Biology subjects

Chakraborty, A.

Publications and source records attributed to Chakraborty, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Interactions between thermal and nutritional stress reveal cryptic adaptive potential

Predicting whether populations can adapt fast enough to persist under global change requires understanding how adaptive potential changes with the environment. Theory predicts that cryptic genetic variation hidden under benign conditions may be released under stress, but empirical tests are rare and typically manipulate single stressors in isolation, which is not realistic under global change. Using a quantitative genetic breeding design with Drosophila melanogaster, we quantified additive genetic variance in fitness (female productivity) across combinations of temperature and nutritional stress. We found that nutritional stress increased genetic variance at cold and hot margins. Temperature and diet also interacted to shape which genotypes performed best: nutritional stress weakened genetic correlations across temperatures at the hot margin, whereas genetic correlations across diets strengthened at the cold margin. Adaptive potential is therefore environment-dependent and multidimensional, meaning single stressor experiments are likely to systematically misrepresent adaptive capacity under realistic global change scenarios.

evolutionary biology↗

bioRxiv2026

Hepatocyte UBE4A protects against MASLD/MASH by maintaining ER homeostasis via UBXN4 interaction

Molecular understanding of the regulation of endoplasmic reticulum (ER) functions in healthy and MASLD livers is limited. Here, we elucidate that Ube4A, a cytosolic E3 ubiquitin ligase, protects against MASLD development by maintaining ER homeostasis during nutritional stress. Hepatocyte-Ube4A deletion (HUKO) augments Gubra-Amylin-diet (GAN-D)-induced steatosis, inflammation, insulin resistance, ER stress and markers of steatohepatitis of the liver in both sexes of mice. Proteomic studies reveal that in addition to altered metabolic and fibrogenic pathways, levels of many ER proteins are also changed in HUKO livers. Impaired ER functions sensitize HUKO livers to nutrition- or chemical (tunicamycin)-induced ER stress. Moreover, HUKO primary hepatocytes exhibit increased ER stress. Furthermore, Ube4A maintains ER homeostasis by facilitating ER-associated protein degradation (ERAD). The lipogenic protein SCD1 is identified as one of the ERAD targets of Ube4A. SCD1 stability and triglyceride accumulation are amplified in HUKO primary hepatocytes. The liver Ube4A-interactome studies identified the ERAD mediator UBXN4 as a major Ube4A interacting protein. Alphafold3-based molecular modelling and interaction site mapping analyses pinpointed the Ube4A binding residues for UBXN4. ER-localized Ube4A-UBXN4 complex facilitates ERAD (e.g., SCD1) and UBXN4-binding deficient Ube4A mutants fail to enhance this process. Finally, Ube4A and UBXN4 are downregulated in the livers of human MASLD and MASH patients and this complex is diminished in GAN-D-fed mouse livers. In conclusion, Ube4A preserves ER homeostasis by maintaining ERAD via its interaction with UBXN4, which maintains liver health. Enhancing levels and/or preserving interaction of these proteins may offer new therapeutic strategies for MASLD treatment.

physiology↗

bioRxiv2026

Tracing the dynamic recruitment trajectory of Optineurin between the centrosome and other macromolecular assemblies, signifying its role in cell cycle progression

Spatiotemporal allocation and dynamic redistribution of centrosomal proteins are critical for coordinating the structural and molecular events that ensure successful cell division. While depletion of the multifunctional protein Optineurin (OPTN) impairs cell-cycle progression, our previous report of the presence of OPTN at key cell-division assemblies raised the question of how its trafficking is coordinated during cell division. In this study, using cell-cycle synchronisation coupled with flow cytometric analysis and confocal microscopy, we report that OPTN progressively dissociates from centrosomes during mitosis, redistributes to cytokinetic structures, accumulates at the spindle midzone, and then concentrates at the midbody during cytokinesis. Moreover, following this transition, OPTN progressively re-emerges at centrosomes during G1 of the subsequent cell cycle. Further, microtubule perturbation and regrowth assays provide evidence that OPTN recruitment and retention at the pericentriolar matrix (PCM) can occur independently of microtubules, whereas its exit is microtubule-dependent. Collectively, this study depicts, for the first time, a detailed movement trail of OPTN, highlighting its centrosome-linked redistribution and microtubule dependency. This context-dependent localisation and trafficking of OPTN across the centrosome-midbody axis establish a definitive spatiotemporal roadmap that can be strategically utilised to study its functional role in cell division.

