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

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

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

Summary of features differentiating Omics Integrator from existing tools and which features are available when Garnet and Forest are used individually.

<p>¹ Some network algorithms model TFs by including protein-DNA interactions in the network or generating TF scores for the protein nodes. ² Some network algorithms optimize the transmission of information from source nodes to target nodes and require the sources to be identified in advance. ³ Time series analysis algorithms require omic data from three or more time points. ⁴ Intermediate proteins, like the Steiner nodes predicted by Forest, are not assigned condition-specific scores but are important for connecting other scored nodes in the subnetwork. ⁵ Negative evidence discourages network algorithms from selecting particular nodes due to prior knowledge or a bias, such as node degree.</p

Anticipated results: Network reconstructed from changes in phosphoproteomic measurements (circles) and gene expression measurements (triangles) in lung cancer cell lines stimulated with Tgf-β.

<p>Blue hexagons represent ‘Steiner nodes’ that were not measured as changing in the original experimental measurements but identified through network reconstruction. Nodes that are not blue were measured in the phosphoproteomic data, with color indicating the degree of change in phosphoproteomic measurements: grey indicates no change and yellow indicates a large amount of change. Network robustness was measured by adding noise to the edges using the --noisyEdges flag. The shade of the edge is correlated with the number of times the edge was selected over all perturbations, and the size of a node represents number of times the node was selected. The width of the edge represents the weight assigned to the interaction in the original interactome.</p

Anticipated results: In silico EGFR knock-out experiment in network modeling.

<p>Blue nodes represent ‘Steiner nodes’ that were not measured as changing in the original experiment but are identified through network reconstruction; yellow nodes represent ‘terminal nodes’ that are the phosphoproteomic hits. The original network and the network with EGFR knock-out have been merged to clearly show the common and different nodes and edges in the two conditions. Common edges in two conditions are black lines, edges only present in EGFR knock-out condition are red dotted lines and edges only present in the wild-type condition are blue dashed lines. Cell surface receptors are arrow-shaped. The parameters are μ = 0.002, ω = 2, β = 150, and D = 10.</p

Description of the parameters used in the Forest.py script.

<p>Description of the parameters used in the Forest.py script.</p

The final PCSF reconstructed from the terminal set formed by the members of mRNA splicing pathway, pyruvate metabolism pathway, and Rho cell motility pathway in ConsensusPathDB.

<p>Each node is colored according to the pathway to which it belongs, and Steiner nodes are colored gray. The parameters are μ = 0.009, ω = 3, β = 5, and D = 5.</p

The flowchart of the software.

<p>Step 1 requires downloading and unzipping the scripts and data files. Step 2 consists of the installation of the necessary tools to run Omics Integrator. Step 3 describes how to prepare input files. Step 4 and 5 are designed for data collection and formatting for Garnet and Forest modules, respectively. At Step 6, configuration files are prepared where parameters are defined for Garnet and Forest separately. Garnet and Forest scripts are run at Step 7. If the initial data contains transcriptional data, then Garnet must be run before Forest. Otherwise Forest can be run independently. Detailed instructions of these steps are in the ‘Procedure’ section of the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004879#pcbi.1004879.s001" target="_blank">S1 Text</a>.</p