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.     

Quantitative mass spectrometry for colorectal cancer proteomics

This review documents the uses of quantitative MS applied to colorectal cancer (CRC) proteomics for biomarker discovery and molecular pathway profiling. Investigators are adopting various labeling and label-free MS approaches to quantitate differential protein levels in cells, tumors, and plasma/serum. We comprehensively review recent uses of this technology to examine mouse models of CRC, CRC cell lines, their secretomes and subcellular fractions, CRC tumors, CRC patient plasma/serum, and stool samples. For biomarker discovery these approaches are uncovering proteins with potential diagnostic and prognostic utility, while in vitro cell culture experiments are characterizing proteomic and phosphoproteomic responses to disrupted signaling pathways due to mutations or to inhibition of drugable enzymes.     

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.     

Application of proteomics in the study of rodent models of cancer

The molecular and cellular mechanisms underlying the multistage processes of cancer progression and metastasis are complex and strictly depend on the interplay between tumor cells and surrounding tissues. Identification of protein aberrations in cancer pathophysiology requires a physiologically relevant experimental model. The mouse offers such a model to identify protein changes associated with tumor initiation and progression, metastasis development, tumor/microenvironment interplay, and treatment responses. Furthermore, the mouse model offers the ability to collect samples at any stage in tumor development from highly matched disease cases and controls with identical environmental and genetic backgrounds, thus providing an excellent method for biomarker discovery. Xenograft and genetically engineered mouse models have been widely used to identify proteomic patterns in tumor tissues and plasma samples associated with different stages of human cancer, including early cancer detection and development of metastasis. Here, we review proteomic strategies to identify proteins involved in key cancer processes within such animal models as well as biomarkers for diagnosis, prognosis, and monitoring of cancer progression and treatment response. Central to such studies is the ability to ensure at an early stage that the identified proteins are of clinical relevance by examining relevant specimens from larger cohorts of cancer patients.     

Proteomic-based approach for biomarkers discovery in early detection of invasive urothelial carcinoma

Identification of reliable non-invasive markers for the detection of invasive phenotype of urothelial carcinoma is needed. This study characterizes and compares protein expression profiles of adjacent non-neoplastic urothelium and invasive urothelial carcinoma to identify biomarkers for early detection of de novo bladder cancer. Differences in protein expression between adjacent non-neoplastic and high-grade, stage T4, grade 3 invasive urothelial carcinoma tissues were investigated using 2-DE, MALDI-TOF-MS, and data processing. Ingenuity Pathway Analysis (IPA) was applied to examine the biological mechanisms represented by the altered proteins. The 2-DE of the adjacent non-neoplastic urothelium and invasive urothelial carcinoma showed reproducibly similar proteomic mapping for each group distinguishing adjacent non-neoplastic urothelium from invasive urothelial carcinoma. Twenty-one proteins were altered in expression and one of these proteins, Choroideremia-like protein (CHML) was significantly overexpressed (p<0.005) and therefore was analyzed further using IHC and Western blot. Urothelial carcinoma presented an elevated expression of CHML but not adjacent non-neoplastic or normal bladder tissues. IPA revealed the involvement of CHML in cell morphology, cellular assembly, and organization. Further investigation is warranted to elucidate the biological significance of CHML and to validate its role as a biomarker for early detection of invasive urothelial carcinoma de novo.     

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.     

Recent advances in targeted proteomics for clinical applications

MS-based approaches using targeted methods have been widely adopted by the proteomics community to study clinical questions such as the evaluation of biomarkers. At present, the most widely used targeted MS method is the SRM technique typically performed on a triple quadrupole instrument. However, the high analytical demands for performing clinical studies in combination with the extreme complexity of the samples involved are a serious challenge. The segmentation of the biomarker evaluation workflow has only partially alleviated these issues by differently balancing the analytical requirements and throughput at different stages of the process. The recent introduction of targeted high-resolution and accurate-mass analyses on fast sequencing mass spectrometers operated in parallel reaction monitoring (PRM) mode offers new avenues to conduct clinical studies and thus overcome some of the limitations of the triple quadrupole instrument. This article discusses the attributes and specificities of the PRM technique, in terms of experimental design, execution, and data analysis, and the implications for biomarker evaluation. The benefits of PRM on data quality and the impact on the consistency of results are highlighted and the definitive progress on the overall output of clinical studies, including high throughput, is discussed.     

