The 20th anniversary of proteomics and some of its origins.

The term "proteome" was first introduced into the scientific literature in July 1995. Almost 20 years ago attempts to characterize the "total protein complement able to be encoded by a given genome" only became possible due to privileged access to what were then the world's most complete sets of genomic data. Today, proteomics has become an important pillar in the fields of disease diagnosis and drug research and development, while also playing a critical role in the much larger field of Healthcare Analytics and Biomarker Discovery and Detection. It is important to note that this industry originated mostly from building blocks in analytical science that predated the term "proteomics" by many decades. However, proteomics, as a discipline, has allowed protein scientists to more favorably compete in the face of highly fashionable Big Science and, more specifically, genomics.

Regionalized GC content of template DNA as a predictor of PCR success.

A set of 1438 human exons was subjected to nested PCR. The initial success rate using a standard PCR protocol required for ligation-independent cloning was 83.4%. Logistic regression analysis was conducted on 27 primer- and template-related characteristics, of which most could be ignored apart from those related to the GC content of the template. Overall GC content of the template was a good predictor for PCR success; however, specificity and sensitivity values for predicted outcome were improved to 84.3 and 94.8%, respectively, when regionalized GC content was employed. This represented a significant improvement in predictability with respect to GC content alone (P < 0.001; chi(2)) and is expected to increase in relative sensitivity as template size increases. Regionalized GC was calculated with respect to a threshold of 61% GC content and a sliding window of 21 bp across the target sequence. Fine-tuning of PCR conditions is not practicable for all target sequences whenever a large number of genes of different lengths and GC content are to be amplified in parallel, particularly if total open reading frame or domain coverage is essential for recombinant protein synthesis. Thus, the present method is proposed as a means of grouping subsets of genes possessing potentially difficult target sequences so that PCR conditions can be optimized separately in order to obtain improved outcomes.

In Silico Screening of Nonsteroidal Anti-Inflammatory Drugs and Their Combined Action on Prostaglandin H Synthase-1.

The detailed kinetic model of Prostaglandin H Synthase-1 (PGHS-1) was applied to in silico screening of dose-dependencies for the different types of nonsteroidal anti-inflammatory drugs (NSAIDs), such as: reversible/irreversible, nonselective/selective to PGHS-1/PGHS-2 and time dependent/independent inhibitors (aspirin, ibuprofen, celecoxib, etc.) The computational screening has shown a significant variability in the IC50s of the same drug, depending on different in vitro and in vivo experimental conditions. To study this high heterogeneity in the inhibitory effects of NSAIDs, we have developed an in silico approach to evaluate NSAID action on targets under different PGHS-1 microenvironmental conditions, such as arachidonic acid, reducing cofactor, and peroxide concentrations. The designed technique permits translating the drug IC50, obtained in one experimental setting to another, and predicts in vivo inhibitory effects based on the relevant in vitro data. For the aspirin case, we elucidated the mechanism underlying the enhancement and reduction (aspirin resistance) of its efficacy, depending on PGHS-1 microenvironment in in vitro/in vivo experimental settings. We also present the results of the in silico screening of the combined action of sets of two NSAIDs (aspirin with ibuprofen, aspirin with celecoxib), and study the mechanism of the experimentally observed effect of the suppression of aspirin-mediated PGHS-1 inhibition by selective and nonselective NSAIDs. Furthermore, we discuss the applications of the obtained results to the problems of standardization of NSAID test assay, dependence of the NSAID efficacy on cellular environment of PGHS-1, drug resistance, and NSAID combination therapy.

Kinetic modelling of NSAID action on COX-1: focus on in vitro/in vivo aspects and drug combinations.

The detailed kinetic model of Prostaglandin H Synthase-1 (COX-1) was developed to in silico test and predict inhibition effects of nonsteroidal anti-inflammatory drugs (NSAIDs) on target. The model takes into account key features of the complex catalytic mechanism of cyclooxygenase-1, converting arachidonic acid to prostaglandin PGH(2), and includes the description of the enzyme interaction with various types of NSAIDs (reversible/irreversible, non-selective and selective to COX-1/COX-2). Two different versions of the model were designed to simulate the inhibition of COX-1 by NSAIDs in two most popular experimental settings - in vitro studies with purified enzyme, and the experiments with platelets. The developed models were applied to calculate the dose-dependence of aspirin and celecoxib action on COX- 1 in vitro and in vivo conditions. The mechanism of the enhancement of aspirin efficiency in platelet as compared to its action on purified COX-1 was elucidated. The dose-dependence of celecoxib simulated with the use of the "in vivo" version of the model predicted potentially strong inhibitory effect of celecoxib on thromboxan production in platelets. Simulation of the combined effect of two NSAIDs, aspirin and celecoxib, on COX-1 allowed us to reveal the mechanism underlying the suppression of aspirin-mediated COX-1 inhibition by celecoxib. We discuss our modelling results in the context of the on-going debates on the potential cardio-vascular risks associated with co-administration of various types of NSAIDs.

