Phosphoproteomics: A possible route to novel biomarkers of breast cancer

Proteomics is rapidly transforming the way that cancer and other pathologies are investigated. The ability to identify hundreds of proteins and to compare their abundance in different clinical samples presents a unique opportunity for direct identification of novel disease markers. Furthermore, recent advances allow us to analyse and compare PTMs. This gives an additional dimension for defining a new class of protein biomarker based not only on abundance and expression but also on the occurrence of covalent modifications specific to a disease state or therapy response. Such modifications are often a consequence of the activation/inactivation of a particular disease related pathway. In this review we evaluate the available information on breast cancer related protein-phosphorylation events, illustrating the rationale for investigating this PTM as a target for breast cancer research with eventual clinical relevance. We present a critical survey of the published experimental strategies to study protein phosphorylation on a system wide scale and highlight recent specific advances in breast cancer phosphoproteomics. Finally we discuss the feasibility of establishing novel biomarkers for breast cancer based on the detection of patterns of specific protein phosphorylation events.     

Microfluidic platforms for discovery and detection of molecular biomarkers

Microfluidics has emerged as a promising platform for discovery and detection of molecular biomarkers recently. With this approach, the discovery of these biomarkers could be more efficient in time and consumes less samples and reagents. Furthermore, the entire discovery process could be automated since all the functional microfluidic devices such as micropumps and microvalves could be integrated on a single chip. Similarly, the detection of the discovered molecular biomarkers is also promising. Detection of nucleic acid biomarkers, protein biomarkers, and metabolite biomarkers has been demonstrated on microfluidic platforms recently. When compared with their large-scale counterparts, the miniature system can perform the detection of these biomarkers within less analysis time while a multiplexed detection scheme could be easily achieved. Furthermore, the entire detection process could be automated on the single chip as well. This review paper is therefore to review the recent development of microfluidic devices and systems for the discovery and detection of the molecular biomarker. Techniques for biomarker discovery, verification, and detection that have been adapted into microfluidics were first reviewed, and their advantages were highlighted. The new approach of biomarker screening based on in vitro-generated affinity reagents such as nucleic acid aptamers and peptide affinity reagents was then reviewed. Finally, in the biomarker detection section, this review placed a special emphasis on commercialized microfluidic-based diagnostics for molecular biomarkers.     

Adipokines: a treasure trove for the discovery of biomarkers for metabolic disorders

Adipose tissue is a major endocrine organ, releasing signaling and mediator proteins, termed adipokines, via which adipose tissue communicates with other organs. Expansion of adipose tissue in obesity alters adipokine secretion which may contribute to the development of metabolic diseases. Consequently, this correlation has emphasized the importance to further characterize the adipocyte secretion profile, and several attempts have been made to characterize the complex nature of the adipose tissue secretome by utilizing diverse proteomic profiling approaches. Although the entirety of human adipokines is still incompletely characterized, to date more than 600 potentially secretory proteins were identified providing a rich source to identify putative novel biomarkers associated with metabolic diseases.     

化学信息学与药物发现研究的开放性

建立在开源软件和公共数据库基础之上的生物信息学所获得的巨大成功,说明药物发现研究的开放性非常重要。然而以大量有机分子结构信息为主要研究对象的化学信息学,还没有达到所期望的开放性。通过对照这两类信息学研究在搜索引擎上查询的资源数量、在专业期刊发表论文的数量及影响因子等,发现编程语言和开发机制的开放性,是造成如此差别的重要原因。因此,化学信息学也应该走开放的道路．才能突破信息障碍,在更广阔的化学空间实现更有效的探索．找到更多、更好先导化合物,也建议在相关科研与教学中大力推广使用开源软件和动态编程语言。     

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.     