cell biology↗

bioRxiv2026

Multi-Model Machine Learning Consensus Identifies a Dual-Interferon Gene Signature in Ischemic Stroke: An Integrative Single-Cell Transcriptomic Analysis

An Ischemic stroke is one of the main causes of death and long-term disability around the world. There are very few treatment options available, especially for the cases where the short time period to give clot dissolving medicine has passed (Powers et al., 2019). While single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for understanding cell-to-cell differences within at issue (cellular heterogeneity) (Tang et al., 2009). However translating these high dimensional datasets into clinically useful biomarkers remains challenging (Rajko-mar et al., 2019). A major problem with current biomarker studies is their reliance on a single machine learning model, where results may reflect model bias rather than true biological signals (Lopez-Rincon et al., 2020). Methods: We developed a multi-model consensus framework integrating eight different machine learning methods from three groups: traditional models (logistic regression, random forest, XGBoost), a hybrid deep learning model (CNN-XGBoost), and transformer-inspired models (scGPT, scBERT, sc-Former, Geneformer). The proposed framework was applied to scRNA-seq data from a mouse middle cerebral artery occlusion (MCAO) model (GSE174574; n=6 mice, 54,599 cells). To prevent data leakage, we implemented rigorous subject-wise train/test splitting, ensuring all cells from individual animals remained either in the training sets or in the testsets. Results: All eight models achieved excellent performance on the independent test data, with AUC scores between 0.9778 and 0.9975. From six models that allowed gene level importance extraction, we identified 63 consensus genes (threshold: [≥] 4/6 models), with 17 genes selected by all 6 models. Pathway enrichment analysis showed significant enrichment of interferon signaling, with a novel dual-interferon pattern. Twelve of the consensus genes respond to interferon, and five of them (Ifitm6, Ifitm1, Gbp2, Igtp, Tgtp1) respond to both Type I (IFN-/{beta}) and Type II (IFN-{gamma}) interferons. Notably the consensus included Il1rn (interleukin-1 receptor antagonist), the target of Anakinra -- an FDA-approved drug that has completed Phase II stroke trials with promising safety profiles (Smith et al., 2018; Emsley et al., 2005). Conclusions: The Multi-model consensus framework, proposed in this paper, provides a robust and reliable approach to biomarker discovery that goes beyond the limitations of any single method and reduces individual algorithm bias. The convergence of classical, ensemble, and deep learning methods on interferon and IL-1 pathway genes supports their biological validity as stroke biomarkers. Our findings provide a prioritized gene list for further clinical validation and highlight interferon signaling as a possible treatment strategy warranting further investigation. Keywords: ischemic stroke; machine learning; single-cell RNA sequencing; biomarker discovery; consensus analysis; interferon signaling; neuroinflammation

bioinformatics↗

bioRxiv2026

Lymphatic ERG Governs Junctional Plasticity and Fluid Clearance via an EDNRB Signaling Axis in Pulmonary Fibrosis

Pulmonary lymphatic vessels are essential for interstitial fluid balance and macromolecular clearance, yet the endothelial mechanisms regulating lymphatic vessel function during chronic fibroproliferative lung injury remain undefined. Here, we demonstrate that the lineage-defining transcription factor ERG in lymphatic endothelial cells (LECs) governs junctional plasticity and fluid drainage in pulmonary fibrosis. In human idiopathic pulmonary fibrosis (IPF) lungs, lymphatic ERG expression is markedly downregulated. Lineage-specific inducible deletion of Erg in murine LECs (Erg-CKO) unexpectedly conferred robust protection against bleomycin-induced pulmonary fibrosis, significantly dampening acute inflammation, reducing edema, and preserving pulmonary compliance. Mechanistically, loss of ERG enhanced lymphatic drainage capacity in vivo. Transcriptomic profiling of isolated LECs revealed that ERG deficiency activates actomyosin contractile pathways and selectively upregulates Endothelin Receptor Type B (Ednrb), driving junctional remodeling from continuous zippers into discontinuous button-like configurations that facilitate interstitial fluid entry. Pharmacological blockade of EDNRB with BQ-788 abolished junctional reorganization, eliminated the enhanced lymphatic drainage, and completely reversed the anti-fibrotic protection in Erg-CKO mice. Together, our findings identify an endothelial ERG-EDNRB regulatory axis that controls lymphatic junctional architecture and demonstrate that promoting lymphatic clearance via endothelial junctional remodeling represents a viable therapeutic strategy for fibrotic vascular remodeling.

