Chemical and pathway proteomics: powerful tools for oncology drug discovery and personalized health care

Selective Recognition of Amino Acids and Peptides by Small Supramolecular Receptors

To this day, the recognition and high affinity binding of biomolecules in water by synthetic receptors remains challenging, while the necessity for systems for their sensing, transport and modulation persists. This problematic is prevalent for the recognition of peptides, which not only have key roles in many biochemical pathways, as well as having pharmacological and biotechnological applications, but also frequently serve as models for the study of proteins. Taking inspiration in nature and on the interactions that occur between several receptors and peptide sequences, many researchers have developed and applied a variety of different synthetic receptors, as is the case of macrocyclic compounds, molecular imprinted polymers, organometallic cages, among others, to bind amino acids, small peptides and proteins. In this critical review, we present and discuss selected examples of synthetic receptors for amino acids and peptides, with a greater focus on supramolecular receptors, which show great promise for the selective recognition of these biomolecules in physiological conditions. We decided to focus preferentially on small synthetic receptors (leaving out of this review high molecular weight polymeric systems) for which more detailed and accurate molecular level information regarding the main structural and thermodynamic features of the receptor biomolecule assemblies is available.

Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD

BACKGROUND

Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS.

OBJECTIVES

This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article.

CONCLUSION

By this thorough study, we have observed that in LBVS algorithms, Support Vector Machines (SVM) and Random Forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.

Emerging and Enabling Technologies in Biodefense

The emergence of new biotechnologies provides great promise for biodefense, especially for key objectives of biosurveillance and early warning, microbial forensics, risk and threat assessment, horizon scanning in biotechnology, and medical countermeasure (MCM) development, scale-up, and delivery. Understanding and leveraging the newly developed capabilities afforded by emerging biotechnologies require knowledge about cutting-edge research and its real or proposed application(s), the process through which biotechnologies advance, and the educational and research infrastructure that promotes multi-disciplinary science. Innovation in research and technology development are driven by sector-specific needs and the convergence of the physical, chemical, material, computer, engineering, and/or life sciences. Biotechnologies developed for other sectors could be applied to biodefense, especially if the individuals involved are able to innovate in concept design and development. Of all biodefense objectives, biosurveillance seems to have reaped the most benefit from emerging biotechnologies, specifically the integration and analysis of diverse clinical, biological, demographic, and other relevant data. More recently, scientists have begun applying synthetic biology, genomics, and microfluidics to the development of new products and platforms for MCMs. Unlike these objectives, investments in microbial forensics have been few, limiting its ability to harness biotechnology advances for collecting and analyzing data. Looking to the future, emerging biotechnologies can provide new opportunities for enhancing biodefense by addressing capability gaps.

Identification and validation nucleolin as a target of curcumol in nasopharyngeal carcinoma cells

Identification of the specific protein target(s) of a drug is a critical step in unraveling its mechanisms of action (MOA) in many natural products. Curcumol, isolated from well known Chinese medicinal plant Curcuma zedoary, has been shown to possess multiple biological activities. It can inhibit nasopharyngeal carcinoma (NPC) proliferation and induce apoptosis, but its target protein(s) in NPC cells remains unclear. In this study, we employed a mass spectrometry-based chemical proteomics approach reveal the possible protein targets of curcumol in NPC cells. Cellular thermal shift assay (CETSA), molecular docking and cell-based assay was used to validate the binding interactions. Chemical proteomics capturing uncovered that NCL is a target of curcumol in NPC cells, Molecular docking showed that curcumol bound to NCL with an -7.8 kcal/mol binding free energy. Cell function analysis found that curcumol's treatment leads to a degradation of NCL in NPC cells, and it showed slight effects on NP69 cells. In conclusion, our results providing evidences that NCL is a target protein of curcumol. We revealed that the anti-cancer effects of curcumol in NPC cells are mediated, at least in part, by NCL inhibition.

