FastMEDUSA: a parallelized tool to infer gene regulatory networks

The feasibility of genome-scale biological network inference using Graphics Processing Units

Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called ‘big data’ applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster.

Biological Network Inference from Microarray Data, Current Solutions, and Assessments

Currently in bioinformatics and systems biology there is a growing interest for the analysis of associations among biological molecules at a network level. A main research in this area is represented by the inference of biological networks from experimental data. Biological network inference aims to reconstruct network of interactions (or associations) among biological molecules (e.g., genes or proteins) starting from experimental observations. The current scenario is characterized by a growing number of algorithms for the inference, while few attention has been posed on the determination of fair assessments and comparisons. Current assessments are usually based on the comparison of the algorithms using reference networks or gold standard datasets. Here we survey some selected inference algorithms and we compare current assessments. We also present a systematic listing of freely available inference and assessment tools for easy reference. Finally we outline some possible future directions of research, such as the use of a prior knowledge into the assessment process.

Master regulators, regulatory networks, and pathways of glioblastoma subtypes

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype- specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.

The inferred cardiogenic gene regulatory network in the mammalian heart

Cardiac development is a complex, multiscale process encompassing cell fate adoption, differentiation and morphogenesis. To elucidate pathways underlying this process, a recently developed algorithm to reverse engineer gene regulatory networks was applied to time-course microarray data obtained from the developing mouse heart. Approximately 200 genes of interest were input into the algorithm to generate putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of putative networks are merged and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve and receiver operator characteristic curve are 9% and 58%, respectively. Of the top 10 ranked predicted interactions, 4 have already been validated. The algorithm is further tested using a network enriched with known interactions and another depleted of them. The inferred networks contained more interactions for the enriched network versus the depleted network. In all test cases, maximum performance of the algorithm was achieved when the purely data-driven method of network inference was combined with a data-independent, functional-based association method. Lastly, the network generated from the list of approximately 200 genes of interest was expanded using gene-profile uniqueness metrics to include approximately 900 additional known mouse genes and to form the most likely cardiogenic gene regulatory network. The resultant network supports known regulatory interactions and contains several novel cardiogenic regulatory interactions. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation.

Molecular mechanisms of system responses to novel stimuli are predictable from public data

Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using ∼ 1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae, we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain-a common problem in laboratory animal and human studies.

Genomic analysis of stress response against arsenic in Caenorhabditis elegans

Arsenic, a known human carcinogen, is widely distributed around the world and found in particularly high concentrations in certain regions including Southwestern US, Eastern Europe, India, China, Taiwan and Mexico. Chronic arsenic poisoning affects millions of people worldwide and is associated with increased risk of many diseases including arthrosclerosis, diabetes and cancer. In this study, we explored genome level global responses to high and low levels of arsenic exposure in Caenorhabditis elegans using Affymetrix expression microarrays. This experimental design allows us to do microarray analysis of dose-response relationships of global gene expression patterns. High dose (0.03%) exposure caused stronger global gene expression changes in comparison with low dose (0.003%) exposure, suggesting a positive dose-response correlation. Biological processes such as oxidative stress, and iron metabolism, which were previously reported to be involved in arsenic toxicity studies using cultured cells, experimental animals, and humans, were found to be affected in C. elegans. We performed genome-wide gene expression comparisons between our microarray data and publicly available C. elegans microarray datasets of cadmium, and sediment exposure samples of German rivers Rhine and Elbe. Bioinformatics analysis of arsenic-responsive regulatory networks were done using FastMEDUSA program. FastMEDUSA analysis identified cancer-related genes, particularly genes associated with leukemia, such as dnj-11, which encodes a protein orthologous to the mammalian ZRF1/MIDA1/MPP11/DNAJC2 family of ribosome-associated molecular chaperones. We analyzed the protective functions of several of the identified genes using RNAi. Our study indicates that C. elegans could be a substitute model to study the mechanism of metal toxicity using high-throughput expression data and bioinformatics tools such as FastMEDUSA.

PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data

MOTIVATION

Protein phosphorylation is critical for regulating cellular activities by controlling protein activities, localization and turnover, and by transmitting information within cells through signaling networks. However, predictions of protein phosphorylation and signaling networks remain a significant challenge, lagging behind predictions of transcriptional regulatory networks into which they often feed.

RESULTS

We developed PhosphoChain to predict kinases, phosphatases and chains of phosphorylation events in signaling networks by combining mRNA expression levels of regulators and targets with a motif detection algorithm and optional prior information. PhosphoChain correctly reconstructed ∼78% of the yeast mitogen-activated protein kinase pathway from publicly available data. When tested on yeast phosphoproteomic data from large-scale mass spectrometry experiments, PhosphoChain correctly identified ∼27% more phosphorylation sites than existing motif detection tools (NetPhosYeast and GPS2.0), and predictions of kinase-phosphatase interactions overlapped with ∼59% of known interactions present in yeast databases. PhosphoChain provides a valuable framework for predicting condition-specific phosphorylation events from high-throughput data.

