Summary Mutations in DMD disrupt the reading frame, prevent dystrophin translation, and cause Duchenne muscular dystrophy (DMD). Here we describe a CRISPR/Cas9 platform applicable to 60% of DMD patient mutations. We applied the platform to DMD-derived hiPSCs where successful deletion and non-homologous end joining of up to 725kb reframed the DMD gene. This is the largest CRISPR/Cas9-mediated deletion shown to date in DMD. Use of hiPSCs allowed for evaluation of dystrophin in disease relevant cell types. Cardiomyocytes and skeletal muscle myotubes derived from reframed hiPSC clonal lines had restored dystrophin protein. The internally deleted dystrophin was functional as demonstrated by improved membrane integrity and restoration of the dystrophin glycoprotein complex in vitro and in vivo. Furthermore, miR31 was reduced upon reframing, similar to observations in Becker muscular dystrophy. This work demonstrates the feasibility of using a single CRISPR pair to correct the reading frame for the majority of DMD patients.

The potential for directed differentiation of human-induced pluripotent stem (iPS) cells to functional postmitotic neuronal phenotypes is unknown. Following methods shown to be effective at generating motor neurons from human embryonic stem cells (hESCs), we found that once specified to a neural lineage, human iPS cells could be differentiated to form motor neurons with a similar efficiency as hESCs. Human iPS-derived cells appeared to follow a normal developmental progression associated with motor neuron formation and possessed prototypical electrophysiological properties. This is the first demonstration that human iPSderived cells are able to generate electrically active motor neurons. These findings demonstrate the feasibility of using iPS-derived motor neuron progenitors and motor neurons in regenerative medicine applications and in vitro modeling of motor neuron diseases.

In amyotrophic lateral sclerosis, down‐regulation of the astrocyte‐specific glutamate excitatory amino acid transporter 2 is hypothesized to increase extracellular glutamate, thereby leading to excitotoxic motor neuron death. The antibiotic ceftriaxone was recently reported to induce excitatory amino acid transporter 2 and to prolong the survival of mutant superoxide dismutase 1 transgenic mice. Here we show that ceftriaxone also protects fibroblasts and the hippocampal cell line HT22, which are not sensitive to excitotoxicity, against oxidative glutamate toxicity, where extracellular glutamate blocks cystine import via the glutamate/cystine‐antiporter system xc−. Lack of intracellular cystine leads to glutathione depletion and cell death because of oxidative stress. Ceftriaxone increased system xc− and glutathione levels independently of its effect on excitatory amino acid transporters by induction of the transcription factor Nrf2 (nuclear factor erythroid 2‐related factor 2), a known inducer of system xc−, and the specific xc− subunit xCT. No significant effect was apparent in fibroblasts deficient in Nrf2 or xCT. Similar ceftriaxone‐stimulated changes in Nrf2, system xc−, and glutathione were observed in rat cortical and spinal astrocytes. In addition, ceftriaxone induced xCT mRNA expression in stem cell‐derived human motor neurons. We conclude that ceftriaxone‐mediated neuroprotection might relate more strongly to activation of the antioxidant defense system including Nrf2 and system xc− than to excitatory amino acid transporter induction.

Stem cell technologies, especially patient‐specific, induced stem cell pluripotency and directed differentiation, hold great promise for changing the landscape of medical therapies. Proper exploitation of these methods may lead to personalized organ transplants, but to regenerate organs, it is necessary to develop methods for assembling differentiated cells into functional, organ‐level tissues. The generation of three‐dimensional human tissue models also holds potential for medical advances in disease modeling, as full organ functionality may not be necessary to recapitulate disease pathophysiology. This is specifically true of lung diseases where animal models often do not recapitulate human disease. Here, we present a method for the generation of self‐assembled human lung tissue and its potential for disease modeling and drug discovery for lung diseases characterized by progressive and irreversible scarring such as idiopathic pulmonary fibrosis (IPF). Tissue formation occurs because of the overlapping processes of cellular adhesion to multiple alveolar sac templates, bioreactor rotation, and cellular contraction. Addition of transforming growth factor‐β1 to single cell‐type mesenchymal organoids resulted in morphologic scarring typical of that seen in IPF but not in two‐dimensional IPF fibroblast cultures. Furthermore, this lung organoid may be modified to contain multiple lung cell types assembled into the correct anatomical location, thereby allowing cell‐cell contact and recapitulating the lung microenvironment. Our bottom‐up approach for synthesizing patient‐specific lung tissue in a scalable system allows for the development of relevant human lung disease models with the potential for high throughput drug screening to identify targeted therapies. Stem Cells Translational Medicine 2017;6:622–633

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, d-Sorbitol, d-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.

Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are several docking programs to estimate the binding position and the binding orientation of protein–ligand complex. Many scoring functions were developed to estimate the binding strength and predict the effective protein–ligand binding. While the accuracy of current scoring function is limited by several aspects, the solvent effect, entropy effect, and multibody effect are largely ignored in traditional machine learning methods. In this paper, we proposed a new deep neural network-based model named DeepBindRG to predict the binding affinity of protein–ligand complex, which learns all the effects, binding mode, and specificity implicitly by learning protein–ligand interface contact information from a large protein–ligand dataset. During the initial data processing step, the critical interface information was preserved to make sure the input is suitable for the proposed deep learning model. While validating our model on three independent datasets, DeepBindRG achieves root mean squared error (RMSE) value of pKa (−logKd or −logKi) about 1.6–1.8 and R value around 0.5–0.6, which is better than the autodock vina whose RMSE value is about 2.2–2.4 and R value is 0.42–0.57. We also explored the detailed reasons for the performance of DeepBindRG, especially for several failed cases by vina. Furthermore, DeepBindRG performed better for four challenging datasets from DUD.E database with no experimental protein–ligand complexes. The better performance of DeepBindRG than autodock vina in predicting protein–ligand binding affinity indicates that deep learning approach can greatly help with the drug discovery process. We also compare the performance of DeepBindRG with a 4D based deep learning method “pafnucy”, the advantage and limitation of both methods have provided clues for improving the deep learning based protein–ligand prediction model in the future.

In this report, we present multiparameter deformability cytometry (m-DC), in which we explore a large set of parameters describing the physical phenotypes of pluripotent cells and their derivatives. m-DC utilizes microfluidic inertial focusing and hydrodynamic stretching of single cells in conjunction with high-speed video recording to realize high-throughput characterization of over 20 different cell motion and morphology-derived parameters. Parameters extracted from videos include size, deformability, deformation kinetics, and morphology. We train support vector machines that provide evidence that these additional physical measurements improve classification of induced pluripotent stem cells, mesenchymal stem cells, neural stem cells, and their derivatives compared to size and deformability alone. In addition, we utilize visual interactive stochastic neighbor embedding to visually map the high-dimensional physical phenotypic spaces occupied by these stem cells and their progeny and the pathways traversed during differentiation. This report demonstrates the potential of m-DC for improving understanding of physical differences that arise as cells differentiate and identifying cell subpopulations in a label-free manner. Ultimately, such approaches could broaden our understanding of subtle changes in cell phenotypes and their roles in human biology.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.