Biology subjects

Gupta, S.

Publications and source records attributed to Gupta, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Tracking Sleep-Linked Brain Fluid Dynamics Using Modified fNIRS: A Novel Noninvasive Window into Glymphatic Function

BackgroundThe glymphatic system, a brain-wide perivascular and interstitial waste and signal transport pathway for cerebrospinal fluid (CSF) to exchange with interstitial fluid (ISF), has pronounced activity during non-rapid eye movement (NREM) sleep and has been implicated in the pathophysiology of traumatic brain injury, Alzheimers disease, and mood disorders. However, direct measurement in humans is limited because current imaging methods rely on intrathecal contrast-enhanced magnetic resonance imaging (MRI), which is unsuitable for routine or naturalistic sleep studies. The absence of real-time, noninvasive monitoring methods that allow for natural sleep poses a major barrier to advancing glymphatic research in clinical settings. MethodsTo address this barrier, we developed a non-invasive functional near-infrared spectroscopy (fNIRS) forehead array in a wearable headband using non-standard wavelengths to allow for better sensitivity for water measurement. We monitored cortical blood and water dynamics during overnight sleep, quantifying oscillatory patterns of oxyhemoglobin (HbO) and water across sleep stages within the outer layers of the frontal cortex, subarachnoid space, and scalp. A component of the extracted water metrics is hypothesized to serve as a proxy for glymphatic transport without the need for contrast agents or surgical intervention. ResultsOur results demonstrate water concentrations were highest in SWS (d = 1.93, p=0.0002), while HbO concentrations also showed a modest elevation (d = 1.06, ns). Additionally, low-frequency oscillations (LFOs) of both water and HbO signals exhibited distinct dynamics of suprathreshold envelope peak (SEP) frequencies during NREM stages (N2 and N3) as compared to REM and wake, with effect size (d= 1.31, p = 0.003) for water. Interestingly, these water-derived metrics correlate with EEG slow-wave activity, linking fluid-sensitive oscillations to established electrophysiological markers of sleep depth. These findings indicate that water-sensitive oscillatory processes are selectively amplified during deep sleep and scale with EEG-defined sleep depth, consistent with a role for glymphatic-related fluid transport in human sleep. ConclusionWe report novel cortical water shift parameters that are robustly sensitive to sleep stage transitions via a wearable, non-invasive, scalable headband, consistent with predicted glymphatic activity. Future work will cross-validate this method with MRI and other techniques. DisclaimerThe views, information or content, and conclusions presented do not represent the official position or policy of, nor should any official endorsement be inferred on the part of, the Uniformed Services University, the Department of War, the U.S. Government, or Walter Reed National Military Medical Center.

neuroscience↗

bioRxiv2025

A simple circuit to sustain intact tumor microenvironments for complex drug interrogations

Deep learning and large language models can integrate complex datasets to uncover biological insights that are often undetectable through conventional analyses. With application to translational cancer research, these computational tools have positioned 3D patient-derived tumor avatars front and center as crucial data input sources. However, a major challenge remains: the lack of standardization in media composition in 3D patient-derived tumor models unpredictably affects cell behavior and limit the utility beyond predicting treatment responses. To address this unmet need, we developed a simple, reproducible perfusion circuit system to approximate in vivo physiology using autologous patient plasma. With peritoneal metastases and core needle biopsies across multiple tumor histologies, we demonstrate preservation of the tumor microenvironment for up to 48 hours using multi-modal interrogation techniques. With proof-of-concept experiments, we display the systems ability to unveil complex drug-dependent biology within this time window. Standardizable, physiologically relevant platforms for 3D patient-derived tumor avatars will yield unprecedented insights through the integration of data from broad groups of patients and the use of an expanding armamentarium of artificial intelligence capabilities.

cancer biology↗

bioRxiv2025

Benchmarking Free Energy Calculations: Analysis of Single and Double Mutations Across Two Simulation Software Platforms for Two Protein Systems

