Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis

ABSTRACT: BACKGROUND: In this study we explored preeclampsia through a bioinformatics approach. We create a comprehensive genes/proteins dataset by the analysis of both public proteomic data and text mining of public scientific literature. From this dataset the associated protein-protein interaction network has been obtained. Several indexes of centrality have been explored for hubs detection as well as the enrichment statistical analysis of metabolic pathway and disease. RESULTS: We confirmed the well known relationship between preeclampsia and cardiovascular diseases but also identified statistically significant relationships with respect to cancer and aging. Moreover, significant metabolic pathways such as apoptosis, cancer and cytokine-cytokine receptor interaction have also been identified by enrichment analysis. We obtained FLT1, VEGFA, FN1, F2 and PGF genes with the highest scores by hubs analysis; however, we also found other genes as PDIA3, LYN, SH2B2 and NDRG1 with high scores. CONCLUSIONS: The applied methodology not only led to the identification of well known genes related to preeclampsia but also to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which eventually need to be validated experimentally. Moreover, new possible connections were detected between preeclampsia and other diseases that could open new areas of research. More must be done in this area to resolve the identification of unknown interactions of proteins/genes and also for a better integration of metabolic pathways and diseases.     

Qualitative networks: a symbolic approach to analyze biological signaling networks

Background  

A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico.     

Bystander effect: biological endpoints and microarray analysis

In cell populations exposed to ionizing radiation, the biological effects occur in a much larger proportion of cells than are estimated to be traversed by radiation. It has been suggested that irradiated cells are capable of providing signals to the neighboring unirradiated cells resulting in damage to these cells. This phenomenon is termed the bystander effect. The bystander effect induces persistent, long-term, transmissible changes that result in delayed death and neoplastic transformation. Because the bystander effect is relevant to carcinogenesis, it could have significant implications for risk estimation for radiation exposure. The nature of the bystander effect signal and how it impacts the unirradiated cells remains to be elucidated. Examination of the changes in gene expression could provide clues to understanding the bystander effect and could define the signaling pathways involved in sustaining damage to these cells. The microarray technology serves as a tool to gain insight into the molecular pathways leading to bystander effect. Using medium from irradiated normal human diploid lung fibroblasts as a model system we examined gene expression alterations in bystander cells. The microarray data revealed that the radiation-induced gene expression profile in irradiated cells is different from unirradiated bystander cells suggesting that the pathways leading to biological effects in the bystander cells are different from the directly irradiated cells. The genes known to be responsive to ionizing radiation were observed in irradiated cells. Several genes were upregulated in cells receiving media from irradiated cells. Surprisingly no genes were found to be downregulated in these cells. A number of genes belonging to extracellular signaling, growth factors and several receptors were identified in bystander cells. Interestingly 15 genes involved in the cell communication processes were found to be upregulated. The induction of receptors and the cell communication processes in bystander cells receiving media from irradiated cells supports the active involvement of these processes in inducing bystander effect.     

Constructing biological pathways by a two-step counting approach

Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network.     

Statistical analysis of microarray data: a Bayesian approach

The potential of microarray data is enormous. It allows us to monitor the expression of thousands of genes simultaneously. A common task with microarray is to determine which genes are differentially expressed between two samples obtained under two different conditions. Recently, several statistical methods have been proposed to perform such a task when there are replicate samples under each condition. Two major problems arise with microarray data. The first one is that the number of replicates is very small (usually 2-10), leading to noisy point estimates. As a consequence, traditional statistics that are based on the means and standard deviations, e.g. t-statistic, are not suitable. The second problem is that the number of genes is usually very large (approximately 10,000), and one is faced with an extreme multiple testing problem. Most multiple testing adjustments are relatively conservative, especially when the number of replicates is small. In this paper we present an empirical Bayes analysis that handles both problems very well. Using different parametrizations, we develop four statistics that can be used to test hypotheses about the means and/or variances of the gene expression levels in both one- and two-sample problems. The methods are illustrated using experimental data with prior knowledge. In addition, we present the result of a simulation comparing our methods to well-known statistics and multiple testing adjustments.     

