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Pharmacoproteomics: A chess game on a protein field
Abstract
The application of proteomics in the field of drug discovery development and the assessment of drug administration is known as pharmacoproteomics. As a branch of proteomics--perhaps the most promising and rapidly evolving field of the post-genomic era--pharmacoproteomics has inherited all the promises that pharmacogenomics has hitherto left unfulfilled. On the road to tailor-made drugs, whole protein profiles of healthy individuals have been progressively expanded, either qualitatively or quantitatively. In this review article, we provide general information about technical advancements in the field of proteomics (the pieces of this intriguing chess game) and show how this progress has furthered our understanding of biological systems. Pitfalls on the field of biomarker individuation and drug discovery and/or testing are also discussed.
... These disciplines were thought to transform the entire process by providing enough information to advance com- pounds more efficiently and to reduce drug attrition. Despite the constant efforts made so far, practical use of these technologies is still limited to discovery applications [2]. ...
... Software advances now enable the production (and computer-readable encoding such as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome [6]. In a similar fashion to functional enrichment of proteomics data (pathway analyses, gene ontology term enrich- ment, protein-protein interaction modeling [2] www.drugdiscoverytoday.com 3 ...
... Features metabolic modeling exploits metabolic net- works or logical graphs, and resources such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) [15]. As is the case for proteomics [2], one of the main challenges for metabolomics is to cope with the diverse range of metabolites found within the cell (either hydrophilic or lipophilic), in conjunction with the large dynamic range of metabolite concentrations [16]. ...
Recent developments in cancer research have led to reconsiderations regarding metabolic dysfunctions in cancer cell proliferation and differentiation. The original concept stemmed from the observation that, even in presence of oxygen, highly proliferating cells tend to generate energy strictly from the glycolytic pathway, through a process called aerobic glycolysis, also known as the Warburg effect. More recently, advances in the field of metabolomics applied to cancer research enabled the documenting of the generality of the Warburg effect in a broad variety of tumors. Through metabolomics, cancer cells told us that oxidative stress, while representing one leading cause of genetic instability underpinning carcinogenesis, could also deliver a window of probable therapeutic opportunities that is worth opening.
... It is an emerging eld rooted in proteomics, one of the fastest-evolving disciplines since the post-genomic era. Pharmacoproteomics possesses the potential to effectively address the unful lled promises of pharmacogenomics and thereby pave the way for novel avenues in drug discovery and development [6]. ...
Proteomics is a field that was initiated by Marc Wilkins in 1995 and has become an essential aspect of various scientific disciplines, particularly in medicine and oncology. Pharmacoproteomics, an extension of proteomics, is set to transform drug discovery, development, and personalized cancer therapy. Pharmacoproteomics examines protein expression patterns, post-translational modifications, and interactions to gain insights into diseases and therapeutic targets. The vital role of pharmacoproteomics in cancer management, outlining its contributions to biomarker discovery, targeted therapies, drug resistance mechanisms, and individualized treatment strategies. Utilizing high-throughput proteomic profiling, pharmacoproteomics can help identify predictive biomarkers, unravel drug mechanisms of action, and monitor treatment response and toxicity in cancer patients. This paper discusses the basic techniques of pharmacoproteomics, including mass spectrometry, gel-based, and gel-free methodologies. It highlights their applications in cancer research and drug development. Integrating pharmacoproteomics into cancer therapy can usher in a new era of precision oncology, customized to the unique molecular profiles of individual patient’s tumors, and improve treatment outcomes and patient care.
... Furthermore, the anticancer effects of beberine are attributable to mitochondrial protein synthesis, TCA, and respiratory electron transport inhibition [4], betulinic acid affects the energy-related proteomic pro ling in pancreatic ductal adenocarcinoma cells [77], celastrol exhibited neuroprotective and anti-in ammatory effects by targeting HSP70 and NF-κB p65 and directly binding to HMGB1 in cerebral I/R injury [79], baicalin targets HSP70/90 to regulate PKR/PI3K/AKT/eNOS signaling pathways [83], PARP16 identi ed as a noncanonical target of the PARP inhibitor talazoparib [84], and arctigenin was identi ed as a phagophore-closure blocker via targeting ESCRT-I subunit VPS28 [87]. These reported outcomes from various pharmacoproteomic studies suggest the versatility of this branch of science in discovering novel drug targets, drug metabolism pathways, drug ef cacy, understanding the mechanism of action of drugs, and drug toxicity [1,2]. ...
Pharmacoproteomics is the proteomic analysis for discovering novel drug targets and studying drug metabolism, efficacy, and toxicity. This chapter aims to review analytical methods used in pharmacoproteomic studies systematically. A search was performed in Scopus, PubMed, and Web of Science online databases using appropriate search queries and reported as per the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines. This review included sixty-seven peer-reviewed articles published between 2006 and 2023. About 72 drugs of diverse classes were studied with human or murine cell lines, parasitic strains, animal models, and human patient samples. Mass spectrometry was the primary analytical method used for pharmacoproteomic studies and sometimes in combination with immunoanalytical methods such as 1D SDS-PAGE and 2D SDS-PAGE. Most studies used label-free mass spectrometry (49/67), while others used labeling reagents like iTRAQ, SILAC, TMT, and ICAT. The pharmacoproteomic studies reported several differentially expressed proteins and unique outcomes about drug targets, mechanism of action, efficacy, and metabolism. This review identified mass spectrometry as the primary analytical method for pharmacoproteomic studies.
... The recent introduction and application of omics approaches in human transfusion medicine 23 has improved our understanding of the mechanisms that contribute to the onset and severity of SLs. 24,25 For example, omics approaches have contributed to the identification of factors other storage duration that affect the onset and severity of SLs. Such factors include blood donor characteristics (eg, donor sex, age, and ethnicity in human blood storage), blood unit processing strategies (such as LR) and storage processing strategies (such as storage additives). ...
Background
Red blood cell (RBC) storage promotes biochemical and morphological alterations, collectively referred to as storage lesions (SLs). Studies in humans have identified leukoreduction (LR) as a critical processing step that mitigates SLs. To date no study has evaluated the impact of LR on metabolic SLs in canine blood units using omics technologies.
Objective
Compare the lipid and metabolic profiles of canine packed RBC (pRBC) units as a function of LR in fresh and stored refrigerated (up to 42 days) units.
Animals
Packed RBC units were obtained from 8 donor dogs enrolled at 2 different Italian veterinary blood banks.
Study Design and Methods
Observational study. A volume of 450 mL of whole blood was collected using Citrate‐Phosphate‐Dextrose‐Saline‐Adenine‐Glucose‐Mannitol (CPD‐SAGM) transfusion bags with a LR filter to produce 2 pRBC units for each donor, without (nLR‐pRBC) and with (LR‐pRBC) LR. Units were stored in the blood bank at 4 ± 2°C. Sterile weekly samples were obtained from each unit for omics analyses.
Results
A significant effect of LR on fresh and stored RBC metabolic phenotypes was observed. The nLR‐pRBC were characterized by higher concentrations of free short and medium‐chain fatty acids, carboxylic acids (pyruvate, lactate), and amino acids (arginine, cystine). The LR‐pRBC had higher concentrations of glycolytic metabolites, high energy phosphate compounds (adenosine triphosphate [ATP]), and antioxidant metabolites (pentose phosphate, total glutathione).
Conclusion and Clinical Importance
Leukoreduction decreases the metabolic SLs of canine pRBC by preserving energy metabolism and preventing oxidative lesions.
... It is the most promising and rapidly evolving branch of proteomics in the post-genomic era. Pharmacoproteomics has inherited all the promises that pharmacogenomics has left unfulfilled [53,116]. Similarly to what was discussed above regarding the difference between proteomics and genomics/transcriptomics, pharmacoproteomics can reveal the patient-to-patient variation more accurately than pharmacogenomics/pharmacotranscriptomics. ...
Simple Summary
The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields.
Abstract
The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.
... interpatient heterogeneity) hem de aynı hastanın tümörünü oluşturan farklı hücrelerin (İng. intratumour heterogeneity) tedavi yanıtındaki farklılık, farmakogenomik ve farmakoproteomik araştırmalara olan ilgiyi arttırmış ve daha spesifik hedefleme potansiyeline sahip kişiselleştirilmiş tedavi yaklaşımlarının gelişmesini sağlamıştır (D'Alessandro ve Zolla, 2010;Whirl-Carrillo vd., 2012;Hess, 2013;Wheeler vd., 2013;Jamal-Hanjani vd., 2015;Relling ve Evans, 2015;Chambliss ve Chan, 2016;Nandal ve Burt, 2017;Crabtree, 2019;Roden vd., 2019). Kişiselleştirilmiş kanser tedavisi, tümörü oluşturan hücrelerin genetik, epigenetik ve proteomik profillerinin göz önünde bulundurulması suretiyle hastaların sınıflandırılarak konvansiyonel veya hedeflenmiş tedavi ajanlarıyla tedavi edilmesi şeklinde tanımlanabilir. ...
