FARE2021WINNERS Sorted By Institute Swati Shah Postdoctoral Fellow CC Radiology/Imaging/PET and Neuroimaging Characterization of CNS involvement in Ebola-Infected Macaques using Magnetic Resonance Imaging, 18F-FDG PET and Immunohistology The Ebola (EBOV) virus outbreak in Western Africa resulted in residual neurologic abnormalities in survivors. Many case studies detected EBOV in the CSF, suggesting that the neurologic sequelae in survivors is related to viral presence. In the periphery, EBOV infects endothelial cells and triggers a “cytokine stormâ€. However, it is unclear whether a similar process occurs in the brain, with secondary neuroinflammation, neuronal loss and blood-brain barrier (BBB) compromise, eventually leading to lasting neurological damage. We have used in vivo imaging and post-necropsy immunostaining to elucidate the CNS pathophysiology in Rhesus macaques infected with EBOV (Makona). Whole brain MRI with T1 relaxometry (pre- and post-contrast) and FDG-PET were performed to monitor the progression of disease in two cohorts of EBOV infected macaques from baseline to terminal endpoint (day 5-6). Post-necropsy, multiplex fluorescence immunohistochemical (MF-IHC) staining for various cellular markers in the thalamus and brainstem was performed. Serial blood and CSF samples were collected to assess disease progression. The linear mixed effect model was used for statistical analysis. Post-infection, we first detected EBOV in the serum (day 3) and CSF (day 4) with dramatic increases until euthanasia. The standard uptake values of FDG-PET relative to whole brain uptake (SUVr) in the midbrain, pons, and thalamus increased significantly over time (p<0.01) and positively correlated with blood viremia (p≤0.01). On MF-IHC, EBOV antigen (VP40) co-localized with endothelial cells as expected, but also with neurons (NeuN), which is consistent with direct neuronal infection and has not been shown before. Many of the infected neurons were apoptotic (cleaved caspase-3/PARP1 staining co-localizing with NeuN and VP40) and showed increased expression of glucose transporters (mainly GLUT-3). Elevated GLUT-3 levels could explain the increased FDG uptake in these regions. On MRI, significant increases in pre-contrast T1-relaxation times with decreased post contrast T1 values suggested BBB disruption and leakage. This was confirmed by MF-IHC showing extravascular albumin staining. Using imaging and histopathology, we showed that acute EBOV infection in macaques leads to BBB disruption, neuronal infection, apoptosis and increased GLUT3 expression. Our findings shed light on the pathophysiology of CNS involvement in EBOV that could explain residual neurologic manifestations in survivors. __________________________________________________________________________________ Yen-Ting Tung Postdoctoral Fellow NCATS Biophysics and Biomedical Engineering A Patient-derived Glioblastoma Model in a Three Dimensional Bioprinted Neurovascular Unit tissue: Moving towards in Tissue Personalized Medicine Glioblastoma (GBM) is a fast-growing, aggressive brain cancer with high heterogeneity of subpopulations. The prognosis for patients with high-grade GBM remains extremely poor, thus, there is a tremendous medical need for more effective treatments. Using three dimensional (3D) biofabrication technologies, including tissue bioprinting, we have successfully established a 3D neurovascular unit (NVU) tissue in a multiwell plate format. The biofabricated NVU includes human GFP-tagged brain endothelial cells, brain pericytes, and astrocytes. A ring-shaped bioprinted structure of vasculature with an inner circular-shape drop of mCherry expressing GBMCs was created in each well to facilitate the quantification of vascular morphology and GBM invasion and growth, respectively. Time dependent vascular angiogenesis in the presence or absence of GBMCs was monitored using a high content fluorescence imaging system. In the tissues without the inclusion of the GBMCs, we confirmed vasculogenic and angiogenic activities occurring for two weeks after bioprinting. The total length of angiogenetic vessel gradually increased with time, along with deposition of brain relevant extra cellular matrix components, like collagen IV, laminin, and fibronectin, demonstrating formation of a 3D neurovascular unit. When introducing the GBMCs into this NVU model, we observed cancer cell infiltration into the outer ring vascular area through the branched vessels. Moreover, the microvascular structure in contact with the GBMCs area collapsed, even though the endothelial cells remained viable and proliferated inside the GBM area. The GBM-microvessel interaction observed is similar to that described in human GBM pathological condition, which showed an abnormal and collapsed