Group A streptococcus cell-associated pathogenic proteins as revealed by growth in hyaluronic acid-enriched media.

Group A streptococcus (GAS), also know as Streptococcus pyogenes, is a human pathogen and can cause several fatal invasive diseases such as necrotising fasciitis, the so-called flesh-eating disease, and toxic shock syndrome. The destruction of connective tissue and the hyaluronic acid (HA) therein, is a key element of GAS pathogenesis. We therefore propagated GAS in HA-enriched growth media in an attempt to create a simple biological system that could reflect some elements of GAS pathogenesis. Our results show that several recognised virulence factors were up-regulated in HA-enriched media, including the M1 protein, a collagen-like surface protein and the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase, which has been shown to play important roles in streptococcal pathogenesis. Interestingly, two hypothetical proteins of unknown function were also up-regulated and detailed bioinformatics analysis showed that at least one of these hypothetical proteins is likely to be involved in pathogenesis. It was therefore concluded that this simple biological system provided a valuable tool for the identification of potential GAS virulence factors.

Analysis of high throughput protein expression in Escherichia coli.

The ability to efficiently produce hundreds of proteins in parallel is the most basic requirement of many aspects of proteomics. Overcoming the technical and financial barriers associated with high throughput protein production is essential for the development of an experimental platform to query and browse the protein content of a cell (e.g. protein and antibody arrays). Proteins are inherently different one from another in their physicochemical properties; therefore, no single protocol can be expected to successfully express most of the proteins. Instead of optimizing a protocol to express a specific protein, we used sequence analysis tools to estimate the probability of a specific protein to be expressed successfully using a given protocol, thereby avoiding a priori proteins with a low success probability. A set of 547 proteins, to be used for antibody production and selection, was expressed in Escherichia coli using a high throughput protein production pipeline. Protein properties derived from sequence alone were correlated to successful expression, and general guidelines are given to increase the efficiency of similar pipelines. A second set of 68 proteins was expressed to investigate the link between successful protein expression and inclusion body formation. More proteins were expressed in inclusion bodies; however, the formation of inclusion bodies was not a requirement for successful expression.

A human proteome project with a beginning and an end.

Research activities centered on the ensemble of and individual human proteins have taken on numerous guises, some of which fall under the banner of what could be defined as a Human Proteome Project (HPP). However, the latter has yet to take-on the apparent global focus of its predecessor, the Human Genome Project. The reasons for this are both financial and technical. The disparate properties afforded to each protein by a 20-letter code render a single unifying approach difficult to implement, while the current limit of analytical detection has yet to deliver an entire proteome for even the simplest of microbes. The situation is complicated further by the fact that low abundance proteins dominate within any living cell. Thus, enhancement of signal-to-noise ratio by affinity ligands becomes of paramount importance if whole-organism proteomics is to be realized. The generation of such ligands (molecules exhibiting desirable affinity and selectivity for target) could provide the necessary focus and a task list with a definable beginning and end. Such a finite task list is considered essential if an HPP might one day deliver global coverage on a scale seen currently for the total DNA sequence of some 200 living organisms.

Lung proteome alterations in a mouse model for nonallergic asthma.

A mouse model for nonatopic asthma was employed to study the alterations of the lung proteome to gain insight into the underlying molecular mechanisms of disease pathophysiology post-challenge. Lung samples from asthmatic and control mice were used to generate 24 high quality two-dimensional electrophoresis gels wherein 2115 proteins were examined for disease relevance. In total, 23 proteins were significantly up- or down-regulated following hapten-challenge of dinitro-fluorobenzene-hypersensitive mice. Twenty proteins were identified by mass spectrometry, of which 18 could be linked to asthma related symptoms, such as stress and inflammation, lung detoxification, plasma exudation and/or tissue remodeling. As such, proteomics was clearly vindicated as a means of studying this complex disease phenomenon. The proteins found in this study may not necessarily play a role in the immunological mechanisms and/or pathophysiology of asthma development. However, they may prove useful as surrogate biomarkers for quantitatively monitoring disease state progression or response to therapy. The mathematics of achieving statistical confidence from low numbers of gel replicates containing large numbers of independent variables stress the need for high numbers of replicates to better sample the population of proteins revealed by two-dimensional gel electrophoresis.