Biology subjects

Bera, K.

Publications and source records attributed to Bera, K..

12 recordsLinked to original sources

bioRxiv2026

Charting the Structure and Functional Landscape of the SF3B1 Intrinsically Disordered Region

Despite its essential role in pre-mRNA splicing, the structure and functional significance of the intrinsically disordered N-terminal domain (NTD) of SF3B1, a core component of the SF3b spliceosome complex, remain poorly understood. Here, we combine nuclear magnetic resonance (NMR) spectroscopy, small-angle X-ray scattering (SAXS), biochemistry, bioinformatics approaches, with molecular dynamics simulations to characterize the conformational landscape of the SF3B1-NTD and its phosphorylation-dependent remodelling. Specifically, we focus on the 186-341 region (186-341SF3B1), which contains at least four regulatory phosphorylation sites. We reveal that 186-341SF3B1 is highly dynamic, lacking a stable secondary structure, and that phosphorylation of T211, T235, T313, and T328 induces a shift toward more compact states driven by the formation of intramolecular interactions. Additionally, we unveil that 186-341SF3B1 interacts with RNA primarily via the arginine residues flaking one of the conserved tryptophan-centred motifs present within SF3B1-NTD. These interactions are transient and heterogeneous, reflecting a nonspecific yet sequence-dependent recognition mechanism. Collectively, this study provides a comprehensive description of the conformational landscape and functional features of the SF3B1-NTD, and expands the portfolio of its possible interactors, thus broadening our understanding of how intrinsic disorder, post-translational modifications, and sequence-encoded motifs cooperate to regulate pre-mRNA splicing.

biochemistry↗

bioRxiv2026

HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neuro-cognitive Modeling

Computational models are central to cognitive neuroscience, but their rigorous application to experimental datasets is often constrained to a narrow set of canonical models that afford tractable analytical computations. We introduce the HSSM (Hierarchical Sequential Sampling Model) ecosystem, a Python toolbox that democratizes access to a broad, extensible array of neurocognitive process models through hierarchical Bayesian inference. Naturally leveraging simulation-based inference via likelihood surrogates, HSSM enables fast parameter estimation for models lacking closed-form likelihoods. Built atop PyMC and Bambi, HSSM provides a user-friendly formula syntax for specifying hierarchical mixed-effects regressions on model parameters, incorporating trial-by-trial neural or physiological covariates. The ecosystem allows fast model simulation and training data generation, as well as the neural network training utilities to deploy surrogate likelihood networks via HuggingFace. Contributions are designed to benefit not only the single researcher working on a problem, but organically, the entire research community. Together, the tools in the HSSM ecosystem bridge the interests of computational theorists as well as experimentalists, accelerating the cycle from model development to rigorous empirical testing.

neuroscience↗

bioRxiv2026

Exploring Disordered Regions of Human Spliceosome Proteins

Introns are removed from messenger RNAs by the spliceosome, a protein-RNA machinery enriched with intrinsically disordered regions (IDRs). Lacking stable 3D structures, IDRs can adopt diverse conformations interlacing proteins and RNAs components of the spliceosome and regulating splicing. Here we performed a comprehensive bioinformatics analysis of the human spliceosome proteome revealing that many proteins contain over 40% of disordered residues. Spliceosome IDRs are mainly driven by compositional bias due to an excess of charged and RS-like sequences, with both the nature and extent of this disorder being broadly conserved evolutionarily. Additionally, these IDRs are frequent targets of post-translational modifications, especially phosphorylation, and are hotspots for cancer-associated mutations, implicated in different cancer types. Our results collectively underscore central role of IDRs in splicing regulation and disease. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/698225v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@a6ed0dorg.highwire.dtl.DTLVardef@11514b7org.highwire.dtl.DTLVardef@103387borg.highwire.dtl.DTLVardef@713fde_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies

BackgroundMajor depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Understanding both the similarities and differences in these mechanisms is important to guide the development of new, targeted interventions. Methods255 participants diagnosed with MDD (n=54), BP (n=47), SCZ (n=67) or without any diagnoses (CTRL; n=87) performed an associative learning task. Computational modeling quantified the mechanistic interplay between working memory (WM) and reinforcement learning (RL). The latent RL and WM signatures in the EEG dynamics showed shared and distinct neurocognitive mechanisms underlying learning. ResultsAll clinical groups showed learning impairments at the behavioral level. Model-based EEG analyses linked these impairments to distinct patterns in the dynamic interplay between latent RL and WM mechanisms, contrasting with the typical patterns observed in CTRL. SCZ was characterized by reduced neural markers of WM, weakening the cooperative influence of WM onto RL (reduced WM recruitment), and reduced integration of negative feedback. Conversely, MDD was characterized by reduced reciprocal influence of RL onto WM, reducing the tendency to upregulate WM contribution with reward history (impaired WM management). Finally, BP was characterized by deficits in both WM and RL recruitment, along with higher WM decay. ConclusionsBehavioral learning impairments that appear similar across clinical groups can be linked to distinct neurocognitive mechanisms via integrative neurocomputational modeling. Our approach provides insights into the interplay of underlying learning mechanisms and how they manifest differently across psychopathologies. CitationThis manuscript is a preprint version of the later manuscript accepted for publication in Biological Psychiatry: Global Open Science. The content may differ from the final published version following peer review and editorial revisions. Ging-Jehli, N.R., Rac-Lubashevsky, R., Bera, K., Roberts, A., Loder, A., Boudewyn, M.A., Carter, C.S., Erickson, M., Gold, J., Luck, S.J., Ragland, J.D., Yonelinas, A.P., MacDonald III, A.W., Barch, D.M., & Frank, M.J. (2025). Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies. Preprint at bioRxiv.

