Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package

High-throughput, ‘omic’ methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of ‘omic’ data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.National Institutes of Health (U.S.) (grant U54CA112967)National Institutes of Health (U.S.) (grant U01CA184898)National Institutes of Health (U.S.) (grant U54NS091046)National Institutes of Health (U.S.) (grant R01GM089903

PCSF: An R-package for network-based interpretation of high-throughput data

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis

Huntington’s Disease iPSC-Derived Brain Microvascular Endothelial Cells Reveal WNT-Mediated Angiogenic and Blood-Brain Barrier Deficits

Brain microvascular endothelial cells (BMECs) are an essential component of the blood-brain barrier (BBB) that shields the brain against toxins and immune cells. While BBB dysfunction exists in neurological disorders, including Huntington's disease (HD), it is not known if BMECs themselves are functionally compromised to promote BBB dysfunction. Further, the underlying mechanisms of BBB dysfunction remain elusive given limitations with mouse models and post-mortem tissue to identify primary deficits. We undertook a transcriptome and functional analysis of human induced pluripotent stem cell (iPSC)-derived BMECs (iBMEC) from HD patients or unaffected controls. We demonstrate that HD iBMECs have intrinsic abnormalities in angiogenesis and barrier properties, as well as in signaling pathways governing these processes. Thus, our findings provide an iPSC-derived BBB model for a neurodegenerative disease and demonstrate autonomous neurovascular deficits that may underlie HD pathology with implications for therapeutics and drug delivery.American Heart Association (12PRE10410000)American Heart Association (CIRMTG2-01152)National Institutes of Health (U.S.) (NIHNS089076

Integrating Omics data : a new software tool and its use in implicating therapeutic targets in Huntington's disease

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references.High-throughput "omics" data are becoming commonplace in biological research and can provide important translational insights, but there is a need for well-crafted user-friendly tools for integrating and analyzing these data. In this thesis, I present versions 1 and 2 of Omics Integrator, a software tool designed to take advantage of the Prize-Collecting Steiner Forest algorithm from graph theory to provide users with high-confidence biological networks informed by their omics results. I show the results of using this flexible tool in several studies of Huntington's disease (HD), a fatal neurodegenerative disorder with no cure. By leveraging Omics Integrator on omics datasets from induced pluripotent stem cell (iPSC) derived models of HD, I discovered and highlighted several pathways that are altered in these cell line models, including neurodevelopment and glycolytic metabolism, which may lead to important therapeutic targets in the disease. Finally, I compare omics data derived from three iPSC-derived models differentiated towards a striatal neuron cell type using different protocols, and show that by performing this large comparative analysis I can implicate functions and pathways common to several models of HD. Future integrative and comparative studies like these will be made easier by the Omics Integrator tool.by Amanda Joy Kedaigle.Ph. D

Bioenergetic deficits in Huntington’s disease iPSC-derived neural cells and rescue with glycolytic metabolites

Altered cellular metabolism is believed to be an important contributor to pathogenesis of the neurodegenerative disorder Huntington’s disease (HD). Research has primarily focused on mitochondrial toxicity, which can cause death of the vulnerable striatal neurons, but other aspects of metabolism have also been implicated. Most previous studies have been carried out using postmortem human brain or non-human cells. Here, we studied bioenergetics in an induced pluripotent stem cell-based model of the disease. We found decreased adenosine triphosphate (ATP) levels in HD cells compared to controls across differentiation stages and protocols. Proteomics data and multiomics network analysis revealed normal or increased levels of mitochondrial messages and proteins, but lowered expression of glycolytic enzymes. Metabolic experiments showed decreased spare glycolytic capacity in HD neurons, while maximal and spare respiratory capacities driven by oxidative phosphorylation were largely unchanged. ATP levels in HD neurons could be rescued with addition of pyruvate or late glycolytic metabolites, but not earlier glycolytic metabolites, suggesting a role for glycolytic deficits as part of the metabolic disturbance in HD neurons. Pyruvate or other related metabolic supplements could have therapeutic benefit in HD.National Institutes of Health (U.S.) (Grant NS089076)National Institutes of Health (U.S.) (Grant R01GM089903)National Institutes of Health (U.S.) (Grant T32GM008334)National Science Foundation (U.S.) (Grant DB1-0821391)University of California, Irvine. Genomic High Through-put Facility Shared Resource of the Cancer Center (Support Grant (CA-62203)National Institutes of Health (U.S.) (Grant P30-ES002109

Discovering altered regulation and signaling through network-based integration of transcriptomic, epigenomic, and proteomic tumor data

© 2018, Springer Science+Business Media LLC. With the extraordinary rise in available biological data, biologists and clinicians need unbiased tools for data integration in order to reach accurate, succinct conclusions. Network biology provides one such method for high-throughput data integration, but comes with its own set of algorithmic problems and needed expertise. We provide a step-by-step guide for using Omics Integrator, a software package designed for the integration of transcriptomic, epigenomic, and proteomic data. Omics Integrator can be found at http://fraenkel.mit.edu/omicsintegrator

Turning omics data into therapeutic insights

Omics technologies have made it easier and cheaper to evaluate thousands of biological molecules at once. These advances have led to novel therapies approved for use in the clinic, elucidated the mechanisms behind disease-associated mutations, led to increased accuracy in disease subtyping and personalized medicine, and revealed novel uses and treatment regimes for existing drugs through drug repurposing and pharmacology studies. In this review, we summarize some of these milestones and discuss the potential of integrative analyses that combine multiple data types for further advances.National Institutes of Health (U.S.) (Grant R01 NS089076

Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package.

High-throughput, 'omic' methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of 'omic' data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator