Integrated proteomic profiling of cell line conditioned media and pancreatic juice for the identification of pancreatic cancer biomarkers

Pancreatic cancer is one of the leading causes of cancer-related deaths, for which serological biomarkers are urgently needed. Most discovery-phase studies focus on the use of one biological source for analysis. The present study details the combined mining of pancreatic cancer-related cell line conditioned media and pancreatic juice for identification of putative diagnostic leads. Using strong cation exchange chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer, we extensively characterized the proteomes of conditioned media from six pancreatic cancer cell lines (BxPc3, MIA-PaCa2, PANC1, CAPAN1, CFPAC1, and SU.86.86), the normal human pancreatic ductal epithelial cell line HPDE, and two pools of six pancreatic juice samples from ductal adenocarcinoma patients. All samples were analyzed in triplicate. Between 1261 and 2171 proteins were identified with two or more peptides in each of the cell lines, and an average of 521 proteins were identified in the pancreatic juice pools. In total, 3479 nonredundant proteins were identified with high confidence, of which ～ 40% were extracellular or cell membrane-bound based on Genome Ontology classifications. Three strategies were employed for identification of candidate biomarkers: (1) examination of differential protein expression between the cancer and normal cell lines using label-free protein quantification, (2) integrative analysis, focusing on the overlap of proteins among the multiple biological fluids, and (3) tissue specificity analysis through mining of publically available databases. Preliminary verification of anterior gradient homolog 2, syncollin, olfactomedin-4, polymeric immunoglobulin receptor, and collagen alpha-1(VI) chain in plasma samples from pancreatic cancer patients and healthy controls using ELISA, showed a significant increase (p < 0.01) of these proteins in plasma from pancreatic cancer patients. The combination of these five proteins showed an improved area under the receiver operating characteristic curve to CA19.9 alone. Further validation of these proteins is warranted, as is the investigation of the remaining group of candidates.     

Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

In this paper, we will present and review the most usual methods to detect linear and nonlinear causality between signals: linear Granger causality test (Geweke in J Am Stat Assoc 77:304–313, 1982) extended to direct causality in multivariate case (LGC), directed coherence (DCOH, Saito and Harashima in Recent advances in EEG and EMG data processing, Elsevier, Amsterdam, 1981), partial directed coherence (PDC, Sameshima and Baccala 1999) and nonlinear Granger causality test of Baek and Brock (in Working Paper University of Iowa, 1992) extended to direct causality in multivariate case (partial nonlinear Granger causality, PNGC). All these methods are tested and compared on several ARX, Poisson and nonlinear models, and on neurophysiological data (depth EEG). The results show that LGC, DCOH and PDC are not very robust in relation to nonlinear linkages but they seem to correctly find linear linkages if only the autoregressive parts are nonlinear. PNGC is extremely dependent on the choice of parameters. Moreover, LGC and PNGC may give misleading results in the case of causality on a spectral band, which is illustrated by our neurophysiological database.Electronic Supplementary Material Supplementary material is available to authorised users in the online version of this article at .     

Analysis of reported SCO2 gene mutations affecting cytochrome c oxidase activity in various diseases

A large number of mutations have been reported in SCO2 (synthesis of cytochrome c oxidase) gene in association with COXdeficiency reported in different diseases such as cardioencephalomyopathy, cardiomyopathy and Leigh syndrome. However, veryfew of these mutations have been functionally analyzed.SCO2 gene encodes for an essential assembly factor for the formation ofcytochrome c oxidase (COX). It is a nuclear encoded protein that helps in transfer of copper ions to COX. This study is an attemptto understand the possible effect of these mutations on the structure and function of SCO2 protein, by using different in silico tools.As per Human Gene Mutation Database, total 11 non synonymous variations have been reported in SCO2 gene. Among these 11variations, only E140K and R171W are functionally proven to cause COX deficiency. They have been used as controls in this study.The remaining variations were further analyzed using ClustalW, SIFT, PolyPhen-2, GOR4, MuPro and Panther softwares. Ascompared to the results of the controls, most of these variations were predicted to affect the structure of SCO2 protein and hence,may cause COX dysfunction. Thus, we hypothesize that these variations have the potential to result in a disease phenotype andshould be investigated by subsequent functional analyses. This will help in an appropriate diagnosis and management of the widespectrum of COX deficiency diseases.     

PCA based population generation for genetic network optimization

A gene regulatory network (GRN) represents a set of genes and its regulatory interactions. The inference of the regulatory interactions between genes is usually carried out using an appropriate mathematical model and the available gene expression profile. Among the various models proposed for GRN inference, our recently proposed Michaelis–Menten based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Considering various issues associated with such population based stochastic optimization approaches (e.g. diversity, premature convergence due to local optima, accuracy, etc.), it becomes important to seed the initial population with good individuals which are closer to the optimal solution. In this paper, we exploit the inherent strength of principal component analysis (PCA) in a novel manner to initialize the population for GRN optimization. The benefit of the proposed method is validated by reconstructing in silico and in vivo networks of various sizes. For the same level of accuracy, the approach with PCA based initialization shows improved convergence speed.