cell biology↗

bioRxiv2026

An Evolutionarily Conserved Ypk1/SGK1 Kinase Pathway Regulates the Unfolded Protein Response by Modulating the Ire1 Protein Abundance

The unfolded protein response (UPR) is a cellular mechanism that maintains protein homeostasis (proteostasis) under conditions of endoplasmic reticulum (ER) stress. The dual kinase/RNase Ire1 is a conserved regulator of the UPR, mediating the unconventional cytosolic splicing of HAC1 mRNA in yeast and XBP1 mRNA in human cells. The resulting spliced HAC1/XBP1 transcript encodes a transcription factor that induces the expression of protein-folding chaperones and stress-responsive genes, thereby restoring proteostasis. In our previous work, we showed that the MAP kinase Slt2 (homolog of human ERKs) contributes to UPR signaling by promoting IRE1 expression through the transcription factor Rlm1 (homolog of human MEF2C). Here, we demonstrate that Hac1 expression is reduced in yeast strains deficient in essential protein kinase Cdc28, Pkc1, Rio2, Tor2, Pkh1, or Ypk1, suggesting that these kinases also serve as UPR regulators. We focused on the kinase Ypk1, the yeast ortholog of human SGK1 (serum/glucocorticoid-regulated kinase 1). We provide genetic and biochemical evidence that Ypk1/SGK1 acts upstream of the Pkc1/PKC{delta} signaling pathway and is required for maintaining IRE1 protein abundance in both yeast and human cells. Collectively, our results identify an evolutionarily conserved Ypk1/SGK1 signaling pathway that regulates the HAC1 and XBP1 mRNA splicing by modulating the Ire1 protein abundance.

genetics↗

bioRxiv2026

Sparse Machine Learning Pipeline with Stabl Identifies Cord Blood Multi-Omic Signatures of Bronchopulmonary Dysplasia

Background: Several omics studies have been completed in recent years, with the goal of identifying biomarkers of complex multifactorial diseases, such as bronchopulmonary dysplasia (BPD). Objective: To evaluate the performance of 3 distinct omics platforms, using a machine learning pipeline with integration of sparse, reliable and adaptive biomarker identification (Stabl). Methods: Using a well-characterized birth cohort, cord blood metabolomics, proteomics and adductomics data were integrated with Least Absolute Shrinkage and Selection Operator (LASSO) regression and Stabl, to evaluate predictive performance for BPD. Results: Sparse multivariable modeling of 45,000 features measured in 217 infants (52 term, 165 extremely preterm <28 weeks; 82 with BPD and 35 with severe BPD/death) identified a perfect signature for preterm birth with both LASSO and Stabl (AUROC=1.0; p<0.001). Analysis of the preterm group yielded excellent predictive power for severe BPD (AUROC=0.83; p=0.005). Stabl identified a set of 12 biomarkers (2 adducts, 3 proteins and 7 metabolites) with good performance for predicting grade III BPD (AUROC=0.76; P=0.03). Biomarkers across the 3 omics platforms revealed dysregulated pathways of innate/adaptive immune responses, metabolic programming and oxidative stress. Conclusions: The sparse machine learning pipeline is a complementary approach for identifying novel pathways and biomarkers of multifactorial BPD and its endotypes.

bioinformatics↗

bioRxiv2026

Mutation bias influences the emergence and effects of antibiotic resistance

Evolution of antibiotic resistance is a major global public health problem. Rapid emergence of antibiotic resistance is often linked to hypermutator bacteria with defective DNA repair, leading to high mutation rates that are broadly advantageous. However, depending on which DNA repair pathway is dysfunctional, mutators may sample only specific types of mutations at a higher rate. Thus, their mutation spectrum can be biased towards specific mutation types, influencing the identity of resistance mutations. Under strong antibiotic selection, an overall high mutation rate should generally shorten the time to sample a resistance mutation and increase the probability of resistance. However, recent work suggests that the mutation rate for specific types of mutations in target genes that drive high resistance is more important than the overall mutation rate. To systematically test this prediction, we exposed Escherichia coli mutators with varying mutation rates and spectra to antibiotics targeting different cellular functions. For each strain, we determined the highest antibiotic concentration at which resistance could emerge overnight, quantifying both the magnitude and probability of resistance. High level antibiotic resistance was generally better predicted by specific rather than overall mutation rate, and resistance mutations matched the mutation spectrum of the respective mutator. Despite the varying magnitude of resistance, at the highest antibiotic concentration survived by each strain, the respective resistance mutations were generally costly in the absence of antibiotic. Given that mutators often arise in laboratory, natural, and clinical settings under antibiotic selection, we suggest that their mutation spectra deserve more attention.

