SwitchFinder - a novel method and query facility for discovering dynamic gene expression patterns.

BACKGROUND: Biological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery. The fundamental idea behind it is the following: there are change-points (switches) in the gene behaviour separating intervals of increasing and decreasing activity, whereas the intervals may have different durations. Elucidating the switch-points is important for the identification of biologically meanigfull features and patterns of the gene dynamics. RESULTS: We developed a statistical method, called SwitchFinder, for the analysis of time-series data, in particular gene expression data, based on a change-point model. Fitting the model to the gene expression time-courses indicates switch-points between increasing and decreasing activities of each gene. Two types of the model - based on linear and on generalized logistic function - were used to capture the data between the switch-points. Model inference was facilitated with the Bayesian methodology using Markov chain Monte Carlo (MCMC) technique Gibbs sampling. Further on, we introduced features of the switch-points: growth, decay, spike and cleft, which reflect important dynamic aspects. With this, the gene expression profiles are represented in a qualitative manner - as sets of the dynamic features at their onset-times. We developed a Web application of the approach, enabling to put queries to the gene expression time-courses and to deduce groups of genes with common dynamic patterns. SwitchFinder was applied to our original data - the gene expression time-series measured in neuroblastoma cell line upon treatment with all-trans retinoic acid (ATRA). The analysis revealed eight patterns of the gene expression responses to ATRA, indicating the induction of the BMP, WNT, Notch, FGF and NTRK-receptor signaling pathways involved in cell differentiation, as well as the repression of the cell-cycle related genes. CONCLUSIONS: SwitchFinder is a novel approach to the analysis of biological time-series data, supporting inference and interactive exploration of its inherent dynamic patterns, hence facilitating biological discovery process. SwitchFinder is freely available at https://newbioinformatics.eu/switchfinder.

Reduced retinoids and retinoid receptors' expression in pancreatic cancer: A link to patient survival.

Pancreatic ductal adenocarcinoma (PDAC) represents one of the deadliest cancers in the world. All-trans retinoic acid (ATRA) is the major physiologically active form of vitamin A, regulating expression of many genes. Disturbances of vitamin A metabolism are prevalent in some cancer cells. The main aim of this work was to investigate deeply the components of retinoid signaling in PDAC compared to in the normal pancreas and to prove the clinical importance of retinoid receptor expression. For the study, human tumor tissues obtained from PDAC patients and murine tumors from the orthotopic Panc02 model were used for the analysis of retinoids, using high performance liquid chromatography mass spectrometry and real-time RT-PCR gene expression analysis. Survival probabilities in univariate analysis were estimated using the Kaplan-Meier method and the Cox proportional hazards model was used for the multivariate analysis. In this work, we showed for the first time that the ATRA and all-trans retinol concentration is reduced in PDAC tissue compared to their normal counterparts. The expression of RARα and ß as well as RXRα and ß are down-regulated in PDAC tissue. This reduced expression of retinoid receptors correlates with the expression of some markers of differentiation and epithelial-to-mesenchymal transition as well as of cancer stem cell markers. Importantly, the expression of RARα and RXRß is associated with better overall survival of PDAC patients. Thus, reduction of retinoids and their receptors is an important feature of PDAC and is associated with worse patient survival outcomes.

Multiparametric image analysis reveals role of Caveolin1 in endosomal progression rather than internalization of EGFR.

Endosomes constitute a central layer in the regulation of growth factor signaling. We applied flow cytometry, confocal microscopy and automated image quantification to define the role of Caveolin1 (Cav1) in epidermal growth factor (EGF) receptor (i) internalization and (ii) endosomal trafficking. Antisense-downregulation of Cav1 did not affect internalization of EGF:EGFR-complexes from the plasma membrane. Instead, Cav1-knockdown had a profound effect on endosomal trafficking and caused a shift in EGF vesicle distribution towards Rab7-negative compartments at late timepoints. Moreover, image quantification with single-endosome resolution revealed that EGF:Cav1-complexes undergo a maturation pattern reminiscent of late endosomes. Our data suggest a model in which Caveolin1 acts upon EGF endosomes internalized via the Clathrin-pathway and functions at the transition from early to late endosomes.

Bayesian statistical modelling of human protein interaction network incorporating protein disorder information.