Quantitative proteomics for identification of cancer biomarkers

Quantitative proteomics can be used for the identification of cancer biomarkers that could be used for early detection, serve as therapeutic targets, or monitor response to treatment. Several quantitative proteomics tools are currently available to study differential expression of proteins in samples ranging from cancer cell lines to tissues to body fluids. 2-DE, which was classically used for proteomic profiling, has been coupled to fluorescence labeling for differential proteomics. Isotope labeling methods such as stable isotope labeling with amino acids in cell culture (SILAC), isotope-coded affinity tagging (ICAT), isobaric tags for relative and absolute quantitation (iTRAQ), and (18) O labeling have all been used in quantitative approaches for identification of cancer biomarkers. In addition, heavy isotope labeled peptides can be used to obtain absolute quantitative data. Most recently, label-free methods for quantitative proteomics, which have the potential of replacing isotope-labeling strategies, are becoming popular. Other emerging technologies such as protein microarrays have the potential for providing additional opportunities for biomarker identification. This review highlights commonly used methods for quantitative proteomic analysis and their advantages and limitations for cancer biomarker analysis.     

Neuroproteomics and systems biology-based discovery of protein biomarkers for traumatic brain injury and clinical validation

The rapidly growing field of neuroproteomics has expanded to track global proteomic changes underlying various neurological conditions such as traumatic brain injury (TBI), stroke, and Alzheimer's disease. TBI remains a major health problem with approximately 2?million incidents occurring annually in the United States, yet no affective treatment is available despite several clinical trials. The absence of brain injury diagnostic biomarkers was identified as a significant road-block to therapeutic development for brain injury. Recently, the field of neuroproteomics has undertaken major advances in the area of neurotrauma research, where several candidate markers have been identified and are being evaluated for their efficacy as biological biomarkers in the field of TBI. One scope of this review is to evaluate the current status of TBI biomarker discovery using neuroproteomics techniques, and at what stage we are at in their clinical validation. In addition, we will discuss the need for strengthening the role of systems biology and its application to the field of neuroproteomics due to its integral role in establishing a comprehensive understanding of specific brain disorder and brain function in general. Finally, to achieve true clinical input of these neuroproteomic findings, these putative biomarkers should be validated using preclinical and clinical samples and linked to clinical diagnostic assays including ELISA or other high-throughput assays.     

Genetic programming for computational pharmacokinetics in drug discovery and development

The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient’s organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesized compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient’s organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterize the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalization ability.

Discovery and validation of urinary biomarkers for prostate cancer

Only 30% of patients with elevated serum prostate specific antigen (PSA) levels who undergo prostate biopsy are diagnosed with prostate cancer (PCa). Novel methods are needed to reduce the number of unnecessary biopsies. We report on the identification and validation of a panel of 12 novel biomarkers for prostate cancer (PCaP), using CE coupled MS. The biomarkers could be defined by comparing first void urine of 51 men with PCa and 35 with negative prostate biopsy. In contrast, midstream urine samples did not allow the identification of discriminatory molecules, suggesting that prostatic fluids may be the source of the defined biomarkers. Consequently, first void urine samples were tested for sufficient amounts of prostatic fluid, using a prostatic fluid indicative panel (“informative” polypeptide panel; IPP). A combination of IPP and PCaP to predict positive prostate biopsy was evaluated in a blinded prospective study. Two hundred thirteen of 264 samples matched the IPP criterion. PCa was detected with 89% sensitivity, 51% specificity. Including age and percent free PSA to the proteomic signatures resulted in 91% sensitivity, 69% specificity.     