evolutionary biology↗

bioRxiv2026

MSGPCA: Multi-Slice Graph PCA for replicate-aware Spatial Omics analysis

As spatial transcriptomics (ST) and spatial proteomics (SP) technologies mature, experimental designs are increasingly moving beyond single-slice analyses toward multi-slice studies involving one or more donors and experimental conditions. Although these designs enable the identification of reproducible spatial signals, they also introduce substantial biological heterogeneity, particularly when integrating non-serial slices or anatomically distinct regions. If not modeled carefully, such variation can blur slice-specific tissue structure, mask conserved molecular patterns, and limit the discovery of biologically relevant latent structure. Although dimension reduction is essential for representing high-dimensional molecular data in a lower-dimensional space, existing multi-slice methods typically enforce a globally shared representation that inadequately accommodates slice-level heterogeneity. To address this limitation, we propose Multi-Slice Graph Principal Component Analysis (MSGPCA), which decomposes molecular variation into shared spatial factors conserved across slices and slice-specific factors that capture local tissue microarchitecture. In downstream analyses, MSGPCA-derived representations recover spatial tissue structure, denoise molecular profiles, and reveal biologically interpretable metafeatures associated with shared and slice-specific biology. In a mass spectrometry imaging dataset comprising nonserial slices of ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC), the shared factors captured broad biological differences across tissue regions, whereas the slice-specific factors revealed intratumoral spatial variation within the IBC microenvironment. In human dorsolateral prefrontal cortex ST data, MSGPCA recovered laminar cortical architecture across adjacent slices, closely aligning with expert pathologist annotations. Together, these findings demonstrate that MSGPCA resolves shared tissue architecture while preserving local microenvironmental variation in complex multi-slice spatial omics datasets.

bioinformatics↗

bioRxiv2026

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of actionable panels for targeted validation and downstream translation. Existing pipelines rely largely on univariate differential-expression analyses, which ignore joint molecular structure and provide limited predictive insight. Multivariate machine-learning methods, including random forest,XGBoost, elastic net, and specialized single-cell panel-selection approaches, can capture predictive patterns but typically lack explicit spatial modeling and probabilistic feature selection, relying instead on model-specific importance scores or user-specified panel sizes. We develop rapid kernel machine regression (RKMR), a scalable framework for spatial-omics marker discovery that integrates nonlinear kernel modeling, spike-and-slab variable selection, and spatial dependence. RKMR uses automatic relevance determination (ARD) kernels and sparsity-inducing priors to capture nonlinear marker-outcome relationships and implicit feature interactions while producing approximate posterior inclusion probabilities (PIPs) that quantify model-based uncertainty in feature inclusion. To scale inference to large spatial datasets, RKMR combines low-rank kernel approximations with stochastic variational optimization. In simulations, RKMR consistently achieves higher AUPRC than competing methods across a range of molecular-signal and spatial-effect settings. Across spatial transcriptomics and scRNA-seq datasets, RKMR identifies parsimonious marker sets that recover reported cell-type signatures and reproducible tissue-layer markers. These results establish RKMR as a scalable and uncertainty-aware framework for translating high-dimensional spatial omics data into robust, experimentally actionable marker panels.

bioinformatics↗

bioRxiv2026

FusedFCR: A Fused Forward Continuation-Ratio model for marker selection along cell-fate trajectories

