Augmented Sparse Reconstruction of Protein Signaling Networks

The problem of reconstructing and identifying intracellular protein signaling and biochemical networks is of critical importance in biology today. We sought to develop a mathematical approach to this problem using, as a test case, one of the most well-studied and clinically important signaling networks in biology today, the epidermal growth factor receptor (EGFR) driven signaling cascade. More specifically, we suggest a method, augmented sparse reconstruction, for the identification of links among nodes of ordinary differential equation (ODE) networks from a small set of trajectories with different initial conditions. Our method builds a system of representation by using a collection of integrals of all given trajectories and by attenuating block of terms in the representation itself. The system of representation is then augmented with random vectors, and minimization of the 1-norm is used to find sparse representations for the dynamical interactions of each node. Augmentation by random vectors is crucial, since sparsity alone is not able to handle the large error-in-variables in the representation. Augmented sparse reconstruction allows to consider potentially very large spaces of models and it is able to detect with high accuracy the few relevant links among nodes, even when moderate noise is added to the measured trajectories. After showing the performance of our method on a model of the EGFR protein network, we sketch briefly the potential future therapeutic applications of this approach.Comment: 24 pages, 6 figure

Definition of valid proteomic biomarkers: a bayesian solution

Clinical proteomics is suffering from high hopes generated by reports on apparent biomarkers, most of which could not be later substantiated via validation. This has brought into focus the need for improved methods of finding a panel of clearly defined biomarkers. To examine this problem, urinary proteome data was collected from healthy adult males and females, and analysed to find biomarkers that differentiated between genders. We believe that models that incorporate sparsity in terms of variables are desirable for biomarker selection, as proteomics data typically contains a huge number of variables (peptides) and few samples making the selection process potentially unstable. This suggests the application of a two-level hierarchical Bayesian probit regression model for variable selection which assumes a prior that favours sparseness. The classification performance of this method is shown to improve that of the Probabilistic K-Nearest Neighbour model

Dual Action of miR-125b As a Tumor Suppressor and OncomiR-22 Promotes Prostate Cancer Tumorigenesis

MicroRNAs (miRs) are a novel class of small RNA molecules, the dysregulation of which can contribute to cancer. A combinatorial approach was used to identify miRs that promote prostate cancer progression in a unique set of prostate cancer cell lines, which originate from the parental p69 cell line and extend to a highly tumorigenic/metastatic M12 subline. Together, these cell lines are thought to mimic prostate cancer progression in vivo. Previous network analysis and miR arrays suggested that the loss of hsa-miR-125b together with the overexpression of hsa-miR-22 could contribute to prostate tumorigenesis. The dysregulation of these two miRs was confirmed in human prostate tumor samples as compared to adjacent benign glandular epithelium collected through laser capture microdissection from radical prostatectomies. In fact, alterations in hsa-miR-125b expression appeared to be an early event in tumorigenesis. Reverse phase microarray proteomic analysis revealed ErbB2/3 and downstream members of the PI3K/AKT and MAPK/ERK pathways as well as PTEN to be protein targets differentially expressed in the M12 tumor cell compared to its parental p69 cell. Relevant luciferase+3’-UTR expression studies confirmed a direct interaction between hsa-miR-125b and ErbB2 and between hsa-miR-22 and PTEN. Restoration of hsa-miR-125b or inhibition of hsa-miR-22 expression via an antagomiR resulted in an alteration of M12 tumor cell behavior in vitro. Thus, the dual action of hsa-miR-125b as a tumor suppressor and hsa-miR-22 as an oncomiR contributed to prostate tumorigenesis by modulations in PI3K/AKT and MAPK/ERK signaling pathways, key pathways known to influence prostate cancer progression

Core-Shell Hydrogel Particles Harvest, Concentrate and Preserve Labile Low Abundance Biomarkers