İnsan hücreleri endojen ve eksojen nedenlerle oluşan genomik hasarlara karşı kompleks bir DNA hasar yanıt mekanizmasına sahiptir. Hücreler, DNA hasar yanıt işlevsizliği durumunda onarılmadıklarından genom stabilitesini tehdit eden çeşitli DNA lezyonlarını biriktirmeye başlar. Yetersiz DNA hasar yanıt aktivitesi; neoplastik transformasyona, antikanser ilaç direncine ve ilgili tedaviler neticesinde oluşan ciddi yan etkilere neden olmanın yanı sıra, tedavi yanıtının tahmininde kullanılabilir bir biyogösterge veya kanser hücrelerini mevcut tedavilere karşı daha duyarlı hale getirebilecek farmakolojik bir hedef olarak da kullanılabilmektedir. Poli (ADP-riboz) (PARP) enzimleri, DNA tek zincir kırıklarının onarılması dahil birçok hücresel mekanizmada rol oynamaktadır. BRCA1/2 proteinleri ise DNA çift zincir kırıklarının homolog rekombinasyon yolağıyla tamir edilmesinde görev almaktadır. Yapılan çalışmalar, BRCA1/2 mutasyonu neticesinde homolog rekombinasyon defektif hale gelen hücrelerin PARP inhibitörlerine karşı hassasiyet kazandığı göstermiştir. BRCA1/2 ve PARP arasında tanımlanan söz konusu sentetik letal etkileşimin başarılı klinik uygulaması, araştırmacıları homolog rekombinasyon durumunu bildirecek farklı biyogöstergeleri araştırmaya ve PARP inhibitör direncinin üstesinden gelmek için diğer potansiyel sentetik letal etkileşimleri ortaya çıkarmaya yönlendirmiştir. Bu derlemede öncelikle DNA hasar yanıt yolağının mevcut durumu özetlenmiş, sonrasında HR tamir sistemi ve PARP inhibisyonu arasındaki sentetik letalite anlatılmıştır.
... interpatient heterogeneity) hem de aynı hastanın tümörünü oluşturan farklı hücrelerin (İng. intratumour heterogeneity) tedavi yanıtındaki farklılık, farmakogenomik ve farmakoproteomik araştırmalara olan ilgiyi arttırmış ve daha spesifik hedefleme potansiyeline sahip kişiselleştirilmiş tedavi yaklaşımlarının gelişmesini sağlamıştır (D'Alessandro ve Zolla, 2010;Whirl-Carrillo vd., 2012;Hess, 2013;Wheeler vd., 2013;Jamal-Hanjani vd., 2015;Relling ve Evans, 2015;Chambliss ve Chan, 2016;Nandal ve Burt, 2017;Crabtree, 2019;Roden vd., 2019). Kişiselleştirilmiş kanser tedavisi, tümörü oluşturan hücrelerin genetik, epigenetik ve proteomik profillerinin göz önünde bulundurulması suretiyle hastaların sınıflandırılarak konvansiyonel veya hedeflenmiş tedavi ajanlarıyla tedavi edilmesi şeklinde tanımlanabilir. ...
Günümüzde enerji kadar önemli bir diğer problem de gelişen teknolojinin, artan nüfus ve yaşam standartlarının en önemli sonucu olan artan atık miktarıdır. Kentlerde oluşan atıklar, şehrin nüfus yoğunluğuna ve bölgenin tüketim alışkanlıklarına bağlı olarak değişmektedir. İçerisinde sıvı ve gaz atıkların bulunmadığı kentsel katı atıkları düzenli depolama alanları kullanarak hem bertaraf etmek hem de enerji elde etmek mümkündür. Çünkü katı atıklar biyolojik olarak bozunmakta ve bozunma sonucunda ortama %45-60 oranında metan, %40-60 oranında karbondioksit gazı salmaktadır. Depo gazı olarak da tanımlanan bu gazın potansiyeli, ülke nüfusunun ortalama 84 milyon, kişi başı atık miktarının ortalama 1,13 kg olduğu göz önüne alınırsa ne kadar yüksek olduğu aşikardır. Bu nedenle tüketimin ana sebebi olan insanoğlu doğada var olduğu sürece, kentsel katı atıklar sürdürülebilir bir enerji kaynağıdır. Bu çalışmada; 2020 yılı TUİK verileri kullanılarak her geçen gün artan düzenli katı atık depolama alanlarımızdan elde edilebilecek depo gazının güncel enerji potansiyeli araştırılmış ve diğer enerji kaynakları ile karşılaştırılması yapılmıştır.
... The purpose of this study was to identify the proteins differentially expressed upon B10 transplantation in the acute phase of ischemic stroke. Recently, a proteomics-based approach has been developed to identify proteins modulated by pharmaceutical agents (D'Alessandro and Zolla, 2010). A focus on proteins is important, as they not only serve as vital building blocks in all cells, but are also critical components of enzymes and hormones involved in essential biological processes within living organisms. ...
Transplantation with mesenchymal stem cells (MSCs) has been reported to promote functional recovery in animal models of ischemic stroke. However, the molecular mechanisms underlying the therapeutic effects of MSC transplantation have been only partially elucidated. The purpose of this study was to comprehensively identify changes in brain proteins in rats treated with MSCs for ischemic stroke, and to explore the multi-target mechanisms of MSCs using a proteomics-based strategy. Twenty-eight proteins were found to be differentially expressed following B10 MSC transplantation in adult male Wistar rats, as assessed using isobaric tagging for relative and absolute protein quantification (iTRAQ). Subsequent bioinformatic analysis revealed that these proteins were mainly associated with energy metabolism, glutamate excitotoxicity, oxidative stress, and brain structural and functional plasticity. Immunohistochemical staining revealed decreased expression of EAAT1 in the phosphate-buffered saline group as opposed to normal levels in the B10 transplantation group. Furthermore, ATP levels were also significantly higher in the B10 transplantation group, thus supporting the iTRAQ results. Our results suggest that the therapeutic effects of B10 transplantation might arise from the modulation of the acute ischemic cascade via multiple molecular pathways. Thus, our findings provide valuable clues to elucidate the mechanisms underlying the therapeutic effects of MSC transplantation in ischemic stroke.
... Proteomics approaches have expanded considerably in recent years, and pharmacoproteomics can reveal disease-modifying parameters and indicators of treatment outcomes by comparing the proteomes of specimens with or without therapeutic drug administration (D'Alessandro and Zolla, 2010). Pharmacoproteomics is a rapidly evolving area due to progress in analytical technologies that enable the processing of complex interactions of large numbers of unique proteins (Zohaib et al., 2018). ...
... The pharmacoproteomics has been widely applied to various aspects of current pharmaceutical researches by discovering disease-related genes (Mrozek et al., 2013;Quiros et al., 2017;Zeng et al., 2017) or new drug targets Saei et al., 2018), constructing pharmacology screening model (Hauser et al., 2005), and revealing the drug mechanism of action (Yue et al., 2016;Zhu et al., 2018), resistance (Paul et al., 2016), and toxicity (Tan et al., 2017;Wang et al., 2017b). Recent findings uncover its potentials to fulfill the promise that the pharmacogenomics has not accomplished yet (D'Alessandro and Zolla, 2010;Chambliss and Chan, 2016;Yang et al., 2016). ...
Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.
... Proteomics is an emerging field in medical science focusing on the library of transcripts specific to a given biosystem. Pharmacoproteomics is the application of proteomic technologies in drug development and assessment of drug administration (D'Alessandro and Zolla 2010;Yu 2011). Analyses of the transcriptome and the proteome of human HSCs have yielded valuable data (Buchholz et al. 2005;Paulo et al. 2013) that promote our understanding of the physiology of HSCs. ...
Diallyl trisulfide (DATS), a major garlic derivative, inhibits cell proliferation and triggers apoptosis in a variety of cancer cell lines. However, the effects of DATS on hepatic stellate cells (HSCs) remain unknown. The aim of this study was to analyze the effects of DATS on cell proliferation and apoptosis, as well as the protein expression profile in rat HSCs. Rat HSCs were treated with or without 12 and 24 μg/mL DATS for various time intervals. Cell proliferation and apoptosis were determined using tetrazolium dye (MTT) colorimetric assay, bromodeoxyuridine (5-bromo-2′-deoxyuridine; BrdU) assay, Hoechst 33342 staining, electroscopy, and flow cytometry. Protein expression patterns in HSCs were systematically studied using 2-dimensional electrophoresis and mass spectrometry. DATS inhibited cell proliferation and induced apoptosis of HSCs in a time-dependent manner. We observed clear morphological changes in apoptotic HSCs and dramatically increased annexin V-positive – propidium iodide negative apoptosis compared with the untreated control group. Twenty-one significant differentially expressed proteins, including 9 downregulated proteins and 12 upregulated proteins, were identified after DATS administration, and most of them were involved in apoptosis. Our results suggest that DATS is an inducer of apoptosis in HSCs, and several key proteins may be involved in the molecular mechanism of apoptosis induced by DATS.
... Pharmacoproteomics, as a subdiscipline of proteomics, examines how a pharmacologic agent perturbs a system of proteins that map to canonical or novel biological pathways. Such a discourse provides a more unbiased and holistic approach to understanding pharmacologic on-target or off-target effects at the protein level [6][7][8][9]. ...
The aim of this study was to examine hemisphere asymmetry of response to pharmacologic treatment in an Alzheimer's disease mouse model using cilostazol as a chemical stimulus. Eight-month-old mice were assigned to vehicle or cilostazol treatment for three months and hemispheres were analyzed using quantitative proteomics. Bioinformatics interpretation showed that following treatment, aggregation of blood platelets significantly decreased in the right hemisphere whereas neurodegeneration significantly decreased and synaptic transmission increased in the left hemisphere only. Our study provides novel evidence on cerebral laterality of pharmacologic activity, with important implications in deciphering regional pharmacodynamic effects of existing drugs thus uncovering novel hemisphere-specific therapeutic targets.
... Another important factor which cannot be underestimated is the time between drug treatment/administration and analysis. In fact if the drug does not reach the pharmacokinetic steady state, the protein profiles changes may not correlate to the drug treatment/administration [115]. Therefore, an in depth assessment of proteomic variability is mandatory for a better understanding and data interpretation of clinical proteomics studies. ...
In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.
... In all these fields proteomics has already proved its worth and will possibly expand its influence in the upcoming years, with the introduction of new investigative technologies, such as the ones enabling the assessment of quantitation, post-translational modification, post hoc in silico elaborations and tissue analysis through imaging mass spectrometry (81). ...