Time-course single-cell RNA sequencing (scRNA-seq) data collected across ordered stages provide population-level snapshots of differentiation, disease progression, and aging. Supervised pseudotime methods use observed stage labels to reconstruct continuous progression but generally do not identify marker genes associated with changes from one stage to the next. Unsupervised pseudotime-based marker selection methods infer latent trajectories directly from expression data and identify trajectory-associated genes, but do not explicitly link these associations to the observed stages. We propose FusedFCR, a regularized forward continuation-ratio model that represents cellular progression through a sequence of conditional transitions across ordered stages. FusedFCR combines a lasso penalty for gene selection with a fusion penalty that encourages similar effects across adjacent transitions while allowing transient and direction-changing associations. The resulting transition-specific coefficients support interpretable gene selection and a continuous pseudotime-like projection anchored to the observed developmental stages. In simulations, FusedFCR accurately recovers gene-effect trajectories and improved predictive performance relative to alternative methods. Applied to one mouse and three human datasets (mouse pancreatic beta-cells, human extravillous trophoblast, human induced pluripotent stem cell derived astrocytes, and human endometrial cells during the secretory phase), FusedFCR identifies biologically interpretable genes associated with distinct developmental transitions. Gene set enrichment analysis further reveals stage-specific pathway activity consistent with known developmental biology, while held-out stage-classification accuracy was competitive or superior across both datasets. Together, these results show that FusedFCR complements pseudotemporal ordering by identifying which molecular programs change and when those changes emerge along the developmental trajectory. An accompanying R package is available on GitHub.

bioinformatics↗

bioRxiv2026

A lipid acyl code-based Dip2-Pkc1 signalling axis maintains mitochondrial integrity in eukaryotes

Organelle membranes employ diverse lipids to relay key signals for efficient coordination of cellular processes. Diacylglycerol (DAG) is a simple yet critical lipid secondary messenger, but the regulatory mechanisms and functional implications for its distribution remain poorly understood. We have recently shown that Protein Kinase C (Pkc1) activation is driven by selective DAGs (C36:0, C36:1), whose levels are governed by Disco-interacting protein 2 (Dip2) (Shambhavi et al., 2025). Here, through genetic, chemical, and lipidomic screens, we show that the absence of Dip2 leads to specific DAG accumulation on the mitochondrial membrane and impacts its morphology, function, and quality control in yeast. Remarkably, the elevated DAGs in{Delta} dip2 promote translocation of Pkc1 to mitochondria via its DAG-binding C1 domain, but not the HR1 domain, suggesting functional partitioning between the regulatory domains. We also show that only specific DAGs, not the bulk DAGs, are required for Pkc1s targeting and inactivating Phospholipase C (Plc1) restores Pkc1 localisation and the associated mitochondrial defects. In addition, we identify that respiratory growth triggers specific DAG (C36:1) accumulation in the mitochondria, thereby promoting Pkc1 recruitment. Furthermore, we establish that the Psi1-Plc1-Dip2 axis is required for survival under respiratory growth conditions. Taken together, our study uncovers a novel, Dip2-mediated, unconventional Pkc1 signalling axis for maintaining mitochondrial homeostasis under nutrient transition and highlights how distinct lipid fingerprints enable precise control over organellar homeostasis.

cell biology↗

bioRxiv2026

APOBEC3-driven neoantigen-rich cancers co-opt 1q23.3 amplification for tumor-intrinsic immune cloaking

Hypermutational processes, including those driven by the APOBEC3 family of cytidine deaminases, generate abundant neoantigens yet give rise to tumors that evade immune recognition. Here, using multi-omics analyses followed by functional validation, we identified a tumor-intrinsic immune-cloaking mechanism in neoantigen-rich epithelial cancers, characterized by coordinated suppression of antigen presentation, immune-recruiting cytokines and immune-checkpoint programs. In bladder cancer, genome-wide copy-number analysis identified recurrent 1q23.3 amplification as a genomic feature of a neoantigen-high/CD8-low tumor state. Within this locus, NECTIN4 emerged as the dominant candidate effector, outperforming extrachromosomal DNA status as a predictor of immune-neoantigen discordance. Similar associations were observed across breast and lung cancers. Functional studies demonstrated that NECTIN4 was sufficient to establish a T-cell-poor tumor microenvironment and confer resistance to PD-1 blockade in immunocompetent mice. Mechanistically, NECTIN4 engaged a DDR1-SHP2 axis that suppressed STAT1 phosphorylation, silencing tumor-cell immune-engagement programs. NECTIN4 blockade restored STAT1 activity and reduced tumor growth, indicating that the cloaked state is pharmacologically reversible. Mutational signature, breakpoint motif, timing and clonality analyses, together with APOBEC3B expression and germline genetic evidence, linked APOBEC3-mediated mutagenesis to recurrent 1q23.3 amplification encompassing NECTIN4. These findings reveal how neoantigen-generating mutational processes can be coupled to structural genome evolution to enable tumor-intrinsic immune cloaking through a therapeutically targetable NECTIN4-DDR1-SHP2 axis.