Background: The blood proteome is thought to represent a rich source of biomarkers for early stage disease detection. Nevertheless, three major challenges have hindered biomarker discovery: a) candidate biomarkers exist at extremely low concentrations in blood; b) high abundance resident proteins such as albumin mask the rare biomarkers; c) biomarkers are rapidly degraded by endogenous and exogenous proteinases. Methodology and Principal Findings: Hydrogel nanoparticles created with a N-isopropylacrylamide based core (365 nm)-shell (167 nm) and functionalized with a charged based bait (acrylic acid) were studied as a technology for addressing all these biomarker discovery problems, in one step, in solution. These harvesting core-shell nanoparticles are designed to simultaneously conduct size exclusion and affinity chromatography in solution. Platelet derived growth factor (PDGF), a clinically relevant, highly labile, and very low abundance biomarker, was chosen as a model. PDGF, spiked in human serum, was completely sequestered from its carrier protein albumin, concentrated, and fully preserved, within minutes by the particles. Particle sequestered PDGF was fully protected from exogenously added tryptic degradation. When the nanoparticles were added to a 1 mL dilute solution of PDGF at non detectable levels (less than 20 picograms per mL) the concentration of the PDGF released from the polymeric matrix of the particles increased within the detection range of ELISA and mass spectrometry. Beyond PDGF, the sequestration and protection from degradation for a series of additional very low abundance and very labile cytokines were verified. Conclusions and Significance: We envision the application of harvesting core-shell nanoparticles to whole blood for concentration and immediate preservation of low abundance and labile analytes at the time of venipuncture. © 2009 Longo et al

BioSunMS: a plug-in-based software for the management of patients information and the analysis of peptide profiles from mass spectrometry

<p>Abstract</p> <p>Background</p> <p>With wide applications of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), statistical comparison of serum peptide profiles and management of patients information play an important role in clinical studies, such as early diagnosis, personalized medicine and biomarker discovery. However, current available software tools mainly focused on data analysis rather than providing a flexible platform for both the management of patients information and mass spectrometry (MS) data analysis.</p> <p>Results</p> <p>Here we presented a plug-in-based software, BioSunMS, for both the management of patients information and serum peptide profiles-based statistical analysis. By integrating all functions into a user-friendly desktop application, BioSunMS provided a comprehensive solution for clinical researchers without any knowledge in programming, as well as a plug-in architecture platform with the possibility for developers to add or modify functions without need to recompile the entire application.</p> <p>Conclusion</p> <p>BioSunMS provides a plug-in-based solution for managing, analyzing, and sharing high volumes of MALDI-TOF or SELDI-TOF MS data. The software is freely distributed under GNU General Public License (GPL) and can be downloaded from <url>http://sourceforge.net/projects/biosunms/</url>.</p

Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery

<p>Abstract</p> <p>Background</p> <p>As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer diagnostic paradigm is that robustly identifying cancer molecular patterns from high-dimensional protein expression data is still a challenge in machine learning and oncology research. As a well-established dimension reduction technique, PCA is widely integrated in pattern recognition analysis to discover cancer molecular patterns. However, its global feature selection mechanism prevents it from capturing local features. This may lead to difficulty in achieving high-performance proteomic pattern discovery, because only features interpreting global data behavior are used to train a learning machine.</p> <p>Methods</p> <p>In this study, we develop a nonnegative principal component analysis algorithm and present a nonnegative principal component analysis based support vector machine algorithm with sparse coding to conduct a high-performance proteomic pattern classification. Moreover, we also propose a nonnegative principal component analysis based filter-wrapper biomarker capturing algorithm for mass spectral serum profiles.</p> <p>Results</p> <p>We demonstrate the superiority of the proposed algorithm by comparison with six peer algorithms on four benchmark datasets. Moreover, we illustrate that nonnegative principal component analysis can be effectively used to capture meaningful biomarkers.</p> <p>Conclusion</p> <p>Our analysis suggests that nonnegative principal component analysis effectively conduct local feature selection for mass spectral profiles and contribute to improving sensitivities and specificities in the following classification, and meaningful biomarker discovery.</p

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitud