Biology subjects

Mehdi, S.

Publications and source records attributed to Mehdi, S..

5 recordsLinked to original sources

bioRxiv2026

Sequential intra-articular HCAd-NFκB-IL-1Ra delivery improves therapeutic efficacy in post-traumatic osteoarthritis

Osteoarthritis is the most common joint disease for which disease-modifying therapies remain unavailable. Intra-articular gene delivery of interleukin-1 receptor antagonist (IL-1Ra) using high-capacity adenovirus (HCAd) has shown therapeutic promise; however, the duration of therapeutic benefit and the feasibility of repeat dosing under anti-adenoviral immunity remain unresolved. Using the murine anterior cruciate ligament transection model of osteoarthritis, we show that a single intraarticular injection of HCAd5-NF{kappa}B-IL-1Ra provides structural preservation and functional improvement in early-stage osteoarthritis but fails to sustain cartilage protection as disease progresses. However, HCAd5 transduction following repeated treatment is limited due to pre-existing immunity against this serotype. Notably, exchanging serotypes for repeated treatments effectively restores vector transduction and transgene expression in both healthy and osteoarthritic joints. Leveraging this strategy, we demonstrate that sequential intra-articular HCAd-NF{kappa}B-IL-1Ra administration does not further improve pain or motor function compared to the initial treatment, but preserves cartilage as assessed by histopathology and phase-contrast CT, irrespective of serotype. These findings establish HCAd serotype switching as a feasible approach to overcome immune barriers to repeat intra-articular gene therapy. Importantly, sequential HCAd-NF{kappa}B-IL-1Ra administration enhances therapeutic durability in post-traumatic osteoarthritis, providing a translational framework for repeat intra-articular gene delivery strategies aimed at long-term disease modification in osteoarthritis.

molecular biology↗

bioRxiv2026

Ab initio prediction of RNA structure ensembles with RNAnneal

RNA utilizes three-dimensional structure in addition to sequence to carry out diverse functions in gene expression and disease. Much like well-folded proteins, RNAs adopt specific three-dimensional structures to carry out their function. Yet comparatively few RNA structures have been solved by atomic resolution structural techniques, in part because unlike structured proteins, RNAs fold into heterogeneous ensembles of interconverting structures that pose a challenge for high-resolution structure probing methods. In this work, we introduce RNAnneal as a method for RNA structural ensemble prediction that seamlessly integrates generative deep learning with statistical physics and molecular dynamics modeling. Given the primary sequence, RNAnneal uses ab inito (i.e., first principles) modeling to sample an ensemble of 3D structures, which, in turn, are used to train an ensemble of unsupervised deep learning models. The RNAnneal score, representing the consensus of the deep learning models, is then used to evaluate the 3D structures. We evaluated RNAnneal structures against 16 experimentally-resolved conformations (ERCs) of riboswitch RNAs and found that pseudoknot-free (PK-free) ERCs were well-reproduced by RNAnneal, with clear avenues for improving performance even on PK-comprising structures. Furthermore, we found that the RNAnneal score outperforms the Rosetta score and a state-of-the-art RNA forcefield on the task of classifying ERCs from decoys. We then introduce the interaction entropy as a measure of conformational heterogeneity within an ensemble and use it to assess our predictions. RNAnneal thus provides a generalizable framework for predicting RNA structural ensembles that will accelerate RNA-targeted drug discovery and the design of functional RNA molecules.

biophysics↗

bioRxiv2023

Learning high-dimensional reaction coordinates of fast-folding proteins using State Predictive Information Bottleneck and Bias Exchange Metadynamics

