Identifying dysfunctional crosstalk of pathways in various regions of Alzheimer's disease brains

Pathway crosstalk analysis based on protein-protein network analysis in ovarian cancer

Ovarian cancer is the fifth leading cause of cancer death in women aged 35 to 74 years. Although there are several popular hypothesis of ovarian cancer pathogenesis, the genetic mechanisms are far from being clear. Recently, systems biology approaches such as network-based methods have been successfully applied to elucidate the mechanisms of diseases. In this study, we constructed a crosstalk network among ovarian cancer related pathways by integrating protein-protein interactions and KEGG pathway information. Several significant pathways were identified to crosstalk with each other in ovarian cancer, such as the chemokine, Notch, Wnt and NOD-like receptor signaling pathways. Results from these studies will provide the groundwork for a combination therapy approach targeting multiple pathways which will likely be more effective than targeting one pathway alone.

Inferring pathway crosstalk networks using gene set co-expression signatures

Constructing molecular interaction networks in cells is important for understanding the underlying mechanisms of biological processes. Except for single gene analysis, several gene set-based methods have been proposed to infer pathway crosstalk by analyzing large-scale gene expression data. But most of them take all pathway genes as a whole to infer the crosstalk. Biological evidence suggests that the pathway crosstalk usually occurs between some subsets rather than the whole sets of pathway genes. In this study, we propose a novel method, sGSCA (signature-based gene set co-expression analysis) which can use the co-expression correlations between subsets of pathway genes to infer the pathway crosstalk networks. The method applies sparse canonical correlation analysis (sCCA) to measure the pathway level co-expression and simultaneously obtain the subsets or signature genes that contribute to the co-expression of pathways. On simulated datasets, sGSCA can efficiently detect pathway crosstalk and the corresponding highly correlated signature genes. We applied sGSCA to two cancer gene expression datasets (one for hepatocellular cancer and the other for lung cancer). In the inferred networks, we found several important pathway crosstalks related to the cancers. The identified signature genes also show high enrichment for the cancer related genes. sGSCA can infer pathway crosstalk networks using large-scale gene expression data, and should be a useful tool for systematically studying the molecular mechanisms of complex diseases on both pathway and gene levels at the same time.

Discovering pathway cross-talks based on functional relations between pathways

Background

In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations.

Results

This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis.

Conclusions

This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.

Genetic interactions associated with 12-month atrophy in hippocampus and entorhinal cortex in Alzheimer's Disease Neuroimaging Initiative

Missing heritability in late onset Alzheimer disease can be attributed, at least in part, to heterogeneity in disease status and to the lack of statistical analyses exploring genetic interactions. In the current study, we use quantitative intermediate phenotypes derived from magnetic resonance imaging data available from the Alzheimer's Disease Neuroimaging Initiative, and we test for association with gene-gene interactions within biological pathways. Regional brain volumes from the hippocampus (HIP) and entorhinal cortex (EC) were estimated from baseline and 12-month magnetic resonance imaging scans. Approximately 560,000 single nucleotide polymorphisms (SNPs) were available genome-wide. We tested all pairwise SNP-SNP interactions (approximately 151 million) within 212 Kyoto Encyclopedia of Genes and Genomes pathways for association with 12-month regional atrophy rates using linear regression, with sex, APOE ε4 carrier status, age, education, and clinical status as covariates. A total of 109 SNP-SNP interactions were associated with right HIP atrophy, and 125 were associated with right EC atrophy. Enrichment analysis indicated significant SNP-SNP interactions were overrepresented in the calcium signaling and axon guidance pathways for both HIP and EC atrophy and in the ErbB signaling pathway for HIP atrophy.

PathNet: a tool for pathway analysis using topological information

Background

Identification of canonical pathways through enrichment of differentially expressed genes in a given pathway is a widely used method for interpreting gene lists generated from high-throughput experimental studies. However, most algorithms treat pathways as sets of genes, disregarding any inter- and intra-pathway connectivity information, and do not provide insights beyond identifying lists of pathways.

Results

We developed an algorithm (PathNet) that utilizes the connectivity information in canonical pathway descriptions to help identify study-relevant pathways and characterize non-obvious dependencies and connections among pathways using gene expression data. PathNet considers both the differential expression of genes and their pathway neighbors to strengthen the evidence that a pathway is implicated in the biological conditions characterizing the experiment. As an adjunct to this analysis, PathNet uses the connectivity of the differentially expressed genes among all pathways to score pathway contextual associations and statistically identify biological relations among pathways. In this study, we used PathNet to identify biologically relevant results in two Alzheimer’s disease microarray datasets, and compared its performance with existing methods. Importantly, PathNet identified de-regulation of the ubiquitin-mediated proteolysis pathway as an important component in Alzheimer’s disease progression, despite the absence of this pathway in the standard enrichment analyses.

