Biology subjects

Venkatesh, M.

Publications and source records attributed to Venkatesh, M..

8 recordsLinked to original sources

bioRxiv2026

A Saturated Aminotriester Lipid Enhances Amonafide Activity in Human Colorectal Cancer Cells

The delivery of small-molecule therapeutics across cellular membranes remains a fundamental challenge in drug development, particularly for compounds whose physicochemical properties limit intracellular bioavailability. Inspired by the low toxicity, biodegradability, and synthetic accessibility of triethanolamine-derived lipids, we investigated a series of C18 aminotriester lipids as excipients for the codelivery of anticancer therapeutics. Four structurally related triester lipids bearing saturated, cis-monounsaturated, trans-monounsaturated, and di-unsaturated C18 tails were synthesized through straightforward esterification reactions with triethanolamine. These lipids were evaluated for their ability to enhance the antiproliferative activity of several anticancer agents, including paclitaxel, doxorubicin, amonafide, homoharringtonine, camptothecin, and SN-38. Across multiple drug classes, the aminotriester lipids exhibited minimal intrinsic cytotoxicity and generally produced limited enhancement of drug potency. However, the fully saturated tris-stearate aminotriester lipid selectively enhanced the antiproliferative activity of amonafide in HCT-116 colorectal cancer cells, particularly at lower concentrations. In contrast, unsaturated lipid analogues displayed negligible enhancement effects, and the activity of the tris-stearate lipid was substantially attenuated in A549 lung adenocarcinoma cells, weakly observed in HT-29 colorectal cancer cells, and absent in CT26 murine colorectal cancer cells. Lactate dehydrogenase release assays indicated that these effects were not associated with membrane lysis or nonspecific cytotoxicity. Further, fluorescence microscopy time course experiments suggest improved membrane trafficking of amonafide when codelivered stoichiometrically with fully saturated C18 aminotriester lipids. Collectively, these findings demonstrate that saturated triethanolamine-based aminotriester lipids may function as selective and biocompatible codelivery excipients for certain small-molecule therapeutics. The observed dependence on both lipid saturation state and cellular context suggests that membrane organization and lipid packing may play important roles in mediating these effects. These results establish a foundation for further mechanistic investigations into aminotriester lipid-assisted small-molecule delivery systems.

cancer biology↗

bioRxiv2026

High performance sorting of motor unit action potentials with EMUsort

Understanding how neural signals control muscle activity during behavior is a key challenge in motor neuroscience. To this end, recent advances in intramuscular multielectrode arrays have enabled high-quality multichannel recordings of many motor unit action potentials (MUAPs) in freely moving subjects. However, identifying individual MUAP events within multichannel recordings is a significant challenge for existing spike sorting methods, which are typically optimized for identifying action potentials from neurons in the brain. To overcome this challenge, we developed the Enhanced Motor Unit sorter (EMUsort), an extension of Kilosort4 (KS4) that achieves high-performance MUAP spike sorting. We applied EMUsort to high-resolution intramuscular recordings from rat forelimb during locomotion and monkey forelimb during a reaching task. EMUsort improves upon prior methods by addressing key challenges encountered with MUAP datasets, including: 1) long time delays across electrodes due to propagation along muscle fibers, 2) more complex waveform shapes compared to neuronal action potentials, and 3) a high degree of MUAP overlap due to cumulative motor unit recruitment. We compared EMUsort to existing spike sorting methods quantitatively using simulated datasets that closely emulated the rat and monkey datasets we recorded. EMUsort provided median error rate reductions of 67.5% and 49.9% during periods of high motor unit activation for the rat and monkey datasets, respectively. In sum, EMUsort provides a substantial improvement to MUAP spike sorter accuracy, especially during regions of high MUAP overlap, in an easy-to-use software package.

neuroscience↗

bioRxiv2025

Allosteric Disordering of eIF2B Regulates the Integrated Stress Response

The ternary complex (TC), composed of translation initiation factor eIF2, GTP, and initiator methionyl tRNA, delivers the first amino acid to the ribosome to initiate protein synthesis. The activity of the decameric eukaryotic initiation factor 2B complex (eIF2B) initiates TC assembly by catalyzing GDP to GTP exchange on eIF2, thereby setting the TC levels in the cell. Stress-induced phosphorylation converts eIF2 from the substrate of the GDP/GTP exchange reaction into an inhibitor (eIF2-P) of eIF2B. This conversion reduces the cells TC levels and induces the widespread reprogramming of translation known as the Integrated Stress Response (ISR). Here, we chart an allosteric axis running through eIF2B, revealing the importance of a protrusive -helix in its {beta}-subunit, the latch-helix, that locks onto the -subunit to induce eIF2B activity. eIF2-P binding unhooks the latch-helix, opening eIF2B, which inhibits its GDP/GTP exchange activity. Distinct viral proteins have convergently evolved to bind to eIF2B and stabilize the latch-helix-bound active state. Using these insights, we generated ISR-ACTivating compounds, ISRACTs, that stabilize eIF2B in its inhibited, unlatched state. Our study thus highlights how state-transitions in eIF2B are regulated via long-range allostery.

biochemistry↗

bioRxiv2022

Robust eIF4B levels undermine invasive growth and immune evasion mechanisms in murine triple negative breast cancer models.