Over the last decade, proteomics has been successfully applied to the study of quality control in production processes of food (including meat, wine and beer, transgenic plants and milk) and food safety (screening for food-derived pathogens). Indeed, food quality and safety and their influence on the health of end consumers have growingly become a founding principle in the international agenda of health organizations. The application of proteomics in food science was at first characterized by exploratory analyses of food of various origin (bovine, swine, chicken or lamb meat, but also transgenic food such as genetically modified maize, for example) and beverages (beer, wine), in parallel to the genomic and transcriptomic approaches seeking determination of quantitative trait loci. In the last few years, technical improvements such as microbial biotyping strategies have growingly allowed proteomicists to address the safety issue as well. The newly introduced technical improvements (instrumentation characterized by higher sensitivity such as mass spectrometers) have paved the way for the individuation of food-contaminating pathogens in a fast and efficient workflow which is mandatory in industrial food production chains.
... This holds true in the diagnosis and monitoring of pathologies, but also in quality assessment of therapeutic products and their deterioration, such as PCs. Here the use of PC supernatants entirely suspended in plasma did not facilitate proteomic analysis since the presence of high-abundance proteins hampered the detection of low-abundance protein populations in which potential biomarkers for the so-called ''PLT-storage lesions'' are thought to reside [30]. However, such a recalcitrant system still represents a product which is prepared in many hospitals, thus our profiling of stored PC releasates will mainly target the real modifications occurring therein. ...
The present study was aimed at revealing new insights into the analysis of storage-related processes occurring in the supernatants of platelet concentrates (PCs) derived from pooled buffy coats suspended in whole plasma. To reduce the dynamic range of plasma protein concentrations and access low-abundance proteins, we made use of a solid-phase combinatorial peptide ligand library, known under the trade name of ProteoMiner™. Afterwards, two-dimensional electrophoresis (2-DE) was coupled with mass spectrometry (MS) to reveal changes in proteomic profiles. Several storage-induced protein alterations were identified including changes to major plasma proteins. In particular, a precursor of the secretory form of clusterin was shown to accumulate during storage of PC supernatants, together with platelet-derived tropomyosin, suggesting a progressive loss of platelet integrity. Platelet-released proteins following activation have also been detected (alpha-1-B-glycoprotein, kininogen-1, and serpin proteinase inhibitor 8). Moreover, specific protein fragments (vitronectin, plakoglobin, hornerin, and apolipoprotein A-IV) were found to be modulated upon storage, possibly indicating a time-dependent buffy-coat PC deterioration. Globally, our findings provided the disclosure of unique proteins in PC supernatants with respect to previous studies conducted in similar experimental conditions, suggesting ProteoMiner enrichment technology to be a possible complementary tool in the identification of diagnostically relevant proteins as age/quality biomarkers of therapeutic products.
Preventive medicine Epigenetics a b s t r a c t Precision medicine is the new age medicine and refers to tailoring treatments to a sub-population who have a common susceptibility to a particular disease or similar response to a particular drug. Although the concept existed even during the times of Sir William Osler, it was given a shot in the arm with the Precision Medicine Initiative launched by Barack Obama in 2015. The main tools of precision medicine are Big data, artificial intelligence, the various omics, pharmaco-omics, environmental and social factors and the integration of these with preventive and population medicine. Big data can be acquired from electronic health records of patients and includes various biomarkers (clinical and omics based), laboratory and radiological investigations and these can be analysed through machine learning by various complex flowcharts setting up an algorithm for the management of specific subpopulations. So, there is a move away from the traditional "one size fits all" treatment to precision-based medicine. Research in "omics" has increased in leaps and bounds and advancements have included the fields of genomics, epigenomics, proteomics, transcriptomics, metabolomics and microbiomics. Pharmaco-omics has also come to the forefront with development of new drugs and suiting a particular drug to a particular subpopulation, thus avoiding their prescription to non-responders, preventing unwanted adverse effects and proving economical in the long run. Environmental, social and behavioural factors are as important or in fact more important than genetic factors in most complex diseases and managing these factors form an important part of precision medicine. Finally integrating precision with preventive and public health makes "precision medicine" a complete final product which will change the way medicine will be practised in future.
Colon cancer is one of the most lethal malignancies worldwide. Berberine has been found to exert potential anti-colon cancer activity in vitro and in vivo, although the detailed regulatory mechanism is still unclear. This study aims to identify the underlying crucial proteins and regulatory networks associated with berberine treatment of colon cancer by using proteomics as well as publicly available transcriptomics and tissue array data. Proteome profiling of berberine-treated colon cancer cells demonstrated that among 5130 identified proteins, the expression of 865 and 675 proteins were changed in berberine-treated HCT116 and DLD1 cells, respectively. Moreover, 54 differently expressed proteins that overlapped in both cell lines were mainly involved in mitochondrial protein synthesis, calcium mobilization, and metabolism of fat-soluble vitamins. Finally, GTPase ERAL1 and mitochondrial ribosomal proteins including MRPL11, 15, 30, 37, 40, and 52 were identified as hub proteins of berberine-treated colon cancer cells. These proteins have higher transcriptional and translational levels in colon tumor samples than that of colon normal samples, and were significantly down-regulated in berberine-treated colon cancer cells. Genetic dependency analysis showed that silencing the gene expression of seven hub proteins could inhibit the proliferation of colon cancer cells. This study sheds a light for elucidating the berberine-related regulatory signaling pathways in colon cancer, and suggests that ERAL1 and several mitochondrial ribosomal proteins might be promising therapeutic targets for colon cancer.
Berberine (BBR), a natural isoquinoline alkaloid, has been shown to be a promising therapeutic agent for colorectal cancer (CRC), but the molecular mechanism remains unclear. Here, we used mass spectrometry-based label-free proteomics to explore the potential targets of BBR in CRC cells. Comprehensive proteomic profiles demonstrated that of 8051 identified proteins, 503 and 277 differentially expressed proteins (DEPs) were screened out of CACO2 and LOVO cells, respectively. 83 DEPs were overlapped and most of these were down-regulated. A pathway enrichment analysis pinpointed mitochondrial translation, respiratory electron transport and the citric acid (TCA) cycle as biological effectors. The data of proteomics was subsequently confirmed by citrate synthase (CS), Tu translation elongation factor (TUFM), pentatricopeptide repeat domain 3 (PTCD3) and mitochondrial ribosomal protein L48 (MRPL 48) protein measurement. CS protein expression in CRC cells and tissues was higher than it was in normal specimens. Additionally, forcible downregulation of CS led to remarkable cell proliferation inhibition. Taken together, we concluded that the anticancer effects of BBR are attributable to mitochondrial protein synthesis, TCA and respiratory electron transport inhibition and that CS might be a useful therapeutic target in CRC treatment.
The products resulting for biotechnologies continue to grow at an exponential rate, and the expectations are that an even greater percentage of drug development will be in the area of the biologics. In 2011, worldwide there were over 800 new biotech drugs and treatments in development including 23 antisense, 64 cell therapy, 50 gene therapy, 300 monoclonal antibodies, 78 recombinant proteins, and 298 vaccines (PhRMA 2012). Pharmaceutical biotechnology techniques are at the core of most methodologies used today for drug discovery and development of both biologics and small molecules. While recombinant DNA technology and hybridoma techniques were the major methods utilized in pharmaceutical biotechnology through most of its historical timeline, our ever-widening understanding of human cellular function and disease processes and a wealth of additional and innovative biotechnologies have been, and will continue to be, developed in order to harvest the information found in the human genome. These technological advances will provide a better understanding of the relationship between genetics and biological function, unravel the underlying causes of disease, explore the association of genomic variation and drug response, enhance pharmaceutical research, and fuel the discovery and development of new and novel biopharmaceuticals. These revolutionary technologies and additional biotechnology-related techniques are improving the very competitive and costly process of drug development of new medicinal agents, diagnostics, and medical devices. Some of the technologies and techniques described in this chapter are both well established and commonly used applications of biotechnology producing potential therapeutic products now in development including clinical trials. New techniques are emerging at a rapid and unprecedented pace and their full impact on the future of molecular medicine has yet to be imagined.
In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.
Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.
A naturally occurring benzofuran derivative, Ebenfuran III (Eb III) was investigated for its antiproliferative effects using the DU-145 prostate cell line. Eb III was isolated from Onobrychis ebenoides of the Leguminosae family, a plant endemic in Central and Southern Greece. We have previously reported that Eb III exerts significant cytotoxic effects on certain cancer cell lines. This effect is thought to occur via the isoprenyl moiety at the C-5 position of the molecule. The study aim was to gain a deeper understanding of the pharmacological effect of Eb III on DU-145 cell death at the translational level using a relative quantitative and temporal proteomics approach. Proteins extracted from the cell pellets were subjected to solution phase trypsin proteolysis followed by iTRAQ-labelling. The labelled tryptic peptide extracts were then fractionated using strong cation exchange chromatography and the fractions were analyzed by nano-flow reverse phase ultra-performance liquid chromatography- nano-electrospray ionization-tandem mass spectrometry analysis using a hybrid QqTOF platform. Using this approach, we compared the expression levels of 1360 proteins analysed at ≤ 1% global protein false discovery rate (FDR), commonly present in untreated (control, vehicle only) and Eb III-treated cells at the different exposure time points. Through the iterative use of Ingenuity Pathway Analysis with hierarchical clustering of protein expression patterns, followed by bibliographic research, the temporal regulation of the Calpain-1, ERK2, PAR-4, RAB-7, and Bap31 proteins, were identified as potential nodes of multi-pathway convergence to Eb III induced DU-145 cell death. These proteins were further verified with Western blot analysis. This gel-free, quantitative 2DLC-MS-MS proteomics method effectively captured novel modulated proteins in the DU-145 cell line as a response to Eb III treatment. This approach also provided greater insight to the multifocal and combinatorial signaling pathways implicated in Eb III-induced cell death.