Conclusions

PathNet is a novel method for identifying enrichment and association between canonical pathways in the context of gene expression data. It takes into account topological information present in pathways to reveal biological information. PathNet is available as an R workspace image from http://www.bhsai.org/downloads/pathnet/.

Gene expression profiling in human neurodegenerative disease

Transcriptome study in neurodegenerative disease has advanced considerably in the past 5 years. Increasing scientific rigour and improved analytical tools have led to more-reproducible data. Many transcriptome analysis platforms assay the expression of the entire genome, enabling a complete biological context to be captured. Gene expression profiling (GEP) is, therefore, uniquely placed to discover pathways of disease pathogenesis, potential therapeutic targets, and biomarkers. This Review summarizes microarray human GEP studies in the common neurodegenerative diseases amyotrophic lateral sclerosis (ALS), Parkinson disease (PD) and Alzheimer disease (AD). Several interesting reports have compared pathological gene expression in different patient groups, disease stages and anatomical areas. In all three diseases, GEP has revealed dysregulation of genes related to neuroinflammation. In ALS and PD, gene expression related to RNA splicing and protein turnover is disrupted, and several studies in ALS support involvement of the cytoskeleton. GEP studies have implicated the ubiquitin-proteasome system in PD pathogenesis, and have provided evidence of mitochondrial dysfunction in PD and AD. Lastly, in AD, a possible role for dysregulation of intracellular signalling pathways, including calcium signalling, has been highlighted. This Review also provides a discussion of methodological considerations in microarray sample preparation and data analysis.

Concerted perturbation observed in a hub network in Alzheimer's disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease involving the alteration of gene expression at the whole genome level. Genome-wide transcriptional profiling of AD has been conducted by many groups on several relevant brain regions. However, identifying the most critical dys-regulated genes has been challenging. In this work, we addressed this issue by deriving critical genes from perturbed subnetworks. Using a recent microarray dataset on six brain regions, we applied a heaviest induced subgraph algorithm with a modular scoring function to reveal the significantly perturbed subnetwork in each brain region. These perturbed subnetworks were found to be significantly overlapped with each other. Furthermore, the hub genes from these perturbed subnetworks formed a connected hub network consisting of 136 genes. Comparison between AD and several related diseases demonstrated that the hub network was robustly and specifically perturbed in AD. In addition, strong correlation between the expression level of these hub genes and indicators of AD severity suggested that this hub network can partially reflect AD progression. More importantly, this hub network reflected the adaptation of neurons to the AD-specific microenvironment through a variety of adjustments, including reduction of neuronal and synaptic activities and alteration of survival signaling. Therefore, it is potentially useful for the development of biomarkers and network medicine for AD.

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

BACKGROUND

Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges.

RESULTS

To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes.

CONCLUSIONS

We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes.

Identification of significant pathways in gastric cancer based on protein-protein interaction networks and cluster analysis

Gastric cancer is one of the most common and lethal cancers worldwide. However, despite its clinical importance, the regulatory mechanisms involved in the aggressiveness of this cancer are still poorly understood. A better understanding of the biology, genetics and molecular mechanisms of gastric cancer would be useful in developing novel targeted approaches for treating this disease. In this study we used protein-protein interaction networks and cluster analysis to comprehensively investigate the cellular pathways involved in gastric cancer. A primary immunodeficiency pathway, focal adhesion, ECM-receptor interactions and the metabolism of xenobiotics by cytochrome P450 were identified as four important pathways associated with the progression of gastric cancer. The genes in these pathways, e.g., ZAP70, IGLL1, CD79A, COL6A3, COL3A1, COL1A1, CYP2C18 and CYP2C9, may be considered as potential therapeutic targets for gastric cancer.

Identifying dysregulated pathways in cancers from pathway interaction networks

Background

Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of pathways. Identifying biomarkers that can characterize cancers would help to understand and diagnose cancers. Traditional computational methods that detect genes differentially expressed between cancer and normal samples fail to work due to small sample size and independent assumption among genes. On the other hand, genes work in concert to perform their functions. Therefore, it is expected that dysregulated pathways will serve as better biomarkers compared with single genes.