pathology↗

bioRxiv2025

Nuclei sense complex tissue shape and direct intestinal stem cell fate

Tissue architecture and function are influenced by mechanical cues. Yet, how cell nuclei sense forces within 3D tissues and dictate differentiation remains unknown as prior studies focused on isolated mesenchymal cells, which fail to fully predict tissue-level mechanical properties. We fill this knowledge gap utilizing live reporters and material-based organoid models. We posit the nucleus as an active mechanosensor of tissue shape, with levels of the nuclear scaffolding protein lamin-A varying across intestinal stem cell differentiation trajectory. Elevated forces on differentiated Paneth cell nuclei, in both organoids and tissue explants, increase lamin-A and nuclear wrinkling. Enhancing nuclear mechanotransduction primes cell differentiation, in otherwise stem promoting conditions, revealing that nuclear mechanics can direct stem cell fate. By engineering spatiotemporally controlled de novo tissue curvature with photo-degradable hydrogels, we direct spatially patterned lamin-A levels across mouse and human organoids of healthy and diseased origin, uncovering conserved nuclear mechanosensing pathway in epithelial tissues.

bioengineering↗

bioRxiv2024

Fully synthetic hydrogels promote robust crypt formation in intestinal organoids

Initial landmark studies in the design of synthetic hydrogels for intestinal organoid culture identified precise matrix requirements for differentiation, namely decompression of matrix-imposed forces and supplementation of laminin. But beyond stating the necessity of laminin, organoid-laminin interactions have gone largely unstudied, as this ubiquitous requirement of exogenous laminin hinders investigation. In this work, we exploit a fast stress relaxing, boronate ester based synthetic hydrogel for the culture of intestinal organoids, and fortuitously discover that unlike all other synthetic hydrogels to date, laminin does not need to be supplemented for crypt formation. This highly defined material provides a unique opportunity to investigate laminin-organoid interactions and how it influences crypt evolution and organoid function. Via fluorescent labeling of non-canonical amino acids, we further show that adaptable boronate ester bonds increase deposition of nascent proteins, including laminin. Collectively, these results advance the understanding of how mechanical and matricellular signaling influence intestinal organoid development.

bioengineering↗

bioRxiv2024

PTEN Regulates Myofibroblast Activation in Valvular Interstitials Cells based on Subcellular Localization

Aortic valve stenosis (AVS) is characterized by altered mechanics of the valve leaflets, which disrupts blood flow through the aorta and can cause left ventricle hypotrophy. These changes in the valve tissue result in activation of resident valvular interstitial cells (VICs) into myofibroblasts, which have increased levels of SMA in their stress fibers. The persistence of VIC myofibroblast activation is a hallmark of AVS. In recent years, the tumor suppressor gene phosphatase and tensin homolog (PTEN) has emerged as an important player in the regulation of fibrosis in various tissues (e.g., lung, skin), which motivated us to investigate PTEN as a potential protective factor against matrix-induced myofibroblast activation in VICs. In aortic valve samples from humans, we found high levels of PTEN in healthy tissue and low levels of PTEN in diseased tissue. Then, using pharmacological inducers to treat VIC cultures, we observed PTEN overexpression prevented stiffness-induced myofibroblast activation, whereas genetic and pharmacological inhibition of PTEN further activated myofibroblasts. We also observed increased nuclear PTEN localization in VICs cultured on stiff matrices, and nuclear PTEN also correlated with smaller nuclei, altered expression of histones and a quiescent fibroblast phenotype. Together, these results suggest that PTEN not only suppresses VIC activation, but functions to promote quiescence, and could serve as a potential pharmacological target for the treatment of AVS.

cell biology↗

bioRxiv2024

Automated Prediction of Fibroblast Phenotypes Using Mathematical Descriptors of Cellular Features

Fibrosis is caused by pathological activation of resident fibroblasts to myofibroblasts that leads to aberrant tissue stiffening and diminished function of affected organs with limited pharmacological interventions. Despite the prevalence of myofibroblasts in fibrotic tissue, existing methods to grade fibroblast phenotypes are typically subjective and qualitative, yet important for screening of new therapeutics. Here, we develop mathematical descriptors of cell morphology and intracellular structures to identify quantitative and interpretable cell features that capture the fibroblast-to-myofibroblast phenotypic transition in immunostained images. We train and validate models on features extracted from over 2,500 primary heart valve interstitial cells (VICs) and test their predictive performance on VICs treated with the small molecule drug 5-azacytidine, which inhibited myofibroblast activation. Collectively, this work introduces an analytical framework that unveils key features associated with distinct fibroblast phenotypes via quantitative image analysis and is broadly applicable for high-throughput screening assays of candidate treatments for fibrotic diseases.