The emerging field of chemo- and pharmacoproteomics studies the mechanisms of action of bioactive molecules in a systems pharmacology context. In contrast to traditional drug discovery, pharmacoproteomics integrates the mechanism of a drug's action, its side effects including toxicity and the discovery of new drug targets in a single approach. Thus, it determines early favorable (e.g.; multiple kinase target in cancer drugs) and unfavorable (e.g.; side effects) polypharmacology. Target profiling is accomplished using either active site-labeling probes or immobilized drugs. This strategy identifies direct targets and has in fact enabled even the determination of binding curves and half maximum inhibitory concentrations (IC(50) ) of these targets. In addition, the enrichment greatly reduces the complexity of the proteome to be analyzed by quantitative mass spectrometry, Complementary to these approaches, global proteomics profiling studying drug treatement induced changes in protein expression levels and/or posttranslational modification status have started to become possible mostly due to significant improvements in instrumentation. Particularly, when using multidimensional separations, a considerable proteome depth of up to 10,000 proteins can be achieved with current state-of-the-art mass spectrometers and bioinformatics tools. In summary, chemo- and pharmacoproteomics has already contributed significantly to the identification of novel drug targets and their mechanisms of action(s). Aided by further technological advancements, this interdisciplinary approach will likely be used more broadly in the future.
In a systemic viewpoint, relevant biological information on living systems can be grasped from the study of small, albeit pivotal molecules which constitute the fundamental bricks of metabolic pathways. This holds true for liver which plays, among its unique functions, a key role in metabolism. The nonbiased analysis of all this small-molecule complement in its entirety is known as metabolomics. However, no practical approach currently exists to investigate all metabolic species simultaneously without including a technical bias towards acidic or basic compounds, especially when performing mass spectrometry-based investigations. Technical aspects of rapid resolution reversed phase HPLC online with mass spectrometry are hereby described. Such an approach allows to discriminate and quantify a wide array of metabolites with extreme specificity and sensitivity, thus enabling to perform complex investigations even on extremely low quantities of biological material. The advantages also include the possibility to perform targeted investigations on a single (or a handful of) metabolite(s) simoultaneously through single (multiple) reaction monitoring, which further improves the dynamic range of concentrations to be monitored.
Such an approach has already proven to represent a valid tool in the direct (on the liver) or indirect (on human red blood cell metabolism which is hereby presented as a representative model, but also on blood plasma or other biological fluids) assessment of metabolic poise modulation and pharmacokinetics for drug development.
Lovastatin (lova), a 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor, can induce differentiation in cancer cells at low concentration, thus having potential to be used as an auxiliary agent in cancer therapy. However, biological networks associated with the differentiation effect of lova have not been elucidated. To investigate molecular mechanisms of lova, the present study was aimed at proteomics and bioinformatics analyses on anaplastic thyroid cancer cell line ARO differentiated with low concentration of lova. Thyroid differentiation was induced by treating ARO cells with 25 μM of lova and confirmed by checking upregulation of some thyroid differentiation markers. Gel-based proteomics analysis was then performed to identify proteins differentially expressed between undifferentiated and lova-differentiated ARO cells. Bioinformatics analysis was finally performed to estimate biological networks regulated by lova. Our results showed that lova impacted on proteins involved in protein folding, biomolecule metabolism, signal transduction, protein expression and protein degradation. Specifically, transfecting ARO cells with plasmid DNA encoding flotillin 1 (FLOT1) up-regulated the thyroid differentiation markers, indicating that FLOT1 might at least partially mediate the lova-induced thyroid differentiation. These data may shed light on the mechanism underlying lova-induced re-differentiation of thyroid cancer, and give a rationale for clinical use of lova as an auxiliary agent in cancer therapy.
Hemoglobin (Hb) approximately constitutes 98% of the protein composition of a red blood cell (RBC), thus masking the remaining 2% which has still to be discovered completely due to the difficulty in its analysis. Here, we proposed a large-scale native gel electrophoresis that effectively tackles this limitation through a novel sample preparation strategy able to concentrate low-abundance species by removing Hb by means of electrophoretic instruments. Clear native PAGE was performed in a gel electrophoresis tube where the run was intermittently interrupted and different fractions were recovered in liquid phase into a collection chamber placed at the end of the tube. In this way, fractions containing multi-protein complexes with different molecular weights were collected in the native form by a simple elution. Red fraction containing Hb multi-protein complexes can be excluded from subsequent analyses, or rather be analyzed separately, reducing therefore the dynamic range of erythrocyte cytosolic protein concentrations and increasing the number of protein identifications. In particular, 838 protein spots in total were detected when fractions were analyzed by 2-D IEF-SDS-PAGE. This depletion method is inexpensive, simple to perform, reproducible and makes it possible to process large amounts of sample (up to 150 mg), thus making it suitable for in-depth proteome investigations. Furthermore, this strategy has the potential to be applied both to native and denatured proteomes of different biological samples.
Background
Pharmacometabonomics is a new branch of science, first described in 2006 and defined as ‘the prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles’. Pharmacometabonomics has been used to predict drug metabolism, pharmacokinetics (PK), drug safety and drug efficacy in both animals and humans and is complementary to both pharmacogenomics (PGx) and pharmacoproteomics.
Methods
A literature review using the search terms pharmacometabonomics, pharmacometabolomics, pharmaco-metabonomics, pharmaco-metabolomics and the singular form of all those terms was conducted in October 2012 using PubMed and Web of Science. The review was updated until mid April 2013.
Results
Since the original description of pharmacometabonomics in 2006, 21 original publications and eight reviews have emerged, covering a broad range of applications from the prediction of PK to the prediction of drug metabolism, efficacy and safety in humans and animals.
Conclusions
Pharmacometabonomics promises to be an important new approach to the delivery of personalized medicine to improve both drug efficacy and safety for patients in the future. Pharmacometabonomics is particularly powerful as it is sensitive to both genetic and environmental factors such as diet, drug intake and most importantly, a person’s microbiome. PGx is now over 50 years old and although it has not achieved as much as some hoped, it is starting to have important applications in personalized medicine. We predict that pharmacometabonomics will be equally important in the next few decades and will be both valuable in its own right and complementary to pharmacoproteomics and PGx.
Traumatic brain injury is a significant disease affecting 1.4 to 2 million patients every year in the USA. Currently, there are no FDA-approved therapeutic remedies to treat TBI despite the fact that there have been over 200 clinical drug trials, all which have failed. These drugs used the traditional single drug-to-target approach of drug discovery and development. An alternative based upon the advances in genomics, proteomics, bioinformatic tools, and systems biology software has enabled us to use a Systems Biology-based approach to drug discovery and development for TBI. It focuses on disease-relevant converging pathways as potential therapeutic intervention points and is accompanied by downstream biomarkers that allow for the tracking of drug targeting and appears to correlate with disease mitigation. When realized, one is able to envision that a companion diagnostic will be codeveloped along the therapeutic compound. This "theranostic" approach is perfectly positioned to align with the emerging trend toward "personalized medicine".
Epithelial ovarian cancer (EOC) ranks fifth as a cause of cancer deaths in women. Current diagnostic and monitoring markers have limited reliability for the detection of disease. We have tested the possibility of identifying candidate biomarkers present at low nanogram to picogram levels after removing both the 12 most abundant and 77 moderately abundant proteins from serum samples of EOC patients using antibody affinity columns. We showed that this approach allows the identification of proteins that are expressed at nanogram per liter levels in the serum. Using ICAT/MS/MS analysis, we identified 51 proteins that are differentially expressed by at least twofold. These proteins include leucine-rich alpha-2-glycoprotein, matrix metalloproteinase-9 (MMP-9), inter-alpha-trypsin inhibitor heavy chain H1, insulin-like growth factor-binding protein 6, insulin-like growth factor-binding protein 3, isoform 1 of epidermal growth factor receptor, angiopoietin-like protein 3 (ANGPTL3) and phosphatidylcholine-sterol acyltransferase. We confirmed the differential expression of MMP9 and ANGPTL3 in normal and ovarian cancer sera by ELISA assays. Further robust clinical evaluation of the candidate markers identified is necessary.
Blood-based biomarkers present a considerable challenge: technically, as blood is a complex tissue and conceptually, as blood lacks direct contact with brain. Nonetheless, increasing evidence suggests that there is a blood protein signature, and possibly a transcript signature, that might act to increase confidence in diagnosis, be used to predict progression in either disease or prodromal states, and that may also be used to monitor disease progression. Evidence for this optimism comes partly from candidate protein studies, including those suggesting that amyloid-beta measures might have value in prediction and those studies of inflammatory markers that consistently show change in Alzheimer's disease, and partly from true proteomics studies that are beginning to identify markers in blood that replicate across studies and populations.
Most small-molecule probes and drugs alter cell circuitry by interacting with 1 or more proteins. A complete understanding of the interacting proteins and their associated protein complexes, whether the compounds are discovered by cell-based phenotypic or target-based screens, is extremely rare. Such a capability is expected to be highly illuminating--providing strong clues to the mechanisms used by small-molecules to achieve their recognized actions and suggesting potential unrecognized actions. We describe a powerful method combining quantitative proteomics (SILAC) with affinity enrichment to provide unbiased, robust and comprehensive identification of the proteins that bind to small-molecule probes and drugs. The method is scalable and general, requiring little optimization across different compound classes, and has already had a transformative effect on our studies of small-molecule probes. Here, we describe in full detail the application of the method to identify targets of kinase inhibitors and immunophilin binders.
