Biology subjects

Misra, J.

Publications and source records attributed to Misra, J..

6 recordsLinked to original sources

bioRxiv2024

Brain dynamics and spatiotemporal trajectories during threat processing

In the past decades, functional MRI research has investigated task processing in a largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard paradigms with limited dynamics. In the present paper, we applied Switching Linear Dynamical Systems to uncover the dynamics of threat processing during a continuous threat-of-shock paradigm. First, we demonstrated that the SLDS model learned the regularities of the experimental paradigm, such that states and state transitions estimated from fMRI time series data from 85 regions of interest reflected threat proximity and threat approach vs. retreat. After establishing that the model captured key properties of threat-related processing, we characterized the dynamics of the states and their transitions. Importantly, we characterized both endogenous and exogenous contributions to dynamics. The results revealed how threat processing can be viewed in terms of dynamic multivariate patterns whose trajectories are a combination of intrinsic and extrinsic factors that jointly determine how the brain temporally evolves during dynamic threat. Furthermore, we developed a measure of region importance that quantifies the contributions of an individual brain region to system dynamics, which complements the system-level characterization that is obtained with the state-space SLDS formalism. Finally, we investigated the generalizability of the modeling approach. The successful application of the SLDS model trained on one paradigm to a separate experiment illustrates the potential of this approach to capture fMRI dynamics that generalize across related but distinct threat-processing tasks. We propose that viewing threat processing through the lens of dynamical systems offers important avenues to uncover properties of the dynamics of threat that are not unveiled with standard experimental designs and analyses.

neuroscience↗

bioRxiv2023

Allosteric Modulation of YAP/TAZ-TEAD Interaction by Palmitoylation and Small Molecule Inhibitors

The Hippo signaling pathway is a highly conserved signaling network that plays a central role in regulating cellular growth, proliferation, and organ size. This pathway consists of a kinase cascade that integrates various upstream signals to control the activation or inactivation of YAP/TAZ proteins. Phosphorylated YAP/TAZ is sequestered in the cytoplasm; however, when the Hippo pathway is deactivated, they translocate into the nucleus, where they associate with TEAD transcription factors. This partnership is instrumental in regulating the transcription of pro-growth and anti-apoptotic genes. Thus, in many cancers, aberrantly hyperactivated YAP/TAZ promotes oncogenesis by contributing to cancer cell proliferation, metastasis, and therapy resistance. Because YAP and TAZ exert their oncogenic effects by binding with TEAD, it is critical to understand this key interaction to develop cancer therapeutics. Previous research has indicated that TEAD undergoes an auto-palmitoylation at a conserved cysteine, and small molecules that inhibit TEAD palmitoylation disrupt effective YAP/TAZ binding. However, how exactly palmitoylation contributes to YAP/TAZ-TEAD interactions and how the TEAD palmitoylation inhibitors disrupt this interaction remains unknown. Utilizing molecular dynamics simulations, our investigation not only provides a detailed atomistic insight into the YAP/TAZ-TEAD dynamics but also unveils that the inhibitor studied influences YAP and TAZ binding to TEAD in distinct manners. This discovery holds significant implications for the design and deployment of future molecular interventions targeting this interaction.

biophysics↗

bioRxiv2022

GCN2 eIF2 kinase promotes prostate cancer by maintaining amino acid homeostasis

A stress adaptation pathway termed the integrated stress response has been suggested to be active in many cancers including prostate cancer (PCa). Here, we demonstrate that the eIF2 kinase GCN2 is required for sustained growth in androgen-sensitive and castration-resistant models of PCa both in vitro and in vivo, and is active in PCa patient samples. Using RNA-seq transcriptome analysis and a CRISPR-based phenotypic screen, GCN2 was shown to regulate expression of over 60 solute-carrier (SLC) genes, including those involved in amino acid transport and loss of GCN2 function reduces amino acid import and levels. Addition of essential amino acids or expression of 4F2 (SLC3A2) partially restored growth following loss of GCN2, suggesting that GCN2 targeting of SLC transporters is required for amino acid homeostasis needed to sustain tumor growth. A small molecule inhibitor of GCN2 showed robust in vivo efficacy in androgen-sensitive and castration-resistant mouse models of PCa, supporting its therapeutic potential for the treatment of PCa. SignificanceThe ISR kinase GCN2 is critical for maintaining tumor amino acid levels to facilitate growth, suggesting a novel therapeutic strategy for the treatment of prostate cancer by inducing starvation for essential amino acids.

cancer biology↗

bioRxiv2022

Structure-based discovery of a novel small-molecule inhibitor of TEAD palmitoylation

