Maharjan, Sushila

Abstract INTRODUCTION Lung cancer is the most common tumor to metastasize to the brain, as 40% of patients develop brain metastasis (BM). Once diagnosed, survival is only 6.5-10 months. A potential target to prevent BM is circulating tumor cells (CTCs). The mechanisms that mediate a CTCs adaptation to the harsh environment of circulation and BM have not been elucidated. We have demonstrated that primary lung cancer cells (LCCs) subjected to circulation have increased migration across brain endothelial cell monolayers, increased side population, and decrease survival in rodents when injected intracardially vs plated controls. METHODS We engineered a microfluidic device capable of subjecting tumor cells to prolonged circulatory shear stress (CSS) that allows the isolation/modeling of CTC-like cells. Primary LCCs (A549, H1563) were subjected to circulation, suspension, or plated (2D) for 72h as controls. At this time point, LCCs were collected, and total mRNA was extracted. Samples were submitted for RNA sequencing. Weighted gene co-expression network analysis (WCGNA) and Ingenuity Pathway Analysis were performed. (FC >2 and p-value <0.05). RESULTS Transcriptomic analysis indicates that cells subjected to CSS activate specific gene networks involved in cell survival, migration, invasion, adhesion of tumor cells, and cytoskeletal reorganization (z-score: >2, p-value: 0.05). Canonical pathways in CTCs included ILK-signaling and Xenobiotic Metabolism. LCCs exposed to CSS had increased expression of multidrug resistance pumps ABCC2, ABCF2 (P:<0.001), and side population on flow cytometry (P: <0.05). The top differentially expressed genes in CTC-like cells (MMP1, ANGPTL4, and CEMIP) were involved in the invasion, angiogenesis, and pre-metastatic niche (PMN) formation of BM. CONCLUSION LCCs that survive CSS have increased expression of genes associated with migration, invasion, and chemoresistance compared to 2D controls. Targeting genes in CTCs that mediate survival and preparation of the PMN may be useful in preventing BM.

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors—S/Ace2, Tmprss2, Cathepsins L and K, and Mpro—to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

Abstract Patients with primary cancers often develop delayed brain metastases. One of the most common cancer types and sources of brain metastasis is lung cancer. For metastasis from lung cancer the 3-year survival is < 5%. Cancer cells in circulation are responsible for metastatic spread. The mechanical microenvironment plays an important role in cancer cells behavior. When cancer cells reach the bloodstream they are exposed to hemodynamic shear stress. It has been shown that most of the circulating tumor cells die once they reach the bloodstream, but the biology of the survival cells is poorly understood. We designed a microfluidics system that simulated the mechanical stress in the human circulating system such as turbulence, change in pressure (0.4-15dyn/cm2) and flow rate (1.06–106.1mm/s). Lung adenocarcinoma cells (A549) were put into circulation and collected after 72 hours. It was found that, 1.4±0.3% of the cells survived, and viability was evaluated by LDH and calcein. CD133, SOX2, and NANOG were downregulated and EMT genes were upregulated in the circulating cancer cells (CCCs) compared with cells in static-suspension or 2D. After re-seeding in 2D, CCCs overexpressed CD133. Female athymic nude rats (6-8weeks,n=16) received intracardiac injections of CCCs or 2D cells (GFP-LUC lentivirus traduced). Bioluminescence imaging was performed every week; the survival in of the CCCs group was lower than the 2D group. One-way ANOVA test was used to analyze survival and gene expression. Survival data was plotted on a Kaplan-Meier curve and compared using the Mantel-Cox logrank test, p=< 0.05. We have isolated a cellular subpopulation of lung adenocarcinoma with high resistance to hemodynamic shear stress that shows a higher metastatic capacity and genetic plasticity compared with cells growing in suspension or 2D. Targeting these cells would potentially allow us to develop personalized treatments to help to stop metastatic spread and improve actual therapeutical strategies.

AbstractConvergence of life sciences, engineering, and basic sciences has opened new horizons for biologically inspired innovations, and a considerable number of organ‐on‐a‐chip platforms have been developed for mimicking physiological systems of biological organs such as the brain, heart, lung, kidney, liver, and gut. Various biophysicochemical factors can also be introduced into such organ‐on‐a‐chip platforms to study metabolic and systemic effects spanning from drug toxicity to different pathologic manifestations. There is also a pressing need to develop better disease models for common pathologies using variations of these platforms. This can be achieved by recapitulating the unique microenvironment of a disease to investigate the cause and development of abnormal conditions as well as the structural and functional changes resulting from such a pathology. In this review, the organ‐on‐a‐chip platforms that have been developed to model different pathologies of neurodegenerative, cardiovascular, respiratory, hepatic, and digestive systems, along with cancer are summarized. Although the field is still in its infancy, it is anticipated that developing disease model‐on‐a‐chip platforms will likely be a valuable addition to the field of disease modeling, pathology studies, and improved drug discovery.