Results

In this paper, we propose a novel approach to identify dysregulated pathways in cancer based on a pathway interaction network. Our contribution is three-fold. Firstly, we present a new method to construct pathway interaction network based on gene expression, protein-protein interactions and cellular pathways. Secondly, the identification of dysregulated pathways in cancer is treated as a feature selection problem, which is biologically reasonable and easy to interpret. Thirdly, the dysregulated pathways are identified as subnetworks from the pathway interaction networks, where the subnetworks characterize very well the functional dependency or crosstalk between pathways. The benchmarking results on several distinct cancer datasets demonstrate that our method can obtain more reliable and accurate results compared with existing state of the art methods. Further functional analysis and independent literature evidence also confirm that our identified potential pathogenic pathways are biologically reasonable, indicating the effectiveness of our method.

Conclusions

Dysregulated pathways can serve as better biomarkers compared with single genes. In this work, by utilizing pathway interaction networks and gene expression data, we propose a novel approach that effectively identifies dysregulated pathways, which can not only be used as biomarkers to diagnose cancers but also serve as potential drug targets in the future.

Dynamic network of transcription and pathway crosstalk to reveal molecular mechanism of MGd-treated human lung cancer cells

Recent research has revealed various molecular markers in lung cancer. However, the organizational principles underlying their genetic regulatory networks still await investigation. Here we performed Network Component Analysis (NCA) and Pathway Crosstalk Analysis (PCA) to construct a regulatory network in human lung cancer (A549) cells which were treated with 50 uM motexafin gadolinium (MGd), a metal cation-containing chemotherapeutic drug for 4, 12, and 24 hours. We identified a set of key TFs, known target genes for these TFs, and signaling pathways involved in regulatory networks. Our work showed that putative interactions between these TFs (such as ESR1/Sp1, E2F1/Sp1, c-MYC-ESR, Smad3/c-Myc, and NFKB1/RELA), between TFs and their target genes (such as BMP41/Est1, TSC2/Myc, APE1/Sp1/p53, RARA/HOXA1, and SP1/USF2), and between signaling pathways (such as PPAR signaling pathway and Adipocytokines signaling pathway). These results will provide insights into the regulatory mechanism of MGd-treated human lung cancer cells.

Relationships between the amyloid precursor protein and its various proteolytic fragments and neuronal systems

Alzheimer's disease (AD) is a progressive neurodegenerative disease and in its familial form is associated with mutations in the amyloid precursor protein (APP) and the presenilins (PSs). Much data regarding the interactions of APP, its proteolytic fragments and PS have been generated, expanding our understanding of the roles of these proteins in mechanisms underlying cognitive function and revealing many complex relationships with wide ranging cellular systems. In this review, we examine the multiple interactions of APP and its proteolytic fragments with other neuronal systems in terms of feedback loops and use these relationships to build a map. We highlight the complexity involved in the APP proteolytic system and discuss alternative perspectives on the roles of APP and its proteolytic fragments in dynamic processes associated with disease progression in AD. We highlight areas where data are missing and suggest potential confounding factors. We suggest that a systems biology approach enhances representations of the data and may be more useful in modelling both normal cognition and disease processes.

Translational bioinformatics in psychoneuroimmunology: methods and applications

Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.

Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach

Background

The incidence of congenital heart disease (CHD) is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide. Various studies suggest that both genetic and environmental factors lead to CHD, and therefore identifying its candidate genes and disease-markers has been one of the central topics in CHD research. By using the high-throughput genomic data of CHD which are available recently, network-based methods provide powerful alternatives of systematic analysis of complex diseases and identification of dysfunctional modules and candidate disease genes.

Results

In this paper, by modeling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network, we extracted dysfunctional modules which were then validated by various types of measurements and independent datasets. Network topology analysis of these modules revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules. We also prioritized a list of candidate CHD genes from these modules using a guilt-by-association approach, which are well supported by various kinds of literature and experimental evidence.

Conclusions

We provided a network-based analysis to detect dysfunctional modules and disease genes of CHD by modeling the information transmission from source disease genes to targets of differentially expressed genes. Our method resulted in 12 modules from the constructed CHD subnetwork. We further identified and prioritized candidate disease genes of CHD from these dysfunctional modules. In conclusion, module analysis not only revealed several important findings with regard to the underlying molecular mechanisms of CHD, but also suggested the distinct network properties of causal disease genes which lead to identification of candidate CHD genes.