Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

A comparative study of synthetic winged peptides for absolute protein quantification

A proper internal standard choice is critical for accurate, precise, and reproducible mass spectrometry-based proteomics assays. Synthetic isotopically labeled (SIL) proteins are currently considered the gold standard. However, they are costly and challenging to obtain. An alternative approach uses SIL peptides or SIL "winged" peptides extended at C- or/and N-terminus with an amino acid sequence or a tag cleaved during enzymatic proteolysis. However, a consensus on the design of a winged peptide for absolute quantification is missing. In this study, we used human serum albumin as a model system to compare the quantitative performance of reference SIL protein with four different designs of SIL winged peptides: (i) commercially available SIL peptides with a proprietary trypsin cleavable tag at C-terminus, (ii) SIL peptides extended with five amino acid residues at C-terminus, (iii) SIL peptides extended with three and (iv) with five amino acid residues at both C- and N-termini. Our results demonstrate properties of various SIL extended peptides designs, e.g., water solubility and efficiency of trypsin enzymatic cleavage with primary influence on quantitative performance. SIL winged peptides extended with three amino acids at both C- and N-termini demonstrated optimal quantitative performance, equivalent to the SIL protein

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding <it>in silico </it>prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites.</p> <p>Results</p> <p>Experimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species <it>Arabidopsis thaliana</it>. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using <it>Arabidopsis thaliana </it>phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms.</p> <p>Conclusions</p> <p>This work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos <url>http://csb.cse.yzu.edu.tw/PlantPhos/</url>. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos.</p

Mining expressed sequence tags identifies cancer markers of clinical interest

BACKGROUND: Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers. RESULTS: We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that HM200 and the shorter gene list HM100 are very competitive cancer biomarker sets. Indeed, when compared to 13 published cancer marker gene lists, HM200 achieves the best or second best classification performance in 79% of the classifications considered. CONCLUSION: These results indicate the existence of at least one general cancer marker set whose predictive value spans several tumor types and classification types. Our comparison with other marker gene lists shows that HM200 markers are mostly novel cancer markers. We also identify the previously published Pomeroy-400 list as another general cancer marker set. Strikingly, Pomeroy-400 has 27 genes in common with HM200. Our data suggest that a core set of genes are responsive to the deregulation of pathways involved in tumorigenesis in a variety of tumor types and that these genes could serve as transcriptional cancer markers in applications of clinical interest. Finally, our study suggests new strategies to select and evaluate cancer biomarkers in microarray studies

Mapping differential interactomes by affinity purification coupled with data independent mass spectrometry acquisition

Characterizing changes in protein-protein interactions associated with sequence variants (e.g. disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies where cost and time are major considerations. To this end, we have coupled AP to data-independent mass spectrometric acquisition (SWATH), and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. Here, we use AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes, and propose a scalable pipeline for systems biology studies

Comparative proteomics using 2-D gel electrophoresis and mass spectrometry as tools to dissect stimulons and regulons in bacteria with sequenced or partially sequenced genomes

We propose two-dimensional gel electrophoresis (2-DE) and mass spectrometry to define the protein components of regulons and stimulons in bacteria, including those organisms where genome sequencing is still in progress. The basic 2-DE protocol allows high resolution and reproducibility and enables the direct comparison of hundreds or even thousands of proteins simultaneously. To identify proteins that comprise stimulons and regulons, peptide mass fingerprint (PMF) with matrix-assisted laser desorption ionization/time-of-flight mass spectrometry (MALDI-TOF-MS) analysis is the first option and, if results from this tool are insufficient, complementary data obtained with electrospray ionization tandem-MS (ESI-MS/MS) may permit successful protein identification. ESI-MS/MS and MALDI-TOF-MS provide complementary data sets, and so a more comprehensive coverage of a proteome can be obtained using both techniques with the same sample, especially when few sequenced proteins of a particular organism exist or genome sequencing is still in progress

Identifying New Therapeutic Targets via Modulation of Protein Corona Formation by Engineered Nanoparticles

We introduce a promising methodology to identify new therapeutic targets in cancer. Proteins bind to nanoparticles to form a protein corona. We modulate this corona by using surface-engineered nanoparticles, and identify protein composition to provide insight into disease development.Using a family of structurally homologous nanoparticles we have investigated the changes in the protein corona around surface-functionalized gold nanoparticles (AuNPs) from normal and malignant ovarian cell lysates. Proteomics analysis using mass spectrometry identified hepatoma-derived growth factor (HDGF) that is found exclusively on positively charged AuNPs ((+)AuNPs) after incubation with the lysates. We confirmed expression of HDGF in various ovarian cancer cells and validated binding selectivity to (+)AuNPs by Western blot analysis. Silencing of HDGF by siRNA resulted s inhibition in proliferation of ovarian cancer cells.We investigated the modulation of protein corona around surface-functionalized gold nanoparticles as a promising approach to identify new therapeutic targets. The potential of our method for identifying therapeutic targets was demonstrated through silencing of HDGF by siRNA, which inhibited proliferation of ovarian cancer cells. This integrated proteomics, bioinformatics, and nanotechnology strategy demonstrates that protein corona identification can be used to discover novel therapeutic targets in cancer

The pancreatic beta cell surface proteome

The pancreatic beta cell is responsible for maintaining normoglycaemia by secreting an appropriate amount of insulin according to blood glucose levels. The accurate sensing of the beta cell extracellular environment is therefore crucial to this endocrine function and is transmitted via its cell surface proteome. Various surface proteins that mediate or affect beta cell endocrine function have been identified, including growth factor and cytokine receptors, transporters, ion channels and proteases, attributing important roles to surface proteins in the adaptive behaviour of beta cells in response to acute and chronic environmental changes. However, the largely unknown composition of the beta cell surface proteome is likely to harbour yet more information about these mechanisms and provide novel points of therapeutic intervention and diagnostic tools. This article will provide an overview of the functional complexity of the beta cell surface proteome and selected surface proteins, outline the mechanisms by which their activity may be modulated, discuss the methods and challenges of comprehensively mapping and studying the beta cell surface proteome, and address the potential of this interesting subproteome for diagnostic and therapeutic applications in human disease