Mutation analysis of single and double mutations of proteins including for the two widely studied proteins: Staphylococcus nuclease (S. nuclease) and T4 lysozyme, provide critical insights into their stability and fitness. Furthermore, such analysis facilitates a deeper understanding of the complex interplay between a proteins sequence, structural conformation, and functional output. Free Energy Perturbation (FEP) is an alchemical, all-atom molecular dynamics based computational approach that determines the free energy change ({Delta}{Delta}G) from wild type to mutant states. Two widely adopted software platforms used for this purpose are Schrodinger and GROMACS. We have compared the results of FEP simulations for mutations using these platforms, employing the OPLS4 force field implemented in Schrodinger, and the Amber99SB-ILDN force field implemented in GROMACS for this work. For the 38 single mutants of S. nuclease, the Pearson r between the experimental and the calculated free energy change ({Delta}{Delta}G) was 0.86 using Schrodinger and 0.87 using GROMACS. The reliability of the FEP method using the two software platforms with the specified force fields was further demonstrated by its performance on 24 single mutants of T4 lysozyme, yielding strong correlation between predicted and experimental{Delta}{Delta} G values, with Pearson r of 0.80 for Schrodinger and 0.85 for GROMACS. Additionally, the computed folding free energy changes for 45 double mutations in S. nuclease ([Formula]) using Schrodinger correlated well with both experimental measurements (Pearson r = 0.74) and previously reported GROMACS values (Pearson r = 0.71). Correspondingly, the nonadditivities([Formula]) of the double mutations derived using Schrodinger for these 45 double mutants of S. nuclease were also found to be in good agreement with the experimental values (Pearson r = 0.79), as well as with the previously reported GROMACS results (Pearson r = 0.61). A good correlation was also observed between computed values from Schrodinger and GROMACS, with a Pearson r of 0.71 for double mutants and 0.61 for their nonadditivities. Collectively, these findings establish the efficiency, accuracy, and reliability of Schrodinger FEP+ and GROMACS in predicting mutation induced free energy changes in S. nuclease and T4 lysozyme. The integration of FEP based computational methodologies with experimental validation provides a framework for quantifying mutation induced changes in protein thermostability, which can be used as a tool for protein engineering and design.

biophysics↗

bioRxiv2025

H19 is a PERK-regulated long non-coding RNA that fine-tunes UPR signalling and inhibits endoplasmic reticulum stress-induced cell death

The endoplasmic reticulum (ER) responds to stimuli that disrupt its homeostasis by activating a signalling network known as unfolded protein response (UPR), that restores cellular balance and determines cell fate through three key sensors: IRE1, PERK, and ATF6. Emerging evidence suggests that the UPR regulates the expression of numerous long non-coding RNAs (lncRNAs), which play critical roles in modulating stress responses. Here we show that expression of lncRNA H19 is downregulated in response to ER stress in breast cancer cells. Using genetic and pharmacological approaches, we demonstrate that this downregulation is primarily mediated by the PERK arm of the UPR. Specifically, knockdown or chemical inhibition of PERK compromised the ER stress-mediated H19 repression, while PERK activation significantly reduced H19 expression. H19 overexpression promotes the optimal activation of ATF6 and PERK pathways, while it attenuates the signalling by IRE1-XBP1 axis of the UPR. Bioinformatic analyses across multiple breast cancer cohorts revealed that high H19 expression correlates with poor prognosis, particularly in basal-like subtypes. Furthermore, in triple-negative breast cancer (TNBC) cells, H19 was found to provide resistance to ER stress-induced apoptosis. Collectively, our findings establish H19 as a key regulatory node in the UPR network, where PERK-mediated repression of H19 shapes cellular outcomes under ER stress. These insights position H19 as a potential therapeutic target in breast cancer, especially in aggressive subtypes such as TNBC.

cancer biology↗

bioRxiv2025

Structural Insights into Zn2+ and Ca2+ binding to Human Protein Z and Their Impact on Membrane Association

Calcium ions (Ca{superscript 2}) bound to the {gamma}-carboxy glutamic acid-rich (Gla) domain of coagulation proteins are essential for their membrane anchoring. Zinc ions (Zn{superscript 2}), abundant in plasma, also bind coagulation proteins and modulate their activity. However, their role in membrane-protein interactions remains less understood. The current study examines how Ca{superscript 2} and Zn{superscript 2} influence the structure and function of human protein Z (PZ), a cofactor of protein Z-dependent protease inhibitor (ZPI) that inhibits factor Xa on phospholipid surfaces. Zn2+ was found to substitute for Ca2+ in the Gla domain of PZ and promote its membrane binding by inducing conformational changes like Ca2+. Additionally, a Zn2+ located in the epidermal growth factor1 (EGF1) domain of PZ appeared to synergize with Ca2+ to enhance the membrane affinity of PZ. Furthermore, both Ca2+ and Zn2+ were suggested to destabilize PZ-ZPI complex, thereby modulating the cofactor activity of PZ, a finding that warrants further experimental investigations. Collectively, this study provides the first structural and functional characterization of Zn{superscript 2}-bound PZ, offering new insights into metal ion driven structural dynamics of PZ and their implications in coagulation regulation.