Sample preparation and fractionation technologies are one of the most crucial processes in proteomic analysis and biomarker discovery in solubilized samples. Chromatographic or electrophoretic proteomic technologies are also available for separation of cellular protein components. There are, however, considerable limitations in currently available proteomic technologies as none of them allows for the analysis of the entire proteome in a simple step because of the large number of peptides, and because of the wide concentration dynamic range of the proteome in clinical blood samples. The results of any undertaken experiment depend on the condition of the starting material. Therefore, proper experimental design and pertinent sample preparation is essential to obtain meaningful results, particularly in comparative clinical proteomics in which one is looking for minor differences between experimental (diseased) and control (nondiseased) samples. This review discusses problems associated with general and specialized strategies of sample preparation and fractionation, dealing with samples that are solution or suspension, in a frozen tissue state, or formalin-preserved tissue archival samples, and illustrates how sample processing might influence detection with mass spectrometric techniques. Strategies that dramatically improve the potential for cancer biomarker discovery in minimally invasive, blood-collected human samples are also presented.
An assessment of the number of molecular targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry. Now that we know the size of the human genome, it is interesting to consider just how many molecular targets this opportunity represents. We start from the position that we understand the properties that are required for a good drug, and therefore must be able to understand what makes a good drug target.
The human plasma proteome holds the promise of a revolution in disease diagnosis and therapeutic monitoring provided that major challenges in proteomics and related disciplines can be addressed. Plasma is not only the primary clinical specimen but also represents the largest and deepest version of the human proteome present in any sample: in addition to the classical "plasma proteins," it contains all tissue proteins (as leakage markers) plus very numerous distinct immunoglobulin sequences, and it has an extraordinary dynamic range in that more than 10 orders of magnitude in concentration separate albumin and the rarest proteins now measured clinically. Although the restricted dynamic range of conventional proteomic technology (two-dimensional gels and mass spectrometry) has limited its contribution to the list of 289 proteins (tabulated here) that have been reported in plasma to date, very recent advances in multidimensional survey techniques promise at least double this number in the near future. Abundant scientific evidence, from proteomics and other disciplines, suggests that among these are proteins whose abundances and structures change in ways indicative of many, if not most, human diseases. Nevertheless, only a handful of proteins are currently used in routine clinical diagnosis, and the rate of introduction of new protein tests approved by the United States Food and Drug Administration (FDA) has paradoxically declined over the last decade to less than one new protein diagnostic marker per year. We speculate on the reasons behind this large discrepancy between the expectations arising from proteomics and the realities of clinical diagnostics and suggest approaches by which protein-disease associations may be more effectively translated into diagnostic tools in the future.
The MALDI-TOF spectra of peptides from the sera of normal and myocardial infarction patients produced patterns that provided an accurate diagnostic of MI. In myocardial infarction, the spectral pattern originated from the cleavage of complement C3 alpha chain to release the C3f peptide and cleavage of fibrinogen to release peptide A. The fibrinogen peptide A and complement C3f peptide were in turn progressively truncated by aminopeptidases to produce two families of fragments that formed the characteristic spectral pattern of MI. Time course and inhibitor studies demonstrated that the peptide patterns in the serum reflect the balance of disease-specific-protease and aminopeptidase activity ex vivo.
Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Chemical genetics and reverse chemical genetics parallel classical genetics but target genes at the protein level and have proven useful in recent years for screening combinatorial libraries for compounds of biological interest. However, the performance of combinatorial chemistry in filling pharmaceutical pipelines has been lower than anticipated and the tide may be turning back to Nature in the search for new drug candidates. Even though diversity oriented synthesis is now producing molecules that are natural product-like in terms of size and complexity, these molecules have not evolved to interact with biomolecules. Natural products, on the other hand, have evolved to interact with biomolecules, which is why so many can be found in pharmacopoeias. However, the cellular targets and modes of action of these fascinating compounds are seldom known, hindering the drug development process. This review focuses on the emergence of chemical proteomics and reverse chemical proteomics as tools for the discovery of cellular receptors for natural products, thereby generating protein/ligand pairs that will prove useful in identifying new drug targets and new biologically active small molecule scaffolds. Such a system-wide approach to identifying new drugable targets and their small molecule ligands will help unblock the pharmaceutical product pipelines by speeding the process of target and lead identification.
The mass-spectrometry-based identification of proteins has created opportunities for the study of organelles, transport intermediates and large subcellular structures. Traditional cell-biology techniques are used to enrich these structures for proteomics analyses, and such analyses provide insights into the biology and functions of these structures. Here, we review the state-of-the-art proteomics techniques for the analysis of subcellular structures and discuss the biological insights that have been derived from such studies.
Blood-based therapeutics are cellular or plasma components derived from human blood. Their production requires appropriate selection and treatment of the donor and processing of cells or plasma proteins. In contrast to clearly defined, chemically synthesized drugs, blood-derived therapeutics are highly complex mixtures of plasma proteins or even more complex cells. Pathogen transmission by the product as well as changes in the integrity of blood constituents resulting in loss of function or immune modulation are currently important issues in transfusion medicine. Protein modifications can occur during various steps of the production process, such as acquisition, enrichment of separate components (e.g. coagulation factors, cell populations), virus inactivation, conservation, and storage.
Contemporary proteomic strategies allow a comprehensive assessment of protein modifications with high coverage, offer capabilities for qualitative and even quantitative analysis, and for high-throughput protein identification. Traditionally, proteomics approaches predominantly relied on two-dimensional gel electrophoresis (2-DE). Even if 2-DE is still state of the art, it has inherent limitations that are mainly based on the physicochemical properties of the proteins analyzed; for example, proteins with extremes in molecular mass and hydrophobicity (most membrane proteins) are difficult to assess by 2-DE. These limitations have fostered the development of mass spectrometry centered on non-gel-based separation approaches, which have proven to be highly successful and are thus complementing and even partially replacing 2-DE-based approaches.
Although blood constituents have been extensively analyzed by proteomics, this technology has not been widely applied to assess or even improve blood-derived therapeutics, or to monitor the production processes. As proteomic technologies have the capacity to provide comprehensive information about changes occurring during processing and storage of blood products, proteomics can potentially guide improvement of pathogen inactivation procedures and engineering of stem cells, and may also allow a better understanding of factors influencing the immunogenicity of blood-derived therapeutics. An important development in proteomics is the reduction of inter-assay variability. This now allows the screening of samples taken from the same product over time or before and after processing. Optimized preparation procedures and storage conditions will reduce the risk of protein alterations, which in turn may contribute to better recovery, reduced exposure to allogeneic proteins, and increased transfusion safety.
>L'industrie pharmaceutique a pour ambition de contribuer au progres therapeutique. Elle doit faire face au defi constamment renouvele de decouvrir de nouveaux medicaments efficaces et mieux toleres pour repondre aux multiples besoins medicaux insatisfaits. Malgre les progres therapeutiques deja accomplis, de nouveaux besoins sont apparus. En effet, en raison de l'evolution demographique et des modes de vie de la population mondiale, on constate une augmentation de l'incidence de certaines maladies comme la maladie d'Alzheimer, l'obesite et le diabete de type 2 et de multiples besoins medicaux non couverts emergent, en particulier dans le domaine du cancer. De plus, l'utilisation des nouvelles technologies issues de la genomique, de la proteomique et de la metabolomique ainsi que de nouvelles approches therapeutiques issues des biotechnologies comme l'utilisation des anticorps monoclonaux humanises, des proteines recombinantes, des siARN, ouvre egalement de multiples voies de decouverte et de potentiel therapeutique. Les connaissances nouvelles et les progres technologiques associes devraient permettre d'ameliorer l'identification et la validation du choix de cibles therapeutiques nouvelles, le developpement et l'integration de biomarqueurs au sein des projets de la recherche et du developpement, d'approfondir la connaissance du mecanisme d'action moleculaire et d'ameliorer l'effet therapeutique des futurs medicaments. Ils permettront egalement une meilleure caracterisation des modeles precliniques afin d'ameliorer leur pouvoir predictif par rapport a la pathologie humaine. C'est dans ce contexte de renouvellement des approches et des methodes que nous evoquerons, dans cette revue, les evolutions des modeles de fonctionnement de l'industrie. Ces evolutions permettent de passer d'une recherche traditionnelle a une recherche translationnelle, ou le patient et la physiopathologie sont mis au centre du processus de recherche et developpement. Ce processus integre l'utilisation de nouveaux outils de medecine moleculaire et de decouverte de nouveaux biomarqueurs.
Subcellular fractionation has provided the means required to analyze the composition and properties of purified cellular elements. In particular, subcellular fractionation has helped to define membrane boundaries and became necessary for the development of cell-free assays that reconstitute complicated cellular processes. Although cell fractionation techniques have improved over the last decades the purification of organelles to homogeneity is still a barely accessible goal in cell biology. In this article, we will first briefly review the basic principles of subcellular fractionation, and the establishment of different organelle fractions by density centrifugation, using tissue culture cells as a paradigm. Then we will discuss some of the intrinsic problems and will compare gradient purification of cellular extracts with electromigration analysis. Finally, we will describe alternative approaches, such as immunoisolation and flow cytometry to purify organelles from tissue culture cells.
Proteome analysis using human serum is a technological advancement that will enable the discovery of novel biomarkers and biomarker patterns of various human diseases. Although proteome analysis using serum has potential in disease prevention, early diagnosis and treatment of diseases, and evaluation of pharmacotherapies, this technology is still in its infancy. Thus, we sought to develop an advanced method of conducting proteome analysis on human serum. In this study, we report the development of the semi-comprehensive protein analytical technique, which involves the systematic use of iTRAQ labeling, HPLC, nano-LC and MS. We compared the composition of the serum proteome in males and females using this technique and detected gender-based differences in serum protein composition. This technology will enable the generation of databases that may ultimately lead to the discovery of specific biomarkers or biomarker patterns of various diseases.