cancer biology↗

bioRxiv2026

TIM22 Complex-NADH dehydrogenase crosstalk maintains mitochondrial health by modulating cell death

Programmed cell death is essential for organismal development. When dysregulated, it leads to neurodegenerative diseases and cancer. Although apoptotic pathways are well studied, the role of mitochondrial import translocases in regulating cell death remains unclear. Our study reveals a unique apoptotic pathway controlled by the TIM22 complex, an inner mitochondrial membrane translocase. This pathway involves a multiprotein complex formed by Tim22 and Nde1, a part of the respiratory electron transport chain. Under stress, the cytosol-exposed Nde1 isoform, a pro-apoptotic factor, is stabilised by the TIM22 complex, which includes the Tim18 subunit and Tim22s transmembrane segments. Notably, impairing the TIM22 complex and deleting Nde1 suppresses apoptosis and restores mitochondrial health. Beyond its role in import, our study uncovers a moonlighting function of the TIM22 complex in regulating mitochondria-dependent apoptotic cell death.

Cell Biology↗

bioRxiv2026

A Toxic Tau-PFKFB3 Circuit Reduces F2,6BP Levels and Drives Neurodegeneration

Alzheimers disease (AD) and related dementias are progressive neurodegenerative disorders manifested by aggregation of Tau and Amyloid beta (A{beta}). Emerging evidence suggests that metabolic dysregulation contributes to AD pathogenesis, yet how metabolic alterations interface with neuronal integrity remains unclear. Here, we identify dysfunction in PFKFB3-F2,6BP (fructose-2,6-bisphosphate) metabolic axis as a key feature of AD. We show that pathological Tau aggregates aberrantly sequester PFKFB3, limiting its activity and resulting in F2,6BP depletion. F2,6BP exerts protective effects through multiple convergent mechanisms: (i) direct activation of polynucleotide kinase 3-phosphatase (PNKP) to facilitate DNA strand break repair; (ii) transcriptional upregulation of the protein phosphatase 2A catalytic subunit (PP2CA) to limit Tau phosphorylation; (iii) stabilization of PFKFB3 to diminish its sequestration into aggregates; and (iv) direct inhibition of Tau aggregation. These findings establish F2,6BP as a central node linking metabolic regulation to both genomic stability and proteostasis in AD. Importantly, exogenous F2,6BP supplementation rescues multiple pathological features across diverse model systems, including induced neuronal cell lines (iN), primary neurons, organotypic hippocampal slice cultures, and in a Drosophila model of AD. These findings redefine F2,6BP as a metabolite that directly coordinates genome maintenance and proteostasis in neurons. Overall, this study identifies the PFKFB3-F2,6BP axis as a central driver of AD pathogenesis and a promising therapeutic target. HighlightsO_LITau aggregates sequester PFKFB3 depletes neuronal F2,6BP C_LIO_LIF2,6BP links metabolism to DNA repair and Tau proteostasis C_LIO_LIF2,6BP activates PNKP and upregulates PP2A to counter Tau pathology C_LIO_LIF2,6BP supplementation rescues AD phenotypes across models C_LI

neuroscience↗

bioRxiv2026

CRISPR activation of endogenous PKD1 increases polycystin-1 levels and suppresses cellular features of ADPKD