High confidence rule mining for microarray analysis

We present an association rule mining method for mining high confidence rules, which describe interesting gene relationships from microarray datasets. Microarray datasets typically contain an order of magnitude more genes than experiments, rendering many data mining methods impractical as they are optimised for sparse datasets. A new family of row-enumeration rule mining algorithms have emerged to facilitate mining in dense datasets. These algorithms rely on pruning infrequent relationships to reduce the search space by using the support measure. This major shortcoming results in the pruning of many potentially interesting rules with low support but high confidence. We propose a new row-enumeration rule mining method, MaxConf, to mine high confidence rules from microarray data. MaxConf is a support-free algorithm which directly uses the confidence measure to effectively prune the search space. Experiments on three microarray datasets show that MaxConf outperforms support-based rule mining with respect to scalability and rule extraction. Furthermore, detailed biological analyses demonstrate the effectiveness of our approach -- the rules discovered by MaxConf are substantially more interesting and meaningful compared with support-based methods.     

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to −1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components.     

Reconstruction of genetic association networks from microarray data: a partial least squares approach

MOTIVATION: Gene association/interaction networks provide vast amounts of information about essential processes inside the cell. A complete picture of gene-gene associations/interactions would open new horizons for biologists, ranging from pure appreciation to successful manipulation of biological pathways for therapeutic purposes. Therefore, identification of important biological complexes whose members (genes and their products proteins) interact with each other is of prime importance. Numerous experimental methods exist but, for the most part, they are costly and labor intensive. Computational techniques, such as the one proposed in this work, provide a quick 'budget' solution that can be used as a screening tool before more expensive techniques are attempted. Here, we introduce a novel computational method based on the partial least squares (PLS) regression technique for reconstruction of genetic networks from microarray data. RESULTS: The proposed PLS method is shown to be an effective screening procedure for the detection of gene-gene interactions from microarray data. Both simulated and real microarray experiments show that the PLS-based approach is superior to its competitors both in terms of performance and applicability. AVAILABILITY: R code is available from the supplementary web-site whose URL is given below.     

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.     

RCA combined nanoparticle-based optical detection technique for protein microarray: a novel approach

Developing a readily available biosensor with excellent performances is the main focus of many research groups. Recently, major breakthroughs in miniaturization of molecular analysis have produced DNA and protein microarrays. The aim of our group is to develop a sensitive technique for analyzing signals on protein microarray by applying the surface plasmon resonance (SPR) method. This new detection technique for specific molecular binding utilizes rolling circles amplification (RCA) post-signal processing method [Nat. Genet. 19 (1998) 225-232] and optical visualization by nanogold particle-labeled molecules on a micro-structured chip surface. By covalent bonding of the RCA primer to the detection antibody guarantees that the linkage between the analyte and the amplified RCA product is maintained during the assay. Experimental results show that RCA has significantly enhanced sensitivity compared to conventional methods. This combination of an easily detectable signal with chip technology should have the potential to become a successful commercial application.     

BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis

biomaRt is a new Bioconductor package that integrates BioMart data resources with data analysis software in Bioconductor. It can annotate a wide range of gene or gene product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Furthermore biomaRt enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis. Fast and up-to-date data retrieval is possible as the package executes direct SQL queries to the BioMart databases (e.g. Ensembl). The biomaRt package provides a tight integration of large, public or locally installed BioMart databases with data analysis in Bioconductor creating a powerful environment for biological data mining.     

A mouse protein interactome through combined literature mining with multiple sources of interaction evidence

Protein–protein interactions (PPIs) play crucial roles in a number of biological processes. Recently, protein interaction networks (PINs) for several model organisms and humans have been generated, but few large-scale researches for mice have ever been made neither experimentally nor computationally. In the work, we undertook an effort to map a mouse PIN, in which protein interactions are hidden in enormous amount of biomedical literatures. Following a co-occurrence-based text-mining approach, a probabilistic model—naïve Bayesian was used to filter false-positive interactions by integrating heterogeneous kinds of evidence from genomic and proteomic datasets. A support vector machine algorithm was further used to choose protein pairs with physical interactions. By comparing with the currently available PPI datasets from several model organisms and humans, it showed that the derived mouse PINs have similar topological properties at the global level, but a high local divergence. The mouse protein interaction dataset is stored in the Mouse protein–protein interaction DataBase (MppDB) that is useful source of information for system-level understanding of gene function and biological processes in mammals. Access to the MppDB database is public available at http://bio.scu.edu.cn/mppi.     

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.