There has been considerable interest in recent years in the development of miniaturized and parallelized array technology for protein-protein interaction analysis and protein profiling, namely 'protein-detecting microarrays'. Protein-detecting microarrays utilize a wide variety of capture agents (antibodies, fusion proteins, DNA/RNA aptamers, synthetic peptides, carbohydrates, and small molecules) immobilized at high spatial density on a solid surface. Each capture agent binds selectively to its target protein in a complex mixture, such as serum or cell lysate samples. Captured proteins are subsequently detected and quantified in a high-throughput fashion, with minimal sample consumption. Protein-detecting microarrays were first described by MacBeath and Schreiber in 2000, and the number of publications involving this technology is rapidly increasing. Furthermore, the first multiplex immunoassay systems have been cleared by the US Food and Drug Administration, signaling recognition of the usefulness of miniaturized and parallelized array technology for protein detection in predictive/early diagnosis. Although genetic tests still predominate, with further development protein-based diagnosis will become common in clinical use within a few years.
The use of proteomics to profile biological fluids and identify therein biomarkers for cancer and other diseases was initially received with considerable excitement. However, results have fallen short of the expectations. Traditionally, protein biomarkers have been identified by measurement of relative expression changes between case and control samples from which differentially expressed proteins are then considered to represent biomarker candidates. We argue that current individual proteomics-based biomarker discovery studies lack the statistical strength for the identification of high-confidence biomarkers. Instead, multi-group efforts are necessary to facilitate the generation of sufficient sample sizes. This is contingent on the ability to collate and cross-compare data from different studies, which will require the use of a common metric or standards. Though profound, the technical challenges for absolute protein quantification can be overcome. The use of matrix specific, shared standards for absolute quantitation presents an opportunity to facilitate the much needed, but currently impossible, comparisons of different studies. In addition to community-wide approaches to standardize pre-analytical biomarker research studies, it is also important to establish means to integrate experimental data from different studies in order to assess the usefulness of proposed biomarkers with sufficient statistical certainty.
In silico drug discovery is a complex process requiring flexibility and ingenuity in method selection and a careful validation of work protocols. GPCR in silico drug discovery poses additional challenges due to the paucity of crystallographic data. This paper starts by reviewing selected GPCR in silico screening programs reported in the literature, including both structure-based and ligand-based approaches. Particular emphasis is given to library design, binding mode selection, process validation and compound selection for biological testing. Following literature review, we provide insights into in silico methodologies and process workflows used at EPIX to drive over 20 highly successful screening and lead optimization programs performed since 2001. Applications of the various methodologies discussed are demonstrated by examples from recent programs that have not yet been published.
We employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumor as well as in the brain adjacent to tumor (BAT). Reference samples were collected subcutaneously from the patients' abdomen. The samples were analyzed by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analyzed with orthogonal partial least-squares (OPLS). To enable detection of treatment-induced alterations, the data was processed by individual treatment over time (ITOT) normalization. One-hundred fifty-one metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected samples from tumor and the BAT region. There was also a marked difference between the intracranially and the subcutaneously collected samples. Furthermore, we observed systematic metabolic changes induced by radiotherapy treatment among both tumor and BAT samples. The metabolite patterns affected by treatment were different between tumor and BAT, both containing highly discriminating information, ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response.
New biomarkers are urgently needed to accelerate efforts in developing new drugs and treatments of known diseases. New clinical and translational proteomics studies emerge almost every day. However, discovery of new diagnostic biomarkers lags behind because of variability at every step in proteomics studies (e.g., assembly of a cohort of patients, sample preparation and the nature of body fluids, selection of a profiling method and uniform protocols for data analysis).Quite often, the validation step that follows the discovery phase does not reach desired levels of sensitivity and specificity or reproducibility between laboratories. Mass spectrometry and gel-based methods do not provide enough throughput for screening thousands of clinical samples. Further development of protein arrays may address this issue.Despite many obstacles, proteomics delivers vast amounts of information useful for understanding the molecular mechanisms underlying diseases.
The analysis of proteins in biological membranes forms a major challenge in proteomics. Despite continuous improvements and the development of more sensitive analytical methods, the analysis of membrane proteins has always been hampered by their hydrophobic properties and relatively low abundance. In this review, we describe recent successful strategies that have led to in-depth analyses of the membrane proteome. To facilitate membrane proteome analysis, it is essential that biochemical enrichment procedures are combined with special analytical workflows that are all optimized to cope with hydrophobic polypeptides. These include techniques for protein solubilization, and also well-matched developments in protein separation and protein digestion procedures. Finally, we discuss approaches to target membrane-protein complexes and lipid-protein interactions, as such approaches offer unique insights into function and architecture of cellular membranes.
Combinatorial peptide ligand libraries have recently allowed considerable advances in the mapping of chicken egg yolk and white proteomics. Data from literature have been regrouped and elaborated for network and pathway analyses in order to convey a unified view of these proteomes. Redundant proteins were excluded, while isoforms of the same proteins were maintained to reach a total of 260 distinct gene products for egg yolk and 148 for egg white having a match in the database. From these analyses, a role for proteins involved in cell development, proliferation and migration, cell-to-cell interaction and hematological system development emerged. Although it might turn out that, notwithstanding the extensive mapping, the currently available datasets might be still incomplete, a valuable insight could still be obtained about specific proteins playing a crucial role in antimicrobial responses, mainly histones, lysozyme and vitamin-binding proteins. In particular, SERPINB3 (ovalbumin Y, or Squamous Cell Carcinoma Antigen, SCCA1) was individuated in 8 out of 10 top score pathways in egg yolk and in 6 out 10 in egg white. SERPINB3 is a member of the ov-serpin family, participating in coagulation and inflammation responses. However, it is yet to be assessed how these observations could correlate with previous analyses about the role of egg yolk derived proteins in counteracting blood coagulation.
Advances in proteomics technologies, in particular the parallel development of highly sensitive mass spectrometers and accurate protein quantitation technologies, have allowed the detection and accurate measurement of low abundance proteins in bodily fluids and tissues. Furthermore, the application of these technologies in biomedical research has led to the identification of proteins and genes with expression patterns that change as a consequence of disease; detection and quantitation of these proteins and genes could provide valuable information for disease diagnosis and prognosis. For example, cell-surface protein expression can change in diseased cells. These proteins may then be secreted or shed from the cell surface; the levels of these proteins in blood or urine could provide valuable information for the diagnosis of disease and disease severity. The focus of this review is the methods by which proteomics-based technologies can be applied to characterize cell-surface proteins and to measure changes to expression levels in diseased states; the review also discusses the soluble counterparts of these surface proteins in the blood; these proteins could be important diagnostic and/or prognostic indicators of disease.
Although a preliminary portrait of the red blood cell proteome and interactome has already been provided, the recent identification of 1578 gene products from the erythrocyte cytosol asks for an updated and improved view. In this paper, we exploit data available from recent literature to compile a nonredundant list of 1989 proteins and elaborate it with pathway and network analyses. Upon network analysis, it is intuitively confirmed that red blood cells likely suffer of exacerbated oxidative stress and continuously strive against protein and cytoskeletal damage. It also emerges that erythrocyte interaction networks display a high degree of maturity. Indeed, a series of core proteins were individuated to play a central role. A catalytic ring of proteins counteracting oxidative stress was individuated as well. In parallel, pathway analysis confirmed the validity of observations about the SEC23B gene role in CDA II in a fast and unbiased way.
The low-molecular weight fraction (LMF) of the human plasma proteome is an invaluable source of biological information, especially in the context of identifying plasma-based biomarkers of disease. In this study, a separation and enrichment strategy based on centrifugal ultrafiltration was developed for the LMF (i.e., <or=25K) of plasma routinely prepared from normal, healthy volunteers. Four commercially-available filter membranes of similar nominal molecular weight cut-off (NMWC), but differing membrane chemistries and filter orientations (Microcon, Millipore; Centrisart, Sartorius; Amicon Ultra, Millipore; Vivaspin, Sartorius), were evaluated. Of these filtration devices, only the Sartorius Vivaspin tangential membrane, NMWC 20K was effective in the non-retention of M(r)>50K, and recovery and enrichment of low-M(r) components from human plasma. This filter membrane device was further optimized with respect to plasma buffer composition, centrifugal force, duration and temperature. Optimal ultrafiltration conditions were obtained using 100 microL of normal plasma in 10% acetonitrile, and a centrifugation force of 4000x g for 35 min at 20 degrees C. In this LMF, 44 proteins (from 266 unique peptides) were identified using a combination of 1D-SDS-PAGE / nano-LC-MS/MS and a stringent level of identification (FDR <1%). We report the identification of several proteins (e.g., protein KIAA0649 (Q9Y4D3), rheumatoid factor D5, serine protease inhibitor A3, and transmembrane adapter protein PAG) previously not reported in extant high-confidence Human Proteome Organization (HUPO) Plasma Proteome Project datasets. When compared with the low-M(r) human plasma/serum proteome datasets of Zhou et al. (Electrophoresis, 2004. 25, 1289-98), Gundry et al. (Proteomics Clin. Appl., 2007. 1, 73-88) and Villanueva et al. (Anal Chem, 2004. 76, 1560-70), 64% of our identifications (28 proteins) were novel; these include cofilin-1, PPIase A, and the SH3 domain-binding glutamic acid-rich-like protein 3. In addition to intact proteins, many peptide fragments from high-abundance proteins (e.g., fibrinogen, clusterin, Factor XIIIa, transferrin, kinogen-1, and inter-alpha-trypsin inhibitor), presumably derived by ex vivo proteolysis, were observed.
Here we review the use of informatics in structural interactomics, with particular emphasis on understanding interfacial contacts in the development of novel therapeutics and the interpretation of genetic variation. We describe the availability and applicability of structural databases of protein interactions which facilitate this endeavour. We demonstrate the applicability of a structural interactomics approach to the study of the fibroblast growth factor (FGF)-stimulated mitogen-activated protein kinase (MAPK) pathway.
