Biology subjects

Geris, L.

Publications and source records attributed to Geris, L..

12 recordsLinked to original sources

bioRxiv2026

An early mechanosensitive window in bone fracture healing shapes long-term repair

Mechanical stability critically influences bone fracture healing, yet how the early mechanical environment directs the transition from inflammation to regeneration remains unclear. Using a murine femoral osteotomy model, we compared rigid, semirigid, and dynamically adjusted semirigid-to-rigid fixation. Semirigid fixation delayed healing on day 21, whereas a strategy of early compliance followed by increased stiffness after 7 days restored bridging and improved bone regeneration beyond constant rigid fixation, pointing to a critical early mechanosensitive window after fracture. Single-cell RNA sequencing across the first week, comparing semirigid with rigid fixation, showed that fixation stiffness altered intercellular signaling within one day of injury. Signaling among myeloid populations was more broadly increased under semirigid fixation, with monocytes the single exception, and signaling associated with resolution of inflammation was reduced. From day 5, periosteal and skeletal progenitor populations increasingly directed cartilage-associated matrix and growth factor signaling toward macrophages and chondrocytes, accompanied by increased Sox9 regulon activity and increased COL10+ hypertrophic cartilage matrix by day 7. Reduced fixation stiffness therefore promotes a chondrogenic trajectory of repair, but increasing stiffness is required to redirect this response toward bone formation. Piezo1 was expressed across the responding populations and shifted between compartments as healing progressed. PIEZO1 activation with Yoda1 under rigid fixation enhanced bone formation but provided no additional benefit under semirigid or dynamically adjusted fixation, whereas inhibition of mechanosensitive signaling with GsMTx4 impaired repair across all conditions. Together, these findings establish the early mechanical environment as a determinant of fracture-healing trajectory, shaping the transition from inflammation to regeneration, and show that successful repair depends not on maximal stability but on when stiffness is applied. Increasing either early mechanical stimulation or cellular mechanosensitivity enhanced regeneration, supporting mechanosensitive signaling as a key component of the early healing response.

bioengineering↗

bioRxiv2026

User-driven development and evaluation of an agentic framework for analysis of large pathway diagrams

As biomedical knowledge keeps growing, resources storing available information multiply and grow in size and complexity. Such resources can be in the format of molecular interaction maps, which represent cellular and molecular processes under normal or pathological conditions. However, these maps can be complex and hard to navigate, especially to novice users. Large Language Models (LLMs), particularly in the form of agentic frameworks, have emerged as a promising technology to support this exploration. In this article, we describe a user-driven process of prototyping, development, and user testing of Llemy, an LLM-based system for exploring these molecular interaction maps. By involving domain experts from the very first prototyping in the form of a hackathon and collecting both fine-grained and general feedback on more refined versions, we were able to evaluate the perceived utility and quality of the developed system, in particular for summarising maps and pathways, as well as prioritise the development of future features. We recommend continued user-driven development and benchmarking to keep the community engaged. This will also facilitate the transition towards open-weight LLMs to support the needs of the open research environment in an ever-changing technology landscape.

bioinformatics↗

bioRxiv2026

Intracellular crowding links dimensionality to cell fate through a mechano-metabolic signalling axis

Three-dimensional (3D) cell culture systems exhibit more native-like cell behaviour compared to conventional two-dimensional (2D) cell culture systems. However, it remains largely unknown how dimensionality alters cell behaviour. Here, we identify intracellular crowding as a key biophysical parameter altered by cell culture dimensionality, which directs cell fate over a mechano-metabolic axis. Specifically, culture dimensionality controlled intracellular crowding by altering cell volume, which was confirmed across multiple cell types and cell culture platforms. Using chondrogenesis as a model system, we demonstrated that dimensionality-induced intracellular changes lead to improved chondrogenic stem cell differentiation in microtissue culture via a FOXO1 signalling axis. Our findings highlight intracellular crowding as an important parameter of cell culture systems, and present a novel strategy for engineering biomimetic cell culture systems, which has implications for a multitude of applications including disease modelling, in vitro drug screening models, developmental biology, and cell-based therapies.

cell biology↗

bioRxiv2025

A multiscale modeling approach to study the role of mechanics and inflammation in pathophysiology of articular cartilage

