Ovarian Cancer Cells in Ascites Form Aggregates That Display a Hybrid Epithelial-Mesenchymal Phenotype and Allows Survival and Proliferation of Metastasizing Cells

Peritoneal metastases are the leading cause of morbidity and mortality in ovarian cancer. Cancer cells float in peritoneal fluid, named ascites, together with a definitely higher number of non neo-neoplastic cells, as single cells or multicellular aggregates. The aim of this work is to uncover the features that make these aggregates the metastasizing units. Immunofluorescence revealed that aggregates are made almost exclusively of ovarian cancer cells expressing the specific nuclear PAX8 protein. The same cells expressed epithelial and mesenchymal markers, such as EPCAM and αSMA, respectively. Expression of fibronectin further supported a hybrid epithelia-mesenchymal phenotype, that is maintained when aggregates are cultivated and proliferate. Hematopoietic cells as well as macrophages are negligible in the aggregates, while abundant in the ascitic fluid confirming their prominent role in establishing an eco-system necessary for the survival of ovarian cancer cells. Using ovarian cancer cell lines, we show that cells forming 3D structures neo-expressed thoroughly fibronectin and αSMA. Functional assays showed that αSMA and fibronectin are necessary for the compaction and survival of 3D structures. Altogether these data show that metastasizing units display a hybrid phenotype that allows maintenance of the 3D structures and the plasticity necessary for implant and seeding into peritoneal lining.

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators

Although the 3D structures of active and inactive cannabinoid receptors type 2 (CB2) are available, neither the X-ray crystal nor the cryo-EM structure of CB2-orthosteric ligand-modulator has been resolved, prohibiting the drug discovery and development of CB2 allosteric modulators (AMs). In the present work, we mainly focused on investigating the potential allosteric binding site(s) of CB2. We applied different algorithms or tools to predict the potential allosteric binding sites of CB2 with the existing agonists. Seven potential allosteric sites can be observed for either CB2-CP55940 or CB2-WIN 55,212-2 complex, among which sites B, C, G and K are supported by the reported 3D structures of Class A GPCRs coupled with AMs. Applying our novel algorithm toolset-MCCS, we docked three known AMs of CB2 including Ec2la (C-2), trans-β-caryophyllene (TBC) and cannabidiol (CBD) to each site for further comparisons and quantified the potential binding residues in each allosteric binding site. Sequentially, we selected the most promising binding pose of C-2 in five allosteric sites to conduct the molecular dynamics (MD) simulations. Based on the results of docking studies and MD simulations, we suggest that site H is the most promising allosteric binding site. We plan to conduct bio-assay validations in the future.

Converting Auroral Observations into 3D Structures

Using 1D and 2D data sources as model constraints yields fine-scale insights into real-world aurorae.

Polylactic Acid Nanonetworks as High Reflectance Distributed Bragg Reflectors

Natural systems found ways to exploit light at the nanoscale, devising complex 3D structures that behave as photonic crystals, able to produce structural coloration. Distributed Bragg reflectors are a particular...

Computer-Based Drug Design of Positive Modulators of Store-Operated Calcium Channels to Prevent Synaptic Dysfunction in Alzheimer’s Disease

Store-operated calcium entry (SOCE) constitutes a fine-tuning mechanism responsible for the replenishment of intracellular stores. Hippocampal SOCE is regulated by store-operated channels (SOC) organized in tripartite complex TRPC6/ORAI2/STIM2. It is suggested that in neurons, SOCE maintains intracellular homeostatic Ca2+ concentration at resting conditions and is needed to support the structure of dendritic spines. Recent evidence suggests that positive modulators of SOC are prospective drug candidates to treat Alzheimer’s disease (AD) at early stages. Although STIM2 and ORAI2 are definitely involved in the regulation of nSOC amplitude and a play major role in AD pathogenesis, growing evidence suggest that it is not easy to target these proteins pharmacologically. Existing positive modulators of TRPC6 are unsuitable for drug development due to either bad pharmacokinetics or side effects. Thus, we concentrate the review on perspectives to develop specific nSOC modulators based on available 3D structures of TRPC6, ORAI2, and STIM2. We shortly describe the structural features of existing models and the methods used to prepare them. We provide commonly used steps applied for drug design based on 3D structures of target proteins that might be used to develop novel AD preventing therapy.

Modeling Tumor: Lymphatic Interactions in Lymphatic Metastasis of Triple Negative Breast Cancer

Breast cancer frequently metastasizes to lymphatics and the presence of breast cancer cells in regional lymph nodes is an important prognostic factor. Delineating the mechanisms by which breast cancer cells disseminate and spatiotemporal aspects of interactions between breast cancer cells and lymphatics is needed to design new therapies to prevent lymphatic metastases. As triple-negative breast cancer (TNBC) has a high incidence of lymphatic metastasis, we used a three-dimensional (3D) coculture model of human TNBC cells and human microvascular lymphatic endothelial cells (LECs) to analyze TNBC:LEC interactions. Non-invasive analyses such as live-cell imaging in real-time and collection of conditioned media for secretomic analysis were facilitated by our novel microfluidic chambers. The volumes of 3D structures formed in TNBC:LEC cocultures are greater than that of 3D structures formed by either LEC or TNBC monocultures. Over 4 days of culture there is an increase in multicellular invasive outgrowths from TNBC spheroids and an association of TNBC spheroids with LEC networks. The increase in invasive phenotype also occurred when TNBC spheroids were cultured in LEC-conditioned media and in wells linked to ones containing LEC networks. Our results suggest that modeling spatiotemporal interactions between TNBC and LECs may reveal paracrine signaling that could be targeted to reduce lymphatic metastasis.

Effect of Disease Causing Missense Mutations on Intrinsically Disordered Regions in Proteins

Background: It is well known that disease-causing missense mutations (DCMMs) reduce the structural stability/integrity of the proteins with well-defined 3D structures, thereby impacting their molecular functions. However, it is not known in what way DCMMs affect the intrinsically disordered proteins (IDPs) that do not adopt well defined stable 3D structures. Methods: In order to investigate how DCMMs may impact intrinsically disordered regions (IDRs) in proteins, we undertook Molecular Dynamics (MD) based studies on three different examples of functionally important IDRs with known DCMMs. Our studies revealed that the functional impact of DCMMs is in reducing the conformational heterogeneity of IDRs, which is intrinsic and quintessential for their multi-faceted cellular roles. Results: These results are reinforced by energy landscapes of the wildtype and mutant IDRs where the former is characterized by many local minima separated by low barriers, whereas the latter are characterized by one global minimum and several local minima separated by high energy barriers. Our MD based studies also indicate that DCMMs stabilize very few structural possibilities of IDRs either by the newly formed interactions induced by the substituted side chains or by means of restricted or increased flexibilities of the backbone conformations at the mutation sites. Conclusion: Furthermore, the structural possibilities stabilized by DCMMs do not support the native functional roles of the IDRs, thereby leading to disease conditions.