SIGNIFICANCE

Many natural products showed high bioactivity, while their mechanisms of action (MOA) are very poor or completely missed. Understanding the MOA of natural drugs can thoroughly exploit their therapeutic potential and minimize their adverse side effects. Identification of the specific protein target(s) of a drug is a critical step in unraveling its MOA. Compound-centric chemical proteomics is a classic chemical proteomics approach which integrates chemical synthesis with cell biology and mass spectrometry (MS) to identify protein targets of natural products determine the drug mechanism of action, describe its toxicity, and figure out the possible cause of off-target. It is an affinity-based chemical proteomics method to identify small molecule-protein interactions through affinity chromatography approach coupled with mass spectrometry, has been conventionally used to identify target proteins and has yielded good results. Curcumol, has shown effective inhibition on Nasopharyngeal Carcinoma (NPC) Cells, interacted with NCL and then initiated the anti-tumor biological effect. This research demonstrated the effectiveness of chemical proteomics approaches in natural drugs molecular target identification, revealing and understanding of the novel mechanism of actions of curcumol is crucial for cancer prevention and treatment in nasopharynx cancer.

Scores for standardization of on-tissue digestion of formalin-fixed paraffin-embedded tissue in MALDI-MS imaging

On-slide digestion of formalin-fixed and paraffin-embedded human biopsy tissue followed by mass spectrometry imaging of resulting peptides may have the potential to become an additional analytical modality in future ePathology. Multiple workflows have been described for dewaxing, antigen retrieval, digestion and imaging in the past decade. However, little is known about suitable statistical scores for method comparison and systematic workflow standardization required for development of processes that would be robust enough to be compatible with clinical routine. To define scores for homogeneity of tissue processing and imaging as well as inter-day repeatability for five different processing methods, we used human liver and gastrointestinal stromal tumor tissue, both judged by an expert pathologist to be >98% histologically homogeneous. For mean spectra-based as well as pixel-wise data analysis, we propose the coefficient of determination R², the natural fold-change (natFC) value and the digest efficiency DE% as readily accessible scores. Moreover, we introduce two scores derived from principal component analysis, the variance of the mean absolute deviation, MAD, and the interclass overlap, J_overlap, as computational scores that may help to avoid user bias during future workflow development. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Identification of plasma protein markers common to patients with malignant tumour and Abnormal Savda in Uighur medicine: a prospective clinical study

Background

Traditional Uighur medicine shares an origin with Greco-Arab medicine. It describes the health of a human body as the dynamic homeostasis of four normal Hilits (humours), known as Kan, Phlegm, Safra, and Savda. An abnormal change in one Hilit may cause imbalance among the Hilits, leading to the development of a syndrome. Abnormal Savda is a major syndrome of complex diseases that are associated with common biological changes during disease development. Here, we studied the protein expression profile common to tumour patients with Abnormal Savda to elucidate the biological basis of this syndrome and identify potential biomarkers associated with Abnormal Savda.

Methods

Patients with malignant tumours were classified by the diagnosis of Uighur medicine into two groups: Abnormal Savda type tumour (ASt) and non-Abnormal Savda type tumour (nASt), which includes other syndromes. The profile of proteins that were differentially expressed in ASt compared with nASt and normal controls (NC) was analysed by iTRAQ proteomics and evaluated by bioinformatics using MetaCore™ software and an online database. The expression of candidate proteins was verified in all plasma samples by enzyme-linked immunosorbent assay (ELISA).

Results

We identified 31 plasma proteins that were differentially expressed in ASt compared with nASt, of which only 10 showed quantitatively different expression between ASt and NC. Bioinformatics analysis indicated that most of these proteins are known biomarkers for neoplasms of the stomach, breast, and lung. ELISA detection showed significant upregulation of plasma SAA1 and SPP24 and downregulation of PIGR and FASN in ASt compared with nASt and NC (p < 0.05).

Conclusions

Abnormal Savda may be causally associated with changes in the whole regulation network of protein expression during carcinogenesis. The expression of potential biomarkers might be used to distinguish Abnormal Savda from other syndromes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12906-015-0526-6) contains supplementary material, which is available to authorized users.

