Biology subjects

Roche, R.

Publications and source records attributed to Roche, R..

10 recordsLinked to original sources

bioRxiv2026

Aquaporin-4 Regulates Edema, Sustains Astrocyte Differentiation, and Constrains Immunosuppressive Remodeling in Glioblastoma

Vasogenic cerebral edema is a severe complication that affects most patients with glioblastoma (GBM). It leads to an increase in intracranial pressure, neurological deficits, and increased mortality rates. While there is considerable research on edema formation, the molecular and cellular mechanisms of edema resolution in GBM, particularly in response to dexamethasone or other anti-edema therapies, remain largely undefined. Our findings demonstrate that the water channel aquaporin-4 (AQP4), a key regulator of brain water homeostasis, is upregulated in human GBM compared to the normal brain. AQP4 expression correlates with peritumoral edema in GBM patients, with the classical (CL) GBM subtype exhibiting the highest AQP4 levels but the lowest edema content relative to the proneural (PN) and mesenchymal (MES) subtypes. Using the RCAS/tv-a system, we generated de novo GBM with defined driver mutations in Aqp4-proficient and -deficient mice. Loss of Aqp4 shortened survival and exacerbated edema, accompanied by reduced astrocytic coverage within the tumor core, but not at the tumor margin. Single-cell RNA-sequencing and spatial transcriptomics revealed that Aqp4 loss reprograms astrocytes toward an immature, progenitor-like state and alters their crosstalk with other cell types in the GBM microenvironment. Additionally, loss of Aqp4 skewed myeloid populations toward immunosuppressive phenotypes while reducing lymphoid infiltration. Treatment with anti-VEGFA antibody, an established anti-edema therapy, increased expression of Aqp4 and reduced vessel leakage, underscoring the role of AQP4 in the therapeutic modulation of brain water balance. Overall, this study highlights the important role of AQP4 in resolving edema and its critical function in maintaining astrocyte identity and shaping the immune landscape of GBM.

cancer biology↗

bioRxiv2025

Custom-Built Electrodes Perform Comparably to a Discontinued Commercial Electrode for Neuromuscular Electrical Stimulation in Mice

ObjectiveA commercial electrode used for transcutaneous neuromuscular electrical stimulation (NMES) in mice is no longer available. In response, we developed two low- cost, customizable alternatives--a 3D-Printed Electrode and a Pen Electrode assembled from off-the-shelf jumper wire components--and evaluated their performance relative to the discontinued commercial standard. MethodsWe conducted in vivo NMES of the left hindlimb ankle dorsiflexors in C57BL/6J mice using three electrode types: a Simple Electrode (previously available as BS4 50-6824, Harvard Apparatus), our novel 3D-Printed Electrode, and our novel Pen Electrode. Contractile torque was recorded during twitch and tetanic contractions, and electrode performance was evaluated based on peak torque values. A repeated measures ANOVA was used to compare torque generated by each animal for the three different types of electrodes. ResultsBoth custom electrodes generated twitch and tetanic responses comparable to those of the Commercial electrode. No statistically significant differences were observed in peak torque values in either twitch (p = 0.181) or tetany (p = 0.438). ConclusionCustom-built 3D-printed and Pen-style electrodes offer low-cost, accessible alternatives to discontinued commercial devices for murine NMES studies. These tools can facilitate continued use of electrical stimulation protocols in preclinical neuromuscular research.

physiology↗

bioRxiv2024

Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models

Ground-breaking progress has been made in structure prediction of biomolecular assemblies, including the recent breakthrough of AlphaFold 3. However, it remains challenging for AlphaFold 3 and other state-of-the-art deep learning-based methods to accurately predict protein-RNA complex structures, in part due to the limited availability of evolutionary and structural information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a new deep-learning framework for protein-RNA complex structure prediction with only single-sequence input. Using a novel geometric attention-enabled pairing of biological language models of protein and RNA, a previously unexplored avenue, ProRNA3D-single enables the prediction of interatomic protein-RNA interaction maps, which are then transformed into multi-scale geometric restraints for modeling 3D structures of protein-RNA complexes via geometry optimization. Benchmark tests show that ProRNA3D-single convincingly outperforms current state-of-the-art methods including AlphaFold 3, particularly when evolutionary information is limited; and exhibits remarkable robustness and performance resilience by attaining better accuracy with only single-sequence input than what most methods can achieve even with explicit evolutionary information. Freely available at https://github.com/Bhattacharya-Lab/ProRNA3D-single, ProRNA3D-single should be broadly useful for modeling 3D structures of protein-RNA complexes at scale, regardless of the availability of evolutionary information.