biophysics↗

bioRxiv2025

BC-Predict Database: A Curated Resource of Experimentally Validated Markers in Multidrug Resistance in Breast Cancer

BackgroundIn this study, we aim to develop a yearly updatable database that could predict chemotherapeutic drug resistance and overall survival probability in breast cancer patients. Existing drug sensitivity databases depend on correlation-based predictions. In our study, candidates involved in drug resistance are chosen based on cell line validation (overexpression or downregulation or inhibition of candidates) studies, curated manually. Method28,773 mRNA expression signatures from 914 breast cancer patients were extracted from cProsite. 106 of these patients had clinical information and log2 fold change information required for this study. We categorized these patients into deceased and surviving groups from TCGA. To prepare a database that can predict drug resistance and overall survival, we included mRNAs that were over-expressed in at least 80% of the breast cancer patients and mRNAs over-expressed in deceased and surviving groups. In addition, we also reported breast cancer-associated drug resistance candidates which have been reported in cell-line based studies. The database matrix preparation involved an approximate of 15000 manual searches of cell validated studies. (750 candidates x 20 drugs). The database was validated using a publicly available breast cancer patient proteomics data. ResultsOur analysis identified a list of top priority candidates associated with multidrug resistance, categorized based on their resistance to >15 drugs, 5-15 drugs, and 2-4 drugs. Analysis of patient profiles in the database revealed that the number of proteins contributing to drug resistance was high in the poor prognosis category compared to the good prognosis category. ConclusionsOur study highlights the probable gaps in breast cancer drug resistance research, as only a small subset of overexpressed mRNA candidates found in patients are studied in vitro or in vivo experiments focusing on drug resistance. We also identified candidates involved in multidrug resistance, whose role in drug resistance has not been studied in more than 15 drugs. After further validations, this will benefit the clinicians and upcoming CRISPR gene therapeutics.

cancer biology↗

bioRxiv2025

Phosphatidylserine and RhoB connect phosphatidylinositol 4-phosphate and phosphatidic acid metabolism at the plasma membrane

Cells tightly control the homeostatic levels and subcellular localizations of membrane phospholipids through the regulation of the activities of numerous lipid-metabolizing enzymes and lipid transfer proteins. Yet, the mechanisms by which lipid imbalances are sensed and corrected to establish and maintain homeostasis are, in most cases, unknown. Here we present an expanded view of plasma membrane (PM) phosphoinositide metabolism by revealing an unexpected metabolic connection between two key anionic lipids in this membrane, phosphatidylinositol 4-phosphate (PI4P) and phosphatidic acid (PA). PM pools of PI4P are generated by PI 4-kinase Type III (PI4KIII/PI4KA), an essential enzyme whose partial dysfunction leads to numerous hereditary human diseases. We find that depletion of PI4P by pharmacological inhibition of PI4KA increases the activity of phospholipase Ds (PLDs) and the levels of their lipid product, PA, in the PM. Guided by RNA-seq analysis and proximity labeling proteomics, we elucidate how cells connect this PI4P decrease to a compensatory increase in PA levels. Loss of PM PI4P induces a concomitant decrease of phosphatidylserine (PS) levels, and this metabolic rewiring activates a reciprocal relationship between PS synthesis and PLD-mediated PA generation. These metabolic changes also lead to transcriptional and translational upregulation of the small GTPase RhoB, which enhances PLD-mediated PA synthesis and subsequent actin cytoskeletal remodeling. Our study reveals how disease-relevant perturbation of phosphoinositide synthesis induces an integrated response that ultimately boosts levels of PA, a key anionic lipid and metabolic intermediate in phosphoinositide resynthesis.

cell biology↗

bioRxiv2025

Constructing Gene Regulatory Network using Chatterjee's Rank Correlation with Single-cell Transcriptomic Data