Dysregulated protein synthesis is seen in many aggressive cancers, including metastatic breast cancer. However, the specific contributions of certain translation initiation factors to in vivo disease remain undefined. This is particularly true of eIF4B, an RNA-binding protein and cofactor of the RNA helicase eIF4A and associated eIF4F cap-binding complex. While eIF4A, eIF4G, and eIF4E are well-known to contribute to the progression of many cancer types including metastatic breast cancers, the role played by eIF4B in breast cancer remains relatively unclear. We therefore explored how naturally divergent and experimentally modulated eIF4B levels impact tumor growth and progression in well-characterized murine triple negative breast cancer (TNBC) models. Surprisingly, we found that higher eIF4B levels in mouse and human breast cancers were associated with less aggressive phenotypes. shRNA-mediated eIF4B knockdown in TNBC lines failed to markedly alter proliferation and global translation in the cells in vitro and only modestly hindered their growth as primary mammary tumors growth in mice. However, eIF4B knockdown significantly enhanced invasive growth in vitro and exacerbated both tumor burden and mortality relative to nontargeting shRNA controls in a model of metastatic disease. Analysis of eIF4B levels and breast cancer patient survival reinforced a link to better outcomes. Interestingly, low eIF4B expression was also associated with more formidable immune evasion in vitro and in vivo, implicating a novel immunomodulatory role for this factor in the malignant setting that suggests a mode of action beyond its historical role as a co-activator of eIF4A/F. Significance StatementMetastasis is the leading cause of cancer-related mortality. Despite many advances in our understanding of this complex process and the molecular and cellular events involved, mechanisms that allow secondary tumors to arise and persist remain incompletely understood. Uncharacterized metastatic determinants active at the level of translational control may be exploitable as novel therapy targets or biomarkers predicting a tumors potential for spread and recurrence. Here we describe previously unrecognized consequences of dysregulated eIF4B levels in murine breast cancer that shed light on how this translation initiation factor contributes to disease outcomes. Our findings suggest that eIF4B levels direct metastatic risk and immune evasion, and further study should establish its value in personalized treatment decisions and development of future therapies.

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↗

bioRxiv2020

Capturing brain dynamics: latent spatiotemporal patterns predict stimuli and individual differences

Insights from functional Magnetic Resonance Imaging (fMRI), and more recently from recordings of large numbers of neurons through calcium imaging, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of neuronal populations. To capture and characterize spatiotemporal properties of brain events, we propose an architecture based on long short-term memory (LSTM) networks to uncover distributed spatiotemporal signatures during dynamic experimental conditions1. We demonstrate the potential of the approach using naturalistic movie-watching fMRI data. We show that movie clips result in complex but distinct spatiotemporal patterns in brain data that can be classified using LSTMs ({approx} 90% for 15-way classification), demonstrating that learned representations generalized to unseen participants. LSTMs were also superior to existing methods in predicting behavior and personality traits of individuals. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational properties of brain dynamics. Finally, we employed saliency maps to characterize spatiotemporally-varying brain-region importance. The spatiotemporal saliency maps revealed dynamic but consistent changes in fMRI activation data. We believe our approach provides a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.

neuroscience↗

bioRxiv2019

Comparing Functional Connectivity Matrices: A Geometry-Aware Approach applied to Participant Identification

Understanding the correlation structure associated with multiple brain measurements informs about potential \"functional groupings\" and network organization. The correlation structure can be conveniently captured in a matrix format that summarizes the relationships among a set of brain measurements involving two regions, for example. Such functional connectivity matrix is an important component of many types of investigation focusing on network-level properties of the brain, including clustering brain states, characterizing dynamic functional states, performing participant identification (so-called \"fingerprinting\") understanding how tasks reconfigure brain networks, and inter-subject correlation analysis. In these investigations, some notion of proximity or similarity of functional connectivity matrices is employed, such as their Euclidean distance or Pearson correlation (by correlating the matrix entries). Here, we propose the use of a geodesic distance metric that reflects the underlying non-Euclidean geometry of functional correlation matrices. The approach is evaluated in the context of participant identification (fingerprinting): given a participants functional connectivity matrix based on resting-state or task data, how effectively can the participant be identified? Using geodesic distance, identification accuracy was over 95% on resting-state data, and exceeded the Pearson correlation approach by 20%. For whole-cortex regions, accuracy improved on a range of tasks by between 2% and as much as 20%. We also investigated identification using pairs of subnetworks (say, dorsal attention plus default mode), and particular combinations improved accuracy over whole-cortex participant identification by over 10%. The geodesic distance also outperformed Pearson correlation when the former employed a fourth of the data as the latter. Finally, we suggest that low-dimensional distance visualizations based on the geodesic approach help uncover the geometry of task functional connectivity in relation to that during resting-state. We propose that the use of the geodesic distance is an effective way to compare the correlation structure of the brain across a broad range of studies.

neuroscience↗

bioRxiv2018

Brain dynamics and temporal trajectories during task and naturalistic processing

Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. Yet, a fuller understanding of the mechanisms that support mental functions necessitates the characterization of dynamic properties. Here, we describe an approach employing a class of recurrent neural networks called reservoir computing, and show the feasibility and potential of using it for the analysis of temporal properties of brain data. We show that reservoirs can be used effectively both for condition classification and for characterizing lower-dimensional \"trajectories\" of temporal data. Classification accuracy was approximately 90% for short clips of \"social interactions\" and around 70% for clips extracted from movie segments. Data representations with 12 or fewer dimensions (from an original space with over 300) attained classification accuracy within 5% of the full data. We hypothesize that such low-dimensional trajectories may provide \"signatures\" that can be associated with tasks and/or mental states. The approach was applied across participants (that is, training in one set of participants, and testing in a separate group), showing that representations generalized well to unseen participants. Taken together, we believe the present approach provides a promising framework to characterize dynamic fMRI information during both tasks and naturalistic conditions.

neuroscience↗