Biology subjects

Gopalakrishnan, V.

Publications and source records attributed to Gopalakrishnan, V..

11 recordsLinked to original sources

bioRxiv2026

REST-AKT-SOX2 axis controls neuronal lineage state and Midkine-mediated communication in SHH-medulloblastoma.

Stem/progenitor-like cells are known drivers of tumor progression, chemoresistance, and relapse in Sonic-Hedgehog medulloblastoma (SHH-MB), yet the regulatory mechanisms that sustain these resilient cellular states remain incompletely defined. Here, we identify the RE1-silencing transcription factor (REST) as a key transcriptional regulator that preserves the progenitor compartment in SHH-MB through stabilization of the stemness factor SOX2. Mechanistically, REST activates AKT signaling, which in turn enhances SOX2 protein stability, revealing a REST-AKT-SOX2 axis that supports stem/progenitor identity and ongoing tumor maintenance. Beyond maintaining intrinsic stem-like programs, REST also orchestrates the extrinsic communication network of SHH-MB tumors. Single cell transcriptomic profiling and ligand-receptor interaction mapping highlight Midkine (MDK)-mediated signaling as one of the most upregulated intercellular communication routes in MB. We demonstrate that REST drives this signaling cascade through its control of SOX2. Perturbation of REST or SOX2 results in reduced MDK and its receptor, SDC2 expression, and chromatin immunoprecipitation assays show that SOX2 directly binds and regulates MDK/SDC2 expression, establishing and reinforcing a REST/SOX2 centered transcriptional mechanism that coordinates both progenitor maintenance and cell-cell communication in the malignant compartment. Together, these findings position REST as an integrator of intrinsic progenitor cell programs and extrinsic MDK-mediated signaling in SHH-MB. By linking stemness, communication and potential for therapeutic resistance, the REST-AKT-SOX2-MDK signaling axis emerges as a targetable vulnerability to suppress stem/progenitor driven tumor program in REST-driven SHH-MBs.

cancer biology↗

bioRxiv2025

REST elevation-dependent chromatin remodeling and alternative Grk6 transcript synthesis hyperactivates Cxcr4-Sdf1 signaling in cerebellar granule cell progenitors

RE1 Silencing Transcription Factor (REST) is a repressor of transcriptional initiation of genes involved in neurogenesis. Here, we show that conditional REST elevation in cerebellar granule cell progenitors (CGNPs) of RESTTG mice perturbed foliation, increased cell migration, and sustained C-X-C motif receptor 4 (Cxcr4) signaling, a pathway key to postnatal CGNP migration. Mechanistic studies uncovered a novel role for REST in controlling transcript diversity and exon skipping in CGNPs. Alternative transcript expression was detected in known Cxcr4 signaling regulator, G-protein-coupled receptor kinase-6 (Grk6). Further analysis of Grk6s isoform expression revealed an upregulation of a transcript lacking exon 10a (Grk6-207) in RESTTG CGNPs. Grk6-207 expression in wildtype CGNPs hyperactivated Cxcr4 signaling and increased chemotaxis. Structural modeling of Grk6-207 predicted changes in active site conformation and interactions with Cxcr4 and {beta}-Arrestin-1, supporting impairment of Cxcr4 signaling desensitization. Interestingly, REST elevation promoted increased chromatin accessibility at the exon10a-10b junction and exon 10a exclusion. Integrated multiomic analyses identified the enhancer of zeste (Ezh2) as a potential mediator of alternative transcript generation which demonstrated increased occupancy at the exon10a-10b locus in RESTTG CGNPs. Pharmacological inhibition of Ezh2 downregulated Grk6-207, confirming a role for Ezh2 in Grk6 exon10a exclusion and the increased migration in RESTTG CGNPs. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/682654v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@166db32org.highwire.dtl.DTLVardef@1c0c5e0org.highwire.dtl.DTLVardef@ff2d6borg.highwire.dtl.DTLVardef@fed170_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Macroscopic Analyses of RNA-Seq Data to Reveal Chromatin Modifications in Aging and Disease