Discovering gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is critical for understanding cellular function. Still, existing methods are limited by strong theoretical assumptions or high computational complexity. We introduce a multiple testing framework for GRN construction using Chatterjee's rank correlation coefficient, a nonparametric measure of dependence. Our approach overcomes the limitations of traditional methods while offering a transparent, scalable, and computationally efficient alternative to recent black-box machine learning models. Crucially, to address the non-independence of cellular observations inherent to scRNA-seq, we develop a data-driven algorithm for estimating robust testing cutoffs. Furthermore, we exploit the asymmetric nature of Chatterjee's correlation to propose a new test for active regulation, enabling the construction of biologically meaningful and directionally informed GRNs. We demonstrate that our method matches or outperforms state-of-the-art approaches in recovering true gene-gene dependencies and directed regulatory interactions from both simulated and real datasets, particularly for complex, non-linear dependencies, providing a powerful tool for dissecting complex GRNs.

bioinformatics↗

bioRxiv2025

Lipid Nanoparticle Delivery of Mesenchymal Stromal Cell-Derived microRNA 187-3p as a First-in-Class Therapy for Myocardial Dysfunction in Sepsis

BackgroundSepsis-induced myocardial dysfunction is a common and critical complication of sepsis. Extracellular vesicles (EVs) from clonally expanded immortalized mesenchymal stromal cells (ciMSCs) contain microRNAs that may be exploited as therapy. MethodsIn mouse models of septic cardiomyopathy induced by caecum ligation and puncture, cardiac function was determined by invasive and echocardiographic assessment. Primary cardiomyocytes derived from foetal murine and human adult ventricular tissue, as well as murine hearts were used for mechanistic studies. Studies using post-mortem human hearts or patient plasma, and clinical and echocardiographic measurements were used to establish translational relevance. ResultsIn preclinical models of sepsis, intravenous administration of either MSCs or ciMSC-EVs, given after the induction of sepsis, prevented a decrease in myocardial ejection fraction, ventricular inflammation, and mortality compared to placebo or platelet-derived control EVs. EV-microRNA sequencing identified enrichment for microRNA-187a-3p (miR-187) in ciMSC-EVs. miR-187 is anti-inflammatory; with interleukin-6 (IL-6) as its major target. Intravenous delivery of lipid nanoparticle (LNP) encapsulated miR-187 improved cardiac function, reduced inflammation, and enhanced survival of septic mice. In cardiomyocytes and in murine hearts, LNP-miR-187 reduces inflammation and expression of myocardial transcription factors linked to fetal gene reactivation in failing septic hearts. In human septic hearts, low circulating miR-187 levels correlate with reduced cardiac function and high sequential organ failure assessment (SOFA) scores. ConclusionThese findings support the development of first-in-class, cell-free, miRNA-based therapy as a novel approach to treat sepsis-induced cardiomyopathy to address a critical gap in sepsis care. One Sentence SummarymiR-based therapy for sepsis The Clinical PerspectiveA. What is NEW? Sepsis accounts for 1 in 5 deaths worldwide. Here, we demonstrate that sepsis-induced myocardial dysfunction represents a discrete, targetable sepsis-trait -- a distinct biological abnormality characterized by cardiomyocyte inflammation and fetal gene reactivation. This component contributes to the propagation of organ dysfunction and overall mortality and may respond to focused epigenetic-based interventions. B. What are the Clinical implications? Currently, there are no effective treatments to reduce, limit, or reverse the immune dysfunction component of sepsis that contributes to multiorgan failure, such as sepsis-induced cardiomyopathy. We identify miR-187 as a clinically relevant post-transcriptional regulator of cardiac inflammation and cardiomyocyte gene expression. Intravenous delivery of miR-187 encapsulated in a lipid nanoparticle (LNP) represents a fundamentally distinct, effective and pathogen-agnostic approach to correcting sepsis-induced cardiac dysfunction through modulation of cardiomyocyte inflammatory and metabolic pathways.

molecular biology↗

bioRxiv2025

Development of a Stable Lyophilized Formulation of a Quadrivalent Frame-Shift Peptide Vaccine for Cancer Prevention in Lynch Syndrome