A new chemical probe for quantitative proteomic profiling of fibroblast growth factor receptor and its inhibitors

Recent advances in mass spectrometry-based chemical proteomics allow unbiased analysis of drug-target interactions under close to physiological conditions. In this study, we designed and synthesized two small molecule probes targeting fibroblast growth factor receptors (FGFRs) and applied them to evaluate the selectivity profiles of the FGFR inhibitors Dovitinib and Orantinib. Probe F2 was capable of enriching all members of the FGF receptor family as well as other kinases involved in cancer such as KDR, FLT4 and RET from lysates of cancer cells or human placenta tissue. In combination with the established Kinobeads™ approach, probe F2 facilitated the identification of the target spectrum of the two inhibitors confirming many of the previously identified (off-) targets such as AURKA, FLT4-VEGFR3, IKBKE and PDGFRβ. The newly synthesized probe enlarges the arsenal of chemical proteomic tools for the expression profiling of kinases and selectivity profiling of their inhibitors. It will also be useful in applications aiming at a better understanding of a drug's cellular mechanisms of action as well as highlighting potential beneficial or adverse side effects.

BIOLOGICAL SIGNIFICANCE

The synthesis of a new chemical affinity probe targeting FGF-receptors and many other kinases improved the general scope of drug selectivity profiling by chemical proteomics. The application of the developed chemical tool identified most of the known targets for the advanced clinical kinase inhibitors Dovitinib and Orantinib thus exemplify the practical utility of the developed probe and the results obtained shed further light on how these drugs exert their anti-cancer activity in cells. More generally speaking, the significance of the work is that the molecular tools presented here extend the application scope of kinobeads based kinase profiling to FGFR/VEGFR/PDGFR families, which thus may be generically employed for selectivity profiling of kinase inhibitors using chemical proteomics. The overall aim of such studies is to improve our understanding of how target as well as off-target profiles can be used to assess or predict the therapeutic efficacy of a drug.

A miniaturized chemical proteomic approach for target profiling of clinical kinase inhibitors in tumor biopsies

While targeted therapy based on the idea of attenuating the activity of a preselected, therapeutically relevant protein has become one of the major trends in modern cancer therapy, no truly specific targeted drug has been developed and most clinical agents have displayed a degree of polypharmacology. Therefore, the specificity of anticancer therapeutics has emerged as a highly important but severely underestimated issue. Chemical proteomics is a powerful technique combining postgenomic drug-affinity chromatography with high-end mass spectrometry analysis and bioinformatic data processing to assemble a target profile of a desired therapeutic molecule. Due to high demands on the starting material, however, chemical proteomic studies have been mostly limited to cancer cell lines. Herein, we report a down-scaling of the technique to enable the analysis of very low abundance samples, as those obtained from needle biopsies. By a systematic investigation of several important parameters in pull-downs with the multikinase inhibitor bosutinib, the standard experimental protocol was optimized to 100 μg protein input. At this level, more than 30 well-known targets were detected per single pull-down replicate with high reproducibility. Moreover, as presented by the comprehensive target profile obtained from miniaturized pull-downs with another clinical drug, dasatinib, the optimized protocol seems to be extendable to other drugs of interest. Sixty distinct human and murine targets were finally identified for bosutinib and dasatinib in chemical proteomic experiments utilizing core needle biopsy samples from xenotransplants derived from patient tumor tissue. Altogether, the developed methodology proves robust and generic and holds many promises for the field of personalized health care.

Characterization of a chemical affinity probe targeting Akt kinases

Protein kinases are key regulators of cellular processes, and aberrant function is often associated with human disease. Consequently, kinases represent an important class of therapeutic targets and about 20 kinase inhibitors (KIs) are in clinical use today. Detailed knowledge about the selectivity of KIs is important for the correct interpretation of their pharmacological and systems biological effects. Chemical proteomic approaches for systematic kinase inhibitor selectivity profiling have emerged as important molecular tools in this regard, but the coverage of the human kinome is still incomplete. Here, we describe a new affinity probe targeting Akt and many other members of the AGC kinase family that considerably extends the scope of KI profiling by chemical proteomics. In combination with the previously published kinobeads, the synthesized probe was applied to selectivity profiling of the Akt inhibitors GSK690693 and GSK2141795 in human cancer cells. The results confirmed the inhibition of all Akt isoforms and of a number of known as well as CDC42BPB as a novel putative target for GSK690693. This work also established, for the first time, the kinase selectivity profile of the clinical phase I drug GSK2141795 and identified PRKG1 as a low nanomolar kinase target as well as the ATP-dependent 5'-3' DNA helicase ERCC2 as a potential new non-kinase off-target.

Mass spectrometry-based proteomics in molecular diagnostics: discovery of cancer biomarkers using tissue culture

Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomics-based approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA) were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches.