bioinformatics↗

bioRxiv2024

Extruded alginate tubes with myogenic potential

BACKGROUNDThere are currently no proven methods to reverse muscle loss in humans, which is caused by trauma (e.g., volumetric muscle loss, VML), genetic neuromuscular diseases (e.g., muscular dystrophies, MDs), and accelerated senescence (e.g., sarcopenia). Since muscle tissue is capable of regeneration through muscle satellite cells (MuSCs), the implantation of autologous (or other) donor MuSCs and MuSC-derived myoblasts into host muscles can promote donor-cell-derived myogenesis. Direct injection or implantation of MuSCs or MuSC-derived myoblasts into host muscles only promotes minimal donor-cell-derived myogenesis, whereas implantation of MuSCs/myoblasts along with associated muscle tissue (muscle fibers, extracellular matrix, neurovascular pathways, etc.) gives better results. METHODSWe aim to leverage the benefits of constraining donor myogenic cells within a template that resembles muscle tissue. In this paper, we present a workflow for basic and translational studies aimed at promoting donor-cell-derived myogenesis to increase functional muscle mass in mice. Our workflow involves preparing a slurry of 10% sodium alginate mixed with myogenic cells in cell culture media, extruding the cell-containing slurry into 10% calcium lactate to form tubes, and implanting the cellularized alginate tubes into host muscle. RESULTSOur data suggest that, the extruded alginate tubes can tolerate a peak stress of 1892 {+/-} 527 mN, that the elastic range is at ~75-125% strain beyond initial length, and that the Youngs modulus (stiffness) is 14.17 {+/-} 1.68 %/mm2. Importantly, these mechanical properties render the alginate tubes suitable for a published technique known as minimally-invasive muscle embedding (MIME) that was developed by us to implant myogenic material into host muscle. MIME involves threading donor myogenic tissue into a needle track created within a host muscle. Cellularized alginate tubes implanted into the tibialis anterior muscle of previously euthanized mice had numerous hematoxylin-stained structures similar to nuclear staining, supporting the idea that our alginate tubes can support cell seeding. Alginate tubes that were seeded with MuSCs, incubated in MuSC/myoblast growth (i.e., proliferation) media for two days, incubated in myotube differentiation media for six days, and then minced and reseeded in new dishes, were able to promote in vitro myoblast outgrowth over several days. DISCUSSIONThis pilot study is limited in its translational scope because it was performed in vitro and with previously euthanized mice. Additional studies are needed to confirm that cellularized alginate tubes can promote the de novo development of donor-cell-derived muscle fibers, which can contribute to contractile force production. CONCLUSIONAlginate tubes with MuSC/myoblasts can be generated by a simple extrusion method. The alginate tubes have sufficient mechanical strength to tolerate insertion into a host muscle, in a minimally-invasive manner, through a needle track. The cellularized alginate tubes demonstrate myogenic potential since they are capable of being maintained in culture conditions for several days, after which they can still facilitate myoblast outgrowth in a dish.

cell biology↗

bioRxiv2023

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks

Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.

bioinformatics↗

bioRxiv2023

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries

Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of the individual interactions between the interfacial residues using a multihead graph attention network and then probabilistically combines the estimated quality of the interfacial residues for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study reveals that the performance gains are connected to the effectiveness of the multihead graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. An open-source software implementation of PIQLE, licensed under the GNU General Public License v3, is freely available at https://github.com/Bhattacharya-Lab/PIQLE.

bioinformatics↗

bioRxiv2022

E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction

Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. Recent studies have demonstrated benefits of employing graph convolution for PPI site prediction, but ignore symmetries naturally occurring in 3-dimensional space and act only on experimental coordinates. Here we present EquiPPIS, an E(3) equivariant graph neural network approach for PPI site prediction. EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to invariant convolutions. EquiPPIS substantially outperforms state-of-the-art approaches based on the same experimental input, and exhibits remarkable robustness by attaining better accuracy with predicted structural models from AlphaFold2 than what existing methods can achieve even with experimental structures. Freely available at https://github.com/Bhattacharya-Lab/EquiPPIS, EquiPPIS enables accurate PPI site prediction at scale.

bioinformatics↗

bioRxiv2022

Accurate self-assessment in 2D: protein contact map quality estimation by deep evolutionary reconciliation