Biomarkers are decision-making tools at the basis of clinical diagnostics and essential for guiding therapeutic treatments. In this context, autoimmune diseases represent a class of disorders that need early diagnosis and steady monitoring. These diseases are usually associated with humoral or cell-mediated immune reactions against one or more of the body's own constituents. Autoantibodies fluctuating in biological fluids can be used as disease biomarkers and they can be, thus, detected by diagnostic immunoassays using native autoantigens. However, it is now accepted that post-translational modifications may affect the immunogenicity of self-protein antigens, triggering an autoimmune response and creating neo-antigens. In this case, post-translationally modified peptides represent a more valuable tool with respect to isolated or recombinant proteins. In fact, synthetic peptides can be specifically modified to mimic neo-antigens and to selectively detect autoantibodies as disease biomarkers. A 'chemical reverse approach' to select synthetic peptides, bearing specific post-translational modifications, able to fishing out autoantibodies from patients' biological fluids, can be successfully applied for the development of specific in vitro diagnostic/prognostic assays of autoimmune diseases. Herein, we report the successful application of this approach to the identification of biomarkers in different autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis and multiple sclerosis.
Blood-related proteomics is an emerging field, recently gaining momentum. Indeed, a wealth of data is now available and a plethora of groups has contributed to add pieces to the jigsaw puzzle of protein complexity within plasma and blood cells. In this review article we purported to sail across the mare magnum of the actual knowledge in this research endeavour. The main strides in proteomic investigations on red blood cells, platelets, plasma and white blood cells are hereby presented in a chronological order. Moreover, a glance is given at prospective studies which promise to shift the focus of attention from the end product to its provider, the donor, in a sort of Kantian "Copernican revolution". A well-rounded portrait of the usefulness of proteomics in blood-related research is accurately given. In particular, proteomic tools could be adopted to follow the main steps of the blood-banking production processes (a comparison of collection methods, pathogen inactivation techniques, storage protocols). Thus proteomics has been recently transformed from a mere basic-research extremely-expensive toy into a dramatically-sensitive and efficient eye-lens to either delve into the depths of the molecular mechanisms of blood and blood components or to establish quality parameters in the blood-banking production chain totally anew.
The use of combinatorial peptide ligand libraries, containing hexapeptides terminating with a primary amine, or modified with a terminal carboxyl group, allowed discovering and identifying a large number of previously unreported proteins in cow's whey. Whereas comprehensive whey protein lists progressively increased in the last 6 years from 17 unique gene products to more than 100, our findings have considerably expanded this list to a total of 149 unique protein species, of which 100 were not described in previous proteomics studies. As an additional interesting result, a polymorphic alkaline protein was observed with a strong positive signal when blotted from an isoelectric focusing separation in gel and tested with sera of allergic patients. This polymorphic protein, found only after treatment with the peptide library, was identified as an immunoglobulin (Ig), a minor allergen that had been largely amplified. The list of cow's whey components here reported is by far the most comprehensive at present and could serve as a starting point for the functional characterization of low-abundance proteins possibly having novel pharmaceutical, diagnostic, and biomedical applications.
Despite continued increase in global Pharma R & D expenditure, the number of innovative drugs obtaining market approval has declined since 1994. The pharmaceutical industry is now entering a crucial juncture where increasing rates of attrition in clinical drug development as well as increasing development timelines are impacted by external factors such as intense regulatory pricing and safety pressures, increasing sales erosion due to generics, as well as exponential increases in the costs of bringing a drug to market. Despite these difficulties, numerous opportunities exist such as multiple unmet medical needs, the increasing incidence of certain diseases such as Alzheimer's disease, cancer, diabetes and obesity due to demographic changes, as well as the emergence of evolving markets such as China, India, and Eastern Europe. Consequently, Pharma is now responding to this challenge by improving both the productivity and the innovation in its drug discovery and development pipelines. In this regard, the advent of new technologies and expertise such as genomics, proteomics, structural biology, and molecular informatics in an integrated systems biology approach also provides a powerful opportunity for Pharma to address some of these difficulties. The key features behind this new strategy imply a discovery process based on an improved understanding of the molecular mechanism of diseases and drugs, translational research that places the patient at the center of the research process, and the application of biomarkers throughout the discovery and development phases. Moreover, new paradigms are required to improve target validation and develop more predictive cellular and animal models of human pathologies, a greater capacity in informatics-based analysis, and, consequently, a greater access to the vast sources of accumulating biological data and its integrated analysis. In the present review, we will address some of these issues and in particular emphasize how the application of biomarkers could potentially lead to improved productivity, quality, and innovation in drug discovery and ultimately better and safer medicines with improved therapeutic efficacy in specific pathologies for targeted patients.
The HUPO Plasma Proteome Project new phase, PPP-2, held its initial workshop on 17 August, 2008, at the 7(th) World Congress of Proteomics in Amsterdam. Technology platforms, data repositories, informatics, and engagement of research groups for the submission of major datasets were key topics. Plasma is expected to be the common pathway for biomarker development and application through collaboration and integration with other HUPO initiatives.
In plasma proteomics, before a proteome analysis, it is essential to prepare protein samples without high-abundance proteins, including albumin, via specific preparation techniques, such as immunoaffinity capture. However, our preliminary experiments suggested that functional changes with use alter the ability of the immunoaffinity column. Thus, in this study, to evaluate the changes of the removal ability of abundant proteins from plasma by the immunoaffinity column, plasma proteome analysis was performed for the long-term test for the reproducibility of the affinity column using the fluorogenic derivatization-liquid chromatography-tandem mass spectrometry method combined with an IgY column. The specific adsorption for albumin decreased with an increase in the number of the column usage before its expiration date. Moreover, it was demonstrated that hydrophobic high molecular weight compounds in plasma adsorbed onto the column materials surface contributed to the functional changes from specific immunoaffinity adsorption into hydrophobic interaction. These results suggested that, in quantitative plasma proteomics studies, it is important to keep in mind the risk of not only the nonselective loss but also the changes in the adsorption ability of the immunoafinity column.
The most abundant proteins in serum, such as albumin and IgG, act as molecular sponges that bind and transport low molecular weight proteins/peptides and drugs. In the near future, pharmacoproteomics, the use of proteomic technologies in the field of drug discovery and development, and interactomics, the branch of proteomics which is concerned with identifying interactions between proteins, will allow researchers to (i) know the specific protein changes that occur in biological compartments in response to drug administration; (ii) design small novel therapeutic molecules that can have extended half-lives if carried by plasma protein in the blood stream. Advances in these fields will open new avenues of tailor-made molecular therapy, reducing present limitations on treatment arising from toxicity and inefficiency. In this short review we report and discuss the most recent developments arising from the use of proteomic tools in blood plasma protein research, looking at the identification of proteins found in plasma as well as their interactions with small molecules such as drugs, peptides, organic chemicals and metals. We believe this research demonstrates that proteomic technologies, and in particular pharmacoproteomics, interactomics and post-translational modification analysis, could be instrumental in the design of new tailor-made drugs leading to substantial improvements in molecular therapy.
Metastasis is a common phenomenon and the major lethal cause of lung adenocarcinoma (AdC). To discover novel potential biomarkers associated with lymph node metastasis and prognosis in lung AdC, we assessed differences in protein expression between primary lung AdC with (LNM AdC) and without lymph node metastasis (non-LNM AdC) using a quantitative proteomic approach. Laser capture microdissection was performed to purify the cancer cells from primary lung AdC tissues. The differential proteins between the pooled microdissected non-LNM AdC and LNM AdC tissues were identified by two-dimensional difference gel electrophoresis (2D-DIGE) coupled with mass spectrometry (MS). In this study, twenty proteins were found to be differentially expressed in two types of lung AdC. ANXA3, significantly up-regulated in LNM AdC compared with non-LNM AdC, was validated by western blotting. Immunohistochemistry showed that ANXA3 over-expression was frequently observed in LNM AdCs and matched lymph node metastases compared with non-LNM AdCs. ANXA3 over-expression was significantly associated with advanced clinical stage (p < 0.001) and lymph node metastasis (p < 0.001) and increased relapse rate (p < 0.001) and decreased overall survival (p < 0.001) in lung AdCs. Cox regression analysis indicated ANXA3 over-expression was an independent prognostic factor. Our results indicate that ANXA3 might serve as a novel biomarker for lymph node metastasis and prognosis in lung AdC, and play an important role in lung AdC progression.
Biological membranes form an essential barrier between living cells and their external environments, as well as serve to compartmentalize intracellular organelles within eukaryotes. The latter includes membranes that envelope the nucleus, the outer and inner membranes of the mitochondria, membrane cisternae complex of the ER, Golgi apparatus, as well as lysosomes and secretory vesicles. Depending on their localizations in the whole organism and also within the cell, these membranes have different, highly specialized functions. Although 30% of naturally occurring proteins are predicted to be embedded in biological membranes, membrane proteomics is traditionally understudied due to difficulties in solubilizing, separating, and identifying membrane proteins. Given the importance of membrane proteins in the various cellular processes listed in this review, as well as the roles they play in diseases and their potential as drug targets, it is imperative that this class of proteins be better studied. With the recent advancement in technology, it is expected that some of the difficulties in membrane proteomics will be overcome, yielding new data on membrane proteins.
We present here the estimation of the upper limit of the number of molecular targets in the human genome that represent an opportunity for further therapeutic treatment. We select around approximately 6300 human proteins that are similar to sequences of known protein targets collected from DrugBank database. Our bioinformatics study estimates the size of 'druggable' human genome to be around 20% of human proteome, i.e. the number of the possible protein targets for small-molecule drug design in medicinal chemistry. We do not take into account any toxicity prediction, the three-dimensional characteristics of the active site in the predicted 'druggable' protein families, or detailed chemical analysis of known inhibitors/drugs. Instead we rely on remote homology detection method Meta-BASIC, which is based on sequence and structural similarity. The prepared dataset of all predicted protein targets from human genome presents the unique opportunity for developing and benchmarking various in silico chemo/bio-informatics methods in the context of the virtual high throughput screening.