BACKGROUND: We present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each node's contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely social, as revealed based on the local sociality parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: a priori knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network. RESULTS: We applied the statistical modelling to the human protein interaction network obtained with Y2H experiments. Bayesian approach for the estimation of the parameters was employed. We deduced social proteins, essential for the formation of the network, while incorporating into the model information on protein disorder. Intrinsically disordered are proteins which lack a well-defined three-dimensional structure under physiological conditions. We predicted the fold group (ordered or disordered) of proteins in the network from their primary sequences. The network analysis indicated that protein disorder has a positive effect on the connectivity of proteins in the network, but do not fully explains the interactivity. CONCLUSIONS: The approach opens a perspective to study effects of biological properties of individual entities on the structure of biological networks.

Discovery of transcriptional programs in cerebral ischemia by in silico promoter analysis.

In stroke, gene transcription plays a central role in processes such as neuroinflammation and neuroregeneration. To predict new transcriptional regulatory mechanisms in cerebral ischemia, we applied a computational approach combining two kinds of information: the results of a microarray analysis in a mouse model of stroke and in silico detection of transcription factor (TF) binding sites in promoter regions of the genes on the array. By using a discriminative logistic regression model, we identified binding sites significantly associated with the up-regulation of genes. Out of 356 TF binding sites defined in TRANSFAC, we could link 32 to gene up-regulation in cerebral ischemia. These sites bind both TFs with an established and a so far unknown role in cerebral ischemia. To evaluate the results further we investigated whether two TFs, CCAAT/enhancer binding protein beta (C/EBP beta) and vitamin D receptor (VDR), are activated as predicted. Immunohistochemistry demonstrated that C/EBP beta and VDR translocated to the nucleus in cerebral ischemia. Chromatin immunoprecipitation revealed increased binding of C/EBP beta to the promoter of its target gene saa3. In addition, we found evidence for the up-regulation of VDR in brain samples from human stroke patients. These results confirm the activation of C/EBP beta and VDR in cerebral ischemia. Thus, our in silico analysis may provide additional information on transcriptional regulation in stroke and suggests several novel transcriptional programs for further exploration.

New insights into the genetic regulation of Plasmodium falciparum obtained by Bayesian modeling.

The most fatal and prevalent form of malaria is caused by the bloodborne pathogen Plasmodium falciparum (henceforth P.f). Annually, approximately three million people died of malaria. Despite P.f devastating effect globally, the vast majority of its proteins have not been characterized experimentally. In this work, we provide computational insight that explore the modalities of the regulation for some important group of genes of P.f, namely components of the glycolytic pathway, and those involved in apicoplast metabolism. Glycolysis is a crucial pathway in the maintenance of the parasite while the recently discovered apicoplast contains a range of metabolic pathways and housekeeping processes that differ radically to those of the host, which makes it ideal for drug therapy.We have been able to validate some of our findings from available literature and therefore provide a basis to give theoretical insight for some genes regulations, which has not been characterized experimentally.

Inferring genetic regulatory logic from expression data.

MOTIVATION: High-throughput molecular genetics methods allow the collection of data about the expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from these data and to get an insight into the mechanisms of genetic regulation. RESULTS: We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge.

Pathways of urothelial cancer progression suggested by Bayesian network analysis of allelotyping data.

Urothelial cancers of the bladder (UC) comprise biologically heterogeneous group of tumors and display complex genetic alterations. Several genetic changes have been analyzed in detail and some of them are associated with the development and progression of UCs. Only a few studies, however, are focused on identifying the order in which the aberrations may appear during UC tumorigenesis. We have analyzed 123 papillary UCs of the bladder by microsatellites for each of the chromosomal regions that have been suggested to be specifically involved in this type of tumor. We used Bayesian network modeling that enables to uncover multivariate probabilistic dependencies between variables. This methodology applied to LOH data allowed us to discover patterns of losses in UCs. Exploiting the mechanism of probabilistic reasoning in Bayesian networks we suggest primary and secondary events in tumor pathogenesis and reconstruct the possible flow of progression of allelic changes. Losses of chromosome 9p and 9q were found to be the primary events. Losses of 8p and 17p are important events leading to progression of tumor cell clones. The loss of 17p occurs when both abnormalities of chromosome 9 and 8p are already present. There are chromosomal losses related to 8p (1q, 18q, 10q) and some losses like 5q/5p were associated with 17p, leading to the hypothesis of different genetic pathways of UC progression. The abnormalities of chromosome regions 13q, 16q, 6q, 14q, 3p are suggested to be late events being accumulated during the progression of cancer. Although some genetic changes were associated only with the 8p pathway, most secondary genetic changes appear in both pathways. Supplementary material for this article can be found on the International Journal of Cancer website at http://www.interscience.wiley.com/jpages/0020-7136/suppmat/index.html.