Most cases of autosomal dominant polycystic kidney disease (ADPKD) are caused by mutations in PKD1, which reduce polycystin-1 (PC1) levels below a critical functional threshold. Normalizing PC1 dosage mitigates disease progression; therefore, we sought to develop a CRISPR activation (CRISPRa) strategy to transcriptionally upregulate endogenous PKD1. We systematically screened multiple single-guide RNAs using an EGFP-reporter platform and identified potent candidates targeting the proximal PKD1 promoter in mouse and human cell models. Our results demonstrate that CRISPRa effectively increased endogenous Pkd1 mRNA in the mouse collecting duct-derived Pkd1RC/-cell model and in the primary renal epithelial cells from PKD mice. In Pkd1RC/- cells, CRISPRa of Pkd1 increased PC1 protein levels and significantly reduced cell proliferation and in vitro cyst formation in 3D cultures. Mechanistically, Pkd1 activation improved mitochondrial membrane potential, reduced dependency on aerobic glycolysis, and corrected signaling pathways involved in cystogenesis, specifically reducing intracellular cAMP, cMyc, pCreb, and pErk levels, while increasing pYap1 levels. We confirmed the translational potential of this platform by successfully activating PKD1 in primary renal epithelial cells from human kidneys. We observed a heterogeneous response across both normal and ADPKD patient-derived donor lines, with significant upregulation achieved in two of the tested cell preparations. These findings provide a compelling proof-of-concept that CRISPRa-mediated gene augmentation can increase PC1 levels, establishing a foundation for promising gene therapies aimed at successfully suppressing the pathogenic features of ADPKD.

molecular biology↗

bioRxiv2026

Phase composition-specific behaviour of functional RNAs in liquid-liquid phase-separated microenvironment

The internal dynamics of liquid-liquid phase-separated systems are governed primarily by polymer packing, excluded-volume effect, and interactions between polymers and encapsulated macro-molecules. Although one immediate effect of such a constrained microenvironment is diffusion limitation, it remains unclear whether encapsulated macromolecules can also exhibit phase composition-specific functional behaviour that is not observable in a well-mixed aqueous environment. In this regard, different phases in a phase-separated environment can be accessed via a phase diagram that demarcates the region between two-phase (droplets) and one-phase (polymer-rich, no droplets) regimes. While the two-phase region is heterogeneous, most previous work on encapsulating functional macromolecules in phase-separated droplets uses a single point from the phase diagram. This leaves a clear gap in understanding on how the function scales across this landscape of droplets and identifying regions advantageous for the encapsulated macromolecule and its function. Here, using the Spinach light-up RNA aptamer, we show that RNA function does not scale uniformly across the phase diagram. We show that RNA can exhibit phase composition-specific functional behaviour due to constraints imposed by the internal microenvironment of phase-separated droplets. Furthermore, using variants of the Spinach aptamer, we show that fluorescence activity differences among the variants vary differently with phase-separation regimes across the phase map, suggesting that some regions of the phase diagram can confer a selective advantage. Our results highlight the potential of liquid-liquid phase-separated internal microenvironments in guiding the differentiation of functional RNA variants, which could serve as a physical selection pressure in pre-cellular evolution.

evolutionary biology↗

bioRxiv2026

Apelin inhibits cyst growth and improves kidney function in mice with polycystic kidney disease

BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is a common inherited disorder marked by numerous renal cysts that impair kidney function, with about half of affected individuals progressing to kidney failure by midlife. Patients exhibit reduced circulating apelin, a ligand of the apelin receptor, known to regulate cardiovascular function including hypertension. We tested whether diminished apelin signaling contributes to cystogenesis and if exogenous apelin receptor activation can improve disease outcomes. MethodsPlasma samples from age- and sex-matched healthy controls and ADPKD participants were analyzed for circulating apelin peptides. To assess direct cystic effects, primary ADPKD renal epithelial cells were grown as 3D collagen-embedded cysts and treated with apelin agonists. Male and female Pkd1RC/RC; Pkd2+/- (PKD) mice were treated for 27 days with apelin agonists, vehicle, or the standard of care drug, Mozavaptan. Kidney and heart weight ratios, BUN, renal cAMP, and kidney transcriptional profiles were evaluated. ResultsCirculating apelin peptides were significantly reduced in ADPKD patients despite normal kidney function (eGFR, BUN, and creatinine). In vitro, both apelin and the small molecule apelin receptor agonist Azelaprag inhibited cyst growth. Apelin and Mozavaptan reduced kidney weight, cystic index, blood urea nitrogen and renal cAMP in PKD mice, whereas Azelaprag did not. Apelin downregulated expression of genes associated with cyst progression, including Lcn2 (Ngal), Postn, and Havcr1 (Kim-1). Mozavaptan, but not apelin, induced diuresis and reduced urinary concentration. ConclusionApelin receptor activation by exogenous apelin inhibited cAMP synthesis and cyst growth and improved kidney function in an orthologous mouse model of ADPKD. We propose that the apelin receptor may be a potential therapeutic target in ADPKD.

pharmacology and toxicology↗