Mass spectrometry-based chemoproteomic approaches

The term "chemical proteomics" refers to a research area at the interface of chemistry, biochemistry, and cell biology that focuses on studying the mechanism of action of bioactive small molecule compounds, which comprises the mapping of their target proteins and their impact on protein expression and posttranslational modifications in target cells or tissues of interest on a proteome-wide level. For this purpose, a large arsenal of approaches has emerged in recent years, many of which employing quantitative mass spectrometry. This review briefly summarizes major experiment types employed in current chemical proteomics research.

Effects of ubiquitin C-terminal hydrolase L1 deficiency on mouse ova

Maternal proteins are rapidly degraded by the ubiquitin-proteasome system during oocyte maturation in mice. Ubiquitin C-terminal hydrolase L1 (UCHL1) is highly and specifically expressed in mouse ova and is involved in the polyspermy block. However, the role of UCHL1 in the underlying mechanism of polyspermy block is poorly understood. To address this issue, we performed a comprehensive proteomic analysis to identify maternal proteins that were relevant to the role of UCHL1 in mouse ova using UCHL1-deficient gad. Furthermore, we assessed morphological features in gad mouse ova using transmission electron microscopy. NACHT, LRR, and PYD domain-containing (NALP) family proteins and endoplasmic reticulum (ER) chaperones were identified by proteomic analysis. We also found that the 'maternal antigen that embryos require' (NLRP5 (MATER)) protein level increased significantly in gad mouse ova compared with that in wild-type mice. In an ultrastructural study, gad mouse ova contained less ER in the cortex than in wild-type mice. These results provide new insights into the role of UCHL1 in the mechanism of polyspermy block in mouse ova.

Affinity-based profiling of dehydrogenase subproteomes

The high cost of drug discovery and development requires more efficient approaches to the identification and inhibition of tractable protein targets. One strategy is to pursue families of proteins that already possess affinity for a drug lead scaffold, where that scaffold plays the dual role of serving (a) when tethered to a resin, as a ligand to purify a subproteome of interest, and (b) as a lead molecule that has the potential for optimization for a given member of the subproteome. Here, we describe an example of the purification of a subproteome using a scaffold tailored to the dehydrogenase family of enzymes. Combined with modern LC-MS/MS methods and subsequent searching of proteome databases, such affinity chromatography strategies can be used to purify and identify any proteins with affinity for the scaffold molecule. The method is exemplified using the CRAA (catechol rhodanine acetic acid) privileged scaffold, which is tailored to dehydrogenases. CRAA affinity column chromatography, combined with LC-MS/MS, is described as a method for profiling dehydrogenase subproteomes.

Chemical proteomics in drug discovery

Real-world drug discovery and development remains a notoriously unproductive and increasingly uneconomical process even in the Omics era. The dominating paradigm in the industry continues to be target-based drug design, with an increased perception of the role of signaling pathways in homeostasis and in disease. Since proteins represent the major type of drug targets, proteomics-based approaches, which study proteins under relatively physiological conditions, have great potential if they can be reduced to practice such that they successfully complement the arsenal of drug discovery techniques. This chapter discusses examples of drug discovery processes where chemical proteomics-based assays using native endogenous proteins should have substantial impact.

Mass spectrometry-based clinical proteomics: head-and-neck cancer biomarkers and drug-targets discovery

Mass spectrometry (MS)-based proteomics is a rapidly developing technology for both qualitative and quantitative analyses of proteins, and investigations into protein posttranslational modifications, subcellular localization, and interactions. Recent advancements in MS have made tremendous impact on the throughput and comprehensiveness of cancer proteomics, paving the way to unraveling deregulated cellular pathway networks in human malignancies. In turn, this knowledge is rapidly being translated into the discovery of novel potential cancer markers (PCMs) and targets for molecular therapeutics. Head-and-neck cancer is one of the most morbid human malignancies with an overall poor prognosis and severely compromised quality of life. Early detection and novel therapeutic strategies are urgently needed for more effective disease management. The characterizations of protein profiles of head-and-neck cancers and non-malignant tissues, with unprecedented sensitivity and precision, are providing technology platforms for identification of novel PCMs and drug targets. Importantly, low-abundance proteins are being identified and characterized, not only from the tumor tissues, but also from bodily fluids (plasma, saliva, and urine) in a high-throughput and unbiased manner. This review is a critical appraisal of recent advances in MS-based proteomic technologies and platforms for facilitating the discovery of biomarkers and novel drug targets in head-and-neck cancer. A major challenge in the discovery and verification of these cancer biomarkers is the typically limited availability of well-characterized and adequately stored clinical samples in tumor and sera banks, collected using recommended procedures, and with detailed information on clinical, pathological parameters, and follow-up. Most biomarker discovery studies use limited number of clinical samples and verification of cancer markers in large number of samples is beyond the scope of a single laboratory. The validation of these potential markers in large sample cohorts in multicentric studies is needed for their translation from the bench to the bedside.