A discontinuous electrophoretic system for the isolation of membrane proteins from acrylamide gels has been developed using equipment for sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Coomassie dyes were introduced to induce a charge shift on the proteins and aminocaproic acid served to improve solubilization of membrane proteins. Solubilized mitochondria or extracts of heart muscle tissue, lymphoblasts, yeast, and bacteria were applied to the gels. From cells containing mitochondria, all the multiprotein complexes of the oxidative phosphorylation system were separated within one gel. The complexes were resolved into the individual polypeptides by second-dimension Tricine-SDS-PAGE or extracted without SDS for functional studies. The recovery of all respiratory chain complexes was almost quantitative. The percentage recovery of functional activity depended on the respective protein complex studied and was zero for some complexes, but almost quantitative for others. The system is especially useful for small scale purposes, e.g., separation of radioactively labeled membrane proteins, N-terminal protein sequencing, preparation of proteins for immunization, and diagnostic studies of inborn neuromuscular diseases.
An approach to the systematic identification and quantification of the proteins contained in the microsomal fraction of cells is described. It consists of three steps: (1) preparation of microsomal fractions from cells or tissues representing different states; (2) covalent tagging of the proteins with isotope-coded affinity tag (ICAT) reagents followed by proteolysis of the combined labeled protein samples; and (3) isolation, identification, and quantification of the tagged peptides by multidimensional chromatography, automated tandem mass spectrometry, and computational analysis of the obtained data. The method was used to identify and determine the ratios of abundance of each of 491 proteins contained in the microsomal fractions of naïve and in vitro- differentiated human myeloid leukemia (HL-60) cells. The method and the new software tools to support it are well suited to the large-scale, quantitative analysis of membrane proteins and other classes of proteins that have been refractory to standard proteomics technology.
Peptides play a central role in many physiological processes. In order to comprehensively analyze all peptides and small proteins of a whole organism or a subsystem (peptidome), technologies other than 2-D gel electrophoresis are required. Although systematic efforts directed at peptides and peptidomes, comparable to the numerous proteomics projects, are largely lacking to date, a number that employ liquid chromatography or affinity purification and mass spectrometric identification have now been developed and applied successfully to the analysis of a variety of different peptide sources. Furthermore, distinct peptide classes, such as antimicrobial peptides or peptides related to metabolic diseases such as diabetes/obesity, are once again receiving attention. Here we discuss peptides in terms of their applicability to serve as diagnostic markers (or more generally as biomarkers), as well as therapeutic targets or lead compounds. There are also a number of technological challenges that need to be overcome in the study of potent animal venoms and plant toxins, both of which are generally peptides and which are discussed as potential lead compounds for therapeutic intervention in diseases such as cancer.
Imaging sciences have grown exponentially during the past three decades, and many techniques, such as magnetic resonance imaging, nuclear tomographic imaging and X-ray computed tomography, have become indispensable in clinical use. Advances in imaging technologies and imaging probes for humans and for small animals are now extending the applications of imaging further into drug discovery and development, and have the potential to considerably accelerate the process. This review summarizes some of the recent developments in conventional and molecular imaging, and highlights their impact on drug discovery.
Cataloging the proteomes of single-celled microorganisms, cells, biological fluids, tissue and whole organisms is being undertaken at a rapid pace as advances are made in protein and peptide separation, detection and identification. For metazoans, subcellular organelles represent attractive targets for global proteome analysis because they represent discrete functional units, their complexity in protein composition is reduced relative to whole cells and, when abundant cytoskeletal proteins are removed, lower abundance proteins specific to the organelle are revealed. Here, we review recent literature on the global analysis of subcellular organelles and briefly discuss how that information is being used to elucidate basic biological processes that range from cellular signaling pathways through protein-protein interactions to differential expression of proteins in response to external stimuli. We assess the relative merits of the different methods used and discuss issues and future directions in the field.
The genomic era has brought with it a basic change in experimentation, enabling researchers to look more comprehensively at biological systems. The sequencing of the human genome coupled with advances in automation and parallelization technologies have afforded a fundamental transformation in the drug target discovery paradigm, towards systematic whole genome and proteome analyses. In conjunction with novel proteomic techniques, genome-wide annotation of function in cellular models is possible. Overlaying data derived from whole genome sequence, expression and functional analysis will facilitate the identification of causal genes in disease and significantly streamline the target validation process. Moreover, several parallel technological advances in small molecule screening have resulted in the development of expeditious and powerful platforms for elucidating inhibitors of protein or pathway function. Conversely, high-throughput and automated systems are currently being used to identify targets of orphan small molecules. The consolidation of these emerging functional genomics and drug discovery technologies promises to reap the fruits of the genomic revolution.
An increasing number of proteomic strategies rely on liquid chromatography-tandem mass spectrometry (LC-MS/MS) to detect and identify constituent peptides of enzymatically digested proteins obtained from various organisms and cell types. However, sample preparation methods for isolating membrane proteins typically involve the use of detergents and chaotropes that often interfere with chromatographic separation and/or electrospray ionization. To address this problem, a sample preparation method combining carbonate extraction, surfactant-free organic solvent-assisted solubilization, and proteolysis was developed and demonstrated to target the membrane subproteome of Deinococcus radiodurans. Out of 503 proteins identified, 135 were recognized as hydrophobic on the basis of their calculated hydropathy values (GRAVY index), corresponding to coverage of 15% of the predicted hydrophobic proteome. Using the PSORT algorithm, 53 of the proteins identified were classified as integral outer membrane proteins and 215 were classified as integral cytoplasmic membrane proteins. All identified integral cytoplasmic membrane proteins had from 1 to 16 mapped transmembrane domains (TMDs), and 65% of those containing four or more mapped TMDs were identified by at least one hydrophobic membrane spanning peptide. The extensive coverage of the membrane subproteome (24%) by identification of highly hydrophobic proteins containing multiple TMDs validates the efficacy of the described sample preparation technique to isolate and solubilize hydrophobic integral membrane proteins from complex protein mixtures.
The emerging field of biomarkers has applications in the diagnosis, staging, prognosis and monitoring of disease progression, as well as in the monitoring of clinical responses to a therapeutic intervention and the development and delivery of personalized treatments to reduce attrition in clinical trials. Moreover, biomarkers have a positive impact on health economics. The word "biomarker" has been used extensively across therapeutic areas and many disciplines, and its nature takes into consideration clinical, physiological, biochemical, developmental, morphological and molecular measures. In drug trials, biomarkers have been proposed for use in efficacy determination and patient population stratification, in deducing pharmacokinetic-pharmacodynamic relationships and in safety monitoring. The interfacing and integration of different technologies for data collection and analysis are pivotal to biomarker identification, characterization, validation and application. "Integrative functional informatics" represents a novel direction in such technology integration.
Separation of complex protein mixtures that have a wide dynamic range of concentration, such as plasma or serum, is a challenge for proteomic analysis. Sample preparation to remove high-abundant proteins is essential for proteomics analysis. Immunoglobulin yolk (IgY) antibodies have unique and advantageous features that enable specific protein removal to aid in the detection of low-abundant proteins and biomarker discovery. This report describes the efficiency and effectiveness of IgY microbeads in separating 12 abundant proteins from plasma with an immunoaffinity spin column or LC column. The protein separation and sample preparation process was monitored via SDS-PAGE, 2-DE, LC-MS/MS, or clinical protein assays. The data demonstrate the high specificity of the protein separation, with removal of 95-99.5% of the abundant proteins. IgY microbeads against human proteins can also selectively remove orthologous proteins of other mammals such as mouse, rat, etc. Besides the specificity and reproducibility of the IgY microbeads, the report discusses the factors that may cause potential variations in protein separation such as protein-protein interactions (known as "Interactome"), binding and washing conditions of immunoaffinity reagents, etc. A novel concept of Seppromics is introduced to address methodologies and science of protein separation in a context of proteomics.
There is a substantial list of pre-analytical variables that can alter the analysis of blood-derived samples. We have undertaken studies on some of these issues including choice of sample type, stability during storage, use of protease inhibitors, and clinical standardization. As there is a wide range of sample variables and a broad spectrum of analytical techniques in the HUPO PPP effort, it is not possible to define a single list of pre-analytical standards for samples or their processing. We present here a compendium of observations, drawing on actual results and sound clinical theories and practices. Based on our data, we find that (1) platelet-depleted plasma is preferable to serum for certain peptidomic studies; (2) samples should be aliquoted and stored preferably in liquid nitrogen; (3) the addition of protease inhibitors is recommended, but should be incorporated early and used judiciously, as some form non specific protein adducts and others interfere with peptide studies. Further, (4) the diligent tracking of pre-analytical variables and (5) the use of reference materials for quality control and quality assurance, are recommended. These findings help provide guidance on sample handling issues, with the overall suggestion being to be conscious of all possible pre-analytical variables as a prerequisite of any proteomic study.
The human urinary proteome has been reassessed and re-evaluated via a novel concentration/equalization technique, exploiting beads coated with hexameric peptide ligand libraries. These beads act by capturing the whole protein spectra contained in the sample, by drastically reducing the level of the most abundant species, while strongly concentrating the more dilute and rare ones. In a control urine sample, 134 unique proteins could be identified. The first bead eluate (in thiourea, urea, and CHAPS) permitted the identification of 317 gene products, whereas the second eluate (in 9 M urea, pH 3.8) allowed the identification of another 95 unique proteins. By eliminating redundancies, a total of 383 unique gene products could be identified in human urines. This represents a major increment as compared to data reported in recent literature. By comparing our data with those reported to the present, an additional 251 proteins could be added to the list, thus bringing the total unique gene products so far identified in human urines to ca. 800 species.