A stable lyophilized formulation for a cancer vaccine containing quadrivalent frameshift neo antigen peptides TAFB(-1), AIM2(-1), HT001(-1) and TGFBR2(-1), was obtained through an iterative screening process. To develop a lyophilized formulation, the screening included evaluation of pH, stabilizers, bulking agents, and surfactants. The screening resulted in a scalable formulation and lyophilization process that co-formulated four peptides with 100 {micro}g/ml each peptide in10 mM Histidine at pH 5.5, 260 mM trehalose, and 0.02% polysorbate 20 as a clinical formulation. Based on peptide content and peptide purity from a three-month stability study, the lyophilized formulation is estimated to be stable at ambient temperatures for 18 months.

pharmacology and toxicology↗

bioRxiv2025

PRESTIGE-ST: Patch Resolution and Encoder STrategies for Inference of Gene Expression from Spatial Transcriptomics

Spatial Transcriptomics (ST) integrates histology with spatially resolved gene expression, offering rich insights into tissue architecture and function. However, its clinical and large-scale deployment is hindered by high costs, technical complexity, and limited accessibility. To address this, computational pathology methods have emerged to predict gene expression directly from histology images, typically framing the task as a multi-output regression problem mapping image patches to gene expression profiles. While several Convolutional Neural Network (CNN) models have been proposed, little is known about how performance is influenced by (a) the number of trainable parameters and (b) the patch size used for prediction. Moreover, existing studies rely primarily on quantitative metrics and overlook biological relevance of predictions. In this study, we systematically evaluated multiple convolution based models (including a Vision Transformer (ViT) model) with different patch sizes on the Xenium based Autoimmune Machine Learning Challenge (AMLC) dataset. We assessed model performance on both globally expressed genes and subsets enriched for immune or disease associated pathways. Our findings reveal that compact CNNs trained on larger patches outperform deeper models, offering superior accuracy in predicting gene expression, especially for biologically important genes. These insights provide practical guidance for designing efficient and biologically meaningful models in the emerging field of image-based gene expression prediction. CCS CONCEPTSComputing methodologies [->] Computer vision; Neural networks; * Applied computing [->] Computational genomics; Imaging.

pathology↗

bioRxiv2025

Neural Correlates of Perceptual Grouping in Brain

Humans are proficient in detecting even subtle regularities in their sensory inputs. In the specific context of vision, one manifestation of this ability is the perception of structures embedded in noisy backgrounds. Here we use electrophysiology to explore the neural correlates of this proficiency. Observers underwent EEG recordings while being presented with random-dot displays. A few dots in some of the displays were arranged in a line. The results reveal that the two kinds of displays, those with and without the embedded lines, though identical in low-level attributes such as mean luminance and spatial frequency distribution, elicit markedly different neural responses. We find that the first divergence in the ERPs corresponding to these two stimulus classes occurs around 100 milliseconds. These differences are evident in occipital sensors suggesting that the perceptual distinction between structured and non-structured stimuli is accomplished in the very early cortical stages of visual processing. The short latency and early locus of the ERP divergence point to a largely feedforward computation underlying this feat of perceptual organization. The functional connectivity for difference between the two events also supports the ERP findings and thus, exhibits relation between synchrony time-course and ERP dynamics.

physiology↗

bioRxiv2025

Neural Oscillations during Perceptual Grouping: Insights from Alpha and Theta Bands in Visual Perception Tasks

Perceptual grouping, the brains ability to organize visual stimuli into coherent objects and patterns, is thought to rely on distinct neural oscillations across various frequency bands. However, the specific contributions of these frequency bands to perceptual grouping remain a central question in cognitive neuroscience. Our study employs spectral analysis and time-frequency analysis techniques to analyze EEG data collected during visual perception tasks. By examining the temporal dynamics and frequency-specific activity, particularly focusing on the alpha and theta bands immediately following stimulus onset, our goal is to uncover the neural processes underlying perceptual grouping. Our findings indicate significant changes in alpha and theta band activity associated with perceptual grouping in the brain. These results suggest that alpha band oscillations may play a role in directing attention and filtering relevant visual information, while theta band oscillations might facilitate the integration of visual elements into coherent percepts by engaging memory processes and enhancing neural communication across brain regions. This research contributes to a deeper understanding of how neural oscillations coordinate to support perceptual organization, potentially informing future studies and therapeutic approaches aimed at enhancing visual perception and cognitive function.