Vascular proteomics and the discovery process of clinical biomarkers: The case of TWEAK

In the last years, big efforts are devoted to the search of novel biomarkers. Proteomic approaches in healthy and pathological samples may help us to discern differential protein expression patterns. These identified proteins include potential culprits in pathological pathways and/or clinical biomarkers to identify individuals at risk. However, extensively validation must be carried out before their implementation into the clinical practice. Biomarkers need to discriminate between health and disease, detect preclinical disease stages, have impact on survival prediction, and add predictive value beyond traditional risk factors and global risk algorithms. Now, we summarize the data of soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK), a new cardiovascular biomarker identified by proteomic analysis. Decreased sTWEAK concentrations have been shown in patients with carotid atherosclerosis, coronary artery disease, congestive heart failure, peripheral artery disease, or chronic kidney disease (CKD). sTWEAK predicted adverse outcomes in patients with heart failure, myocardial infarction, and CKD. Finally, different drug regimens were able to modify sTWEAK plasma levels in patients with CKD. Although sTWEAK seems so far to fulfill the requisites in the development of a new biomarker, more large-scale studies are warranted to consolidate its usefulness.     

组合化学及其在药物开发中的应用

介绍了组合化学的基本概念和相关技术。组合化学起源于固相多肽合成,目前在包括药物开发在内的许多领域都得到了成功的应用。采用组合化学技术可以在短时间内迅速制备大量的化合物。介绍了化合物库的合成方法和分析、筛选方法。组合化学技术与高通量筛选技术相结合,在现代药物开发中已经起到了非常重要的作用。     

Methods for compound selection focused on hits and application in drug discovery

In the context of virtual screening calculations, a multiple fingerprint-based metric is applied to generate focused compound libraries by database searching. Different fingerprints are used to facilitate a similarity step for database mining, followed by a diversity step to assemble the final library. The method is applied, for example, to build libraries of limited size for hit-to-lead development efforts. In studies designed to inhibit a therapeutically relevant protein–protein interaction, small molecular hits were initially obtained by combined fingerprint- and structure-based virtual screening and used for the design of focused libraries. We review the applied virtual screening approach and report the statistics and results of screening as well as focused library design. While the structures of lead compounds cannot be disclosed, the analysis is thought to provide an example of the interplay of different methods applied in practical lead identification.     

Current application of conformal prediction in drug discovery

We present two applications of conformal prediction relevant to drug discovery. The first application is around interpretation of predictions and the second one around the selection of compounds to progress in a drug discovery project setting.     

A catalogue of proteins released by colorectal cancer cells in vitro as an alternative source for biomarker discovery

Improved methods for the early diagnosis of colorectal cancer by way of sensitive and specific tumour markers are highly desirable. Therefore, efficient strategies for biomarker discovery are urgently needed. Here we present an approach that is based on the direct experimental access to proteins released by SW620 human colorectal cancer cells in vitro. A 2-D map and a catalogue of this subproteome - here termed the secretome - were established comprising more than 320 identified proteins which translate into approximately 220 distinct genes. As the majority of the secretome constituents were nominally cellular proteins, we directly compared the secretome and the total proteome by 2-D-DIGE analysis. We provide evidence that unspecific release through cell death, classical secretion, ectodomain shedding, and exosomal release contribute to the secretome in vitro, presumably reflecting the mechanisms in vivo which lead to the occurrence of tumour-specific proteins in the circulation. These data together with the fact that the SW620 secretome catalogue, as presented here, does comprise a large number of known and novel biomarker candidates, validates our approach to isolate and characterize the tumour cell secretome in vitro as a rich source for tumour biomarkers.     

Surface plasmon resonance imaging (SPRi) as an alternative technique for rapid and quantitative screening of small molecules, useful in drug discovery

Surface plasmon resonance imaging (SPRi) is a label free technology for biomolecular interaction, which gives access to binding kinetic parameters from real time acquisition. It offers the possibility to test in a single run a large number of interactions, allowing rapid identification of the most suitable compounds toward a given biological entity. Until now, this technique has proven to be relevant for interaction between relatively large molecules (protein, antibodies, DNA) but has not been challenged yet for the screening of small molecules that can be of interested in the field of drug discovery. As a proof a principle, we have used SPRi to screen for interaction of several small molecules, referred to as G4-ligands on G-quadruplex DNA. This technology allowed to easy discrimination of the binding properties of four G4-ligands on quadruplex DNA models.