CLIP-Seq analysis enables the design of protective ribosomal RNA bait oligonucleotides against C9ORF72 ALS/FTD poly-GR pathophysiology.

Amyotrophic lateral sclerosis and frontotemporal dementia patients with a hexanucleotide repeat expansion in C9ORF72 (C9-HRE) accumulate poly-GR and poly-PR aggregates. The pathogenicity of these arginine-rich dipeptide repeats (R-DPRs) is thought to be driven by their propensity to bind low-complexity domains of multivalent proteins. However, the ability of R-DPRs to bind native RNA and the significance of this interaction remain unclear. Here, we used computational and experimental approaches to characterize the physicochemical properties of R-DPRs and their interaction with RNA. We find that poly-GR predominantly binds ribosomal RNA (rRNA) in cells and exhibits an interaction that is predicted to be energetically stronger than that for associated ribosomal proteins. Critically, modified rRNA "bait" oligonucleotides restore poly-GR-associated ribosomal deficits and ameliorate poly-GR toxicity in patient neurons and Drosophila models. Our work strengthens the hypothesis that ribosomal function is impaired by R-DPRs, highlights a role for direct rRNA binding in mediating ribosomal dysfunction, and presents a strategy for protecting against C9-HRE pathophysiological mechanisms.

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19.

Objective: This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methods: The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. Results: The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. Discussion: In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. Conclusions: Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

Image-based Characterization of 3D Collagen Networks and the Effect of Embedded Cells.

Collagen microstructure is closely related to the mechanical properties of tissues and affects cell migration through the extracellular matrix. To study these structures, three-dimensional (3D) in vitro collagen-based gels are often used, attempting to mimic the natural environment of cells. Some key parameters of the microstructure of these gels are fiber orientation, fiber length, or pore size, which define the mechanical properties of the network and therefore condition cell behavior. In the present study, an automated tool to reconstruct 3D collagen networks is used to extract the aforementioned parameters of gels of different collagen concentration and determine how their microstructure is affected by the presence of cells. Two different experiments are presented to test the functionality of the method: first, collagen gels are embedded within a microfluidic device and collagen fibers are imaged by using confocal fluorescence microscopy; second, collagen gels are directly polymerized in a cell culture dish and collagen fibers are imaged by confocal reflection microscopy. Finally, we investigate and compare the collagen microstructure far from and in the vicinities of MDA-MB 23 cells, finding that cell activity during migration was able to strongly modify the orientation of the collagen fibers and the porosity-related values.

Matrix architecture plays a pivotal role in 3D osteoblast migration: The effect of interstitial fluid flow.

Osteoblast migration is a crucial process in bone regeneration, which is strongly regulated by interstitial fluid flow. However, the exact role that such flow exerts on osteoblast migration is still unclear. To deepen the understanding of this phenomenon, we cultured human osteoblasts on 3D microfluidic devices under different fluid flow regimes. Our results show that a slow fluid flow rate by itself is not able to alter the 3D migratory patterns of osteoblasts in collagen-based gels but that at higher fluid flow rates (increased flow velocity) may indirectly influence cell movement by altering the collagen microstructure. In fact, we observed that high fluid flow rates (1 µl/min) are able to alter the collagen matrix architecture and to indirectly modulate the migration pattern. However, when these collagen scaffolds were crosslinked with a chemical crosslinker, specifically, transglutaminase II, we did not find significant alterations in the scaffold architecture or in osteoblast movement. Therefore, our data suggest that high interstitial fluid flow rates can regulate osteoblast migration by means of modifying the orientation of collagen fibers. Together, these results highlight the crucial role of the matrix architecture in 3D osteoblast migration. In addition, we show that interstitial fluid flow in conjunction with the matrix architecture regulates the osteoblast morphology in 3D.

Quantifying 3D chemotaxis in microfluidic-based chips with step gradients of collagen hydrogel concentrations.

Cell migration is an essential process involved in crucial stages of tissue formation, regeneration or immune function as well as in pathological processes including tumor development or metastasis. During the last few years, the effect of gradients of soluble molecules on cell migration has been widely studied, and complex systems have been used to analyze cell behavior under simultaneous mechano-chemical stimuli. Most of these chemotactic assays have, however, focused on specific substrates in 2D. The aim of the present work is to develop a novel microfluidic-based chip that allows the long-term chemoattractant effect of growth factors (GFs) on 3D cell migration to be studied, while also providing the possibility to analyze the influence of the interface generated between different adjacent hydrogels. Namely, 1.5, 2, 2.5 and 4 mg ml-1 concentrations of collagen type I were alternatively combined with 5, 10 or 50 ng ml-1 concentrations of PDGF and VEGF (as a negative control). To achieve this goal, we have designed a new microfluidic device including three adjacent chambers to introduce hydrogels that allow the generation of a collagen concentration step gradient. This versatile and simple platform was tested by using dermal human fibroblasts embedded in 3D collagen matrices. Images taken over a week were processed to quantify the number of cells in each zone. We found, in terms of cell distribution, that the presence of PDGF, especially in small concentrations, was a strong chemoattractant for dermal human fibroblasts across the gels regardless of their collagen concentration and step gradient direction, whereas the effects of VEGF or collagen step gradient concentrations alone were negligible.

Quantification of angiogenic sprouting under different growth factors in a microfluidic platform.

Angiogenesis, as example of collective migration of endothelial cells (ECs), is the main dynamic process that culminates in sprout formation from existing vessels. After tissue injury, the vascularity is interrupted, triggering the regeneration process and the release of different growth factors (GFs). The main aim of this work is to quantify the effect of specific GFs during the initial stage of sprout formation, namely: VEGF, PDGF-BB, TGFß and BMP-2, all of them involved in regenerative processes. For this purpose, we designed a novel algorithm implemented in Matlab to quantify the advance of the EC monolayer over time and the sprout structure in 3D. Our results show that VEGF is the main regulatory GF on angiogenesis processes, producing longer sprouts with higher frequency. However, the chemoattractant effect of VEGF depends on its concentration and its spatiotemporal location, having a critical impact on the sprout time evolution. PDGF-BB (namely as PDGF) has a global negative effect on both the number and length of sprouts. TGFß enhances sprout length at earlier times, although its effect gradually disappears over time. Finally, BMP-2 produces, overall, less number and shorter sprouts, but was the only GF with a positive evolution at longer times, producing fewer but longer sprouts.