Bob Amess

Proteomics: a major new technology for the drug discovery process.

Proteomics is a new enabling technology that is being integrated into the drug discovery process. This will facilitate the systematic analysis of proteins across any biological system or disease, forwarding new targets and information on mode of action, toxicology and surrogate markers. Proteomics is highly complementary to genomic approaches in the drug discovery process and, for the first time, offers scientists the ability to integrate information from the genome, expressed mRNAs, their respective proteins and subcellular localization. It is expected that this will lead to important new insights into disease mechanisms and improved drug discovery strategies to produce novel therapeutics.

Structure and sites of phosphorylation of 14-3-3 protein: Role in coordinating signal transduction pathways

The 14-3-3 family are homo- and heterodimeric proteins whose biological role has been unclear for some time, although they are now gaining acceptance as a novel type of ‘adaptor’ protein that modulates interactions between components of signal transduction pathways, rather than by direct activation or inhibition. It is becoming apparent that phosphorylation of the binding partner and possibly also the 14-3-3 proteins may regulate these interactions. 14-3-3 isoforms interact with a novel phosphoserine (Sp) motif on many proteins, RSX1,2SpXP. The two isoforms that interact with Raf-1 are phosphorylated in vivo on Ser185 in a consensus sequence motif for proline-directed kinases. The crystal structure of 14-3-3 indicates that this phosphorylation could regulate interaction of 14-3-3 with its target proteins. We have now identified a number of additional phosphorylation sites on distinct mammalian and yeast isoforms.

The application of selective reaction monitoring confirms dysregulation of glycolysis in a preclinical model of schizophrenia

BackgroundEstablishing preclinical models is essential for novel drug discovery in schizophrenia. Most existing models are characterized by abnormalities in behavioral readouts, which are informative, but do not necessarily translate to the symptoms of the human disease. Therefore, there is a necessity of characterizing the preclinical models from a molecular point of view. Selective reaction monitoring (SRM) has already shown promise in preclinical and clinical studies for multiplex measurement of diagnostic, prognostic and treatment-related biomarkers.MethodsWe have established an SRM assay for multiplex analysis of 7 enzymes of the glycolysis pathway which is already known to be affected in human schizophrenia and in the widely-used acute PCP rat model of schizophrenia. The selected enzymes were hexokinase 1 (Hk1), aldolase C (Aldoc), triosephosphate isomerase (Tpi1), glyceraldehyde-3-phosphate dehydrogenase (Gapdh), phosphoglycerate mutase 1 (Pgam1), phosphoglycerate kinase 1 (Pgk1) and enolase 2 (Eno2). The levels of these enzymes were analyzed using SRM in frontal cortex from brain tissue of PCP treated rats.ResultsUnivariate analyses showed statistically significant altered levels of Tpi1 and alteration of Hk1, Aldoc, Pgam1 and Gapdh with borderline significance in PCP rats compared to controls. Most interestingly, multivariate analysis which considered the levels of all 7 enzymes simultaneously resulted in generation of a bi-dimensional chart that can distinguish the PCP rats from the controls.ConclusionsThis study not only supports PCP treated rats as a useful preclinical model of schizophrenia, but it also establishes that SRM mass spectrometry could be used in the development of multiplex classification tools for complex psychiatric disorders such as schizophrenia.

Proteomic analysis of the maternal protein restriction rat model for schizophrenia: Identification of translational changes in hormonal signaling pathways and glutamate neurotransmission

Previous studies have found that some first onset schizophrenia patients show signs of impaired insulin signaling. Also, epidemiological studies have shown that periods of suboptimal nutrition including protein deficiencies during pregnancy can lead to increased incidence of metabolic conditions and psychiatric disorders in the offspring. For these reasons, we have carried out a molecular profiling analysis of blood serum and brain tissues from adult offspring produced by the maternal low protein (LP) rat model. The results showed similar changes to those seen in schizophrenia. Multiplex immunoassay profiling identified changes in the levels of insulin, adiponectin, and leptin along with alterations in inflammatory and vascular system‐related proteins such as osteopontin, macrophage colony‐stimulating factor 1, and vascular cell adhesion molecule 1. LC‐MSE proteomic profiling showed that glutamatergic pathways were altered in frontal cortex, while signaling pathways and cytoskeletal proteins involved in hormonal secretion and synaptic remodeling were altered in the hypothalamus. Taken together, these studies indicate that the LP rat model recapitulates several pathophysiological attributes seen in schizophrenia patients. We propose that the LP model may have utility for drug discovery efforts, especially to identify compounds that modulate the metabolic and glutamatergic systems.

Analysis of the rat hypothalamus proteome by data-independent label-free LC-MS/MS.

Studies of neuronal, endocrine, and metabolic disorders would be facilitated by characterization of the hypothalamus proteome. Protein extracts prepared from 16 whole rat hypothalami were measured by data‐independent label‐free nano LC‐MS/MS. Peptide features were detected, aligned, and searched against a rat Swiss‐Prot database using ProteinLynx Global Server v.2.5. The final combined dataset comprised 21 455 peptides, corresponding to 622 unique proteins, each identified by a minimum of two distinct peptides. The majority of the proteins (69%) were cytosolic, and 16% were membrane proteins. Important proteins involved in neurological and synaptic function were identified including several members of the Ras‐related protein family and proteins involved in glutamate biosynthesis.

Identification of protein biomarkers in human serum using iTRAQ and shotgun mass spectrometry.

Blood serum is one of the easiest accessible sources of biomarkers and its proteome presents a significant parcel of immune system proteins. These proteins can provide not only biological explanation but also diagnostic and drug response answers independently of the type of disease or condition in question. Shotgun mass spectrometry has profoundly contributed to proteome analysis and is presently considered as an indispensible tool in the field of biomarker discovery. In addition, the multiplexing potential of isotopic labeling techniques such as iTRAQ can increase statistical relevance and accuracy of proteomic data through the simultaneous analysis of different biological samples. Here, we describe a complete protocol using iTRAQ in a shotgun proteomics workflow along with data analysis steps, customized for the challenges associated with the serum proteome.

Application of meta-analysis methods for identifying proteomic expression level differences

We present new statistical approaches for identification of proteins with expression levels that are significantly changed when applying meta‐analysis to two or more independent experiments. We showed that the Euclidean distance measure has reduced risk of false positives compared to the rank product method. Our Ψ‐ranking method has advantages over the traditional fold‐change approach by incorporating both the fold‐change direction as well as the p‐value. In addition, the second novel method, Π‐ranking, considers the ratio of the fold‐change and thus integrates all three parameters. We further improved the latter by introducing our third technique, Σ‐ranking, which combines all three parameters in a balanced nonparametric approach.