Quantitative analysis of cell surface membrane proteins using membrane-impermeable chemical probe coupled with 18O labeling

We report a mass spectrometry-based strategy for quantitative analysis of cell surface membrane proteome changes. The strategy includes enrichment of surface membrane proteins using a membrane-impermeable chemical probe followed by stable isotope (18)O labeling and LC-MS analysis. We applied this strategy for enriching membrane proteins expressed by Shewanella oneidensis MR-1, a Gram-negative bacterium with known metal-reduction capability via extracellular electron transfer between outer membrane proteins and extracellular electron receptors. LC/MS/MS analysis resulted in the identification of about 400 proteins with 79% of them being predicted to be membrane localized. Quantitative aspects of the membrane enrichment were shown by peptide level (16)O and (18)O labeling of proteins from wild-type and mutant cells (generated from deletion of a type II secretion protein, GspD) prior to LC-MS analysis. Using a chemical probe labeled pure protein as an internal standard for normalization, the quantitative data revealed reduced abundances in Delta gspD mutant cells of many outer membrane proteins including the outer membrane c-type cytochromes OmcA and MtrC, in agreement with a previous report that these proteins are substrates of the type II secretion system.

Phosphoproteomic characterization of PYK2 signaling pathways involved in osteogenesis

The PYK2 tyrosine kinase is a negative regulator of bone formation, but aside from the requirement for PYK2 kinase activity there has been little progress toward understanding of the molecular mechanism involved in this function. To gain insight into the signaling pathways modulated by PYK2 we sought to identify PYK2 substrates. Challenges inherent to a quantitative phosphoproteomic analysis for non-receptor tyrosine kinases were overcome by employing an inducible PYK2 overexpression system in NIH3T3 cells in combination with a selective PYK2 inhibitor. The identification of a number of known PYK2 substrates and interacting partners validated the methodology. Results of the inducible cell system were extended to a cell model of osteogenesis, examining the effect of the PYK2 inhibitor on the phosphorylation state of targets identified in the phosphoproteomic study. Consistent with phosphoproteomic analysis, increased osteogenesis associated with a selective PYK2 inhibitor was accompanied by reduced phosphorylation of paxillin, Gab1 and p130(Cas), along with reduction of phosphorylation levels of the Met activation loop. These results further confirmed the utility of the methodology and point to a previously unknown bi-directional activation pathway between PYK2 and Met.

Glioma pathophysiology: insights emerging from proteomics

Proteomics is increasingly employed in both neurological and oncological research to provide insight into the molecular basis of disease but rarely has a coherent, novel pathophysiological insight emerged. Gliomas account for >50% of adult primary intracranial tumors, with malignant gliomas (anaplastic astrocytomas and glioblastoma multiforme) being the most common. In glioma, the application of proteomic technology has identified altered protein expression but without consistency of these alterations or their biological significance being established. A systematic review of multiple independent proteomic analyses of glioma has demonstrated alterations of 99 different proteins. Importantly 10 of the 99 proteins found differentially expressed in glioma [PHB, Hsp20, serum albumin, epidermal growth factor receptor (EGFR), EA-15, RhoGDI, APOA1, GFAP, HSP70, PDIA3] were identified in multiple publications. An assessment of protein-protein interactions between these proteins compiled using novel web-based technology, revealed a robust and cohesive network for glioblastoma. The protein network discovered (containing TP53 and RB1 at its core) compliments recent findings in genomic studies of malignant glioma. The novel perspective provided by network analysis indicates that the potential of this technology to explore crucial aspects of glioma pathophysiology can now be realized but only if the conceptual and technical limitations highlighted in this review are addressed.