bioengineering↗

bioRxiv2024

Nonlinear Elastic Bottlebrush Polymer Hydrogels Modulate Actomyosin Mediated Protrusion Formation in Mesenchymal Stromal Cells

The nonlinear elasticity of many tissue-specific extracellular matrices is difficult to recapitulate without the use of fibrous architectures, which couple strain-stiffening with stress relaxation. Herein, bottlebrush polymers are synthesized and crosslinked to form poly(ethylene glycol)-based hydrogels and used to study how strain-stiffening behavior affects human mesenchymal stromal cells (hMSCs). By tailoring the bottlebrush polymer length, the critical stress associated with the onset of network stiffening is systematically varied, and a unique protrusion-rich hMSC morphology emerges only at critical stresses within a biologically accessible stress regime. Local cell-matrix interactions are quantified using 3D traction force microscopy and small molecule inhibitors are used to identify cellular machinery that plays a critical role in hMSC mechanosensing of the engineered, strain-stiffening microenvironment. Collectively, this study demonstrates how covalently crosslinked bottlebrush polymer hydrogels can recapitulate strain-stiffening biomechanical cues at biologically relevant stresses and be used to probe how nonlinear elastic matrix properties regulate cellular processes.

bioengineering↗

bioRxiv2023

Fibrillar adhesion dynamics govern the timescales of nuclear mechano-response via the vimentin cytoskeleton

The cell nucleus is continuously exposed to external signals, of both chemical and mechanical nature. To ensure proper cellular response, cells need to regulate not only the transmission of these signals, but also their timing and duration. Such timescale regulation is well described for fluctuating chemical signals, but if and how it applies to mechanical signals reaching the nucleus is still unknown. Here we demonstrate that the formation of fibrillar adhesions locks the nucleus in a mechanically deformed conformation, setting the mechanical response timescale to that of fibrillar adhesion remodelling ([~]1 hour). This process encompasses both mechanical deformation and associated mechanotransduction (such as via YAP), in response to both increased and decreased mechanical stimulation. The underlying mechanism is the anchoring of the vimentin cytoskeleton to fibrillar adhesions and the extracellular matrix through plectin 1f, which maintains nuclear deformation. Our results reveal a mechanism to regulate the timescale of mechanical adaptation, effectively setting a low pass filter to mechanotransduction.

biophysics↗

bioRxiv2023

Cytoskeletal activation of NHE1 regulates cell volume and DNA methylation

Mammalian cells can rapidly respond to osmotic and hydrostatic pressure imbalances during an environmental change, generating large fluxes of water and ions that alter cell volume within minutes. While the role of ion pump and leak in cell volume regulation has been well-established, the potential contribution of the actomyosin cytoskeleton and its interplay with ion transporters is unclear. We discovered a cell volume regulation system that is controlled by cytoskeletal activation of ion transporters. After a hypotonic shock, normal-like cells (NIH-3T3, MCF-10A, and others) display a slow secondary volume increase (SVI) following the immediate regulatory volume decrease. We show that SVI is initiated by hypotonic stress induced Ca2+ influx through stretch activated channel Piezo1, which subsequently triggers actomyosin remodeling. The actomyosin network further activates NHE1 through their synergistic linker ezrin, inducing SVI after the initial volume recovery. We find that SVI is absent in cancer cell lines such as HT1080 and MDA-MB-231, where volume regulation is dominated by intrinsic response of ion transporters. A similar cytoskeletal activation of NHE1 can also be achieved by mechanical stretching. On compliant substrates where cytoskeletal contractility is attenuated, SVI generation is abolished. Moreover, cytoskeletal activation of NHE1 during SVI triggers nuclear deformation, leading to a significant, immediate transcriptomic change in 3T3 cells, a phenomenon that is again absent in HT1080 cells. While hypotonic shock hinders ERK-dependent cell growth, cells deficient in SVI are unresponsive to such inhibitory effects. Overall, our findings reveal the critical role of Ca2+ and actomyosin-mediated mechanosensation in the regulation of ion transport, cell volume, transcriptomics, and cell proliferation.

biophysics↗

bioRxiv2022

Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM

Computational modeling has become a central aspect of research in the cognitive neurosciences. As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision making intended to capture processes jointly giving rise to reaction time distributions and choice data in n-alternative choice paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of sequential sampling models. In addition, recent work has motivated the combination of SSMs with reinforcement learning (RL) models, which had historically been considered in separate literatures. Here we provide a significant addition to the widely used HDDM Python toolbox and include a tutorial for how users can easily fit and assess a (user extensible) wide variety of SSMs, and how they can be combined with RL models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.

bioinformatics↗