Biological events occurring on long timescales, such as protein folding, remain hard to capture with conventional molecular dynamics (MD) simulation. To overcome these limitations, enhanced sampling techniques can be used to sample regions of the free energy landscape separated by high energy barriers, thereby allowing to observe these rare events. However, many of these techniques require a priori knowledge of the appropriate reaction coordinates (RCs) that describe the process of interest. In recent years, Artificial Intelligence (AI) models have emerged as promising approaches to accelerate rare event sampling. However, integration of these AI methods with MD for automated learning of improved RCs is not trivial, particularly when working with undersampled trajectories and highly complex systems. In this study, we employed the State Predictive Information Bottleneck (SPIB) neural network, coupled with bias exchange metadynamics simulations (BE-metaD), to investigate the unfolding process of two proteins, chignolin and villin. By utilizing the high-dimensional RCs learned from SPIB even with poor training data, BE-metaD simulations dramatically accelerate the sampling of the unfolding process for both proteins. In addition, we compare different RCs and find that the careful selection of RCs is crucial to substantially speed up the sampling of rare events. Thus, this approach, leveraging the power of AI and enhanced sampling techniques, holds great promise for advancing our understanding of complex biological processes occurring on long timescales. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/550401v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@18a9296org.highwire.dtl.DTLVardef@9dc2f3org.highwire.dtl.DTLVardef@16a0cf8org.highwire.dtl.DTLVardef@1799db1_HPS_FORMAT_FIGEXP M_FIG TABLE OF CONTENT GRAPHIC C_FIG

biophysics↗

bioRxiv2023

A peptide that regulates metalation of the Arabidopsis ethylene receptor.

Ethylene signalling represents one of the classic hormonal pathways in plants, with diverse roles in development and stress responses. The dimeric ethylene receptor localizes to the endoplasmic reticulum (ER) and contains Cu(I) ions essential for ethylene binding and signal transduction. We previously discovered that mutants in the Arabidopsis gene POLARIS (PLS), encoding a 36 amino acid peptide, exhibit enhanced ethylene signalling responses, suggestive of reduced receptor activity, but the role and activity of the peptide in this signalling cascade has not been defined. Here we report PLS binds copper as a 1:2 thiol-dependent Cu(I):PLS2 complex, with an affinity of 3.79 ({+/-}1.5) x1019 M-2, via two cysteine residues also found in the related species Camelina sativa. These residues are also essential for biological function. This affinity precludes PLS as a cytosolic Cu chaperone. We demonstrate that PLS localizes to endomembranes and interacts with the transmembrane domain of receptor protein ETR1. PLS-ETR1 binding is increased in the presence of copper, and this interaction provides a Cu-dependent mechanism for mediating a repression of ethylene responses. PLS transcription is up-regulated by auxin and down-regulated by ethylene, and so PLS-ETR1 interactions also provide a mechanism to modulate ethylene responses in high auxin tissues.

plant biology↗

bioRxiv2020

MSK phosphorylation of H3S28 is required for immediate early gene induction and cardiac hypertrophy

Heart failure is a leading cause of death that develops subsequent to deleterious hypertrophic cardiac remodelling. MAPK pathways play a key role in coordinating the induction of gene expression during hypertrophy. Induction of the immediate early gene (IEG) response is a necessary and early event in this process. How MAPK and IEG expression are coupled during cardiac hypertrophy is not yet resolved. Here, in vitro, in rodent models and in human samples, we demonstrate that MAPK-stimulated IEG induction depends on the Mitogen and Stress activated protein Kinase (MSK) and its phosphorylation of histone H3 at serine 28 (pH3S28). pH3S28 in IEG promoters in turn recruits Brg1, a BAF60 ATP-dependent chromatin remodelling complex component, initiating gene expression. Without MSK activity and IEG induction, the hypertrophic response is suppressed. These studies provide new mechanistic insights and highlight the role of signalling to the epigenome in gene expression regulation during cardiac hypertrophy. Brief summary one sentenceMSK1/2 phosphorylation of Histone 3 Serine 28 couples MAPK signalling with chromatin remodelling and immediate early gene expression to induce pro-hypertrophic cardiac transcriptional responses.

molecular biology↗