Protein contact maps have proven to be a valuable tool in the deep learning revolution of protein structure prediction, ushering in the recent breakthrough by AlphaFold2. However, self-assessment of the quality of predicted structures are typically performed at the granularity of 3D coordinates as opposed to directly exploiting the rotation- and translation-invariant 2D contact maps. Here we present rrQNet, a deep learning method for self-assessment in 2D by contact map quality estimation. Our approach is based on the intuition that for a contact map to be of high quality, the residue pairs predicted to be in contact should be mutually consistent with the evolutionary context of the protein. The deep neural network architecture of rrQNet implements this intuition by cascading two deep modules--one encoding the evolutionary context and the other performing evolutionary reconciliation. The penultimate stage of rrQNet estimates the quality scores at the interacting residue-pair level, which are then aggregated for estimating the quality of a contact map. This design choice offers versatility at varied resolutions from individual residue pairs to full-fledged contact maps. Trained on multiple complementary sources of contact predictors, rrQNet facilitates generalizability across various contact maps. By rigorously testing using publicly available datasets and comparing against several in-house baseline approaches, we show that rrQNet accurately reproduces the true quality score of a predicted contact map and successfully distinguishes between accurate and inaccurate contact maps predicted by a wide variety of contact predictors. The open-source rrQNet software package is freely available at https://github.com/Bhattacharya-Lab/rrQNet.

bioinformatics↗

bioRxiv2020

Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins

Crystallography and NMR system (CNS) is currently the de facto standard for fragment-free ab initio protein folding from inter-residue distance or contact maps. Despite its widespread use in protein structure prediction, CNS is a decade-old macromolecular structure determination system that was originally developed for solving macromolecular geometry from experimental restraints as opposed to predictive modeling driven by interaction map data. As such, the adaptation of the CNS experimental structure determination protocol for ab initio protein folding is intrinsically anomalous that may undermine the folding accuracy of computational protein structure prediction. In this paper, we propose a new CNS-free hierarchical structure modeling method called DConStruct for folding both soluble and membrane proteins driven by distance and contact information. Rigorous experimental validation shows that DConStruct attains much better reconstruction accuracy than CNS when tested with the same input contact map at varying contact thresholds. The hierarchical modeling with iterative self-correction employed in DConStruct scales at a much higher degree of folding accuracy than CNS with the increase in contact thresholds, ultimately approaching near-optimal reconstruction accuracy at higher-thresholded contact maps. The folding accuracy of DConStruct can be further improved by exploiting distance-based hybrid interaction maps at tri-level thresholding, as demonstrated by the better performance of our method in folding difficult free modeling targets from the 12th and 13th rounds of the Critical Assessment of techniques for protein Structure Prediction (CASP) experiments compared to several popular CNS- and fragment-based approaches, some of which even using much finer-grained distance maps than ours. Additional large-scale benchmarking shows that DConStruct can significantly improve the folding accuracy of membrane proteins compared to a CNS-based approach. These results collectively demonstrate the feasibility of greatly improving the accuracy of ab initio protein folding by optimally exploiting the information encoded in inter-residue interaction maps beyond what is possible by CNS.Author summary Predicting the folded and functional 3-dimensional structure of a protein molecule from its amino acid sequence is of central importance to structural biology. Recently, promising advances have been made in ab initio protein folding due to the reasonably accurate estimation of inter-residue interaction maps at increasingly higher resolutions that range from binary contacts to finer-grained distances. Despite the progress in predicting the interaction maps, approaches for turning the residue-residue interactions projected in these maps into their precise spatial positioning heavily rely on a decade-old experimental structure determination protocol that is not suitable for predictive modeling. This paper presents a new hierarchical structure modeling method, DConStruct, which can better exploit the information encoded in the interaction maps at multiple granularities, from binary contact maps to distance-based hybrid maps at tri-level thresholding, for improved ab initio folding. Multiple large-scale benchmarking experiments show that our proposed method can substantially improve the folding accuracy for both soluble and membrane proteins compared to state-of-the-art approaches. DConStruct is licensed under the GNU General Public License v3 and freely available at https://github.com/Bhattacharya-Lab/DConStruct.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2020

DisCovER: distance-based covariational threading for weakly homologous proteins

MotivationThreading a query protein sequence onto a library of weakly homologous structural templates remains challenging, even when sequence-based predicted contact or distance information is used. Contact- or distance-assisted threading methods utilize only the spatial proximity of the interacting residue pairs for template selection and alignment, ignoring their orientation. Moreover, existing threading methods fail to consider the neighborhood effect induced by the query-template alignment. ResultsWe present a new distance- and orientation-based covariational threading method called DisCovER by effectively integrating information from inter-residue distance and orientation along with the topological network neighborhood of a query-template alignment. Our method first selects a subset of templates using standard profile-based threading coupled with topological network similarity terms to account for the neighborhood effect and subsequently performs distance- and orientation-based query-template alignment using an iterative double dynamic programming framework. Multiple large-scale benchmarking results on query proteins classified as hard targets from the Continuous Automated Model Evaluation (CAMEO) experiment and from the current literature show that our method outperforms several existing state-of-the-art threading approaches; and that the integration of the neighborhood effect with the inter-residue distance and orientation information synergistically contributes to the improved performance of DisCovER. Availabilityhttps://github.com/Bhattacharya-Lab/DisCovER Contactbhattacharyad@auburn.edu

bioinformatics↗