AVAILABILITY

PhosphoChain is implemented in Java and available at http://virgo.csie.ncku.edu.tw/PhosphoChain/ or http://aitchisonlab.com/PhosphoChain

Genomic analysis of immune response against Vibrio cholerae hemolysin in Caenorhabditis elegans

Vibrio cholerae cytolysin (VCC) is among the accessory V. cholerae virulence factors that may contribute to disease pathogenesis in humans. VCC, encoded by hlyA gene, belongs to the most common class of bacterial toxins, known as pore-forming toxins (PFTs). V. cholerae infects and kills Caenorhabditis elegans via cholerae toxin independent manner. VCC is required for the lethality, growth retardation and intestinal cell vacuolation during the infection. However, little is known about the host gene expression responses against VCC. To address this question we performed a microarray study in C. elegans exposed to V. cholerae strains with intact and deleted hlyA genes.

Many of the VCC regulated genes identified, including C-type lectins, Prion-like (glutamine [Q]/asparagine [N]-rich)-domain containing genes, genes regulated by insulin/IGF-1-mediated signaling (IIS) pathway, were previously reported as mediators of innate immune response against other bacteria in C. elegans. Protective function of the subset of the genes up-regulated by VCC was confirmed using RNAi. By means of a machine learning algorithm called FastMEDUSA, we identified several putative VCC induced immune regulatory transcriptional factors and transcription factor binding motifs. Our results suggest that VCC is a major virulence factor, which induces a wide variety of immune response- related genes during V. cholerae infection in C. elegans.

Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks

Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched processes related to these biological events were identified. Ranking scores further suggested ability to discern the primary role of a gene (target or regulator). Prototype is available at: http://kdbio.inesc-id.pt/software/regulatorysnapshots.

Computational oncology

Oncology research has traditionally been conducted using techniques from the biological sciences. The new field of computational oncology has forged a new relationship between the physical sciences and oncology to further advance research. By applying physics and mathematics to oncologic problems, new insights will emerge into the pathogenesis and treatment of malignancies. One major area of investigation in computational oncology centers around the acquisition and analysis of data, using improved computing hardware and software. Large databases of cellular pathways are being analyzed to understand the interrelationship among complex biological processes. Computer-aided detection is being applied to the analysis of routine imaging data including mammography and chest imaging to improve the accuracy and detection rate for population screening. The second major area of investigation uses computers to construct sophisticated mathematical models of individual cancer cells as well as larger systems using partial differential equations. These models are further refined with clinically available information to more accurately reflect living systems. One of the major obstacles in the partnership between physical scientists and the oncology community is communications. Standard ways to convey information must be developed. Future progress in computational oncology will depend on close collaboration between clinicians and investigators to further the understanding of cancer using these new approaches.

Integration and analysis of genome-scale data from gliomas

Primary brain tumors are a leading cause of cancer-related mortality among young adults and children. The most common primary malignant brain tumor, glioma, carries a median survival of only 14 months. Two major multi-institutional programs, the Glioma Molecular Diagnostic Initiative and The Cancer Genome Atlas, have pursued a comprehensive genomic characterization of a large number of clinical glioma samples using a variety of technologies to measure gene expression, chromosomal copy number alterations, somatic and germline mutations, DNA methylation, microRNA, and proteomic changes. Classification of gliomas on the basis of gene expression has revealed six major subtypes and provided insights into the underlying biology of each subtype. Integration of genome-wide data from different technologies has been used to identify many potential protein targets in this disease, while increasing the reliability and biological interpretability of results. Mapping genomic changes onto both known and inferred cellular networks represents the next level of analysis, and has yielded proteins with key roles in tumorigenesis. Ultimately, the information gained from these approaches will be used to create customized therapeutic regimens for each patient based on the unique genomic signature of the individual tumor. In this Review, we describe efforts to characterize gliomas using genomic data, and consider how insights gained from these analyses promise to increase understanding of the biological underpinnings of the disease and lead the way to new therapeutic strategies.

Functional genomics of the brain: uncovering networks in the CNS using a systems approach

The central nervous system (CNS) is undoubtedly the most complex human organ system in terms of its diverse functions, cellular composition, and connections. Attempts to capture this diversity experimentally were the foundation on which the field of neurobiology was built. Until now though, techniques were either painstakingly slow or insufficient in capturing this heterogeneity. In addition, the combination of multiple layers of information needed for a complete picture of neuronal diversity from the epigenome to the proteome requires an even more complex compilation of data. In this era of high-throughput genomics though, the ability to isolate and profile neurons and brain tissue has increased tremendously and now requires less effort. Both microarrays and next-generation sequencing have identified neuronal transcriptomes and signaling networks involved in normal brain development, as well as in disease. However, the expertise needed to organize and prioritize the resultant data remains substantial. A combination of supervised organization and unsupervised analyses are needed to fully appreciate the underlying structure in these datasets. When utilized effectively, these analyses have yielded striking insights into a number of fundamental questions in neuroscience on topics ranging from the evolution of the human brain to neuropsychiatric and neurodegenerative disorders. Future studies will incorporate these analyses with behavioral and physiological data from patients to more efficiently move toward personalized therapeutics.