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.     

Semi-automated sample preparation for plasma proteomics

The discovery of new biomarkers will be an essential step to enhance our ability to better diagnose and treat human disease. The proteomics research community has recently increased its use of human blood (plasma/serum) as a sample source for these discoveries. However, while blood is fairly non-invasive and readily available as a specimen, it is not easily analyzed by liquid chromatography (LC)/mass spectrometry (MS), because of its complexity. Therefore, sample preparation is a crucial step prior to the analysis of blood. This sample preparation must also be standardized in order to gain the most information from these valuable samples and to ensure reproducibility. We have designed a semi-automated and highly parallel procedure for the preparation of human plasma samples. Our process takes the samples through eight successive steps before analysis by LC/MS: (1) receipt, (2) reformatting, (3) filtration, (4) depletion, (5) concentration determination and normalization, (6) digestion, (7) extraction, and (8) randomization, triplication, and lyophilization. These steps utilize a number of different liquid handlers and liquid chromatography (LC) systems. This process enhances our ability to discover new biomarkers from human plasma.     

Quantitative proteomics in cardiovascular research: Global and targeted strategies

Extensive technical advances in the past decade have substantially expanded quantitative proteomics in cardiovascular research. This has great promise for elucidating the mechanisms of cardiovascular diseases and the discovery of cardiac biomarkers used for diagnosis and treatment evaluation. Global and targeted proteomics are the two major avenues of quantitative proteomics. While global approaches enable unbiased discovery of altered proteins via relative quantification at the proteome level, targeted techniques provide higher sensitivity and accuracy, and are capable of multiplexed absolute quantification in numerous clinical/biological samples. While promising, technical challenges need to be overcome to enable full utilization of these techniques in cardiovascular medicine. Here, we discuss recent advances in quantitative proteomics and summarize applications in cardiovascular research with an emphasis on biomarker discovery and elucidating molecular mechanisms of disease. We propose the integration of global and targeted strategies as a high-throughput pipeline for cardiovascular proteomics. Targeted approaches enable rapid, extensive validation of biomarker candidates discovered by global proteomics. These approaches provide a promising alternative to immunoassays and other low-throughput means currently used for limited validation.     

Wireless sensor networks for rehabilitation applications: Challenges and opportunities

Rehabilitation supervision has emerged as a new application of wireless sensor networks (WSN), with unique communication, signal processing and hardware design requirements. It is a broad and complex interdisciplinary research area on which more than one hundred papers have been published by several research communities (electronics, bio-mechanical, control and computer science). In this paper, we present WSN for rehabilitation supervision with a focus on key scientific and technical challenges that have been solved as well as interdisciplinary challenges that are still open. We thoroughly review existing projects conducted by several research communities involved in this exciting field. Furthermore, we discuss the open research issues and give directions for future research works. Our aim is to gather information that encourage engineers, clinicians and computer scientists to work together in this field to tackle the arising challenges. We believe that bridging researchers with different scientific backgrounds could have a significant impact on the development of WSN for rehabilitation and could improve the way rehabilitation is provided today.     

The chicken model of spontaneous ovarian cancer

The chicken is a unique experimental model for studying the spontaneous onset and progression of ovarian cancer (OVC). The prevalence of OVC in chickens can range from 5 to 35% depending on age, genetic strain, reproductive history, and diet. Furthermore, the chicken presents epidemiological, morphological, and molecular traits that are similar to human OVC making it a relevant experimental model for translation research. Similarities to humans include associated increased risk of OVC with the number of ovulations, common histopathological subtypes including high-grade serous, and molecular-level markers or pathways such as CA-125 expression and p53 mutation frequency.  Collectively, the similarities between chicken and human OVC combined with a tightly controlled genetic background and predictable onset window provides an outstanding experimental model for studying the early events and progression of spontaneous OVC tumors under controlled environmental conditions. This review will cover the existing literature on OVC in the chicken and highlight potential opportunities for further exploitation (e.g. biomarkers, prevention, treatment, and genomics).     