microvascular morphology. The single cell RNA sequencing and anti-cancer drug treatments are now being used to study changes in cell populations in the tissue as well as finding potential druggable targets driving cancer cell growth in these tissue models. For the future work, neuronal cells will be included in the tissue model to achieve a better physiological relevance of the neurovascular tissue. This GBM in-a-tissue model will ultimately provide extensive opportunities to investigate patient-specific pathology and to find a personalized drugs for patients. __________________________________________________________________________________ Vishal Babu Siramshetty Postdoctoral Fellow NCATS Chemistry Machine Learning versus Artificial Intelligence Methods for Predicting Cardiotoxicity in the ‘Big Data’ Era Cardiotoxicity is a leading concern behind the recall of marketed drugs and failure of clinical candidates. Inhibition of the potassium ion channel, whose alpha subunit is encoded by human Ether-à-go-go- Related Gene, leads to prolonged QT interval in the cardiac action potential. Fatal cases of cardiac arrhythmia have been reported due to drug-induced hERG channel blockade. Several computational methods, including machine learning, have been employed in modeling this endpoint. Reports based on artificial intelligence methods have only recently surfaced in the literature. To this end, we performed a comprehensive comparison of classification models developed using the classical machine learning and modern deep learning methods by employing different molecular descriptors. The training set (~9000 compounds) was constructed by combining hERG bioactivity data from ChEMBL database with data generated in an in-house high-throughput ion flux assay. Prospective validation of the models was performed on ~1700 in-house compounds screened for hERG activity in the same flux assay. We noticed that the deep learning models performed on par with the baseline models (Random Forests and Gradient Boosting). Latent descriptors derived directly from the deep learning architectures provided nearly the same performance as traditional molecular descriptors. We compared our models with other neural network architectures and state-of-the-art models from literature. Our models outperformed these models on the validation data. The study highlights the application of artificial intelligence methods on chemistry big data, readily available from public domain compound repositories, for generation of novel descriptors that are promising for molecular property prediction. __________________________________________________________________________________ Travis Kinder Postdoctoral Fellow NCATS Immunology - Autoimmune Quantitative High Throughput Screening to Repurpose Drugs to Treat Myositis Idiopathic inflammatory myopathies, collectively referred to as myositis, are a group of rare autoimmune diseases characterized by skeletal muscle inflammation, weakness, and often pathologies of the skin, lungs, or vasculature. Current treatment with steroids and immunosuppressive agents are not curative and have serious side-effects. We initiated a drug repurposing program to identify more targeted, less toxic therapeutics to treat myositis. Repurposing is the quickest path for drug development because it involves identifying approved medications currently used to treat other conditions. Although the cause of myositis is not fully understood, muscle from patients have an overabundance of the inflammatory cytokine type 1 interferon (IFN) and a down-stream product major histocompatibility complex (MHC) class I, which targets T cells for cytotoxic attack of host tissue. Genome-wide association studies in myositis have confirmed that alleles of MHC class I are the strongest risk factor, the protein is up-regulated in patient muscles, and forced over-expression in mouse skeletal muscle leads to many features of myositis. We conducted quantitative high throughput screening of >15,000 compound titrations, including all approved drugs, through a series of cell-based assays to identify those that inhibit the IFN-beta stimulated expression of MHC class I in muscle precursor cells (myoblasts). The primary screen utilized CRISPR/Cas9 genome-engineered human myoblasts that contained a pro-luminescent reporter HiBit fused to the C-terminus of endogenous MHC class I. Active compounds were counter-screened for cytotoxicity and validated by MCH class I immunofluorescence, Western blot, and RT-qPCR. All assays were optimized to have Z’-factors >0.5, a statistical measure of assay robustness. Sixty-seven active compounds were identified falling into two major pharmacological mechanisms, Janus kinase (JAK) inhibitors and epigenetic/transcription factor modulators.