Identification and characterization of molecular targets of natural products by mass spectrometry

Natural products, and their derivatives and mimics, have contributed to the development of important therapeutics to combat diseases such as infections and cancers over the past decades. The value of natural products to modern drug discovery is still considerable. However, its development is hampered by a lack of a mechanistic understanding of their molecular action, as opposed to the emerging molecule-targeted therapeutics that are tailored to a specific protein target(s). Recent advances in the mass spectrometry-based proteomic approaches have the potential to offer unprecedented insights into the molecular action of natural products. Chemical proteomics is established as an invaluable tool for the identification of protein targets of natural products. Small-molecule affinity selection combined with mass spectrometry is a successful strategy to "fish" cellular targets from the entire proteome. Mass spectrometry-based profiling of protein expression is also routinely employed to elucidate molecular pathways involved in the therapeutic and possible toxicological responses upon treatment with natural products. In addition, mass spectrometry is increasingly utilized to probe structural aspects of natural products-protein interactions. Limited proteolysis, photoaffinity labeling, and hydrogen/deuterium exchange in conjunction with mass spectrometry are sensitive and high-throughput strategies that provide low-resolution structural information of non-covalent natural product-protein complexes. In this review, we provide an overview on the applications of mass spectrometry-based techniques in the identification and characterization of natural product-protein interactions, and we describe how these applications might revolutionize natural product-based drug discovery.

Biomarker Validation for Aging: Lessons from mtDNA Heteroplasmy Analyses in Early Cancer Detection

The anticipated biological and clinical utility of biomarkers has attracted significant interest recently. Aging and early cancer detection represent areas active in the search for predictive and prognostic biomarkers. While applications differ, overlapping biological features, analytical technologies and specific biomarker analytes bear comparison. Mitochondrial DNA (mtDNA) as a biomarker in both biological models has been evaluated. However, it remains unclear whether mtDNA changes in aging and cancer represent biological relationships that are causal, incidental, or a combination of both. This article focuses on evaluation of mtDNA-based biomarkers, emerging strategies for quantitating mtDNA admixtures, and how current understanding of mtDNA in aging and cancer evolves with introduction of new technologies. Whether for cancer or aging, lessons from mtDNA based biomarker evaluations are several. Biological systems are inherently dynamic and heterogeneous. Detection limits for mtDNA sequencing technologies differ among methods for low-level DNA sequence admixtures in healthy and diseased states. Performance metrics of analytical mtDNA technology should be validated prior to application in heterogeneous biologically-based systems. Critical in evaluating biomarker performance is the ability to distinguish measurement system variance from inherent biological variance, because it is within the latter that background healthy variability as well as high-value, disease-specific information reside.

Target profiling of small molecules by chemical proteomics

The medical and pharmaceutical communities are facing a dire need for new druggable targets, while, paradoxically, the targets of some drugs that are in clinical use or development remain elusive. Many compounds have been found to be more promiscuous than originally anticipated, which can potentially lead to side effects, but which may also open up additional medical uses. As we move toward systems biology and personalized medicine, comprehensively determining small molecule-target interaction profiles and mapping these on signaling and metabolic pathways will become increasingly necessary. Chemical proteomics is a powerful mass spectrometry-based affinity chromatography approach for identifying proteome-wide small molecule-protein interactions. Here we will provide a critical overview of the basic concepts and recent advances in chemical proteomics and review recent applications, with a particular emphasis on kinase inhibitors and natural products.

Proteomics strategy for quantitative protein interaction profiling in cell extracts

We report a proteomics strategy to both identify and quantify cellular target protein interactions with externally introduced ligands. We determined dissociation constants for target proteins interacting with the ligand of interest by combining quantitative mass spectrometry with a defined set of affinity purification experiments. We demonstrate the general utility of this methodology in interaction studies involving small-molecule kinase inhibitors, a tyrosine-phosphorylated peptide and an antibody as affinity ligands.