Regulation of gene expression is fundamental for proper cellular function, and is constrained by the local chromatin environment of each gene, which varies spatially along the chromosome and is shaped by epigenetic modifications. Epigenetic modifications induce changes in the local chromatin structure, which can influence gene expression, by affecting the accessibility of DNA to transcription factors. Such changes are particularly relevant in aging and genetic disorders like Hutchinson-Gilford Progeria Syndrome (HGPS) and Werner Syndrome (WRN), where altered chromatin structure contributes to disease pathology. In this study, we analyze RNA-seq data using macroscopic metrics designed to be explicitly sensitive to chromatin modifications. The first metric, intra-chromosomal gene correlation length, measures spatial correlations in gene expressions along the chromosome. The second metric employs an energy landscape model based on the Arrhenius equation to estimate the energetic barriers associated with chromatin state transitions. We apply these metrics to various aging-related datasets, demonstrating their sensitivity to changes in the chromatin structure and the interpretability of the resulting outputs. The intra-chromosomal gene correlation length is particularly effective in quantifying changes in RNA-seq profiles due to increased chromatin accessibility during aging (and conversely, reduced accessibility due to treatment). This metric not only accurately distinguishes cell states, but also provides insight into the direction of aging. For instance, our observations on the effects of anti-sense oligonucleotide (ASO) treatment align with the existing literature, demonstrating that ASO partially restores chromatin structure in diseased cells. They additionally quantify the more pronounced effects in HGPS compared to WRN. The barrier energy landscape further extends this capability by offering a framework for understanding the progressive degradation of the regulatory mechanisms. Together, these metrics provide robust screening tools that enhance our ability to exploit common measurements such as RNA-seq to derive new phenotypes such as chromatin dynamics on aging and disease, offering an alternative perspective that complements traditional analytical techniques and enriches our understanding of cellular states.

bioinformatics↗

bioRxiv2025

Identification of Raptor and GLI1 as USP37 substrates highlight its context-specific function in medulloblastoma cells.

USP37 gene encodes a deubiquitylase (DUB), which catalyzes the proteolytic removal of ubiquitin moieties from proteins to modulate their stability, cellular localization or activity. Its expression is downregulated in a subgroup of medulloblastomas driven by constitutive activation of sonic hedgehog (SHH) signaling. Patients with SHH-driven medulloblastomas with elevated expression of the RE1 silencing transcription factor (REST) and reduced expression of USP37 have poor outcomes. In previous studies, we showed sustained proliferation of SHH-medulloblastoma cells due to blockade of terminal cell cycle exit and neuronal differentiation stemming from a failure in USP37-dependent stabilization of its target, the cyclin-dependent kinase inhibitor (CDKI)-p27. This finding suggested a tumor suppressive function for USP37. Interestingly, the current study also uncovered Raptor, a component of the mTORC1 complex, as a novel target of USP37. Under conditions of low-USP37 expression, reduced Raptor stability and mTORC1 activity caused a decline in phosphorylation of 4E-binding protein 1 (4EBP1) and increased its interaction with eukaryotic elongation factor 4E (eIF4E), which is known to inhibit CAP-dependent translation initiation. Surprisingly, a subset of patients with SHH-driven medulloblastomas with elevated expression of USP37 and the Glioma associated Oncogene 1 (GLI1), also exhibited poor outcomes. Using genetic and biochemical analyses, we showed that USP37-mediated stabilization of GLI1, a terminal effector of SHH signaling, increases pathway activity and upregulates expression of its target oncogene products, NMYC and CCND1, to drive cell proliferation. These data indicate that USP37 elevation in SHH-driven medulloblastomas has the potential to promote non-canonical activation of SHH signaling. Overall, our findings suggest that USP37 may have context-specific oncogenic and tumor suppressive roles in medulloblastoma cells.

cancer biology↗

bioRxiv2024

Time-Domain Diffuse Optical Tomography for Precision Neuroscience

AbstractRecent years have witnessed a rise in research utilizing neuroimaging for precision neuromedicine, but clinical translation has been hindered by scalability and cost. Time Domain functional Near Infrared Spectroscopy (TD-fNIRS), the gold standard of optical neuroimaging techniques, offers a unique opportunity in this domain since it provides superior depth sensitivity and enables resolution of absolute properties unlike its continuous wave counterparts. However, current TD systems have limited commercial availability, slow sampling rates, and sparse head coverage. Our team has overcome the technical challenges involved in developing a whole-head time-domain diffuse optical tomography (TD-DOT) system. Here, we present the system characterization results using standardized protocols and compare them to the state-of-the-art. Furthermore, we showcase the system performance in retrieving cortical activation maps during standard hemodynamic, sensory, and motor tasks. A combination of the system performance, signal quality, and ease-of-use can enable future studies aimed at investigating TD-DOT clinical applications.