The paralogous oncogenic transcriptional coactivators YAP and TAZ are the distal effectors of the Hippo signaling pathway, which play a critical role in cell proliferation, survival and cell fate specification. The core kinase cascade of the Hippo signaling pathway phosphorylates YAP/TAZ and restrict its activity by promoting cytoplasmic sequestration and degradation. Unphosphorylated YAP/TAZ translocates into the nucleus and associates with the TEA domain (TEAD1-4) transcription factors to regulate their target genes. They are frequently misregulated in most human cancers, where they contribute to multiple aspects of tumorigenesis including growth, metabolism, metastasis and chemo/immunotherapy resistance. Thus, they provide a critical point for therapeutic intervention. However, due to their intrinsically disordered structure, they are challenging to target directly. Since YAP/TAZ exerts oncogenic activity by associating with the TEAD1-4 transcription factors, to regulate target gene expression, YAP activity can be controlled indirectly by regulating TEAD1-4. Interestingly, TEADs undergo autopalmitoylation, which is essential for their stability and function. The palmitic acid occupies a hydrophobic pocket in TEAD1-4, and small-molecule inhibitors that bind to this site can render them unstable, and allosterically inhibit their interaction with YAP/TAZ and chromatin. Here we combined structure-based virtual ligand screening with biochemical and cell biological studies and identified a novel small-molecule that inhibits TEAD palmitoylation and stability. Further, it affects YAP-TEAD interaction and inhibits YAP transcriptional activity and impairs proliferation, colony formation and migration of breast and ovarian cancer cells.

cancer biology↗

bioRxiv2021

Learning brain dynamics for decoding and predicting individual differences

Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To decode brain dynamics, we propose an architecture based on recurrent neural networks to uncover distributed spatiotemporal signatures. We demonstrate the potential of the approach using human fMRI data during movie-watching data and a continuous experimental paradigm. The model was able to learn spatiotemporal patterns that supported 15-way movie-clip classification (~90%) at the level of brain regions, and binary classification of experimental conditions (~60%) at the level of voxels. The model was also able to learn individual differences in measures of fluid intelligence and verbal IQ at levels comparable or better than existing techniques. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational (i.e., classification related) properties of brain dynamics. Finally, saliency maps were employed to characterize brain-region/voxel importance, and uncovered how dynamic but consistent changes in fMRI activation influenced decoding performance. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. We believe our approach provides a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.1 Author summaryBrain signals are inherently dynamic and evolve in both space and time as a function of cognitive or emotional task condition or mental state. To characterize brain dynamics, we employed an architecture based on recurrent neural networks, and applied it to functional magnetic resonance imaging data from humans watching movies or during continuous experimental conditions. The model learned spatiotemporal patterns that allowed it to correctly classify which clip a participant was watching based entirely on data from other participants; the model also learned a binary classification of experimental conditions at the level of voxels. We developed a dimensionality reduction approach that uncovered low-dimensional "trajectories" and captured essential information properties of brain dynamics. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. We believe our approach provides a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.

neuroscience↗

bioRxiv2018

Temporal Multiple Kernel Learning (tMKL) model for predicting resting state FC via characterizing fMRI connectivity dynamics

Over the last decade there has been growing interest in understanding the brain activity in the absence of any task or stimulus captured by the resting-state functional magnetic resonance imaging (rsfMRI). These resting state patterns are not static, but exhibit complex spatio-temporal dynamics. In the recent years substantial effort has been put to characterize different FC configurations while brain states makes transitions over time. The dynamics governing this transitions and their relationship with stationary functional connectivity remains elusive. Over the last years a multitude of methods has been proposed to discover and characterize FC dynamics and one of the most accepted method is sliding window approach. Moreover, as these FC configurations are observed to be cyclically repeating in time there was further motivation to use of a generic clustering scheme to identify latent states of dynamics. We discover the underlying lower-dimensional manifold of the temporal structure which is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to these latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) and finally predicts the grand average functional connectivity (FC) of the unseen subjects by leveraging a state transition Markov model. tMKL thus learns a mapping between the underlying anatomical network and the temporal structure. Training and testing were done using the rs-fMRI data of 46 healthy participants and the results establish the viability of the proposed solution. Parameters of the model are learned via state-specific optimization formulations and yet the model performs at par or better than state-of-the-art models for predicting the grand average FC. Moreover, the model shows sensitivity towards subject-specific anatomy. The proposed model performs significantly better than the established models of predicting resting state functional connectivity based on whole-brain dynamic mean-field model, single diffusion kernel model and another version of multiple kernel learning model. In summary, We provide a novel solution that does not make strong assumption about underlying data and is generally applicable to resting or task data to learn subject specific state transitions and successful characterization of SC-dFC-FC relationship through an unifying framework.

neuroscience↗