Revealing promiscuous drug-target interactions by chemical proteomics

The (poly-)pharmacological activities of a drug can only be understood if its interactions with cellular components are comprehensively characterized. Mass spectrometry-based chemical proteomics approaches have recently emerged as powerful tools for the characterization of drug-target interactions in samples from cell lines and tissues. At the same time, off-target activities can be identified. This information can contribute toward optimization of candidate drug molecules and reduction of side effects. In this review, we describe recent advances in chemical proteomics and outline potential applications in drug discovery.

Off-target decoding of a multitarget kinase inhibitor by chemical proteomics

Unbiased: Chemical proteomics was used to profile compound interactions in an unbiased fashion. We present here the application of different compound-immobilization routes for decoding nonprotein kinase off-targets of the multitarget kinase inhibitor C1, which interacts with distinct compound moieties. Since the approval of the first selective tyrosine kinase inhibitor, imatinib, various drugs have been developed to target protein kinases. However, due to a high degree of structural conservation of the ATP binding site, off-target effects have been reported for several drugs. Here, we report on off-target decoding for a multitarget protein kinase inhibitor by chemical proteomics, by focusing on interactions with nonprotein kinases. We tested two different routes for the immobilization of the inhibitor on a carrier matrix, and thus identified off-targets that interact with distinct compound moieties. Besides several of the kinases known to bind to the compound, the pyridoxal kinase (PDXK), which has been described to interact with the CDK inhibitor (R)-roscovitine, was captured. The PDXK-inhibitor interaction was shown to occur at the substrate binding site rather than at the ATP binding site. In addition, carbonic anhydrase 2 (CA2) binding was demonstrated, and the determination of the IC(50) revealed an enzyme inhibition in the submicromolar range. The data demonstrate that different compound immobilization routes for chemical proteomics approaches are a valuable method to improve the knowledge about the off-target profile of a compound.

Genomic and proteomic approaches for predicting toxicity and adverse drug reactions

BACKGROUND

In the toxicology field, it remains a major challenge to predict and understand drug toxicity and adverse drug reactions (ADRs) in man.

OBJECTIVE

Recent progress in genomics and proteomics technologies and their application in predicting drug toxicity and ADRs.

METHODS

The key genomic and proteomic approaches are outlined, their applications in predicting toxicity and ADRs are described and their future developments in this field are discussed.

CONCLUSION

These technologies, used to measure expression at the transcript and protein levels, each convey different information and have different technical capabilities that can complement each other. The fields of genomics and proteomics continue to develop rapidly and it is already evident that genomic and proteomic approaches have much to contribute to the early prediction of drug toxicity and ADRs.

A single lysis solution for the analysis of tissue samples by different proteomic technologies

Cancer, being a major healthcare concern worldwide, is one of the main targets for the application of emerging proteomic technologies and these tools promise to revolutionize the way cancer will be diagnosed and treated in the near future. Today, as a result of the unprecedented advances that have taken place in molecular biology, cell biology and genomics there is a pressing need to accelerate the translation of basic discoveries into clinical applications. This need, compounded by mounting evidence that cellular model systems are unable to fully recapitulate all biological aspects of human dissease, is driving scientists to increasingly use clinically relevant samples for biomarker and target discovery. Tissues are heterogeneous and as a result optimization of sample preparation is critical for generating accurate, representative, and highly reproducible quantitative data. Although a large number of protocols for preparation of tissue lysates has been published, so far no single recipe is able to provide a "one-size fits all" solubilization procedure that can be used to analyse the same lysate using different proteomics technologies. Here we present evidence showing that cell lysis buffer 1 (CLB1), a lysis solution commercialized by Zeptosens [a division of Bayer (Schweiz) AG], provides excellent sample solubilization and very high 2D PAGE protein resolution both when using carrier ampholytes and immobilized pH gradient strips. Moreover, this buffer can also be used for array-based proteomics (reverse-phase lysate arrays or direct antibody arrays), allowing the direct comparison of qualitative and quantitative data yielded by these technologies when applied to the same samples. The usefulness of the CLB1 solution for gel-based proteomics was further established by 2D PAGE analysis of a number of technically demanding specimens such as breast carcinoma core needle biopsies and problematic tissues such as brain cortex, cerebellum, skeletal muscle, kidney cortex and tongue. This solution when combined with a specific sample preparation technique - cryostat sectioning of frozen specimens - simplifies tissue sample preparation and solves most of the difficulties associated with the integration of data generated by different proteomic technologies.