw and processed data require a combination of various informatics technologies such as databases, sequence comparison, predictive models, and statistical tools. We have demonstrated the utility of bioinformatics in clinical proteomics through the analysis of the cancer antigen survivin and its suitability as a target for cancer immunotherapy.     

Mining the fecal proteome: from biomarkers to personalised medicine

Introduction: Fecal proteomics has gained increased prominence in recent years. It can provide insights into the diagnosis and surveillance of many bowel diseases by both identifying potential biomarkers in stool samples and helping identify disease-related pathways. Fecal proteomics has already shown its potential for the discovery and validation of biomarkers for colorectal cancer screening, and the analysis of fecal microbiota by MALDI-MS for the diagnosis of a range of bowel diseases is gaining clinical acceptance.

Areas covered: Based on a comprehensive analysis of the current literature, we introduce the range of sensitive and specific proteomics methods which comprise the current ‘Proteomics Toolbox’, explain how the integration of fecal proteomics with data processing/bioinformatics has been used for the identification of potential biomarkers for both CRC and other gut-related pathologies and analysis of the fecal microbiome, outline some of the current fecal assays in current clinical practice and introduce the concept of personalised medicine which these technologies will help inform.

Expert commentary: Integration of fecal proteomics with other proteomics and genomics strategies as well as bioinformatics is paving the way towards personalised medicine, which will bring with it improved global healthcare.     

Proteomics in environmental and technical microbiology

Nowadays, the field of proteomics encompasses various techniques for the analysis of the entirety of proteins in biological samples. Not only 2D electrophoresis as the primary method, but also MS‐based workflows and bioinformatic tools are being increasingly applied. In particular, research in microbiology was significantly influenced by proteomics during the last few decades. Hence, this review presents results of proteomic studies carried out in areas, such as fundamental microbiological research and biotechnology. In addition, the emerging field of metaproteomics is addressed because high‐throughput genome sequencing and high‐performance MS facilitate the access to such complex samples from microbial communities as found in sludge from wastewater treatment plants and biogas plants. Both current technical limitations and new concepts in this growing and important area are discussed. Moreover, it was convincingly shown that future prospective applications of proteomics in technical and environmental microbiology might also be closely connected with other Omics approaches as well as bioinformatics for systems biology studies.     

Proteomics approach to identify biomarkers for upper gastrointestinal cancer

Introduction: The prognosis for patients with upper gastrointestinal cancers remains dismal despite the development of multimodality therapies that incorporate surgery, chemotherapy, and radiotherapy. Early diagnosis and personalized treatment should lead to better prognosis. Given the advances in proteomic technologies over the past decades, proteomics promises to be the most effective technique to identify novel diagnostics and therapeutic targets.

Areas covered: For this review, keywords were searched in combination with ‘proteomics’ and ‘gastric cancer’ or ‘esophageal cancer’ in PubMed. Studies that evaluated proteomics associated with upper gastrointestinal cancer were identified through reading, with several studies quoted at second hand. We summarize the proteomics involved in upper gastrointestinal cancer and discuss potential biomarkers and therapeutic targets.

Expert commentary: In particular, the development of mass spectrometry has enabled detection of multiple proteins and peptides in more biological samples over a shorter time period and at lower cost than was previously possible. In addition, more sophisticated protein databases have allowed a wider variety of proteins in samples to be quantified. Novel biomarkers that have been identified by new proteomic technologies should be applied in a clinical setting.     

GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data

Background  

Spectral counting is a shotgun proteomics approach comprising the identification and relative quantitation of thousands of proteins in complex mixtures. However, this strategy generates bewildering amounts of data whose biological interpretation is a challenge.     

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer,COPD, and asthma

Introduction: Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges.

Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma.

Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.     

Advances and challenges in understanding the role of the lipid raft proteome in human health

ABSTRACT

Introduction: Phase separation as a biophysical principle drives the formation of liquid-ordered ‘lipid raft’ membrane microdomains in cellular membranes, including organelles. Given the critical role of cellular membranes in both compartmentalization and signaling, clarifying the roles of membrane microdomains and their mutual regulation of/by membrane proteins is important in understanding the fundamentals of biology, and has implications for health.

Areas covered: This article will consider the evidence for lateral membrane phase separation in model membranes and organellar membranes, critically evaluate the current methods for lipid raft proteomics and discuss the biomedical implications of lipid rafts.

Expert commentary: Lipid raft homeostasis is perturbed in numerous chronic conditions; hence, understanding the precise roles and regulation of the lipid raft proteome is important for health and medicine. The current technical challenges in performing lipid raft proteomics can be overcome through well-controlled experimental design and careful interpretation. Together with technical developments in mass spectrometry and microscopy, our understanding of lipid raft biology and function will improve through recognition of the similarity between organelle and plasma membrane lipid rafts and considered integration of published lipid raft proteomics data.