Pandora, a pathway and network discovery approach based on common biological evidence

Reverse engineering of Alzheimer's disease based on biomarker pathways analysis

Alzheimer's disease (AD) poses an increasingly profound problem to society, yet progress toward a genuine understanding of the disease remains worryingly slow. Perhaps, the most outstanding problem with the biology of AD is the question of its mechanistic origins, that is, it remains unclear wherein the molecular failures occur that underlie the disease. We demonstrate how molecular biomarkers could help define the nature of AD in terms of the early biochemical events that correlate with disease progression. We use a novel panel of biomolecules that appears in cerebrospinal fluid of AD patients. As changes in the relative abundance of these molecular markers are associated with progression to AD from mild cognitive impairment, we make the assumption that by tracking their origins we can identify the biochemical conditions that predispose their presence and consequently cause the onset of AD. We couple these protein markers with an analysis of a series of genetic factors and together this hypothesis essentially allows us to redefine AD in terms of the molecular pathways that underlie the disease.

Platelet aggregation pathway network-based approach for evaluating compounds efficacy

Traditional Chinese medicines (TCMs) contain a large quantity of compounds with multiple biological activities. By using multitargets docking and network analysis in the context of pathway network of platelet aggregation, we proposed network efficiency and network flux model to screen molecules which can be used as drugs for antiplatelet aggregation. Compared with traditional single-target screening methods, network efficiency and network flux take into account the influences which compounds exert on the whole pathway network. The activities of antiplatelet aggregation of 19 active ingredients separated from TCM and 14 nonglycoside compounds predicated from network efficiency and network flux model show good agreement with experimental results (correlation coefficient = 0.73 and 0.90, resp.). This model can be used to evaluate the potential bioactive compounds and thus bridges the gap between computation and clinical indicator.

Merging multiple omics datasets in silico: statistical analyses and data interpretation

By the combinations of high-throughput analytical technologies in the fields of transcriptomics, proteomics, and metabolomics, we are now able to gain comprehensive and quantitative snapshots of the intracellular processes. Dynamic intracellular activities and their regulations can be elucidated by systematic observation of these multi-omics data. On the other hand, careful statistical analysis is necessary for such integration, since each of the omics layers as well as the specific analytical methodologies harbor different levels of noise and variations. Moreover, interpretation of such multitude of data requires an intuitive pathway context. Here we describe such statistical methods for the integration and comparison of multi-omics data, as well as the computational methods for pathway reconstruction, ID conversion, mapping, and visualization that play key roles for the efficient study of multi-omics information.

Genome-scale metabolic models: reconstruction and analysis

Metabolism can be defined as the complete set of chemical reactions that occur in living organisms in order to maintain life. Enzymes are the main players in this process as they are responsible for catalyzing the chemical reactions. The enzyme-reaction relationships can be used for the reconstruction of a network of reactions, which leads to a metabolic model of metabolism. A genome-scale metabolic network of chemical reactions that take place inside a living organism is primarily reconstructed from the information that is present in its genome and the literature and involves steps such as functional annotation of the genome, identification of the associated reactions and determination of their stoichiometry, assignment of localization, determination of the biomass composition, estimation of energy requirements, and definition of model constraints. This information can be integrated into a stoichiometric model of metabolism that can be used for detailed analysis of the metabolic potential of the organism using constraint-based modeling approaches and hence is valuable in understanding its metabolic capabilities.

Disease gene interaction pathways: a potential framework for how disease genes associate by disease-risk modules

Background

Disease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as ‘a form of biological systems’, consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic.

Methodology/Principal Findings

In this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways.

Conclusions/Significance

Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases.

Discovering novel subsystems using comparative genomics

MOTIVATION

Key problems for computational genomics include discovering novel pathways in genome data, and discovering functional interaction partners for genes to define new members of partially elucidated pathways.

RESULTS

We propose a novel method for the discovery of subsystems from annotated genomes. For each gene pair, a score measuring the likelihood that the two genes belong to a same subsystem is computed using genome context methods. Genes are then grouped based on these scores, and the resulting groups are filtered to keep only high-confidence groups. Since the method is based on genome context analysis, it relies solely on structural annotation of the genomes. The method can be used to discover new pathways, find missing genes from a known pathway, find new protein complexes or other kinds of functional groups and assign function to genes. We tested the accuracy of our method in Escherichia coli K-12. In one configuration of the system, we find that 31.6% of the candidate groups generated by our method match a known pathway or protein complex closely, and that we rediscover 31.2% of all known pathways and protein complexes of at least 4 genes. We believe that a significant proportion of the candidates that do not match any known group in E.coli K-12 corresponds to novel subsystems that may represent promising leads for future laboratory research. We discuss in-depth examples of these findings.

AVAILABILITY

Predicted subsystems are available at http://brg.ai.sri.com/pwy-discovery/journal.html.

CONTACT

lferrer@ai.sri.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Glycomics meets lipidomics--associations of N-glycans with classical lipids, glycerophospholipids, and sphingolipids in three European populations

Recently, high-throughput technologies have been made available which allow the measurement of a broad spectrum of glycomics and lipidomics parameters in many samples. The aim of this study was to apply these methods and investigate associations between 46 glycan and 183 lipid traits measured in blood of 2041 Europeans from three different local populations (Croatia - VIS cohort; Sweden - NSPHS cohort; Great Britain - ORCADES cohort). N-glycans have been analyzed with High Performance Liquid Chromatography (HPLC) and lipids with Electrospray Ionization Tandem Mass Spectrometry (ESI-MS/MS) covering sterol lipids, glycerolipids, glycerophospholipids and sphingolipids in eight subclasses. Overall, 8418 associations were calculated using linear mixed effect models adjusted for pedigree, sex, age and multiple testing. We found 330 significant correlations in VIS. Pearson's correlation coefficient r ranged from -0.27 to 0.34 with corresponding p-values between 1.45 × 10(-19) and 4.83 × 10(-6), indicating statistical significance. A total of 71 correlations in VIS could be replicated in NSPHS (r = [-0.19; 0.35], p = [4.16 × 10(-18); 9.38 × 10(-5)]) and 31 correlations in VIS were also found in ORCADES (r = [-0.20; 0.24], p = [2.69 × 10(-10); 7.55 × 10(-5)]). However, in total only 10 correlations between a subset of triantennary glycans and unsaturated phosphatidylcholine, saturated ceramide, and sphingomyelin lipids in VIS (r = [0.18; 0.34], p = [2.98 × 10(-21); 1.69 × 10(-06)]) could be replicated in both NSPHS and ORCADES. In summary, the results show strong and consistent associations between certain glycans and lipids in all populations, but also population-specific correlations which may be caused by environmental and genetic differences. These associations point towards potential interactive metabolic pathways.

Inferring functional modules of protein families with probabilistic topic models

BACKGROUND

Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context.

RESULTS

We describe a Bayesian method, based on a probabilistic topic model, which directly identifies functional modules of protein families. The method explores the co-occurrence patterns of protein families across a collection of sequence samples to infer a probabilistic model of arbitrarily-sized functional modules.

CONCLUSIONS

We show that our method identifies protein modules - some of which correspond to well-known biological processes - that are tightly interconnected with known functional interactions and are different from the interactions identified by pairwise co-occurrence. The modules are not specific to any given organism and may combine different realizations of a protein complex or pathway within different taxa.