Mechanical loading regulates chondrocyte health in articular cartilage. While physiological stimuli maintain homeostasis, supra-physiological stimuli from joint injuries disrupt it, leading to osteoarthritis (OA). OA is a prevalent degenerative joint disease affecting millions worldwide. OA progression involves complex mechanical and biochemical interactions across multiple length scales, which are challenging to investigate experimentally. In silico models provide an effective framework to explore these mechanisms. This study developed an integrated multiscale modeling framework for articular cartilage. It combined finite element (FE) models at tissue and cellular scales with an intracellular gene/protein regulatory network. The network incorporated key chondrocyte mechanotransduction and inflammatory pathways. A Hills function was used to link cellular forces from the FE model to a mechanical loading input to the regulatory network. Hills function constants were calibrated using a genetic algorithm approach. Calibration was performed by matching experimental and simulated expressions of COL-II and ADAMTS5 of cartilage explants under 20% dynamic compression. As a validation step, model simulations were performed at 10% dynamic compression of cartilage explants. COL-II and ACAN were overestimated, and ADAMTS5 was underestimated compared with experimental data. Furthermore, predicted sGAG loss matched the trend of experimental data. Simulated chondrocyte responses for varying spatial locations revealed spatial heterogeneity of chondrocyte activity. Over-all, the multiscale modeling workflow developed in this study provides a first step towards a powerful tool to increase the understanding of the complex interplay of mechanics and inflammation in articular cartilage. By integrating tissue, cellular, and intracellular scales, it offers a comprehensive framework for studying cartilage mechanobiology and guiding future therapeutic strategies. HighlightsO_LIDeveloped an integrated multiscale model linking tissue, cellular mechanics, and gene regulation in articular cartilage. C_LIO_LICoupled cellular mechanical forces to gene regulatory networks using Hills function approach. C_LIO_LICalibrated Hills function parameters via genetic algorithm using experimental cartilage explant compression data. C_LIO_LIPredicted spatial heterogeneity of chondrocyte activity and cartilage biomarker expression under dynamic compression C_LIO_LIEstablished computational framework can be used for studying cartilage mechanobiology and osteoarthritis therapeutic strategies C_LI

bioengineering↗

bioRxiv2025

From human joints to bioreactor setups: quantifying mechanical stimuli in cartilage physiology and regeneration

ObjectiveBioreactors are widely used to apply mechanical stimuli to osteochondral (OC) explants and cartilage tissue-engineered (TE) constructs, yet their ability to replicate native joint mechanics is not well quantified. This study benchmarks common bioreactor loading protocols against a finite element (FE) model of the human knee during gait, enabling direct comparison to physiologically relevant mechanical parameters. MethodsA validated FE model of the human knee joint simulating the stance phase of gait was used to characterize physiological mechanical conditions, including maximum principal stress, maximum shear strain, pore pressure, and fluid velocity. These outputs were compared with those generated by representative bioreactor setups: dynamic unconfined compression (10-30%) and combined compression (10%) with ball rotation ({+/-}25{degrees}), which were applied to both OC plugs and TE constructs, and hydrostatic pressure (0.5-50 MPa), which was applied only to TE constructs. FE simulations evaluated spatial and magnitude-based agreement with native cartilage mechanics. ResultsIn OC plugs, 10% unconfined compression generated maximum principal stresses ([~]7.5 MPa) and pore pressures ([~] 4 MPa) closely matching native tissue ([~] 4.5 MPa and [~]5 MPa, respectively). In TE constructs, even at 30% unconfined strain, maximum principal stresses and pore pressures remained around 100 times lower than physiological values, while fluid velocities were 10 times higher. Hydrostatic loading of TE constructs at 5 MPa matched native pore pressures ([~]5 MPa) but induced negligible strains. ConclusionsThis study provides a quantitative framework for evaluating how well bioreactor loading regimens replicate physiological joint mechanics. The findings offer actionable guidance to experimentalists for selecting bioreactor conditions based on specific mechanical targets. This can lead to more effective design of in vitro cartilage studies and support translational strategies in cartilage repair and tissue engineering.

bioengineering↗

bioRxiv2024

Building a model of sepsis: data integration unravels pathogenic mechanisms in severe P. aeruginosa infections.