bioengineering↗

bioRxiv2024

Labeling Natural Killer cells with superparamagnetic iron oxide nanoparticles for detection by preclinical and clinical-scale magnetic particle imaging

IntroductionClinical adoption of NK cell immunotherapy is underway for medulloblastoma and osteosarcoma, however there is currently little feedback on cell fate after administration. We propose magnetic particle imaging (MPI) for the detection, localization, and quantification of VivoTrax-labeled NK cells. MethodsHuman-derived NK-92 cells were labeled by co-incubation with VivoTrax for 24 hours then the excess nanoparticles were washed with centrifugation. Cytolytic activity of labeled vs. unlabeled NK-92 cells was assessed after 4 hours of co- incubation with medulloblastoma cells (DAOY) or osteosarcoma cells (LM7 or OS17) using bioluminescent or GFP counts. Labeled NK-92 cells at two different doses (0.5 or 1 x 106) were administered to excised mouse brains (cerebellum), tibias, and lungs then imaged by 3D preclinical MPI (MOMENTUM imager) and localized relative to fiducial markers. NK-92 cells were imaged by clinical-scale MPI under development at Magnetic Insight Inc. ResultsNK-92 cells were labeled with an average of 3.17 pg Fe/cell with no measured effects on cell viability or cytolytic activity against 3 tumor cell lines. MPI signal was directly quantitative with the number of VivoTrax-labeled NK-92 cells, with preclinical limit of detection of 3.1 x 104 cells on MOMENTUM imager. Labeled NK-92 cells could be accurately localized in mouse brains, tibias, and lungs within < 1 mm of stereotactic injection coordinates with preclinical scanner. Feasibility for detection of a clinically relevant dose of 4 x 107 labeled NK-92 cells was demonstrated on clinical-scale MPI. ConclusionMPI can provide sensitive, quantitative, and accurate spatial information on NK cell delivery, showing its potential to resolve a significant unmet clinical need to track NK cell treatments in patients.

biophysics↗

bioRxiv2022

Dissociation protocols used for sarcoma tissues bias the transcriptome observed in single-cell and single-nucleus RNA sequencing

BackgroundSingle-cell RNA-seq has emerged as an innovative technology used to study complex tissues and characterize cell types, states, and lineages at a single-cell level. Classification of bulk tumors by their individual cellular constituents has also created new opportunities to generate single-cell atlases for many organs, cancers, and developmental models. Despite the tremendous promise of this technology, recent evidence studying epithelial tissues and diverse carcinomas suggests the methods used for tissue processing, cell disaggregation, and preservation can significantly bias gene expression and alter the observed cell types. To determine whether sarcomas - tumors of mesenchymal origin - are subject to the same technical artifacts, we profiled patient-derived tumor explants (PDXs) propagated from three aggressive subtypes: osteosarcoma, Ewing sarcoma (ES), desmoplastic small round cell tumor (DSRCT). Given the rarity of these sarcoma subtypes, we explored whether single-nuclei RNA-seq from more widely available archival frozen specimens could accurately be identified by gene expression signatures linked to tissue phenotype or pathognomonic fusion proteins. ResultsWe systematically assessed dissociation methods across different sarcoma subtypes. We compared gene expression from single-cell and single-nucleus RNA-sequencing of 125,831 whole-cells and nuclei from ES, DSRCT, and osteosarcoma PDXs. We detected warm dissociation artifacts in single-cell samples and gene length bias in single-nucleus samples. Classic sarcoma gene signatures were observed regardless of dissociation method. In addition, we showed that dissociation method biases can be computationally corrected. ConclusionsWe highlighted transcriptional biases, including warm dissociation and gene-length biases, introduced by the dissociation method for various sarcoma subtypes. This work is the first to characterize how the dissociation methods used for sc/snRNA-seq may affect the interpretation of the molecular features in sarcoma PDXs.