智能合约Gas优化综述

区块链2.0最显著的特征是增加了对智能合约的支持,这使得区块链拥有了运行各种应用程序的能力. 智能合约是一种根据预先定义的代码逻辑自动运行的计算机软件. 区别于传统软件,区块链技术赋予了智能合约不依赖可信中心机构而在相互不信任的节点上正确执行的能力,使其在数字支付、共享经济等领域被广泛地应用. 为了防止滥用智能合约导致计算资源被浪费,以太坊等区块链向部署和执行智能合约这2种活动收取Gas（燃料）费用. 智能合约消耗的计算资源是决定费用高低的因素. 具有低效代码的智能合约浪费资源且易受攻击,此类智能合约的开发者和用户将承担不必要的费用. 因此,优化智能合约以节省资源已经成为开发者和研究者重点关注的问题. 首先详细分析了智能合约Gas优化所面临的主要挑战;然后回顾和总结了近年来提出的各种优化技术;最后展望了该研究方向的未来工作,旨在为智能合约的开发者和研究人员提供参考和借鉴.

     

Computational Intelligence and Games： Challenges and Opportunities

The last few decades have seen a phenomenal increase in the quality, diversity and pervasiveness of computer games. The worldwide computer games market is estimated to be worth around USD 21bn annually, and is predicted to continue to grow rapidly. This paper reviews some of the recent developments in applying computational intelligence （CI） methods to games, points out some of the potential pitfalls, and suggests some fruitful directions for future research.     

Capillary zone electrophoresis on-line coupled to mass spectrometry: A perspective application for clinical proteomics

Clinical proteomics, a rapidly growing field, intends to use specific diagnostic proteomic/peptidomic markers for initial diagnosis or prognosis of the progression of various diseases. Analyses of disease-associated markers in defined biological samples can provide valuable molecular diagnostic information for these diseases. This approach relies on sensitive and highly standardized modern analytical techniques. In the recent years, one of these technologies, CZE online coupled to MS (CZE-MS), has been increasingly used for the detection of peptide biomarkers (<20 kDa) in body fluids such as urine. This review presents the most relevant urinary proteomic studies addressing the application of CZE-MS in clinically relevant biomarker research between the years 2006 and 2014.     

Understanding the pathogenesis of endometriosis through proteomics: Recent advances and future prospects

Endometriosis is a complex gynecological disease, characterized by the presence and growth of endometrial tissue outside the uterus, resulting in pelvic pain and infertility. It occurs in 10% of women in their reproductive age. The viable endometrial cells enter the peritoneal cavity by retrograde menstruation, implant, and cause lesions ectopically; depending on their ability to survive, attach, grow, and invade. These “normal” endometrial cells turn “endometriotic” apparently because of inherent abnormalities present in them. Information on these molecular abnormalities is now being sought through proteomic approaches. Recent proteome-based comparisons between the eutopic endometrium from normal women and patients with endometriosis have revealed several proteins (many of which are shown to have a role in several cancers), of which a few have been validated as potential players in the etiology of endometriosis. After an initial in-flow of information from these proteome studies of eutopic endometrium, focus now needs to be expanded to the changes in the various protein PTMs and their upstream effectors present in these tissues. Early diagnosis of endometriosis through noninvasive means is the need of the hour as well—which would require the use of the presently existing immunoassays, along with the advancing MS-based proteomics. In this review, we aim to discuss these future thrust areas of human endometriosis proteomics and also present the proteomic advances made so far in understanding the molecular basis of endometriosis.