Understanding host-pathogen interactions is crucial for explaining the variability in sepsis outcomes, with Pseudomonas aeruginosa (PA) remaining a significant public health concern. In this work, we explored PA-human host interaction mechanisms through a data integration workflow, focusing on protein-protein and metabolite-protein interactions, along with pathway modulation in affected organs during severe infections. A scoping literature review enabled us to construct a domain-based infection network encompassing pathogenesis concepts, molecular interactions, and host response signatures, providing a wide view of the relevant mechanisms involved in severe bacterial infections. Our analysis yielded a literature-based comprehensive description of PA infection mechanisms and an annotated dataset of 189 PA-human interactions involving 152 proteins/molecules (109 human proteins, 3 human molecules, 34 PA proteins, and 5 PA molecules). This dataset was complemented with gene expression analysis from in vivo PA-infected lung samples. The results indicated a notable overexpression of proinflammatory pathways and PA-mediated modulation of host lung responses. Our comprehensive molecular network of PA infection represents a valuable tool for the understanding of severe bacterial infections and offers potential applications in predicting clinical phenotypes. Through this approach combining omics data, clinical information, and pathogen characteristics, we have provided a foundation for future research in host-pathogen interactions and the mechanistic grounds to build dynamic computational models for clinical phenotype predictions.

systems biology↗

bioRxiv2023

Robotics-driven manufacturing of cartilaginous microtissues for the bio-assembly of skeletal implants

Automated technologies are attractive for enhancing a robust manufacturing of tissue engineered products for clinical translation. In this work, we present an automation strategy using a robotics platform for media changes of cartilaginous microtissues cultured in static microwell platforms. We use an automated image analysis pipeline to extract microtissue displacements and morphological features, which serve as input for statistical factor analysis. To minimize microtissue displacement and suspension leading to uncontrolled fusion, we performed a mixed factorial DoE on liquid handling parameters for large and small microwell platforms. As a result, 144 images, with 51 471 spheroids could be processed automatically. The automated imaging workflow takes 2 minutes per image, and it can be implemented for on-line monitoring of microtissues, thus allowing informed decision making during manufacturing. We found that time in culture is the main factor for microtissue displacements, explaining 10 % of the displacements. Aspiration and dispension speed were not significant at manual speeds or beyond, with an effect size of 1 %. We defined optimal needle placement and depth for automated media changes and we suggest that robotic plate handling could improve the yield and homogeneity in size of microtissue cultures. After three weeks culture, increased expression of COL2A1 confirmed chondrogenic differentiation and RUNX2 shows no osteogenic specification. Histological analysis showed the secretion of cartilaginous extracellular matrix. Furthermore, microtissue-based implants were capable of forming mineralized tissues and bone after four weeks of ectopic implantation in nude mice. We demonstrate the development of an integrated bioprocess for culturing and manipulation of cartilaginous microtissues. We anticipate the progressive substitution of manual operations with automated solutions for manufacturing of microtissue-based living implants.

bioengineering↗

bioRxiv2022

Pharmacologic inhibition of EPHA2 decreases inflammation and pathological endochondral ossification in osteoarthritis

Low-grade inflammation and pathological endochondral ossification are processes underlying the progression of osteoarthritis, the most prevalent joint disease worldwide. In this study, data mining on publicly available transcriptomic datasets revealed EPHA2, a receptor tyrosine kinase associated with cancer, to be associated with both inflammation and endochondral ossification in osteoarthritis. A computational model of cellular signaling networks in chondrocytes predicted that in silico activation of EPHA2 in healthy chondrocytes increases inflammatory mediators and triggers hypertrophic differentiation, the phenotypic switch characteristic of endochondral ossification. We then evaluated the effect of inhibition of EPHA2 in cultured human chondrocytes isolated from individuals with osteoarthritis and demonstrated that inhibition of EPHA2 indeed reduced inflammation and hypertrophy. Additionally, systemic subcutaneous administration of the EPHA2 inhibitor ALW-II-41-27 attenuated joint degeneration in a mouse osteoarthritic model, reducing local inflammation and pathological endochondral ossification. Collectively, we demonstrate that pharmacological inhibition of EPHA2 with ALW-II-41-27 is a promising disease-modifying treatment that paves the way for a novel drug discovery pipeline for osteoarthritis.

pharmacology and toxicology↗

bioRxiv2022

An integrated single-cell atlas of the skeleton from development through adulthood

The recent growth of single-cell transcriptomics has turned single-cell RNA sequencing (scRNA-seq) into a near-routine experiment. Breakthroughs in improving scalability have led to the creation of organism-wide transcriptomic datasets, aiming to comprehensively profile the cell types and states within an organism throughout its lifecycle. To date, however, the skeleton remains a majorly underrepresented organ system in organism-wide atlases. Considering how the skeleton not only serves as the central framework of the vertebrate body but is also the home of the hematopoietic niche and a central player in major metabolic and homeostatic processes, this presents a major deficit in current reference atlas projects. To address this issue, we integrated ten separate scRNA-seq datasets containing limb skeletal cells and their developmental precursors, generating an atlas of 133 332 cells. This limb skeletal cell atlas describes cells across the mesenchymal lineage from the induction of the limb to the adult bone and encompasses 39 different cell states. Furthermore, expanding the repertoire of available time points and cell types within a single dataset allowed for more complete analyses of cell-cell communication or in silico perturbation studies. Taken together, we present a missing piece in the current atlas mapping efforts, which will be of value to researchers in the fields of skeletal biology, hematopoiesis, metabolism and regenerative medicine.