neuroscience↗

bioRxiv2025

Autophagy maintains HEV identity and function during inflammation

High endothelial venules (HEVs) play a crucial role in adaptive immune responses in secondary and tertiary lymphoid organs. They are uniquely equipped with high levels of peripheral node addressins (PNAd), harboring carbohydrate structures that serve as L-Selectin ligands to efficiently facilitate lymphocyte homing. During inflammation, the HEV network expands in SLOs, increasing lymphocyte infiltration, but the underlying mechanisms maintaining HEVs remain underexplored. Here, we report that autophagy is essential for HEV function and expansion. Using single-cell transcriptomics, intravital imaging, and an inducible HEV tracer system in mice, we demonstrate that autophagy deficiency compromises LT{beta}R-signaling and the Unfolded Protein Response in HEVs, leading to disrupted PNAd production, dedifferentiation, and reduced lymphocyte homing. Autophagy deficiency and LT{beta}R blockade impaired HEV function and reduced skin inflammation in psoriasis-bearing mice by limiting immune infiltration and cytokine release. Our work uncovers an unprecedented role of autophagy in safeguarding HEV identity and function during inflammation. HighlightsO_LIHigh endothelial venules (HEVs) exhibit heightened autophagy in comparison to non-HEV blood endothelial cells, which further increases during inflammation. C_LIO_LIAutophagy is pivotal in maintaining HEV fate and function, specifically during inflammation, via LT{beta}R signaling and the Unfolded Protein Response (UPR), ensuring the proper production of peripheral node addressins (PNAd) that serve as L-selectin ligands for the efficient influx of naive lymphocytes. C_LIO_LIBlocking autophagy in HEVs leads to disrupted PNAd production, HEV flattening and dedifferentiation, and reduced lymphocyte homing. C_LIO_LIGenetic and pharmacological perturbation of HEV function in the lymph nodes and skin lesions of psoriasis-bearing mice impaired neutrophil and lymphocyte recruitment, as well as cytokine secretion, thereby alleviating skin inflammation. C_LI

cell biology↗

bioRxiv2025

Transcriptional response to warm temperatures is confounded by organ-specificity

Climate change is causing increases in global average temperatures with detrimental consequences to food security, in part because most plants are sensitive to increases of even 1-2 degrees Celsius. Our molecular understanding of how plants perceive and acclimatise to mild increases in temperature (aka thermomorphogenesis) have largely been built upon transcriptomic analyses of whole seedling using the plant model Arabidopsis thaliana. In this work, we demonstrate that this widely used approach has significant limitations that may have skewed our understanding of gene regulation in thermomorphogenesis. We found a surprisingly poor consensus among published whole seedling transcriptomic datasets. To investigate the source of this variability, we performed a controlled comparison of RNA sequencing data from whole seedlings versus dissected organs (root, hypocotyl, and cotyledon), which revealed strong organ-specific transcriptional responses to warmth. We found that ~70% of DEGs identified in whole seedlings were not differentially expressed in any of the individual organs, suggesting that whole seedling data is confounded by the pooling of distinct, and sometimes opposing, organ-level responses. These findings are further supported by proteomic analysis in each organ. Our work provides a word of caution and guidance for the field to advance when producing and interpreting RNA sequencing data for plant response to warm temperatures.

plant biology↗

bioRxiv2025

Growth-factor ligand functionalization enhances cellular and in vivo uptake of DNA nanodevices

Epidermal Growth Factor (EGF) and Transforming Growth Factor beta (TGF-{beta}) are two important classes of growth factor that regulates cell growth, cell proliferation, differentiation, immune responses, and extracellular matrix generation. Using a short peptide-based ligand coupled to DNA nanocages, we present the enhanced internalization of a receptor-mediated peptide-DNA nanocage. We used here tdDNA as a delivery vehicle. Our study in cellular and in vivo showed excellent internalization, cell growth, and inhibition. We expect that a tdDNA-modified receptor-binding peptides could become a valuable scaffold for use as a cellular programming and regenerative material.

bioengineering↗

bioRxiv2025

Dual Inhibition of CDK4/6 and mTORC1 Establishes a Preclinical Strategy for Translocation Renal Cell Carcinoma