cancer biology↗

bioRxiv2019

Knowledge discovery with Bayesian Rule Learning for actionable biomedicine

Biomarker discovery is critical for both biomedical research and for clinical diagnostic, prognostic, and therapeutic decision-making. They help improve our understanding of the underlying physiological processes within an individual. Discovery of biomarkers from complex biomedical datasets is done using data mining algorithms. Hundreds of thousands of biomarkers have been discovered and reported in literature but only a few dozen have been found to be clinically useful. This discrepancy is because statistical significance is not clinical relevance. Statistical significance only accounts for the correctness of the learned associations. Clinical relevance, in addition to statistical significance, also accounts for clinical utility such as cost-effectiveness, non-invasiveness, efficacy, and safety of the proposed biomarkers. We need models that are statistically significant and clinically relevant, all the while keeping it interpretable. Interpretable classifiers are more actionable in medicine because they offer human-readable explanations for their predictions. Traditional data mining methods cannot account for clinical relevance. We formulate this as a knowledge discovery problem. In computer science, knowledge discovery in databases is \"a non-trivial process of the extraction of valid, novel, potentially useful, and ultimately understandable patterns in data\". Bayesian Rule Learning (BRL) finds an optimal Bayesian network to explain the training data and translates that into an interpretable rule model. In this paper, we extend BRL for knowledge discovery (BRL-KD) to enable BRL to incorporate a clinical utility function to learn models that are clinically more relevant. We demonstrate this using a real-world dataset to predict cardiovascular disease outcome. We evaluate predictive performance with the area under the receiver operating characteristic curve (AUROC) and clinical utility with the cost of the model. We show that BRL-KD successfully generates a set of models offering different trade-offs between AUROC and cost. Based on the clinical standard, a model with an acceptable trade-off can then be chosen.

bioinformatics↗

bioRxiv2019

A low-cost, open source, self-contained bacterial EVolutionary biorEactor (EVE)

The morbidostat automatically adjusts antibiotic concentration as a bacterial population evolves resistance. Although this device has advanced our understanding of the evolutionary and ecological processes that drive antibiotic resistance, no low-cost and open-source systems are available for educators. Here, we present the EVolutionary biorEactor (EVE), an accessible alternative to other morbidostats for use in low-resource classrooms that requires minimal engineering and programming experience. We first compare our system to others, emphasizing how it differs in design and cost. We then describe how we validated the EVE by evolving replicate Escherichia coli populations under chloramphenicol challenge and comparing our results to those in the published literature. Lastly, we detail how high school students used the EVE to learn about bacterial growth and antibiotic resistance.

bioengineering↗

bioRxiv2019

Node-Specific Heritability in the Mouse Connectome

Genome-wide association studies have demonstrated significant links between human brain structure and common DNA variants. Similar studies with rodents have been challenging because of smaller brain volumes. Using high field MRI (9.4T) and compressed sensing, we have achieved microscopic resolution and sufficiently high throughput for rodent population studies. We generated whole brain structural MRI and diffusion connectomes for four diverse isogenic lines of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than human connectomes. We derived volumes, scalar diffusion metrics, and estimates of residual technical error for 166 regions in each hemisphere and connectivity between the regions. Volumes of discrete brain regions had the highest mean heritability (0.71 {+/-} 0.23 SD, n = 332), followed by fractional anisotropy (0.54 {+/-} 0.26), radial diffusivity (0.34 {+/-} 0.022), and axial diffusivity (0.28 {+/-} 0.19). Connection profiles were statistically different in 280 of 322 nodes across all four strains. Nearly 150 of the connection profiles were statistically different between the C57BL/6J, DBA/2J, and CAST/EiJ lines.

neuroscience↗

bioRxiv2019

EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

The genomewide signal profiles from functional genomics experiments are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinct patterns along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in many epigenetic assays such as ChIP-Seq and bisulfite sequencing. Valleys mark locations of cis-regulatory elements such as enhancers. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements using epigenetic data. Nevertheless, the valleys are generally not reported by analysis pipelines. Here, we describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability, and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification and DNA methylation valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding, and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes.

bioinformatics↗