bioinformatics↗

bioRxiv2022

A simple geometrical model of the electrostatic environment around the catalytic center of the ribosome

The central function of the large subunit of the ribosome is to catalyze peptide bond formation. This biochemical reaction is conducted at the peptidyl transferase center (PTC). Experimental evidence shows that the catalytic activity is affected by the electrostatic environment around the peptidyl transferase center. The electrostatic field originates from the particular 3-dimensional space distribution in the phosphate moieties of the 23S (archea and bacteria) or 28S (eukarya) rRNA of the large ribosomal subunit as well as the funnel shape of the cavity at its connection with the ribosome exit tunnel. Here, we set up a minimal geometrical model fitting the available x-ray solved structure of the ribonucleoproteic cavity around the catalytic center of the large subunit of the ribosome. The purpose of this phenomenological model is to estimate quantitatively the electrostatic potential and electric field that are experienced during the peptidyl transfer reaction. At least two reasons motivate the need for developing this quantification. First, we inquire whether the electric field in this particular catalytic environment, made only of nucleic acids, is of the same order of magnitude as the one prevailing in catalytic centers of the proteic enzymes counterparts. Second, the protein synthesis rate is dependent on the nature of the amino acid sequentially incorporated in the nascent chain. The activation energy of the catalytic reaction and its detailed kinetics are expected to be dependent on the mechanical work exerted on the amino acids by the electric field, especially when one of the four charged amino acid residues (R, K, E, D) is newly incorporated in the nascent chain. Physical values of the electric field will provide quantitative knowledge of mechanical work, activation energy and kinetics of the peptide bond formation catalysed by the ribosome.

biophysics↗

bioRxiv2021

An integrated in silico-in vitro approach for identification of therapeutic drug targets for osteoarthritis

Without the availability of disease-modifying drugs, there is an unmet therapeutic need for osteoarthritic patients. During osteoarthritis, the homeostasis of articular chondrocytes is dysregulated and a phenotypical transition called hypertrophy occurs, leading to cartilage degeneration. Targeting this phenotypic transition has emerged as a potential therapeutic strategy. Chondrocyte phenotype maintenance and switch are controlled by an intricate network of intracellular factors, each influenced by a myriad of feedback mechanisms, making it challenging to intuitively predict treatment outcomes. In this study, we developed a regulatory network model using knowledge-based and data-driven modelling technologies. The in silico high-throughput screening of (pairwise) perturbations operated with that network model highlighted conditions impacting the hypertrophic switch. Several combinations were tested in a murine cell line and primary chondrocytes to validate the predicted conditions potential. Our in silico-in vitro strategy opens a new route for developing osteoarthritis targeting therapies by refining the early stages of drug discovery.

systems biology↗

bioRxiv2020

Ribosome Exit Tunnel Electrostatics

The impact of the ribosome exit tunnel electrostatics on the protein elongation rate or on the forces acting upon the nascent polypeptide chain are currently not fully elucidated. In the past, researchers have measured the electrostatic potential inside the ribosome polypeptide exit tunnel at a limited number of spatial points, at least in prokaryotes. Here, we present a basic electrostatic model of the exit tunnel of the ribosome, providing a quantitative physical description of the tunnel interaction with the nascent proteins at all centro-axial points inside the tunnel. We show how the tunnel geometry causes a positive potential difference between the tunnel exit and entry points which impedes positively charged amino acid residues from progressing through the tunnel, affecting the elongation rate in a range of minus 40% to plus 85% when compared to the average elongation rate. The time spent by the ribosome to decode the genetic encrypted message is constrained accordingly. We quantitatively derived, at single residue resolution, the axial forces acting on the nascent peptide from its particular sequence embedded in the tunnel. The model sheds light on how the experimental data point measurements of the potential are linked to the local structural chemistry of the inner wall and the shape and size of the tunnel. The model consistently connects experimental observations coming from different fields in molecular biology, structural and physical chemistry, biomechanics, synthetic and multi-omics biology. Our model should be a valuable tool to gain insight into protein synthesis dynamics, translational control and into the role of the ribosomes mechanochemistry in the co-translational protein folding.

biophysics↗