PurposeTranslocation renal cell carcinoma (tRCC) is a rare and aggressive subtype of kidney cancer driven by an oncogenic fusion involving a transcription factor in the MiT/TFE gene family, most commonly TFE3. Treatment of tRCC currently lacks a clear standard of care, underscoring the pressing need to nominate new therapeutic targets with mechanistic rationale in this cancer. Experimental DesignIn this study, we applied integrative genomic approaches to identify activation of the cyclin-dependent kinase 4/6 (CDK4/6) and mammalian target of rapamycin complex 1 (mTORC1) pathways in tRCC. We tested the activity of CDK4/6 inhibitors (CDK4/6i), alone or in combination with mTORC1-selective inhibition, using in vitro and in vivo models of tRCC. ResultstRCC tumors displayed multiple genomic and transcriptional features associated with activation of the CDK4/6 and mTORC1 signaling pathways. Genetic or pharmacologic inhibition of CDK4/6 suppressed tRCC cell growth and induced cell cycle arrest in vitro but was not cytotoxic, with rapid cell regrowth observed after drug withdrawal. The mTORC1-selective inhibitor, RMC-5552, potently reduced translation of Cyclin D1, which complexes with CDK4/6 proteins to regulate G1-S cell cycle progression. Combined treatment with the CDK4/6 inhibitor, palbociclib, and RMC-5552 resulted in synergistic suppression of tRCC cell viability and increased markers of apoptosis in vitro. The combination of palbociclib and RMC-5552 in a tRCC xenograft model showed greater eOicacy than either single agent while also being well-tolerated. ConclusionsOur study indicates the therapeutic potential of combined CDK4/6 and mTORC1 inhibition in tRCC, providing the rationale for further clinical evaluation of this strategy. Translational RelevanceTranslocation renal cell carcinoma (tRCC) is a rare and aggressive form of kidney cancer that accounts for 2-5% of all RCCs in adults and around 50% of RCCs in children. tRCC lacks effective therapies and represents a significant unmet clinical need. Therapies approved for clear cell RCC (ccRCC), including VEGF/multikinase inhibitors and immune checkpoint blockade, typically show decreased efficacy in tRCC. Via an integrative genomic analysis, we nominate combined inhibition of the cyclin-dependent kinase 4/6 (CDK4/6) and mammalian target of rapamycin complex 1 (mTORC1) pathways in tRCC. We demonstrate that combining the CDK4/6 inhibitor palbociclib with the mTORC1-selective inhibitor RMC-5552 synergistically inhibits the growth of tRCC cells in vitro and of tRCC xenografts in vivo, where the combination is well-tolerated. This proof-of-concept study provides preclinical evidence supporting CDK4/6 inhibitor-based combination regimens tailored to tRCC biology, offering a foundation for future clinical studies in tRCC, which currently has no clear standard of care.

cancer biology↗

bioRxiv2025

Integrated Systems Vaccinology Reveals Distinct Metabolic Responses to SARS-CoV-2 Infection and DNA-Based Vaccines in Ferrets

Understanding systemic effects of vaccination and infection is central to defining correlates of protection against SARS-CoV-2. We used untargeted serum metabolomics to profile the immunometabolic landscape of ferrets after SARS-CoV-2 infection and DNA-/protein-based vaccination. Ferrets were vaccinated with either a multigenic DNA vaccine encoding SARS-CoV-2 RBD, M, and N (OC2), an N-only DNA vaccine (OC12), a recombinant spike protein with QS-21 adjuvant (S+QS21), or a hepatitis B/D control construct (Hep-B/D), and subsequently challenged with SARS-CoV-2. Serum was analyzed longitudinally at baseline, post-vaccination, and post-challenge. SARS-CoV-2 infection induced broad metabolic reprogramming, involving TCA cycle, glutathione metabolism, and nucleotide turnover, reflecting inflammation and cellular activation. OC2 vaccination induced strong metabolic shifts in amino acid and mitochondrial pathways despite low pre-challenge anti-S antibodies. Post-challenge, these shifts extended to redox and nucleotide pathways, correlating with robust anti-S and very strong anti-N antibody responses and complete viral clearance in BAL, but with marked airway pathology, consistent with T cell-mediated clearance of infected cells. S+QS21 and OC12 induced distinct, immunogen-specific signatures with partial protection, while Hep-B/D showed minimal systemic engagement. Metabolite-antibody correlations revealed vaccine-specific associations, highlighting lipid and amino acid pathways as potential immunogenicity biomarkers. Overlap and heatmap analyses showed that metabolic trajectories reflect both the magnitude and quality of immune training. These findings underscore the value of systems vaccinology in resolving mechanistic differences in vaccine responses and support metabolic profiling as a tool for evaluating immune efficacy in preclinical vaccine studies.

immunology↗